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The Indian Roads CongressE-mail: [email protected]/[email protected]

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Edited and Published by Shri Vishnu Shankar Prasad on behalf of the Indian Roads Congress (IRC), New Delhi. The responsibility of the contents and the opinions expressed in Indian Highways is exclusively of the author/s concerned. IRC and the Editor disclaim responsibility and liability for any statement or opinion, originality of contents and of any copyright violations by the authors. The opinions expressed in the papers and contents published in the Indian Highways do not necessarily represent the views of the Editor or IRC.

Volume 41 NumbeR 4 ApRIl 2013 CoNTeNTs IssN 0376-7256

INDIAN HIGHWAYsA ReVIeW oF RoAD AND RoAD TRANspoRT DeVelopmeNT

Page

2-4 From the editor’s Desk

5 Life-Cycle Cost Analysis of Long Lasting Pavements Deepthi Mary Dilip, Praveen Ravi and G.L. Sivakumar Babu

15 Support Loan Concept for the Viability of A BOT Road Project Swapan Kumar Bagui and Ambarish Ghosh

27 Derivation of Capacity Estimates for Urban Expressway Using Computer Simulation Ravikiran Puvvala, Balaji Ponnu and Shriniwas S Arkatkar

36 Forthcoming Event of IBC

37 Determination of Dynamic PCUs of Different Types of Passenger Vehicles on Urban Roads : A Case Study, Delhi Urban Area Probhat Kr. Paul and P.K. Sarkar

48 Road Accident : A Threat Towards Nation’s Peace and Prosperity Bikramjit Das Gupta and Abhijit Kr. Mandal

58 Obituary

59 Cinder Waste Material for the Construction of Road V.G Havanagi, A.K. Sinha, V.K. Kanaujia, A. Ranjan and S. Mathur

63-70 Circular’s Issued by MORT&H

71 Tender Notice of NHs Madurai

2 INDIAN HIGHWAYS, APRIL 2013

Dear Readers,The pace at which India aspires to grow requires the development of infrastructure at a much higher speed. The crucial catalyst role the infrastructure plays in easing supply side constraints to economic growth has been well recognized. Due to these valid reasons, huge investment in infrastructure sector has been envisaged in the 12th Plan Period. However, such a huge investment is a daunting task, which requires not only the investment support from private sector but new financial resources as well as innovative financing methodologies may also be required in financing this growth driver sector of economy.This year we are celebrating the 150th birth anniversary of Swami Vivekananda who once famously state:- “Arise, Awake & stop not till the goal is achieved”. Today his sayings are all the more applicable in harnessing the creative energy of all especially the professionals in the road sector.In today’s scenario a paradigm shift is required towards the road sector so as to re-vitalize & rejuvenate this crucial segment of the infrastructure sector. Rarely, the road infrastructure’s overall impact on economy of the region, on each segment of the society as well as on each segment of the industry have been analyzed and documented. Normally, a conservative view towards spending in the road sector is taken and also not much specific credit is given to this important sector in achieving the growth & prosperity of the economy. However, the multi-dimensional benefits to the overall economy with the increased penetration of roads in different regions of the country are already visible since last more than 10 years.Therefore, in today’s scenario of symptoms of global economic stagnation with symptoms of economic contraction, there is a need to carry out “Road Infrastructure Productivity Assessment”. This “Constructive Risk Analysis” is the need of the hour to help in strengthening fiscal fundamentals as well as for achieving fiscal consolidation in the sector. This will also help in specifically bringing out a focused attention on the contributing role played by the road infrastructure in effectiveness of the deliverance of government policies & schemes in a fair manner. It will also help in bringing out the necessity of investment by different stakeholders in the road sector so as to reap the fruits of a higher growth rate on sustainable basis.Having recognized the essentiality of high level of investment in road infrastructure sector for the overall revival of investment climate leading to sustainable growth in the economy, we need to build on the successes and learn from the failures with an open mind. The public – private partnership in the road sector even though initiated in the 1990’s and in a big way during the last 7 years has witnessed some problems which need to be addressed. It may require some change in the mindset besides institutional restructuring as well.In the absence of proper debt management, financing of road projects have run into difficulty as leveraged companies are unable to raise more debt in the absence of fresh equity. There is a need for development of newer financial models for road infrastructure financing. To bridge the demand-supply gap with suggestive innovative financing mechanisms like take out financing, re-financing & securitization, etc. some of the issues and challenges for increased investment in road sector may be addressed. These comes with a rider of broader understanding and clarity. For resolving these issues broad consensus is to be built and in this it would be better if the Japnese system called “NEMAWASHI” is adopted which means “Consensus in advance”. This may allow timely decisions with a pro-active partnership role played by people, private entities and the public authorities/organizations.This requires a shift in the mindset to understand the collaborative benefits of synergy of different stakeholders in the implementation and operation of road infrastructural projects which generally falls in the long term

From the editor’s Desk

HARNess THe sTReNGTH oF RoADs IN eCoNomY(susTAINAble & INClusIVe DeVelopmeNT WITH FIsCAl CoNsolIDATIoN)

eDIToRIAl

INDIAN HIGHWAYS, APRIL 2013 3

category. This may also address to some extent another important issue of shared obligatory responsibility & accountability in the process of stakeholders ownership and obligations.In PPP projects, another issue may require innovative concept which is related to “exit route”. Generally it is advocated that exit route needs to be eased so that promoters can sale equity position after construction, passing on all benefits and the responsibility to the entities that step in. In this process, the government can also be benefitted in case the premium over sale proceeds are shared between seller and the government, keeping in view that the premium has grown on account of government’s support and public investment (including finances from financial institutions). The government share may be leveraged either to bring down the quantum of toll rate or the tolling period which will benefit the public (road users) directly. This may create a win-win situation.Similarly, the VGF component can be made a rolling proposition which will help not only the sector but economy as a whole as huge rolling funds will also be available to part finance the road sector projects. It is a common fact that the private sector investor asses the investment risk and therefore, they prefer first for “Attractive scheme” and thereafter the “Viable scheme”. The “Unviable scheme” does not find private sector participants and therefore the institutional restructuring should address the issue of making the unviable scheme investor conducive. This may require developing innovative financing models which may help players to move from attractive schemes to completely unviable schemes while fulfilling social responsibility and balance development of the region. Under this concept, optimized utilization of land resources including monetization of vacant land resources for providing public utilities/essential services/storage facilities, etc. as well low-cost housing may be considered along with the required road facility. This concept may help in providing ring roads around congested cities possibly with no cost to the government but government organizations needs to act as facilitators in active partnership with the people of the region. The social benefits as well as environmental benefits of this concept may be enormous.Most of the time roads are considered to be a facility which is in conflict with the environmental issues. However, little thought has been given that roads also contributes towards environmental management system as it helps in improving resource efficiency, reduce waste and drive down production costs in industrial and agricultural sectors. The road building also uses some of the industrial waste and by-products which have otherwise environmental implications.In addition, the roads helps in bridging the gap between “haves” and “have-nots’; between urban areas & rural areas as well as does not discriminate between its road users. Road & road transport system are considered as the most economical way of connecting the people living in tribal & forest areas which enables them to participate in the main-stream activities. Why they should not enjoy the fruits of economic growth and why they should be deprived of the beneficial effects of the economic prosperity of the country without proper road connectivity. Of course this integration should have proper checks and balances in place with a constructive approach rather than a rigid approach.As per the assessment of the working group of 12th Five Year Plan on employment, the rate of employment in last two decades is lower than the rate of growth of the economy and in addition the concern is expressed in the areas of quality of employment and the level of productivity. Harnessing the advantages of flexibility of the road transport system, these issues can be addressed to some extent. The integrated road planning & development may also help in optimizing respective strengths of minor, small and medium enterprises resulting in more inclusive and employment opportunities. Therefore, the conservative mindset needs to be changed to remove the environmental related hurdles to road infrastructure growth.The efficiency of the road transport system can be improved considerably through a proper mix of high grade access controlled expressways with the existing road network. This may help in not only providing relief to the through traffic passing through congested towns but may also bring a corresponding relief to these


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towns. It may also help in not only faster movement of industrial & agricultural produce but may help in faster distribution of the same across the country in a flexible manner, in a timely manner, thereby limiting the inflationary pressure due to scarcity in one area and depression in prices due to surplus or excessive production in other areas. Usually the cost of these facilities comes in their way but this cost should not be considered just as expenditure but it should be considered as a constructive investment resulting into multiple long term benefits spanning over 50 or more years. To some extent the issue of acquisition of land of these new green field facilities can be addressed by constructing totally elevated expressways. This new model needs a closer look as it opens up new opportunities of integration of different sectors of economy besides resulting in better capacity utilization in the down-stream industries even in the period of global economic uncertainty. Perhaps this advantage may not be available with any other mode of transport infrastructure. With each passing day the need & necessity of the expressway is increasing. The same may be considered as necessity instead of luxury.Normally, when there is global economic downtrend and the export market also witnesses a consequential downtrend. In the period of global economic downtrend, the effectiveness of the schemes providing export incentive to the exporters also witness their limitations, as they does not help in bringing down per unit production cost or encourage mass production – economic scale of production. How the exports can be made more competitive on sustainable basis and what role the efficient road network plays towards the same on sustainable basis (even during period of global uncertainty) may suggest for re-channelizing part of this fund in building and developing efficient road transport trade facilities, so that on sustainable basis the transportation cost is slashed both for the raw material as well as finished products. This may also help in framing a comprehensive logistic policy framework in which road sector may be an active partner.Obviously, the industrial sector is also a consumer of the output of the road infrastructure system but at the same time it has also an obligatory role as a consumer to play an active role in such initiatives of road construction. Harnessing of demographic dividend through appropriate developmental efforts – provide an opportunity to achieve inclusive and productivity within the country. It may also help in arresting joblessness. It opens up opportunity for utilization of India inclusive innovation fund which primarily will be focusing on generating employment & supporting livelihood across the country. The synergy which can be provided by the road sector to this effort of the government needs due consideration by all stakeholders.The road sector is yet to be benefitted from the corporate sector social responsibility concept. The potential is enormous and the benefits which may be accrued to the corporate sector from their investment in the road sector under CSR will be enormous. Under CSR the corporate sector may adopt some of the linear road routes or even part of road network which may help not only in enhancing their own productivity and profitability but may help in spreading their product reach. They may invest in the areas of R&D efforts, enhancing road safety, providing road side facilities, undertaking maintenance activities, etc. This may also help in improving satisfaction level of the road users on those stretches besides help in creating more employment opportunities as well as bringing smiles on the faces of local unemployed segment of society thereby contributing towards harmonizing sustainable economic development with higher goals of happiness, good governance, community vitality and well-being. There are many more direct and indirect benefits of the investment in roads under CSR concept by the corporate sector.“Your wish is to discover your work and then with all your heart to give yourself to it. That is the mark of true professional”

Gautam Buddha

Place: New Delhi Vishnu shankar prasad Dated: 18th March, 2013 Secretary General

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AbsTRACTThe concept of designing long lasting pavements is gaining acceptance with the increasing traffic demand and the need for economic and environmental sustainability. In this paper, the theory of perpetual pavements is implemented with a view to compare these long lasting pavements with the conventionally designed pavements and to evaluate their economic feasibility in Indian conditions. The design was based on available literature on perpetual pavements that suggests the use of Mechanistic-Empirical Design (MED) philosophy wherein limiting pavement responses are used to evaluate a proposed design. In order to compute pavement responses to the applied traffic loads, the pavement design software KENPAVE was employed. A horizontal tensile strain of 70µs below the bituminous layer for fatigue cracking, and vertical compressive strain of 200µs on top of the subgrade for structural rutting, was adopted as the endurance limits. The significant role of bituminous layer thickness in the reduction of the overall design thickness was observed at varying levels of traffic. In order to evaluate the cost-effectiveness of the various design alternatives, a life cycle cost analysis was carried out using the software LCCA Express. The significant contribution of the high stiffness base materials and a stable foundation towards a more cost-effective design was highlighted in this study. It was seen that at the end of 50 years, for the long-lasting pavement section considered there is a saving of about 19.4% of the total costs for a project length of 20km, when compared to the costs incurred by the conventional pavement. It was concluded that perpetual pavements can be a viable option for constructing structurally stable and economically feasible roads with minimal maintenance and other overheads, thus necessitating extensive field studies for implementation on Indian roads.

1 INTRoDuCTIoN

A perpetual pavement is an asphalt pavement designed to last for about 50 years without requiring major structural rehabilitation or reconstruction and needing only periodic surface renewal in response to distresses confined to the top of the pavement (APA 2002). The term ‘Perpetual’ is slightly misleading as no pavement

lIFe-CYCle CosT ANAlYsIs oF loNG lAsTING pAVemeNTsDeepthi Mary Dilip,* praveen ravi** anD G.l. SivakuMar BaBu***

can last indefinitely; rather, the term long lasting is more apt. All further use of the word ‘perpetual’ in this paper will imply long lasting pavements of 50 years or more.

Available literature has shown that the early practice to accommodate increasing traffic was to correspondingly increase the pavement thickness. This was a result of empirical extrapolation rather than an engineering analysis which resulted in uneconomical and environmentally unsustainable pavements. Later analysis revealed that most pavements designed in such a way were more than capable to resist the heaviest traffic loads (APA 2002). Such conservative designs exert a heavy financial burden on developing countries where the aim should be to produce economically sound, long lasting and structurally stable pavements. The idea of perpetual pavements thus came into existence as much to prevent over-design as to provide a longer life span (APA 2002).

The basic premise of designing a perpetual pavement is that an adequately thick Hot Mix Asphalt (HMA) pavement placed on a stable foundation will relocate the distresses that originate at the bottom of the pavement to the upper layers (APA 2002). This obviates expensive structural maintenance procedures since the distresses are confined to the wearing course which can be replaced when and if functional requirements such as skid resistance and riding quality are not being met (APA 2002). Thus the potential of fatigue cracking and structural rutting, the two most devastating pavement distresses, is reduced in Perpetual Pavements.

* Research Scholar** Project Associate*** Professor

Department of Civil Engineering and Centre for Infrastructure, Sustainable Transportation & Urban Planning (CiSTUP), Indian Institute of Science, Bangalore-560 012 E-mail: [email protected]

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Research has shown that there are threshold values of horizontal and vertical strains below which no fatigue cracking or structural rutting occur respectively. However, there seems to be a disparity in these values among different researchers, particularly in the Fatigue Endurance Limit (FEL) below which fatigue cracking does not occur. While Tarefder et al (2010) has considered 70 microstrains (μs), Yang et al (2006) has considered 120μs by arguing that 70μs is too conservative for China’s heavier traffic loads. There is however consensus in the limiting value of structural rutting taken as 200μs. The Indian Roads Congress, IRC:37-2012, has proposed the values of 70 and 200μs for the fatigue and rutting endurance limits, to be adopted in the design of perpetual pavements.

In this study, the significance of the HMA layer over the granular layer was showcased for different traffic loads by designing and comparing pavements having greater granular thickness with those having greater HMA thickness. The pavement structure and other parameters were adopted from IRC:37-2012 while MED principles such as limiting damage ratio were used to evaluate the pavement. The significance of stiffer base materials was then demonstrated for a traffic loading of 66.51msa, which corresponds to an initial traffic of 2000 CVPD and design life of 15 years, by comparing the design thickness values adopted from IRC:37-2012 with the long-lasting design alternatives. This was done by varying the resilient modulus of the granular base within the range recommended in Indian design guidelines. These concepts were then extended to design long-lasting pavements by adopting the limiting values of horizontal and vertical strains as 70μs and 200μs for design purposes. A Life Cycle Cost Analysis (LCCA) was performed to gauge the economic superiority of the long lasting design alternatives over the conventional design; and thereby implying the feasibility of implementing the concept of long lasting pavements. The cost of the alternatives was compared with that of a conventional pavement designed, by considering the cost of constructing overlays to extend the life of these pavements to 50 years

2 NeeD FoR peRpeTuAl pAVemeNTs IN INDIA

As India is attaining greater modernization, the number of vehicles on the road is increasing significantly. This is imposing greater distress on the country’s roads in the form of increased fatigue cracking and structural and surface rutting, which directly increases the maintenance cost and resource consumption. Pavements which are traditionally designed for 15-20 years need structural rehabilitation and reconstruction after their design life has been reached; this involves major traffic closures and rerouting adding to the rehabilitation cost. These considerations are especially important on high-traffic volume freeways where user delay costs may be prohibitive (Tarefder et al. 2010). Perpetual pavements have been found to improve this situation as they are capable of maintaining the pavement performance for nearly 50 years without requiring major structural rehabilitation. They have gained a lot of importance in developed countries having been successfully constructed in USA, UK, and France (APA 2010). They are also being extensively studied in developing countries like China (Yang et al. 2006) and recommended for India (Kandhal et al. 2008). The success of these perpetual pavements advocates their study and implementation in India while moving towards sustainable development.

The main feature of perpetual pavements is that they never need to be completely removed and replaced. In the world of pavements, this is the ultimate in economic and environmental sustainability. As only the surface is renewed and the base structure stays in place, there is considerable saving of construction materials. Also, the user-costs associated with construction delays are greatly reduced because routine maintenance can be done quickly in off-peak hours, unlike the remove and replace option which necessitates 24-hour road closures. In addition, significant fuel savings are achieved with pavements kept smooth by routine maintenance involving infrequent milling of the top layer for recycling, then placing a quiet, durable, safe new overlay. All these factors not only result in a more cost-effective design but also a reduction

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in the emission of greenhouse gases. By reducing greenhouse gas emissions, perpetual pavements can mitigate climate change, both now and for generations to come.

3 obJeCTIVes oF THe sTuDY

1 To highlight the effect of increasing the thickness of the Hot Mix Asphalt (HMA) over the granular layer on the critical pavement responses and overall design thickness, by designing pavements according to the philosophy followed in IRC:37-2012.

2 To examine the effect of providing stiffer base materials on the pavement design for conventional pavements (15 years) and long-lasting pavements.

3 To design long-lasting pavements and to perform LCCA for all the design alternatives to highlight the economic advantages of providing perpetual pavements over an equivalent conventional pavement designed according to IRC:37-2012, over a period of 50 years.

4 pAVemeNT DesIGN AND ANAlYsIs meTHoDoloGY

4.1 Asphalt pavements Designed for 20 Years

To showcase the importance of the HMA layer, the pavement structure was adopted for a CBR of 5% from IRC:37-2012 for traffic loads of 30, 100 and 150msa. The structure consists of a bituminous surface course, a granular base and sub-base layer and the subgrade. The bituminous surface layer consists of two courses, namely a Bituminous Concrete (BC) wearing course and Dense Bituminous Macadam (DBM) binder course. The values of material properties like resilient modulus and Poisson’s ratio were adopted from IRC:37-2012 guidelines. Resilient Modulus for asphalt layers as given in the guidelines is presented in Table 1; in this study, it was taken as 3000MPa which corresponds to DBM VG40, and a temperature of 35ºC which has been suggested as the Average

Annual pavement Temperature for the plains in India. The resilient modulus of subgrade and granular layers was calculated using the equations recommended by the guidelines as given by Equations (1) and (2).

Table 1 Resilient modulus of bituminous mixes [IRC:37-2012]

mix Type Temperature (ºC)20 25 30 35 40

BC and DBM (VG10) 2300 2000 1450 1000 800BC and DBM (VG30) 3500 3000 2500 1700 1250BC and DBM (VG40) 6000 5000 4000 3000 2000BC and DBM (Modified Binder)

5700 3800 2400 1650 1300

BM (VG10) 500 MPa at 35 ºCBM (VG30) 700 MPa at 35 ºC

MR = 10 × CBR for CBR ≤ 5

MR = 17.6 (CBR)0.64 for CBR 5 (1)

MR – Modulus of subgrade (MPa)

CBR – California Bearing Ratio of subgrade (%)

E = MR × 0.2 × h0.45 (2)

E – Modulus of granular base (MPa)

h – Thickness of granular base (mm)

Other pavement design parameters such as wheel spacing, tyre contact pressure, tyre contact radius were suitably chosen based on available literature for Indian conditions (Maji and Das, 2006) and are presented in Table 2. The analysis was done using the pavement design and analysis software KENPAVE developed by Huang (2004) at the University of Kentucky. It accepts layer thickness, loading and material characteristics as inputs and has provisions for entering various indigenous data such as rutting and fatigue life coefficients, wheel spacing, tyre pressure etc. The analysis is based on the linear elastic, multilayer theory and the stresses and strains developed in various layers of the pavement are obtained. The pavements were designed on the concept of damage ratio, defined as the ratio of actual load repetitions to allowable load repetitions. A value greater than 1 signifies failure while a value less than 1 implies the

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pavement can be subjected to more number of load repetitions. A tolerance of ± 0.1 was adopted.

Table 2 Design parameters for KeNpAVe (IRC:37-2012)

parameter ValueLane Distribution Factor (F) 0.75Vehicle Damage Factor (D) 4.5Traffic growth rate 5%Resilient Modulus of Subgrade 50MPaPoisson’s ratio for HMA 0.5Poisson’s ratio for GB, TSG and subgrade 0.4

In order to compute the damage ratio, the allowable repetitions are calculated in terms of the fatigue and rutting lives. For National highways and Expressways having design traffic exceeding 30msa, fatigue and rutting equations with 90% reliability is recommended which are given by equations (3) and (4) respectively. Currently, modified binders like Polymer and Crumb Rubber Modified binders are used that have fatigue lives two to ten times higher than the normal mixes depending upon the binder content; this property can be utilized in designing high fatigue life bituminous pavements after carrying out laboratory tests. The equations adopted for this study are as follows:

Nf = 0.711 × 10–4 × 1 13 89 0 854

ε t RM

×

. .

(3)

Nf – Number of cumulative standard axles to produce 20% cracked surface area

εt – Tensile strain at the bottom of asphalt concrete (in micro strain)

E – Modulus of elasticity of bituminous surfacing (MPa)

Nr = 1.41 × 10–8 × 1

4 5337

εv

.

(4)

Nr – Number of cumulative standard axles to produce rutting of 20mm

εv – Vertical subgrade strain (in micro strain).

Though it is possible to determine progression of cracking and rutting in bituminous pavements by

adopting cumulative damage principle, field data is not yet available and the concept of equivalent standard axle load repetitions is currently the best available option for thickness design of bituminous pavements. The expected traffic loading is computed on the basis of the vehicle damage factor, given by

N = 356 1 1× + − ( )r

r

n

× A × D × F (5)

N – Cumulative number of repetitions in terms of million standard axles

r – Expected traffic growth

n – Design life

A – Initial traffic in the year of completion in terms of CVPD

D – Lane Distribution Factor

F – Vehicle Damage Factor

The pavements designed for 50, 100 and 150 msa using MED principles are shown in Table 3, to study the effect of increasing the thickness of the HMA layer as compared to the granular layer. In order to evaluate the monetary consequence of increasing the HMA layer thickness, a pavement section was designed for a traffic of 66.51msa corresponding to an initial traffic of 2000 CVPD and design life 15 years (obtained from Eq. 5). For the assessment, a pavement design for a Bituminous pavement with Granular Base and Granular sub-base corresponding to a CBR of 5% and the traffic range of 51-100msa was adopted from IRC:37-2012; and was compared with the pavement section with an thicker HMA layer. To measure the effect of stiffness of base layer, pavements with granular base modulus varying from 300 to 1000 MPa and a typical value of 450 MPa were considered; and the design alternatives are presented in Table 4. These values of base modulus have been recommended for the aggregate layer sandwiched between a bituminous surface and a cementitious base as the strong support from cementitious base results in higher modulus. In this study, the long-lasting designs include a Treated Subgrade (TSG) which is chemically stabilized subgrade overlying the natural subgrade that provides

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a very strong foundation. Thus the adoption of these values for the granular base modulus can be justified. For the stabilization of the subgrade, a number of new soil stabilizers are available commercially and many of them have undergone trials in different locations in India. They should be evaluated for their durability and structural parameters for pavement design (IRC:37-2012). The resilient modulus of the

treated subgrade can then be determined from field or laboratory tests; in this study, the modulus of TSG was adopted as 210MPa from available literature (Tarefder et al, 2010). The typical values of treated subgrade thickness for perpetual pavements is recommended as 300mm (IDOT 2002); however as the pavement was designed only for 15 years, this value is reduced to 100mm.

Table 3 pavement sections for 50, 100 and 150 msa

Traffic (msa) Type of pavement

pavement thickness (mm) Damage Ratiobituminous

surfacingGranular

baseGranular sub-base

Total

50 Thick granular layer 155 250 300 705 0.844

Thick HMA layer 170 150 200 520 0.981


Thick HMA layer 200 150 200 550 0.99


Thick HMA layer 220 150 200 570 0.98

Table 4 Pavements Designed for 15 Years and 2000 CVPD Initial Traffic (66 msa)

Trial Type of pavement

Thickness (mm) modulus of Gb (mpa)

Remarks

bC Dbm Gb Gsb TsG Total

1 Conventional 50 120 250 300 - 720 171 IRC recommended

2 Long-Lasting 50 170 100 100 420 109 Increased HMA

3 Long-Lasting 50 150 150 - 100 450 300 TSG in place of GSB



4.2 perpetual pavements Designed for 50 Years

The methodology for perpetual pavement design is similar to the one adopted for conventional design with the exception that the limiting parameters are horizontal tensile strain below the HMA layer and vertical compressive strain above the natural

subgrade instead of damage ratio. This is represented diagrammatically in Fig.1. The design is considered adequate if the former and latter are 70µs and the 200µs respectively with a tolerance of ±1 µs. The pavement sections were designed with a treated subgrade layer of thickness, 250mm and the base modulus varied as before. The values are presented in Table 5.

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Table 5 perpetual pavements Designed for 50 Years (656 msa)


Thickness (mm) modulus of Gb (mpa)

strains DR

bC Dbm Gb TsG Total Tensile Compressive

1 Deep-strength 50 230 150 300 730 300 71.3 163.9 0.637

2 Deep-strength 50 210 150 300 710 450 70.05 171.7 0.593

3 Deep-strength 50 170 150 300 670 1000 57.5 177.7 0.461

Fig. 1 Perpetual Pavement Design Concept

In order to evaluate the performance of the perpetual pavement, the designed pavement sections were analysed for an expected initial traffic of 5000 CVPD (in both directions) and a design life of 50 years. In other words, the adequacy of the sections so designed by keeping the critical pavement responses below the endurance limits, was verified by comparing the allowable traffic repetitions with the expected traffic demand of 656 msa, as determined from Equation 5.

5 lIFe CYCle CosT ANAlYsIs

The Life Cycle Cost (LCC) of an asset is defined as the total cost, in present value or annual value that

includes the initial costs, Maintenance, Repair and Renewal (MR&R) costs over the service life or a specified life cycle. LCC is based on an understanding that the value of money changes with time and as a result, expenditures made at different times are not equal. This concept, referred to as the ‘time value of money’, is the basis for Life Cycle Cost Analysis (LCCA). LCCA is a process for evaluating the total economic cost of an asset by analyzing initial costs and discounted future expenditures such as maintenance, operational, user, and social costs over the service life or life cycle of an asset.

In order to evaluate the feasibility and cost-effectiveness of all the design alternatives, LCCA

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was carried out using LCCA Express. It calculates the Net Present Value (NPV) of the cost per lane-mile by accepting load spectra, traffic growth rate, unit cost of materials and construction of various layers, density of various layers and maintenance costs as inputs; Table 6 shows the design parameters that were used in the study. The discount rate is the interest rate by which future costs will be converted to present value, and has been adopted as 4% in this study. In other words, it is the percentage by which the cost of future benefits will be reduced to present value (as if the future benefit takes place in the present day). Real discount rates (as opposed to nominal discount rates) reflect only the true time value of money without including the general rate of inflation and typically range from 3% to 5%. The Consumer Price Index (CPI) that measures changes in the price level of consumer goods and services was assumed as 174.4. The CPI is a statistical estimate constructed using the prices of a sample of representative items whose prices are collected periodically; and the annual percentage change in a CPI is used as a measure of inflation. The densities of the materials for the different layers used in the study, and the unit costs of materials adopted from the 2011-12 Schedule of Rates (Karnataka Public Works Department) are presented in Table 7.

Table 6 Design parameters for lCCA

Parameter Value

Project Length 20km

Number of Lanes 4

Pavement Width 3.5 m

No of shoulders 2

Average shoulder width 2.5 m

Speed limit 90 kmph

Analysis Period 15 or 50 years

Discount rate 4%

Current CPI 173.644

Table 7 layer Densities and Cost

layer modulus (mpa)

Density Cost

pcf kg/m3 (Rs/m3) (Rs/ton)

BC 3000 145 2324 8650 3722

DBM 3000 145 2324 7100 3055

Granular Base/Sub-base

109 135 2161 723 335

171 135 2161 723 335

300 135 2161 733 339

450 135 2161 733 339

1000 135 2161 982 454

TSG 210 130 1998 174 87

The life-cycle costs were calculated for pavement designed for 15 years, to evaluate the monetary consequence of increasing the thickness of the HMA. Further, the requirement of the treated subgrade and the stiffer base materials are evaluated through the life-cycle costs. In the case of the long-lasting pavements, the life-cycle costs are compared for the pavement section designed with the base modulus of 450 MPa, and the costs incurred by designing conventional pavements at the end of 50 years.

maintenance strategy

The maintenance strategy adopted, for the pavements designed for 15 years, included the removal and replacement of the Bituminous Concrete (BC) course every 7 years. In the case of the long lasting pavements, the BC course was replaced every 10 years, as these pavements are designed for little maintenance requirements. This design alternative is compared with the conventional pavement, whose design life is extended to 50 years, by proving overlays at the end of 15 years. The conventional pavements were also subjected to a maintenance strategy every 7 years, by replacing the BC layer. In other words, the life-cycle costs are analysed by considering the total costs incurred at the end of 50 years; where in the case of the conventional pavement the entire HMA layer is removed and replaced, while the long lasting pavements require only the replacement of the BC. The labour costs were assumed to be 5% of the total material costs.

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6 ResulTs AND DIsCussIoN

The basic premise of perpetual pavements is that an adequately thick HMA layer can preclude the distresses developed below the pavement. While the same can be achieved with a thicker granular layer, the pavement thus produced will be thicker. This is observed in Table 3 wherein for each traffic load of 50, 100 and 150msa there is an appreciable reduction in overall pavement thickness of about 25% (180 mm) by increasing the HMA thickness by only 3% (200 mm).

From an analysis of Table 4, a similar observation is made from the first two design alternatives wherein an increase of 50 mm in HMA thickness, led to an overall reduction of 300 mm. However, from Table 8 it can be noted that although the total pavement thickness has been reduced from 720 mm to

420 mm, the total cost of construction is increased by around 5.65% for the second alternative. This increase can be attributed to the high cost of the HMA layer. The other design alternatives included the provision of a TSG layer, with varying values of base modulus. It can be seen that in the case of pavements designed for 15 years, the treated subgrade layer and the increase in stiffness of the base layer had an insignificant effect of the overall design thickness (as compared to the second alternative). However, an analysis of the life-cycle costs shows that the provision of 10cm of the Treated Subgrade layer results in a saving of 1.95% of the total cost while by increasing the stiffness of base modulus to 1000 MPa, the total costs can be reduced by around 10.58%. Thus, the provision of increased HMA thickness can be justified only by the provision of very strong foundation and base materials.

Table 8 lCC for 15 Year pavements

Trial Type of pavement Construction cost Rs/km

maintenance cost Rs/km

user delay cost Rs/km

Total cost Rs/km

Total cost (Rs)

% savings

1 Conventional 38277290 8501069 43661 46822019 93,64,40,380 -2 Conventional 40920680 8501069 43661 49465410 98,93,08,200 -5.65%3 Deep-strength

(300 MPa GB)37366688 8501069 43661 45911418 91,82,28,360 +1.95%

4 Deep-strength (450 MPa GB)

35732359 8501069 43661 44277088 88,55,41,760 +5.43%


33322879 8501069 43661 41867608 83,73,52,160 +10.58%

In the case of long-lasting pavements, a TSG layer of 300 mm is provided above the subgrade to provide a cost-effective design; and base modulus stiffness was increased from 300 MPa to 1000 MPa, which had a significant effect on overall design thickness as seen in Table 5. The adequacy of the design was evaluated for an expected traffic of 656 msa on the basis of the damage ratio. As these values are significantly less than 1, the design is considered adequate for all the alternatives presented in Table 5. The design and

construction of long-lasting pavements is justified only if the life-cycle costs are lower than that required for the construction of the conventional pavements. It can be seen from Table 9, in the case of the design alternative with GB of modulus 300 MPa, the total cost at the end of 50 years is reduced by around 4.47% only, while there is reduction of around 9.76% in the case of the pavement section having GB of modulus 450 MPa. However, it can be seen that there is a saving of around 19.4% of the total cost can be achieved for

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Table 9 lCC for 50 Year pavements


Construction cost Rs/km

maintenance cost Rs/km

user delay cost

Rs/km

Total cost Rs/km

Total cost (Rs)

% savings

1 Conventional ( 3 Overlays )

38277289 27196320 43661 65517270 1310345396 -


51167802 11373411 43661 62584873 1251697466 +4.47576


47899134 11373411 43661 59316206 1186324120 +9.46478


41361791 11373411 43661 52778863 1055577255 +19.4428

long-lasting pavements, with a GB of 1000 MPa, when compared to the conventional pavements whose design life has been extended to 50 years through the provision of 3 overlays.

7 CoNClusIoNs

It can be concluded that perpetual pavements hold a lot of promise, but require extensive field study for suitable implementation on Indian roads. Though the pavement thicknesses proposed are not absolute, they provide a basis of comparing conventional asphalt pavements with perpetual pavements. The study shows that although the concept of perpetual pavements advocates the increase of HMA thickness to keep the critical strains within the threshold limits, the provision of a stable foundation and the high stiffness base materials are equally significant from a financial perspective. This can be attributed to the increasing cost of asphalt in India, with the rising price of crude oil. Thus, the implementation of long-lasting pavements in India calls for development in the area of soil stabilization or the provision of treated subgrade, and the use of high stiffness base materials.

The study is not without its limitations. The values proposed are based on a theoretical approach where uniform stress distribution along the pavement is assumed which is not the case in real life. Elastic layered systems approach is used which assumes

linear elasticity of all layers ignoring the viscoelastic properties of bitumen. Parameters such as Discount Ratio and CPI were arbitrarily assumed. However, the aim of this study was not to propose directly implementable results in the field but only a comparison of perpetual pavement and the MED. The need for extensive study on Perpetual pavements is stressed in the study. This can only be done by the construction of trial sections and subjecting them to real life loads over 3 to 4 years so that pavement responses can be physically measured. Such studies have been performed by the National Centre for Asphalt Technology (NCAT), Auburn University and have yielded positive results which only makes it all the more necessary for India to experiment with Perpetual Pavements.

ReFeReNCes1. Asphalt Pavement Alliance (APA)., Perpetual Pavements:

A Synthesis, APA 101, Lanham, Maryland, 2002.

2. Harvey, J., Monismith, C., Bejarano, M., Tsai B.W. and Kannekanti V., Long-Life AC Pavements: A Discussion of Design and Construction Criteria based on California Experience. Proceedings. Intl. Symp. on Design and Construction of Long Lasting Asphalt Pavements, National Center for Asphalt Technology. Auburn University, Alabama, pp. 285-334, 2004.

3. Illinois Department of Transportation (IDOT), Subgrade Stability Manual, Policy MAT-10, Springfield, 1982.

4. Illinois Department of Transportation (IDOT) (2002), Standard Specifications for Road and Bridge Construction, Illinois Department of Transportation, Springfield.

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5. IRC:37-2012, Tentative Guidelines for the Design of Flexible Pavement, (The Indian Roads Congress: New Delhi).

6. Kandhal, P. S., Sinha, V.K. and Veeraragavan, A., A Critical Review of Bituminous Paving Mixes Used in India, Indian Highways, Indian Roads Congress, pp. 113-132, 2007.

7. Maji, A. and Das, A., Reliability Considerations of Bituminous Pavement Design by Mechanistic-Empirical Approach, International Journal of Pavement Engineering, 2008, 9(1), 19-31.

8. Monismith, C.L. and McLean, D.B. Technology of Thick Lift Construction: Structural Design Considerations, Proceedings of The Association of Asphalt Paving Technologists, Vol. 41, pp. 258-304, 1972.

9. NCHRP, Guide for Mechanistic-Empirical Design of New and Rehabilitated Pavement Structures, Transportation Research Board, National Research Council, 2004.

10. Newcomb, D. E., Willis, R. and Timm, D. H., Perpetual Asphalt Pavements – A Synthesis, Asphalt Pavement Alliance, APA 101, Lanham, Maryland, 2010.

11. Tarefder, R. A. and Bateman, D., Design of Optimal Perpetual Pavement Structure, J. Transp. Engrg., ASCE, 2010,.

12. Walubita, L.F., Liu, W., Scullion, T. and Leidy, J., Modeling Perpetual Pavements Using the Flexible Pavement System (FPS) Software. Paper submitted to 87th Annual Meeting, Transportation Research Board, Washington, 2008.

13. Willis, J. R. and Timm, D. H., 2009. A Comparison of Laboratory Thresholds to Measured Strains in Full-Scale Pavements, Proceedings of Intl. Conf. on Perpetual Pavements, Ohio University, Columbus, 2009.

14. Yang, Y., Gao, G. and Linn, W., Timms, D.H., Priest, A., Huber, G.A. and Andrewski, D.A. Perpetual Pavement Design in China, International Conference on Perpetual Pavement, Ohio Research Institute for Transportation and the Environment, Stocker Center, Athens, Ohio, 2006.


AbsTRACTThis paper presents the viability of a BOT road project based on support loan concept. Interest of debt is one of the most important parameters for the viability of a project. In India interest rate is in the range of 15-18% annually. The road project with low traffic and high project cost may be infeasible. In order to viable the project, support loan concept has been proposed in this paper. This paper presents the changes values of various financial viable parameters with the use of support loan with a real case study. This paper presents the results normal debt and support loan with different interest rates and different payback periods and develops a methodology for support loan for the viability of a project. It has been found longer payback period is also more beneficial. Financial return is more with low rate of interest of debt. A real case study has been compared with support loan and subsidy provision and find out best option after projecting both values at the end of payback period with an example calculation presented in Annexure 1. It has been found that support loan provision is more beneficial for the government instead of subsidy option for the viability of a project. Support loan concept is recommended for the viability of the project.

1 INTRoDuCTIoN

Inadequate transport infrastructure has been recognized as an impediment to the industrial and economic progress of any country. Governments worldwide invariably must cope with the widening gap between needed investments and available budgetary resources. They increasingly attempt to involve the private sector in the financing, design, construction, and operation of major infrastructure projects, with a view to exploit the private initiatives to implement public projects. In this context, the Build Operate Transfer (BOT) concept is becoming a popular mode of privatization of transport infrastructure development (Tiong 1995).

suppoRT loAN CoNCepT FoR THe VIAbIlITY oF A boT RoAD pRoJeCT

Swapan kuMar BaGui* anD aMBariSh GhoSh**

In recent years governments in many countries have begun privatizing transportation infrastructure sectors. Some of the forces driving this movement include a scarcity of public resources, an increase in the demand for better service and a political trend toward the deregulation of infrastructures from public monopoly.

The BOT project is essentially a form of leasing, where the government (project sponsor) allows a private entrepreneur (project promoter) to design, finance, and build an infrastructure facility. In return, the project promoter is permitted to collect tolls (user fee) and operate the facility for a specified period (called the concession period), during which he is expected to recover all of his costs and earn a reasonable profit. At the end of the concession period, the ownership of the facility is transferred to the government. This arrangement facilitates the implementation of capital intensive infrastructure projects by the government with funds from outside the budget allocation, while transferring the risks involved to the private sector.

Prior experience in BOT projects is limited in India, though varied levels of success with such projects have been reported in other countries such as Malaysia, Thailand, Mexico, and China. However, for successful implementation, it is essential for both the government and the private project promoters to be fully aware of the prospects and pitfalls of these projects. The conventional financial analysis with deterministic or ‘‘point’’ estimates of the important parameters is variables of a transport infrastructure project such as the construction, operation, and maintenance costs, the traffic volume, and the toll revenue are not amenable

* PhD Student,** Professor, Dept. of Civil Engg., Bengal Engg. and Science University, Shibpur, Howrah

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to precise prediction, and the financial performance cannot be assessed accurately. For a realistic and meaningful analysis of the financial viability of BOT projects, the consideration of risk and uncertainty should be explicitly incorporated.

Quite often, private investment in public infrastructure occurs within the Build-Operate-Transfer (BOT) model where a public entity, the Government, and a private entity, the Sponsor, enter into an agreement where the Sponsor is bound to design, build, finance and operate an infrastructure project on behalf of the Government for a predetermined period of time, the concession period. At the end of the concession period, the Sponsor transfers its ownership rights back to the Government. Typically, the Sponsor finances the BOT investment through project finance rather than corporate loans .This introduces another active party, the Lender. Thus, the BOT model becomes a trilateral negotiation game with complex interrelationships. The critical success factor for a BOT project is the profit margin of the Concessionaire.

Financing is one of the most significant issues in the BOT project. Only with sufficient capital can a BOT project be successfully carried out (Tiong 1995). However, in the process of financial planning, there are so many details included that appropriate financial planning procedures and financial assessment methods should be developed in order to evaluate the viability of a project and come up with the best scenario.

Four financial assessment methods are generally available for the viability of a BOT project namely, NPV.FIRR, the payback period method, and the discount payback period method. These can be defined as follows (Brigham et al. 1997):

● Net Present Value (NPV) method: This method is to discount all the cash flows back to the present year (or a specific year). A zero value of NPV represents the breakeven point of a project. If the value of NPV is zero or positive, the project is worth investing. Conversely, if the value of NPV is negative, it is better to decline the project.

● Internal Rate of Return (IRR) method: IRR uses the rate of return that assumes the NPV value of a project to be zero. To evaluate a project with IRR, just compare it to the estimated cost of capital. If the IRR is positive, the project is acceptable, depending on need/importance of the project.

● Payback period method: This method involves the discounting. When the sum of zero is reached, the payback period is found. Payback period should be lesser than concession period.

● Discount payback period method: This is almost the same as the payback period method but discounting all cash flows back to a specified year.

A BOT transport infrastructure project may be considered as financially viable, when the following the conditions are simultaneously satisified (Malini 1998):

The NPV for the project should be positive. The discount rate for financial analysis may include a risk premium over the current commercial lending rate.

The financial IRR should have a value greater than the discount rate.

The cash flow (liquidity) situation in each year of the concession period should be satisfactory. In other words, the cash balance at the end of every year should be positive.

Payback period/Break down year should be lesser than concession period.

Above four conditions may not be satisfied in real project case study and project may not be viable financially. To make viable, some modifications may be required.

To make the project viable, following modifications can be considered:

● Payback period to be increased ;

● Recommend for Subsidy ;

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● Modification the percentage of Equity ;and

● Provision of Rigid pavement option with followings-

- 0% Fly ash

- 20% Fly ash

- 30% Fly ash

- 40% Fly ash

- 50% Fly ash

- 60% Fly ash

- Provision of soft Loan and Support loan from financial institutes like World Bank, Asian Development Bank etc.

A soft loan is a loan with a below market rate of interest. This is also known as soft financing. Sometimes soft loans provide other concessions to borrowers, such as long repayment periods or interest holidays. Soft loans are usually provided by governments to projects they think are worthwhile. The World Bank and other development institutions provide soft loans to developing countries.

The bank charges LIBOR plus a spread. LIBOR is the London Interbank interest rate and currently hovering around 2%. the spread is around 25 basis points. The lender has to, however, absorb, the foreign currency risks which would appreciate is significant this point.

LIBOR is defined as: The rate at which an individual Contributor Panel bank could borrow funds, were it to do so by asking for and then accepting inter-bank offers in reasonable market size, just prior to 11.00 London time.

BBA (British Bank Association) LIBOR is not a compounded rate but is calculated on the basis of actual days in funding period/360*. Therefore, the formula is as follows:

Interest Due = Principle Sum × bbalibor Rate100

× interest periodNumber or day in

360*

+ Spread is around 25 basis point

It has been found from case study, these parameters improve net present value /FIRR. Variation of Interest rate of debt, the only single parameter may improve financial parameters.

1.1 literature Review

The Sydney tunnel and the Malaysian expressway received support loans from the governments. For the Malaysian project, the government allocated $235,000,000 (about 13% of the total project cost) in start-up finance toward the construction costs. The loan was payable over 25 years, including a 15-year grace period and a fixed interest rate of 8% per annum. For the Sydney tunnel, the government even provided an interest-free loan of $125,000,000 (about 23% of total project costs) to cover the preliminary construction costs of the tunnel. The loan was to be repaid over 30 years. Instead of providing loans, the Chinese government assisted in arranging an “emergency loan facility” for the sponsors to provide funds in the events of “force majeure.” (Tiong 1990).

2 leAD FRom pAsT sTuDY

From past studies, it is found that research work on support loan ` carried by previous researchers is very limited. So support loan concept may be introduced in road BOT project and details financial analysis with a real case study to be carried out.

2.1 scope

Based on the lead from previous work, it is felt that support loan concept can be introduced for the viability of a project. Present research work is planned to carryout a real case study which was originally infeasible for base case. After modifying interest rate, the same project is found financially viable.

3 CAse sTuDY

A case study has been considered for selected sections of National Highway (NH) No. 4.

The homogeneous sections are presented in Table 1.

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Table 1 Identification of Homogeneous Sections

section length (km)

HS-01 23

HS-02 67HS-03 33

The Annual Average Daily Traffic (AADT) values are used for future projections for 30 years analysis period. Growth rate factors are taken as 5 % as recommended by Model Concession Agreement, NHAI, 2000. Tollable traffic count is shown in Table 2.

Table 2 Annual Average Daily Tollable Traffic

Vehicle Type Hs 1 Hs 2 Hs 3

Car/Van/jeep 2736 3675 4741Mini Bus 74 111 194Bus 1076 864 1205Light Goods Vehicles 443 983 13352 Axle 2180 2179 32983 Axle 855 1168 1367Multi- Axle Vehicle 108 179 315

3.1 Toll Rate

Toll rate is selected using guideline prepared by the Government of India. Inflation rate has been determined based on Source; Reserve Bank of India Bulletin, 2000.Whole price index for all commodities is found out 8.3%.

Using this value future toll rate has been projected for future year and toll rate for the opening year, 2004 is mentioned in Table 3.Toll rate increasing factor for the year 2004 is 1.0837 = 1.74.

Table 3 Toll Rate per/km Vehicle Wise

Year Car Full bus

multi Axle

lCV 2A, 3A Truck

Toll Rate Rs (1997) 0.28 1.05 1.6 0.60 1.10

Toll Rate Rs (2004) 0.52 1.95 2.8 1.03 1.81

Toll Rate Rs (2008) 0.67 2.33 3.74 1.16 2.35

Toll Rate Rs (2012) 0.89 3.12 5.00 1.56 3.12

3.2 project Cost

Project cost worked out for flexible pavement. This cost includes the cost of glare screen barrier. The average project cost per kilometer is found Rs 42.2 Million

3.3 Financial Analysis

Financial analysis for base case has been carried out taking the following major maintenance and operation costs:

Annual Routine Maintenance (repair of pot hole, clearing C D structure etc) Cost (Rs 0.2 million per km).

Periodic Maintenance (Overlay every 5th year) Cost (Rs 2.8 million per km).

Toll Operation (Toll administrative cost) Cost (Rs 6 million for toll plaza per year).

Financial analysis is carried out varying equity from 10% to 90%. Concession period is taken 20 years and payback period is taken 10 years for normal debt and 10 years for support loan for link 1. Interest on normal debt and return on equity are assumed 15% and 20%.Interest rate of support loan is varied. Project road is divided into three contract packages. For analysis, link 1is considered.

4 FoRmulATIoN oF FINANCIAl moDel

A financial model is developed using Excel sheet. It is used to support decision making in project evaluation. The project viability is analyzed from the equity holders’ perspective in the project. The first step in any investment evaluation is to gather the appropriate information on the project costs and calculate the cash.

Assumptions and Theoretical Framework

The following are the assumptions for the model:

1. The financing of a project is raised by a combination of equity and debt. The net cash

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flow during the construction period is negative and positive during the operation period.

2. A loan is available from one source or from multiple sources with the same term of annual equal installments.

3. Land acquisition cost is borne by the Government of India.

5. The cash flows during construction are predestinated.

7. The toll rate vehicle mode wise shall be the rate fixed by the Government of India

8. Complete depreciation of the Total Project Cost (TPC) is allowed during the operation period.

4.1 Theoretical Framework

Ranasinghe (1996) has developed a simplified model to calculate TPC for infrastructure projects in developing countries, which is the starting point of the financial analysis and defined below:

TPC=BC+EDC+IDC+Financing Change (1)

where,

BC = base cost or constant value cost of the project estimated at market prices of a predetermined year;

EDC = the cost escalation during construction; and

IDC = the interest during construction.

After the completion of construction, revenue is generated from toll from vehicle during the operation period, which is fixed based on technical parametre of the project. The net annual cash available in current value given by :

NCAi = PBITi - TAXi + DEPi - Di for i = 1,2, . . . ,m (2)

Where,

PBITi = Profit Before Interest and Tax;

TAXi = tax;

DEPi = Depreciation; and

Di = annual Debt Installment for ith year.

Corporate tax @ 35% to be paid as decided by Government of India.

TAXi = (PBITi - INTi). for i = 1,2, . . . , m (3)

where

INTi = interest to be paid in the ith year.

4.2 Depreciation

Depreciation is a non-cash expense: it only reduces taxable income and provides an annual tax advantage equal to the product of depreciation and the (marginal) tax rate, but it does not lead to a cash outflow from the company. The most common method for depreciation is straight-line depreciation. Under this method, annual depreciation equals a constant proportion of the initial investment. In this model, it is assumed that TPC can be depreciable in its entirety. Thus

DEPi = TPC

m for i = 1,2,3---- (4)

Operation and Maintenance (OM) cost includes OM of road cost, personnel salaries, indirect costs, and insurance cost. These costs are calculated separately and used in financial model.

The view point of equity holders is focused on the main project metrics, internal rate of return and Net Present Value (NPV). The IRR and NPV are the most common and fundamental economic decision criteria employed in practice (Lohmann 1988). Financial analysis has been carried out for concession period of 20 years for base case for the Link HS01(23 km length, Refer Table 1) without any modification for the viability of the project. Equity is considered 20% and debt is equal to 80%.Interest rate of debt and return on equity are assumed as 15% and 20% respectively for carrying out financial analysis. Summary of results of financial analysis for base case and detail calculation are shown in Table 4 and Annexure 1 (attached end of the paper) respectively.

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Table 4 Financial Results for base Case

FIRR (%) NPV (Rs Million)

7.84 -353.7

From Table 4, it is found that the entire project is not viable financially.

In order to viable the project, support loan concept has been adopted with following variables:

● Base case i.e. debt interest 15%;● Debt interest is 7.5% to be availed by the

government;

● Debt interest is 5% to be availed by the government;

● 100% Debt with interest is 0% to be availed by the government;



● 25% Debt with interest is 0% to be availed by the government.

Results are shown in Tables 5, 6, Fig.1 and Fig.2.

Table 5 Financial Return and NpV for Various Debt Interest for payback period 10 Years

equity(%) base Case @ 15% Interest

base Case @ 7.5% Interest

base Case @ 5% Interest base Case @ 0% Interest

NpV (Rs million)

FIRR (%) NpV (Rs million)



FIRR (%)

10 -267.2 8.57 323.3 14.14 701.6 16.26 2064.5 20.88

20 -364.2 7.56 122.7 11.95 402 13.5 1317.1 16.7

30 -455 6.79 -52.44 10.43 156.4 11.67 776.9 14.14

40 -539.8 6.17 -205.3 9.27 -48.4 10.31 377 12.33

50 -618.7 5.68 -340.2 8.35 -221.6 9.23 73.4 10.92

60 -692 5.27 -406.3 7.59 -370.2 8.34 -163.4 9.78

70 -759.3 4.92 -568 6.94 -499.5 7.99 -353.1 8.81

80 -821.2 4.62 -665.2 6.34 -613 6.94 -509.3 7.98

90 -877.6 4.4 -753.3 5.9 -714 6.37 -641.2 7.25

Fig.1 Variation NPV vs Equity

Table 6 Financial Return and NpV for payback period 15 Years

equity (%) base Case @ 0% InterestNpV FIRR

10 2105.9 27.720 1383.9 20.3130 924.85 17.240 465.8 14.1250 196.775 12.560 -72.25 10.9170 -248.125 9.880 -424 8.890 -551.35 7.9

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Changing payback period from 10 years to 15 years, NPV and FIRR values are shown in Tables 5 and 6. From these tables, it is found that NPV and FIRR are more for payback period of 15 years than that of 10 years payback period.

The correlation between NPV vs equity ,FIRR vs equity are shown in Fig.1 and Fig.2. Regression equations and R2 values are shown in Table 7. The results are statically significant.

Fig.2 Variation FIRR vs Equity

Table 7 Regression equation for NpV and FIRR

Interest

(%)

Regression equation for NpV Regression equation for FIRRequation R2 equation R2

15.0 NPV = - 7.62 × E - 218.3 0.99 FIRR = 8.51 - 0.05 × E 0.957.5 NPV = 375.8 - 13.17 × E 0.98 FIRR = 13.86 - 0.097 × E 0.945.0 NPV = 727.6 - 17.28 × E 0.97 FIRR = 18.11 - 0.24 × E + 0.01 × E2 0.990.0 NPV = 2653 - 71.9 × E - 0.4 × E2 0.99 FIRR = 23.6 - 0.355 × E + .002 × E2 0.99

Average Debt Coverage Ratio (ADCR),Time Interest Earned (TIE) and Ź for risk analysis are shown in Table 8 for various support loan. Average debt coverage ratio increases with increasing value of equity and same for interest coverage ratio, TIE. Value of Ź also normal tendency of same value of similar tendency with irregularity of some values. Brigham et al. 1997 reported that Debt coverage ratio shows the concessionaire’s ability to pay debt. The higher

the debt coverage ratio, the better the concessionaire’s debt paying ability. The debt coverage ratio influences the willingness of banks to loan money to the concessionaire. Generally speaking, a debt coverage ratio at least equal to or larger than 1.0 is acceptable. Considering this aspect and compare Table 9, support loan with 5% and 0% is only viable option for the project.

Table 8 Values of ADCR, TIE and Ź for Various Support Loan

equity (%)

Interest @15% Interest @7.5% Interest @ 5% Interest @ 0%ADCR TIe Ź ADCR TIe Ź ADCR TIe Ź ADCR TIe Ź

10 0.59 1.42 0.20 0.91 4.45 -.64 1.05 7.5 -.7 1.49 ∞ -.8720 0.61 1.47 0.28 0.92 4.53 -.49 1.06 7.6 -.5 1.49 ∞ -.8130 0.62 1.52 0.33 0.93 4.62 -.30 1.07 7.7 -.3 1.49 ∞ -.6640 0.64 1.57 0.34 0.95 4.69 -.12 1.08 7.8 -.1 1.49 ∞ -.4350 0.65 1.63 0.35 0.96 4.77 0.03 1.09 7.9 .03 1.50 ∞ -.1960 0.67 1.85 0.36 0.97 4.86 0.13 1.10 8.0 .14 1.50 ∞ -.0170 0.69 1.91 0.36 0.99 4.95 0.02 1.11 8.1 .21 1.50 ∞ 0.1280 0.71 1.98 0.35 1.00 5.04 0.25 1.13 8.2 .25 1.50 ∞ 0.2090 0.73 2.06 0.35 1.02 5.13 0.26 1.14 8.3 .28 1.50 ∞ 0.25

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Financial analysis is also calculated for various combination of support loan and normal debt and

results are shown in Table 9.

Table 9 Financial Return and NpV for Various proportions support loan and Normal Debt for payback period 10 Years

equity(%) support loan 75% support loan 50% support loan 25%NpV FIRR NpV FIRR NpV FIRR

10 932 16.94 302 13.68 -57.5 10.920 562 14 102.3 11.6 -183.1 9.4730 267 12.1 -70.7 10.15 -298.7 8.4740 28.4 10.6 -222 9.05 -395 7.850 -168.3 9.5 -355.6 8.2 -495 7.0660 -332.2 8.9 -474.3 7.4 -585 6.5 70 -473.9 8.57 -580.6 6.8 -668 6.0 80 -595.9 7.1 -676.4 6.3 -745 5.6 90 -703.1 6.5 -763 5.8 -816 5.23

Fig.3 Cash Flow for 10 and 15 Years Payback Period

From Table 9, it is found that NPV and FIRR vary linearly with equity with negative slope. Both are increased with decreased of interest of loan/debt. With decreasing rate of interest, the project has been found viable with equity value varying from 20% to 50%.Retutn is maximum for debt with 0% interest of debt.

Financial results reported in Tables 5 and 6 for payback period 10 and 15 years. Return for payback period 15 years is more than that of 10 years. This is due to more positive cash flow (first 15 years) for 15 years payback periods than that of 10 years payback period. This is shown in Fig.3.

5 suppoRT loAN Vs subsIDY

To viable the project at equity proportion 20%,support loan and subsidy option are studied. It has been found that the Government provided 41% subsidy for the viability of the project. Subsidy amount is Rs 397.8 million. Assuming, this cost assumed to be alloted to the Concessionaire in the three years of construction periods @30%, 30% and 40% i.e Rs 119.4,119.4 and 159.2 million. The same project has been found viable for support loan @ 41.2 % of total debt amount @ rate of interest 0%. Support loan can be phased out @ Rs 95.97,95.97 and 127.97 million at 1st, 2nd and 3rd year respectively. Assuming government will provide support loan from any financial institute @ 15% interest. Total future values for both cases are calculated at the end of payback period and shown below:

Future values of subsidy = 119.4* (1.1513) + 119.4* (1.1512) + 159.2* (1.1511) = 2114.1

Future value of support loan = 95.97* (1.1513) + 95.97* (1.1512) + 127.97* (1.1511) = 1699.3.

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From support loan government will return money @ Rs 32 million for ten installment. Future value of return money is 32 (1.1510 + 1.159 + 1.158 + 1.157 + 1.156 + 1.155 + 1.154 + 1.153 + 1.152 + 1.151) = 747.2.

Net expense of the government in term of future value at the end of payback period = 1699.3 - 747.2 = 952.1

Comparing above future values of net expense of support loan, subsidy, it is clearly found that support loan is better option than that of subsidy of the project.

Again considering low rate interest of other country like 8%. Analysis has been carried out. Subsidy and support loan were found 8.7 % and 10 % to be required for the viability of the project. Future values of support loan and subsidy were calculated and found Rs 412.5 and 466.7 million. Hence support loan choice is best option for the present project.

Subsidy and support loan option are studied .Subsidy and support loan amount are found Rs 788.8 and Rs 604 million. Hence support loan is best option .

6 CoNClusIoNs

Based on the present research work following conclusions can be drawn:

● Financial viability of a project should be checked based on support loan concept.

● If a project is not viable financially, modification should be carried out by modifying rate of interest of debt/introducing support loan concept.

● Net present value, financial internal rate of return vary with negative slope with varying equity proportion.

● Average debt coverage ratio varies with positive slope with equity.

● Net present value, financial internal rate of return vary with payback period. Higher payback period yields better return.

● Interest coverage ratio varies with positive slope with varying equity for a given interest rate of support loan. This varies with negative slope with varying interest rate.

● Support loan and subsidy options are studied, it is found that support loan is best option and it should be considered for viability of a BOT project.

ReFeReNCes1. Brigham, E. F., and Gapenski, L. C. (1997). Financial

Management—Theory and Practice, 8th Ed., Dryden Press, Fort Worth.

2. National Highways Authority of India (2000). “Detailed Project Reports of NH 4 km 592 to km 725, 2000.”

3. Tiong, R. L. K. (1995). “Impact of Finance Package Versus Technical Solution in a BOT Tender.” J. Constr. Eng. Manage., ASCE,121(30), 304–311.

4. Tiong, R. L. K. (1990). “COMPARATIVE STUDY OF BOT PROJECTS.” J. Constr. Eng. Manage., ASCE, 121(30), 107-122.

5. Ranasinghe, M.(1996). “Total Project Cost: A Simplified Model for Decision Makers” Journal of Construction Management Econom., 14(3), 497-505.

6. Malini, E. (1998). ‘‘Evaluation of Financial Viability of BOT Transport Infrastructure Projects.’’ Journal of the Indian Road Congress 58(1), 87–123.

7. Lohman, J. R.(1988). “The IRR, NPV and the Fallacy of the Reinvestment Rate Assumption” Engg. Econom., 33(4), 303-330.

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AbsTRACTExpressways in India are vastly different from other roads of the country as vehicles such as bicycles, two-wheelers, three-wheelers and bullock carts are not allowed in these roads and additionally, there is no strict lane discipline. Nevertheless, there is not much research literature specific to these categories of roads. Hence, this work aims to model traffic flow on Indian Expressways and estimate its capacity using the micro-simulation model, VISSIM.

1 INTRoDuCTIoN

An urban expressway is defined as an arterial highway for motorized traffic, with divided carriageways for high speed travel, with full control of access and usually provided with grade separators at location of intersections. They are the highest class of roads in the Indian Road Network. Higher design speeds, restriction on slow moving vehicles, varied traffic composition with high amount of cars characterize these roads. With such operational difference and with many urban expressways such as Delhi-Gurgaon, Eastern and Western Express Highways in Mumbai being in existence and more number of them such as the Yamuna and Kundli-Manesar-Palwal Expressways being built, a thorough understanding of their operation assumes high importance. The traffic flow characteristics on expressways can be understood very well by developing relationships among fundamental parameters and also by determining the capacity and Level-of-Service (LoS) by modelling the system using appropriate analytical techniques, which will enable the study of the characteristics over a wide range of the influencing factors. Additionally, there is a very less literature available on capacity of expressways which has also found a mention in the 11th Five Year plan

DeRIVATIoN oF CApACITY esTImATes FoR uRbAN expRessWAY usING CompuTeR sImulATIoN

ravikiran puvvala*, Balaji ponnu** anD ShriniwaS S arkatkar***

(2007-2012) report. Considering all the above, it is imperative and timely to initiate a study on the capacity and LoS criteria of expressways depending upon the carriageway/roadway widths and other relevant parameters. Thus this study is aimed at developing capacity estimates for urban expressway segments under varying roadway and traffic conditions, which will help in meeting the country’s need for design, analysis, operations and management of expressways. To this end, the traffic flow on the Delhi-Gurgaon Expressway has been studied and modelled through both empirical and simulation approaches.

2 lITeRATuRe ReVIeW

Simulation has been recognized as one of the best tools for modeling of traffic flow under homogeneous as well as heterogeneous conditions. Fellendorf and Vortisch (2001) presented the possibilities of validating the microscopic traffic flow simulation model VISSIM, both on a microscopic and a macroscopic level in homogeneous flows. Matsuhashiet. al. (2005) assessed the traffic situation in Hochiminh city in Vietnam, using image processing technique and traffic simulation model (VISSIM). It was found that the high number of motorcycles in the network interfere with other vehicles which reduces average speed of traffic stream drastically. Further, the simulation model was applied for deriving the benefits of increasing the share of public transport. Chandra, S. (2004) developed a method for estimating the capacity for a two lane road under mixed traffic conditions. Zhang et al. (2008) conducted a study using VISSIM to

* Project Associate, BITS-Pilani** Project Associate, IIT-Madras*** Assistant Professor, Department of Civil Engineering, BITS, Pilani, E-mail: [email protected]

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evaluate a proposed feedback- based tolling algorithm to dynamically optimize High Occupancy Toll (HOT) lane operations and performance. Hossain (2004) calibrated the heterogeneous traffic model in VISSIM to match saturation flows measured by video at an intersection in the city of Dhaka, Bangladesh. Velmurugan et al., (2010) studied free speed profiles and plotted speed–flow equations for different vehicle types for varying types of multi-lane highways based on traditional and microscopic simulation model VISSIM and subsequently estimated roadway capacity for four-lane, six-lane and eight-lane roads under heterogeneous traffic conditions with reasonable degree of authenticity.

Many researchers tried to build their own simulation software for studying heterogeneous traffic flow. Arasan and Koshy (2005) developed a heterogeneous-traffic-flow simulation model to study the various characteristics of the traffic flow at micro level under mixed traffic condition on urban roads. The vehicles are represented, with dimensions, as rectangular blocks occupying a specified area of road space. The positions of vehicles are represented using coordinates with reference to an origin. For the purpose of simulation, the length of road stretch as well as the road width can be varied as per user specification. The model was implemented in C++ programming language with modular software design. The model is also capable of showing the animation of simulated traffic movements over the road stretch. Dey et al. (2008) developed a simulation program coded in Visual Basic language. Arkatkar S (2012) analysed heterogeneous Traffic Flow Using Microscopic Simulation Technique. Bains Ponnu and Arkatkar S (2012) developed a model in VISSIM for simulating the Mumbai-Pune Expressway traffic and estimating the capacity values. The authors performed number of simulation runs to determine the capacity of a two-lane road and to study the effect of traffic mix, slow moving vehicles and directional distribution of traffic on capacity and speed.

3 THe sImulATIoN moDel

Simulation technique is one of the well-known techniques to study traffic flow and its characteristics.

Simulation gives us the advantage of being able to study how the created model behaves dynamically over time or after a certain span of time. Traffic characteristics on roads as a system vary with time and with a considerable amount of randomness and simultaneous interactions. The most difficult and critical process in simulating any traffic flow scenario or for that matter any physical phenomena is to calibrate the simulated model to capture or replicate the ground reality with the desired accuracy. Given this, the results obtained through a validated simulation model would be more accurate than those obtained through analytical results.

The simulation model followed in the present study is shown in the form of a flow chart in Fig.1. Data in the form of videos collected from the study site was analyzed and this information is used for building the simulation model in the software VISSIM 5.40. Then the model was calibrated and validated for rendering it suitable for replicating the conditions at site. Using this validated simulation model, roadway capacity estimation was done.

Fig. 1 The Simulation Model

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4 moDel CAlIbRATIoN AND VAlIDATIoN

Model calibration is an iterative process of comparing the model to reality, making adjustments (or even major changes) to the model, comparing the revised model to real conditions, making additional adjustments, comparing again, and so on. The comparison of the model to reality is carried out by tests that require data on the system’s behavior plus the corresponding data produced by the model. The input data required for the above mentioned heterogeneous traffic-flow model are related to four aspects viz. road geometrics, traffic characteristics, driver reaction time and vehicle performance. The power of simulation as a tool for the study of traffic flow lies in ability of the model to include the effect of the random nature of traffic. Hence, the random variables associated with traffic flow such as headway distribution are expressed as frequency distributions and input into the simulation model. These data, pertaining to one direction of traffic flow, was collected at a selected stretch of an expressway for model calibration and validation purposes.

5 sTuDY sTReTCH & DATA ColleCTIoN

The Delhi-Gurgaon Expressway is an 8-lane divided facility that connects the city of Delhi with one of its busiest suburbs, Gurgaon. The traffic on the expressway was video graphed from a vantage point, during both peak and non-peak hours on 20th March, 2012. It was also ensured that the study location was free from any traffic interferences such as bus-stops or intersections.

Free-flow speeds were ascertained by observing 100 vehicles each in different categories of vehicles during non-peak hours when a flow of 1000 vph prevailed. Then on the same day, the traffic flow on the road was observed for two hours in the evening peak period from 16:24 to 18:24 hours. Macroscopic parameters such as flow and speed aggregated at every 5-min intervals were extracted from the videos were extracted at a rate of 25 frames per second for achieving a high accuracy. The traffic flows observed were 7200 vph

and 8573 vph in the first and second hours respectively. Composition of the traffic stream is given in Column (2) of Table 1. The speeds of the different categories of vehicles were measured by noting the time taken by the vehicles to traverse a trap length of 30 m. The free speeds of the different categories of vehicles were also measured for the traffic under free-flow conditions. The observed maximum, minimum and mean speeds of various classes of vehicles and the corresponding standard deviations are shown in columns (3), (4) and (5) respectively of Table 1.

The overall dimensions of all categories of vehicles are shown in columns (7) and (8) of Table 1. Any vehicle moving in a traffic stream has to maintain sufficient lateral clearance on the left and right sides with respect to other vehicles/curb/median to avoid side friction. These lateral clearances depend upon the speed of the vehicle being considered, speed of the adjacent vehicle in the transverse direction, and their respective vehicle categories. The minimum and maximum values of lateral-clearance share are given in columns (9) and (10) of Table 1 respectively. The minimum and the maximum clearance-share values correspond to zero speed and free speed conditions of respective vehicles respectively. The acceleration values of the different categories of vehicles over different speed ranges used for simulation are shown in Table 2.

6 sImulATIoN moDel DeVelopmeNT

A model which accurately represents the design and operational attributes of the study stretch in the simulation software is known as the ‘base model’. The design attributes can be road configuration (carriageways, medians & shoulders), horizontal curvature and vertical gradient. Operational attributes can be the vehicle or driver characteristics and the traffic flow data. When this base model is calibrated and validated to replicate the actual or ground conditions, the model can be used to study different characteristics that were not defined by the user as an input. For example, the width of the road can be defined and in turn the capacity of this road could be

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measured. The validated base model can also be used to develop a simulated scenario which is desired to be known. The base model development involves the following steps:

a) Development of Base Link/Network.

b) Defining Model Parameters.

c) Calibrating the Network.

d) Validating the Model

7.1 Development of base link/Network

Development of a link/network that accurately depicts the physical attributes of a test site is an important stage in the modeling process. The basic key network building components in VISSIM are

links and connectors. In the present simulation model, a unidirectional four lane test section link spanning 1000 m was created representing the study stretch located on the Delhi-Gurgaon Expressway as explained above.

7.2 Defining Model Parameters

7.2.1 Vehicle Model

Vehicle model deals with defining the dimensions of each vehicle type that are plying on the test stretch and are hence considered for the simulation. It is also used to define the acceleration values of vehicles. The dimensions namely the width and the length are considered for the present simulation model as per the description given in Table 1. The acceleration values are given as per Table 2.

Table 1 Input Data for Heterogeneous Traffic Flow Simulation

Vehicle Type Composition (%)

observed speeds, km/h Vehicle Dimension, m lat. clear. share, mmax. speed

min. speed

mean speed

std. Deviation

length Width min. max.

Car 70.80 103 78 90 4.00 4.4 1.75 0.40 0.60Two-wheeler 22.50 87 33 58 8.33 1.8 0.60 0.10 0.30Three-wheeler 3.30 63 38 50 4.00 2.6 1.4 0.30 0.40Bus 2.20 93 64 79 5.00 10.3 2.5 0.40 0.60LCV 0.70 80 63 73 3.33 5.0 1.9 0.40 0.60Truck 0.50 69 48 60 4.00 7.5 2.5 0.40 0.60

Note : LCVs – Light Commercial Vehicles

Table 2 Acceleration Values for Different Vehicle Categories

Vehicle Type 0-30 km/hr. (m/s2)

30-60 km/hr.

(m/s2)

Above 60 km/hr. (m/s2)

Car 2.15 1.80 1.10Two-wheeler 1.10 0.70 0.45Three-wheeler 0.80 0.30 0.25Bus 1.40 1.00 0.45LCV 1.30 0.80 0.55Truck 1.00 0.62 0.46

7.2.2 Desired Speed Distribution

The desired speed distribution for each vehicle category was given as input for the simulation model

in VISSIM. The maximum & minimum values of the speeds and distribution between these values were defined in the model. The desired speed profile for the vehicle type car is given as an example is shown in Fig.2. The desired distribution curve for any vehicle category is generally an ‘S’ shaped curve as shown in the figure. Adequate care was taken to ensure that the speed distribution defined in VISSIM represented the values observed in the field.

7.2.3 Vehicle Composition and Vehicle Flow

Vehicle composition and vehicle flow based on field observations is given as an input to simulation model for the given time interval.

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and lateral distance. These inputs were given in the simulation model as shown in Fig.3a and 3b respectively. Thepsycho physical driver behavior based Wiedermann 99 Car-following model has been used for simulating the vehicle following behavior. The parameters of the ‘Indian driving’ behavior model is shown in Fig.3a. The cars following parameters considered in simulation are shown in Fig. 3b.

7.3 Calibration of the simulation model

Calibration is a process of adjusting the model to replicate observed data and observed site conditions to a sufficient level to satisfy the model objectives. This process involves adjusting the following characteristics: desired speed distribution, acceleration/deceleration of vehicle, mechanical characteristics of the vehicle, minimum safety distance, minimum lateral distance and driving behavior characteristics.

By giving these parameters as an input to simulation model, simulation runs have to be carried out in order to estimate the output. In the present simulation model,

Fig. 2 Desired Speed Distribution of Small Car Considered in VISSIM

Fig. 3a Indian Driving Behaviour Modelled

7.2.4 Driving Behavior Characteristics

The driving behavior characteristics mainly include these two features viz. car following behaviour

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the outputs were the traffic volumes and average speeds of the vehicles for 10 different random seed values. All the simulations were run for a total time of 7400 seconds including a temporal warm-up period of 200 seconds to ensure accurate simulation results. Flow for each 1 hour from the two hour field data was fed which improved the degree of match between the empirical and the simulated.

As explained above, a different driving behavior was considered for each vehicle type to account for heterogeneity in the traffic. There was no strict lane discipline among the vehicles was as observed from the video. Hence, an entire road width based simulation where there was a one lane model having an effective with of three lanes was considered in the simulation. Thus each vehicle was free to choose any lateral position and overtake from any side during the simulation on this three lane width without any lane discipline similar to site conditions.

The minimum look ahead distance which defines the distance a vehicle can see forward in order to react to vehicles in front or to the side of it was set to a value of 40 m was found to be appropriate for the present situation. Similar calibration was done for minimum look back distance. Time headway plays a major role for capacity estimation in VISSIM and hence these values were chosen carefully for each vehicle type according to the observed traffic flow as shown in the Figs.3a and 3b. The other values were chosen as per the defaults considered in VISSIM which produced the observed conditions with required accuracy. The estimated values and the observed values were compared and the error was computed. If the error was within the limits, the calibration process was stopped or otherwise the parameters were modified and simulation runs were carried out. This process was repeated and the simulation runs were made till the error was within the satisfactory limits. The calibration process in the form of a flow chart is shown in Fig.4.

Fig. 3b Small Car Following Parameters Considered in Simulation

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7.4 Validation of the simulation model

Validation is the process of checking the results obtained from the calibrated model in terms of simulated values against field measurements for parameters such as traffic volumes and average speeds. The observed traffic volume and composition was given as input to the simulation process. The simulation runs were made with 10 random number

seeds for a total run time of 7400 seconds including temporal warm-up period of 200 seconds to ensure accurate simulation results. A sample simulation run is shown in Fig.5. The average speeds of vehicles from a single run was noted and then the average speed for each vehicle category from all the ten runs were taken as the final output from the model. The inter-arrival time gaps of the heterogeneous traffic flow (similar to headway of homogeneous traffic) of vehicles was assumed to follow negative exponential distribution (Arasan and Koshy, 2003) and the free speeds of different categories of vehicles, based on the results of an earlier study (Velmurugan et al. 2010), was assumed to follow normal distribution. These distributions formed the basis for input of the two parameters for the purpose of simulation. To check for the validity of the model, the vehicle speeds simulated by the model were compared with the field observed speed values for each vehicle category. The comparison of the observed and simulated speeds, for an observed traffic volume, is shown in Fig.6.

Fig. 5 A Snapshot of Simulation Runs in VISSIM

Fig. 6 Comparison of Observed and Simulated Stream Speeds for Every 5-min

It can be seen that the simulated speed values, simulated flow values are quite closer to the speeds and flows observed from the field for all the vehicle categories and for most of the, if not all 24 five minute intervals (Figs. 6 & 7) and eight 15 minute intervals (Figs. 8 and 9). A paired t-test results obtained for different input flow intervals are listed in the Table 3. The critical value of p statistic for a level of

Fig. 4 Calibration of the Simulation Model

1. Desired speed distribution2. Desired accel-eration & deceleration. of vehicle3. Weight of the vehicle4. Minimum Safety Distance5. Min. and max. lateral gaps6. Traffic volume and composition 7. Vehicle dimensions (length and width)8. Roadway geometry 9. Total simulation time

Define Simulation Parameters

Simulation Run in VISSIM

Estimation of Output Speed

Comparison with observed data

If Error < Defined

Accuracy

Speed

Stop

NO

YES

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significance of 0.05 for 4 degrees of freedom is 0.05. This implies that there is no significant difference between the observed and simulated speeds.

7.4.1 Model Application

The VISSIM model can be applied to study various traffic scenarios for varying roadway and traffic conditions. In this study, the application of the model is to study the relationship between traffic volume and speed on Indian Expressways with seven categories of vehicles as shown in Fig. 6.

7.4.2 Speed-Flow Relationships and Capacity

One of the basic studies in traffic flow research is to examine the relationship between speed and volume of traffic. The capacity of the facility under different roadway and traffic conditions can be estimated using these relationships. In this study, speed-flow relationship was developed using the validated simulation model for a heterogeneous flow with vehicle composition and roadway conditions same as that observed in the field. The average speed of the stream was plotted for different simulated volumes, starting from 500 vph to the capacity of the road.

The following procedure was adopted for finding the capacity of the facility when developing the above speed-flow relationships. During successive simulation runs with increments in traffic volume from near-zero volume level, there will be a commensurate increase in the exit volume at the end of simulation stretch. After a specific number of runs, the increments in the input traffic volumes will not result in the same increase in the exit volume. Such a decrease in exit volume (in spite of increase in the input) in successive runs indicates that the facility has reached its capacity. The speed-volume relationships for a four lane expressway are shown in the Figs.10 and 11. It is clear from the figures that the curves follow the established trend and the capacity in terms of vehicles per hour decreases as we proceed from car, light commercial vehicle and trucks in that order, which is quite intuitive. The values of capacity obtained from the simulation for the observed flow and simulated one are given in Table 4.

Fig. 7 Comparison of Observed and Simulated Flows for Every 5-min



Table 3 Degree of match of the simulated and observed speed/Flow (p-Values obtained from the

student’s T-test)

property Flow Interval

p-Value Critical p-Value

Speed 5-min 0.22746 0.05

Speed 15-min 0.35308 0.05

Speed 30-min 0.38017 0.05

Speed 60-min 0.19236 0.05

Flow 5-min 0.35181 0.05

Flow 15-min 0.44942 0.05

Flow 30-min 0.61559 0.05

Flow 60-min 0.5054 0.05

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Table 4 estimated Roadway Capacity of eight lane (bidirectional)/Four lane

(unidirectional) expressways

Flow Type Traffic Composition

estimated Capacity, veh/hr/dir

Observed Heterogeneous (Table 1)

10800

Simulated (1-min) Heterogeneous (Table 1)

10560

Simulated (5-min) Heterogeneous (Table 1)

9606

7 CoNClusIoNs

It has been found from this study that the micro-simulation model VISSIM is suitable for simulating

and hence studying heterogeneous traffic flow in expressways with statistical significance. The simulated flow and speed from the calibrated model best fits with the observed data when the input flow is given for every 15 minutes and output collected for every 30 minutes, whereas the capacity value estimated from the model is closer to the observed value when the output for every 1 minute is collected. This is quite intuitive as lower speeds and lower flows would be observed at more frequencies when the level of time aggregation is decreased. Hence when the interval considered is smaller, lower and higher mean speeds as well as lower and higher flow levels are recorded resulting in a more fully-developed flow-speed curve. The capacity estimated for the Delhi-Gurgaon Expressway from this study is 10560 vehicles/hour in one direction of travel considering the observed vehicle class mix during the study period. These results are local and are pertaining only to the expressway that has been studied and more studies are envisaged to extend the results for expressways in general.

8 lImITATIoNs oF THe sTuDY

The driver behavior, considered in this study can be refined further to consider many more physiological and psychological factors.

9 FuTuRe ReseARCH sCope

This study can be further extended to study the following aspects:

1. Developing a concept of stochastic capacity estimates under heterogeneous traffic conditions prevailing on expressways in India. Such estimates would account for vehicle composition in the traffic stream.

2. Studying lane utilization and lane discipline in Indian Expressways to determine the degree of heterogeneity in these facilities with clear

Fig. 10 Simulated 1-Minute Traffic Flow

Fig. 11 Simulated 5-Minute Traffic Flow

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demarcation between vehicles following a lane discipline and those who do not.

3. Area-Occupancy can be used as a parameter of surrogate measure for the density

4. The effect of vehicle composition on PCU values can be determined from the calibrated model.

10 ACKNoWleDGemeNTs

The authors would like to thank Central Road Research Institute (CRRI), New Delhi India for providing the traffic data used in this study. They would also like to thank PTV AG, Germany for providing the software VISSIM used in this study.

ReFeReNCes1. Fellendorf, M., and Vortisch, P. (2001).Validation of the

Microscopic Traffic Flow Model VISSIM in Different Real-World Situations. Proceedings of 80th Annual Meeting of the Transportation Research Board, Washington D.C.

2. Matsuhashi, N., Hyodo, T., Takahashi, Y. (2005). Image Processing Analysis on Motorcycle Oriented Mixed Traffic Flow in Vietnam. Proceedings of Eastern Asia society for transportation studies (EAST), Tokyo, 929–944.

3. Zhang, G., Wang, Y., Wei, H., Yi, P., (2008). A Feedback Based Dynamic Tolling Algorithm for High-occupancy Toll Lane Operations. Transportation Research Record, 2065, 54-63.

4. Hossain, M.J. (2004).Calibration of the Microscopic Traffic Flow Simulation Model VISSIM for Urban Conditions in Dhaka city. Master thesis, University of Karlsruhe, Germany.

5. Velmurugan, S., Errampalli, M., Ravinder, K., Sitaram Anjaneyulu, K., and Gangopadhyay, S. (2010). Critical Evaluation of Roadway Capacity of Multi-lane High Speed Corridors under Heterogeneous Traffic Conditions through Traditional and Microscopic Simulation Models. Journal of Indian Roads Congress, 235-264.

6. Arasan, V.T., and Koshy, R.Z. (2005). Methodology for Modeling Highly Heterogeneous Traffic Flow. Journal of Transportation Engineering, 131, 544 – 551.

7. Arasan, V. T., and Koshy, R. Z. (2003). Headway Distribution of Heterogeneous Traffic on Urban Arterials. Journal of Institution of Engineers (India), 84, 210–215.

8. Dey, P.P., S. Chandra and S. Gangopadhyay (2008).Simulation of Mixed Traffic Flow on Two-lane Roads. Journal of Transportation Engineering, ASCE, 134, 361-369.

9. Manraj Singh Bains, Balaji Ponnu, Shriniwas S Arkatkar (2012). Modeling of Traffic Flow on Indian Expressways using Simulation Technique. Procedia - Social and Behavioral Sciences 43 ( 2012 ) 475 – 493.

10 Chandra, S. (2004). Capacity Estimation Procedure for Two Lane Roads under Mixed Traffic Conditions. Journal of Indian Road Congress, 165, 139-170.

11. Capacity Manual, National Research Council, Transportation Research Board, Washington, D.C.

12. Shriniwas S. Arkatkar “Analysis of Heterogeneous Traffic Flow Using Microscopic Simulation Technique” National Symposium on Innovations and Advances in Civil Engineering, March 16-17, 2012, GGCT, Jabalpur, India, 29-40.

FoRTHComING eVeNT oF IbCIndian Building Congress (IBC) have intimated that on the invitation of Government of Bihar, IBC is organizing its Mid-Term Session & Seminar on “State-of-The –Art Building Technology” from 26th to 28th April, 2013 at Shri Krishna Memorial Hall, North Gandhi Maidan, Patna – 800 004 (Bihar). For more information regarding this event IBC may be contacted at Shri Pradeep Mittal, Honorary Secretary, Indian Building Congress, Sector-6, R.K. Puram, New Delhi – 110 022, Tele. + 91 11 2616 9531, 2617 0197, Fax: + 91 11 2619 6391


AbsTRACTTraffic in India is highly heterogeneous, comprising different types of vehicles with widely varying static and dynamic characteristics. Due to these variations in traffic composition, the Passenger Car Unit (PCU) is normally used as a standard unit of measurement of traffic volume. The guidelines provided by Indian Roads Congress (IRC) in respect of PCU of different categories of vehicles are more or less based on the static characteristics of the vehicles whereas the dynamic characteristics have been not taken into account during the formulation.

The heterogeneous traffic in India comprised of not only fast moving motorized vehicles but also slow moving vehicles like cycle and cycle rickshaws. The speed of these vehicles vary from 8 to 55 km/h. Due to the widely varying physical dimensions and speeds, each vehicle type is unique in itself and cannot be compared, using direct values, with other vehicle type due to the fact that it demonstrates different effects on behavior of traffic flow stream, on varying composition within the traffic flow.

A number of studies have been carried out abroad suggesting that the behavior of traffic stream were highly influenced by the presence of heavy vehicles and slow-moving vehicles in the flow. Various studies have been conducted abroad and in India suggesting how the traffic flows get affected with the change in percentage of these type of vehicles in traffic volume, but very few studies were carried out stating the effect of these vehicles on other type of vehicle. The lessons learnt from various studies are that each type of vehicle has an effect on the performance of other types vehicles depending upon its own static & dynamic characteristics. In this study, an attempt has been made to build a number of relationships to appreciate the characteristics of different types of vehicles in regard to their performance and their effect on the varying composition of the traffic stream. These include the studies of the effects of the share of NMT and Heavy vehicles on PCU values of Bus and bicycle, variation of PCU values of different modes of transport as against the speed of traffic stream. The above studies forms the basis for formulation of dynamic PCU values under varying traffic flow composition and speed. Further attempt has been made to work out the capacity

DeTeRmINATIoN oF DYNAmIC pCus oF DIFFeReNT TYpes oF pAsseNGeR VeHICles oN uRbAN RoADs: A CAse sTuDY,

DelHI uRbAN AReAproBhat kr. paul* anD p.k Sarkar**

of arterial road for varying road width by considering values of dynamic PCU values.

1 bACKGRouND

Traffic in India is highly heterogeneous, comprising different types of vehicles with widely varying static and dynamic characteristics. The heterogeneous traffic in India comprised not only of fast moving motorized vehicles but also of slow moving vehicles like bicycles and cycle rickshaws. The speeds of these vehicles vary from 8 to 55 km/h. Due to the widely varying physical dimensions and speeds, every vehicle type is unique in itself and cannot be compared, using direct values, with other vehicle types due to the fact that it has different effects on behavior of traffic flow, on different composition within the traffic flow.

Passenger Car Unit (PCU) is used to express the capacity of roads. The values of PCU’s of any other modes except car is worked out by considering car as one unit in the prevailing traffic flow condition. It is therefore, a measure of the performance of any mode in the traffic stream with respect to Car which is defined as one unit. The guidelines provided by Indian Roads Congress (IRC) in respect of PCUs of different categories of vehicles are more or less based on the static characteristics, like size of the vehicles. Whereas the dynamic characteristics like speed of the vehicle, lateral clearance between two consecutive vehicles and its composition within the traffic stream, are among the few major factors which are not taken into consideration during the formulation of passenger car unit.

* Ex. Student, School of Planning and Architecture, New Delhi** Professor, Department of Transport Planing, School of Planning and Architecture, New Delhi

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A number of studies have been conducted abroad suggesting that the behavior of traffic stream is highly influenced by the presence of heavy vehicles and slow-moving vehicles in the flow. Various studies have been carried out abroad and in India (CRRI, 1982) suggesting how the traffic flows get affected with the increase in percentage of these types of vehicles in traffic volume, but very few were carried out stating the effect of these vehicles on other types of vehicles. It is clear from various studies that the effect of performance of each vehicle type on other vehicle types depends upon its own static & dynamic characteristics.

2 ReVIeW oF THe eARlIeR sTuDIes

In the past, various methods have been adopted for estimation of PCU values of vehicles. The basis used for estimation process is (i) Arterial road (e.g. Sumner, et.al. 1980), (ii) speed (e.g. Aerde Van and Yagar, 1984; and Elefteriadou et.al. 1997), (iii) density (e.g. Huber, 1982; Webster and Elefteriadou, 1999), (iv) freeway capacity (e.g. Linzer,), and (v) queue discharge (e.g. Al-Kaisy et.al. 2005). All these studies, however, are mainly related to estimation of Passenger Car Equivalents (PCE) for heavy vehicles (Trucks or Buses) under homogeneous traffic conditions and hence, the results of these studies are not applicable for Indian conditions. Justo and Tuladhar (1984) concluded that the PCU value of each vehicle category is not a constant, but varies with several factors such as traffic composition, Volume-to-Capacity ratio and other factors associated with the roadway, traffic and environment.

Chandra and Sikdar (2000) observed that PCU for a vehicle type is mainly controlled by homogeneity/heterogeneity of the traffic stream, which in turn, depend upon the relative proportion of different types of vehicle. PCU for large size vehicle i.e. bus/truck

increases and for small size vehicles like 3-wheeler and 2-wheeler decreases with increase in their own proportions in the traffic stream. The basic philosophy involved in the development of concept of dynamic PCU is that capacity estimation in a common unit must be same irrespective of stream composition under given physical and control conditions. The researchers developed a computer program to evaluate PCU for a vehicle type on urban roads.

Chandra (2000) calculated the PCU values for two-lane undivided roads using two variables (i) speed ratio of the car to the subject vehicle (for which PCU value is to be calculated) and (ii) space occupancy ratio of the car to the subject vehicle. However, these values are empirical and are based on limited traffic data.

Malliarjuna and Rao (2006) used area occupancy in place of density, as equivalency criteria to estimate the PCU values for buses, trucks and motorized two-wheelers using a simulation model. The estimated PCU values, for all the considered vehicle categories are found to decrease with increase in their respective proportions.

Recently, a study on Dynamic PCU Value for Urban Roads is carried out by Bais (2007) in the School of Planning and Architecture which provides the variation of PCUs of different types of vehicles as against the change in traffic volume along with the change in composition of vehicle for which PCU values for various modes are determined while the present work examines the derivation of dynamic PCU of different modes of transport with respect to variation of NMT, HMV along with the change in traffic stream speed Table 1 presents the variation of Dynamic PCU values under varying traffic magnitude and composition of the traffic flows

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Table 1 Variation of PCUs Under Varying Traffic Flow and Composition of the Vehicle

Composition Vehicles 1000 2000 5000

bicycle 2-W 3-W bus bicycle 2-W 3-W bus bicycle 2-W 3-W bus

1 0.63 3.05 0.6 3.05 0.51 3.28

5 0.67 0.85 2.84 0.64 0.82 2.9 0.54 0.72 3.07

10 0.7 0.62 0.85 2.58 0.68 0.58 0.82 2.63 0.58 0.46 0.73 2.81

15 0.62 0.86 2.31 0.58 0.84 2.57 0.46 0.73

20 0.61 0.87 0.57 0.84 0.45 0.74

25 0.61 0.88 0.57 0.85 0.45 0.75

30 0.58 0.89 0.56 0.86 0.44 0.76

50 0.55 0.54 0.42

80 0.58 0.51 0.39

3 CoNCepT

As per IRC:106-1990, urban roads are characterized by mixed traffic conditions, with varying degree of interaction between various kinds of vehicles. This has led to the understanding of road capacity with its maximum number of vehicles passing through a road section in unit time. It is usual to express the capacity of urban roads in term of a common unit. The unit generally employed is the Passenger Car Unit (PCU) and each vehicle type is converted into equivalent PCU based on its relative size, weight, speed and influential catchment area in the traffic stream.

According to HCM 1965, PCE was defined as “number of passenger cars displaced in the traffic flow by a truck or a bus, under the prevailing roadway and traffic conditions. Most of the studies used the following formula to work out the PCU value for a particular mode.

PCU of a particular vehicle= (Ai x Vc) / (Vi x Ac)..............................Equation No.1

Where Vc and Vi are speeds of car and particular vehicle i respectively and Ac and Ai are their influence area.

4 meTHoDs oF DeTeRmINING p.C.u. VAlue

A common method used in the USA is the density method. However, the PCU values derived from the

density method are based on underlying homogeneous traffic concepts such as strict lane discipline, car following etc. On the other hand, highways in India carry heterogeneous traffic, where road space is shared among many modes of transport with different physical dimensions in which loose lane discipline prevails coupled with non-adherence to car following norm to a great extent. This complicates the computing of PCE or PCU values for different types of vehicles.

The different methods for determining the PCU Value are presented as under:

● Homogenization coefficient

● Semi-empirical method

● Walker’s method

● Headway method

● Multiple linear regression method

● Simulation method

Factors on which the PCU values of different vehicle classes depend upon can be stated as under:

● Dimensions, power, speed, acceleration and braking characteristics of the vehicle.

● Road characteristics such as geometrics including gradients, curves, access controls, type of road: rural or urban, presence and the type of intersections etc.

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● Transverse and longitudinal clearances between vehicles moving on road, which in turn depends upon the speeds, driver characteristics and the classes of other moving vehicles.

● Traffic stream composition of different classes of vehicles.

These factors are not constant and change dynamically under different conditions. Therefore, there is a need to develop a modified approach considering the various traffic interaction and flow characteristic as application of single set of PCU values poses problems resulting in inaccuracy for determination of magnitude of traffic flow.

5 A CAse sTuDY & ITs TRAFFIC CHARACTeRIsTICs

Urban arterial roads of Delhi city are considered as the case study in this research shown in the Fig. 1. The roads which are taken into consideration for this study are as under:

● Vasant Kunj Marg

● Willingdon Crescent Marg

● Vandemataram Marg

● Shahjahan Road

Fig. 1 Arterial Roads Selected for the Study

Traffic flow on the major roads of Delhi continues to increase over a period of time. The construction of flyovers at various locations has only slowed down the process of congestion and delay. The adverse

conditions have shifted from the intersections to other locations or next adjoining at-grade intersections. The road capacity has been reduced by encroachments and parking. A study was conducted on the city network

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in 2002 by C.R.R.I. to assess urban road traffic conditions, which showed that roads are carrying more than 100,000 vehicles in the inner and middle areas of Delhi. The magnitude of traffic flow is most critical on bridges across Yamuna – I.T.O. Bridge carrying the maximum traffic followed by Nizamuddin Bridge. The traffic is constantly increasing at a rate of more

than 2.5% per annum and heavy intensity of traffic continues to add to the problems causing degradation of environment of the city. In fact traffic is the largest contributor to pollution levels in Delhi.

Vehicular compositions on the selected stretch of arterial roads as mentioned above are shown below:-

Fig. 2 Vasant Kunj

Fig. 3 Willingdon Crescent Marg

Fig. 4 Vandematarm Marg

Fig. 5 Shahjahan Road

Table 2 Dynamic pCus for Different Categories of Vehicles

Vehicle Type Average speed (km/h) for Vasant Kunj road

Average speed (km/h) for Willingdon Crescent marg

Average speed (km/h) for Vandemataram Road

Average speed (km/h) for shahjahan Road

2-wheeler 40 39 40 37

3-wheeler 33 34 33 32

Bus 23 22 23 22

Car 39 38 39 36

Cycle 12 12 12 11

Truck 29 29 30 28

Stream 36.5 35.6 36.2 33.5

Average Speed of various vehicle types on the selected stretch of arterial roads as mentioned above

are presented in the Table 2

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6 seleCTIoN oF moDel AND FACToRs

The passenger car unit may be considered as a measure of relative space requirement of a vehicle class compared to that of a passenger car under a specified set of vehicle characteristic, stream characteristics, and roadway characteristic.

6.1 Factors Considered in the model building

To determine the variation in PCU values for a vehicle type, the following factors are considered:

● Influence area of each vehicle.

● Traffic Composition

● Speed of each category of vehicle

● Headway

● Lateral clearance

6.2 selection of equation

The reason behind selecting the specific equation is it’s widely used in the determination of PCU values for various modes of transport.

The passenger car equivalency of a vehicle type is inversely proportional to the ratio of speed and directly proportional to the space requirement of a vehicle with respect to car

PCU = (Ai x Vc) / (Vi x Ac)..............................Equation No.1

Where Vc and Vi are speeds of car and vehicle i respectively and Ac and Ai are their influence area.

6.3 Data Collection

The data is collected by Videography recording on real time basis. A trap is made on the road section of 30 m and the traffic is observed for a fixed interval. The following data is collected:

● Classified Volume count

● Speed

● Headway

● Lateral Clearance

The survey is conducted for the duration of 3 hours on a single road during peak hour period. The data analysis is carried out by running the recorded tapes several times to measure the data with 90% of accuracy.

6.4 Conceptual model

In order to appreciate the functioning of the model, an attempt has been made to develop to a conceptual model to start with as shown in the Fig. 6.

Fig. 6 A Conceptual Model for Determining Dynamic PCU

For analyzing the headway and lateral clearance data has been measured from recorded data.

The above diagram shows how the PCU value for any type of vehicles can be worked out. It can be seen from the Fig.6 that the influence area has been the determinant factor to work out the PCU of any type of vehicles. The determination of PCU as given in the Equation No. 1 has been taken into account in the calculation of PCU which is the ratio of speed of any type of vehicle to the proportional space requirement of the vehicle with respect to car. Therefore, PCU for any type of modes has been worked out under different traffic conditions by considering traffic composition and respective vehicular speeds.

7 ANAlYsIs oF DYNAmIC pCus

The PCU values are developed according to the influence area of each vehicle, and speed in the section from the collected data with the help of regression equation and developing relationship between influence area and volume, speed and composition.

The PCU values developed are dynamic and according to the change of traffic stream taken from the collected data with respect to speed, vehicular mix, volume, headway and lateral clearance. PCUs for various types

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of vehicles are calculated based on the Equation No.1. Table 3 presents the dynamic PCU values for different

categories of vehicles for the urban roads selected for the study.

Table 3 Dynamic pCus for Different Categories of Vehicles

location pCu of 2-Wheeler

pCu of 3-Wheeler

pCu of bus pCu of Cycle pCu of Truck

max min max min max min max min max minMehrauli to Mahipalpur 0.53 0.40 1.31 1.12 3.82 3.43 0.61 0.42 2.69 2.06Mahipalpur to Mehrauli 0.40 0.30 1.44 1.09 4.26 3.75 0.54 0.45 2.54 2.22Vasant kunj Road Karol Bagh to Dhola Kuan 0.45 0.36 1.40 1.11 4.16 3.50 0.59 0.40 2.48 2.05Dhola Kuan to Karol Bagh 0.48 0.33 1.31 1.05 3.88 3.45 0.56 0.38 2.28 2.04Vandemataram marg 11 Murti to RML hospital 0.43 0.30 1.18 1.07 4.42 3.32 0.48 0.40 2.52 2.03RML hospital to 11 Murti 0.50 0.31 1.37 1.08 4.07 3.60 0.66 0.44 2.56 2.19Willingdon Crescent margIndia Gate to Safdarjang 0.53 0.35 1.30 1.12 4.26 3.67 0.56 0.43 2.43 2.22Safdarjang to India Gate 0.35 0.30 1.23 1.11 4.22 3.46 0.50 0.40 2.60 2.24shahjahan Road

unit: pCu

After calculating the PCU values for different roads at different time intervals, a number of relationships are developed to determine the PCU of different types vehicle categories depending on varying percentage of NMT & HMV in traffic composition, and following results are obtained as shown in the Figs.7-9,

along with its statistics. It can be seen from the statistics that most of the relationships developed offer high R2 values and are statistically sound with respect to t values with significance at 5% and 1% level. Even we look at the correlations tables, the dependence between the independent variables are not significant.

Effect of HMV & NMT on PCU of Bus

Equation : Y = 3.37 – 0.3156a + 0.185b

r2 = 0.79

Fig. 7 Effect of share of Non-Motorized Transport (NMT) & Heavy Motor Vehicle (HMV) on Passenger Car Unit (PCU) value of Bus

Where; Y = PCU of Bus

a = % of HMV

b = % of NMT

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Equation : Y = 0.285 + 0.097a – 0.025b

r2 = 0.54

Effect of HMV & NMT on PCU of 2-wheeler motorized vehicleFig. 8 Effect of the Share of NMT & HMV on PCU Value of 2-Wheeler Motorized Vehicles

Where; Y = PCU of 2-wheeler

a = % of HMV

b = % of NMT

Equation : Y = 0.877 – 0.091a – 0.015b

r2 = 0.56

Where; Y = PCU of Cycle

a = % of HMV

b = % of NMT

Depending upon the above equations, the graphs show the effect of different composition of NMT & HMV in traffic composition on PCU values of different vehicles. Fig.10 shows the variation of PCU values of cycle, bus and 2-Wheeler motorized vehicles as against the composition of NMT and HMV.

Effect of HMV & NMT on PCU of CycleFig. 9 Effect of Share of NMT & HMV on PCU Value of Cycle

Fig. 10 PCU of different vehicdles on different HMV & NMT%

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Another interesting analysis is carried out showing the effect of speed on the PCU values of different

effect of stream’s speed on pCu values of different vehicles

vehicles, resulting in development of the following relationships:

For BUS

Equation: Y = - 0.0449x + 5.4688

r2 = 0.4584

Where; Y = PCU of Bus

a = Speed

For 2-Wheeler

Equation: Y = 0.0164x – 0.2008

r2 = 0.7677

Where; Y = PCU of 2-Wheeler

x = Speed

For 3-Wheeler

Equation: Y = 0.0106x + 0.7756

r2 = 0.5127

Where; Y = PCU of 3-Wheeler

x = Speed

For Cycle

Equation: Y = 0.0091x + 0.1559

r2 = 0.4807

Where; Y = PCU of Cycle

x = Speed

Variation of PCU with respect to truck could not be established from the existing set of data.

Fig.11 shows the variation of PCU values of various modes of transport against the change in vehicular stream speed.

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Fig. 11 Graph Showing the Variation of PCU Values at Different Speed

Based on the above equations and the traffic composition, the nomogram presented below is developed showing the variation of PCU of different vehicles at different speeds for an average composition of composition of traffic.

PCUs for various types of vehicles are calculated based on the equation indicated above.

The capacity which is worked out at the next stage using the above method is given in Table 4:-

Fig. 12 Nomogram Showing Variation of PCU wrt Varried Stream’s Speed for an Average Vehicular Composition

Table 4 Capacity for Different Carriageway width

Actual Carriageway width (m)

Capacity through Dynamic pCu (pCu/hr)

6.87 2570

7.34 2818

7.37 2939

7.23 2774

7.40 3245

7.87 3237

7.70 3216

8.10 3302

8 A CompARIsoN oF VAlues oF DYNAmIC pCus beTWeeN THe pReseNT AND bAIs’s sTuDY

Table 5 presents a comparative assessment of the ranges of PCU values obtained from the two studies as mentioned above.Table 5 Comparison of pCu Values between the Two

studies

present study bais’s studymax min max min

Two-Wheeler 0.53 .030 0.62 0.19Three- Wheeler 1.44 1.05 0.89 0.60Bus 4.42 3.32 3.57 2.31Bicycle 0.56 0.38 0.63 0.36

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1. The values obtained from the studies closely match with each other apart from the Three –wheeler with respect to maximum and minimum values. There is also difference in the values of PCU of buses. The variation of value of Three-wheeler is primarily attributable to non-adherence to the traffic lane to a great extent as well as for its more maneuverability with little safety which poses complexity in the calculation in the PCU.

9 lImITATIoNs

Some of the limitations of the said project are:-

1. Study was carried out on urban arterial roads.

2. For Heavy vehicles, only Buses were considered and for slow-moving vehicles only Cycles were considered.

3. The study was conducted for a flow ranging from 800 PCU/hr to 1800 PCU/hr.

10 CoNClusIoNs

The above study is conclusive with the following observations.

1. The PCU of 2-wheeler increases with increase in HMV% and decrease in NMT%.

2. The PCU of Bus decreases with the increase in HMV % and decrease in NMT%.

3. No significant relationship was observed between PCU of Auto-rickshaw and change in percentage of HMV & NMT.

4. As the speed of the stream decreases, the PCU of Bus increases. This shows that Busses experience a more freedom of space with higher speed as compared to other type of vehicles. The reason behind the variation of PCU of Bus is due to the speed of the bus which varies significantly from low speed of old DTC buses to high speed of Volvo and chartered buses

5. The PCU value of 2-wheeler decreases with decrease of stream’s speed which is due to its vehicle characteristics like speed and size.

6. The PCU value of cycle also decreases with decrease in speed.

7. The PCU value of 3-wheeler also decreases with decrease in speed.

8. More studies need to be conducted for different traffic flow ranges so as to derive a generic relation irrespective of the traffic flow.

ReFReNCes1. CRRI 1982, Road User Cost Study Final Report

New Delhi.2. HCM 1965,1995 & 20003. Linzer TRB Circular 212 (1979)4. Huber, M.J. “1982 Estimation of Passenger Car Equivalent

of truck in Traffic Stream”5. Cunagin, W.D. and Messer, C.J. “Passenger Car Equivalent

for Rural Highways “TRRL 905, 19836. Van Aerde, M., and Yagar, S. “Capacity, Speed, and

Platooning Vehicle Equivalents for Two-Lane Rural Highways”. In Transportation Research Record 971. TRB, National Research Council, Washington, DC., 1984, pp. 58-67.

7. Huber, M. “Estimation of Passenger Car Equivalents of Trucks in Traffic Stream”. In Transportation Research Record 869. TRB, National Research Council, Washington, DC., 1982, pp. 60-70

8. Webster, N., and Elefteriadou, L. “A Simulation Study of Truck Passenger Car Equivalents (PCE) on Basic Freeway Sections”. In Transportation Research, Vol. 33B, 1999, pp. 323-336

9. Al-Kaisy, A., Hall, F., and Reisman, E. “Developing Passenger Car Equivalents for Heavy Vehicles on Freeways During Queue Discharge Flow”. In Transportation Research, Vol. 36A, 2002, pp. 725-742.

10. Justo, C.E.G. and Tuladhar, S.B.S” Passenger Car Unit Values for Urban Roads” Journal of IRC 1984

11. CRRI, Capacity of Roads in Urban Areas 1988 New Delhi

12. Satish Chandra, Virendra Kumar and Sikdar, P.K.” Dynamic PCU and Estimation of Capacity of Urban Roads “ Indian Highways 1995

13. Gopakumar, Nair,S , Basu,B and Maitra,B “Modeling of Passenger Car Equivalency on Urban Mid-Block”, IIT, Kharagpur

14. Paul,PK,” Thesis – Capacity of Urban Arterial Roads,” 2009, SPA.

15. Sumner, R., Hill, D., and Shapiro, S. “Segment Passenger Car Equivalent Values for Cost Allocation on Urban Arterial Roads”. In Transportation Research, Vol. 18A, No. 5/6, 1984, pp. 399-406.

16. Linzer, E., Roess, R., and McShane, W. “Effect of Trucks, Buses, and Recreational Vehicles on Freeway Capacity and Service Volume”. In Transportation Research Record 699.

17. Bais (2007 “Dynamic PCU Value for Urban Roads” the School of Planning and Architecture, 2009

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AbsTRACTRoad accident is an unfortunate event that creates ecological imbalance and social disaster. More than a collision, it is a situation that leaves behind distress, sorrow & sufferings. Therefore, identification of the causes of road accidents become highly essential for adopting necessary preventive measures against this critical event. The damage created by road accidents is to a large extent unrepairable and therefore needs attention to eradicate this continuously increasing trend of awful epidemic. The objective of this research paper is to highlight & focus on the causes resulting in road accidents in the North-Eastern region of India by collecting data from various sources. Though we have concentrated on causes of road accidents, the paper to follow will highlight on some possible remedies for lessening/eliminating road accidents of the region specific in nature.

1 INTRoDuCTIoN

North-Eastern region of India consists of 8 States viz. Assam, Manipur, Meghalaya, Mizoram, Arunachal Pradesh, Nagaland, Tripura & Sikkim. At present the North-Eastern States of India are mainly suffering from poor infrastructure regarding transportation & connectivity problems. Connectivity through rail & air mode is restricted only to selected places of this region. Therefore, roads are the only means for travelling to various places and they act as the veins and arteries for the flow & movement of people, goods & other consumables, supporting the business activities in this region. Each state of this region is dependent on the other for its business activities, which highlights the need for good durable & sustainable roads in the North-Eastern region of India.

Silchar acts as a business hub supporting many states of the North Eastern region. To analyse the problem better a survey was conducted at Silchar, Assam to identify the major ones among the various causes resulting in road accidents in the North-Eastern region. Also, the paper will help to project the rate

RoAD ACCIDeNT: A THReAT ToWARDs NATIoN’s peACe AND pRospeRITY

BikraMjit DaS Gupta* anD aBhijit kr ManDal**

of growth in the vehicle population in the last decade (2000-2010) in the various states of North-East India and the statistics of the road accidents in those states during that period.

* Engineer** Deputy Director (Tech.)

A Truck Loaded with Logs Rammed into the School Bus at Saw Mer, Upper Shillong (Meghalaya) on 15th June, 2012

(Ref. The Shilong Times 16th June, 2012)

Table 1 state-wise Comparison of Road Accidents at North-eastern Region of India in 2008 & 2009

sl. No.

state Road Accidents in

2008

Road Accidents in

2009

% Growth

1 Assam 4262 4585 7.57

2 Tripura 767 865 12.77

3 Manipur 502 578 15.13

4 Meghalaya 191 314 64.39

5 Mizoram 87 125 43.67

6 Nagaland 126 47 -62.69

Source: NCRB data bank

National Automotive Testing and R&D Infrastructure Project (NATRiP), Silchar Centre, E-mail: [email protected]

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Road accident rate in India is among the highest in the world, with at least 1,34,000 killed each year on the road. Road fatalities are an “epidemic” and will become the world’s fifth biggest killer by 2030. North-Eastern region of India is an ecologically sensitive place but lacks the necessary facilities and infrastructure regarding road transportation, ultimately resulting in the critical event- road accidents, creating a major problem for the common people of this region.

2 meTHoDoloGY

Initially, for the identification of problem (i.e. causes of road accidents in North-East India), the need for a survey was realised for collection of primary data. The survey was conducted at Silchar, Assam. To conduct the survey, a questionnaire of likert-scale type was prepared and then the target group was selected to conduct the survey. The response of the target group (total respondents: 40 nos) was collected during the survey and was thoroughly observed & studied for further analysis.

3 DATA ColleCTIoN

3.1 primary Data (Through survey)

Primary data is collected from the survey conducted at Silchar, Assam. A questionnaire (likert-scale type) is used to conduct the survey. The target group for this survey consists of various officials from State/Central Govt. organizations, traffic dept. officials, motor vehicle association, service engineers from vehicle dealers, surveyor & loss assessor from various insurance companies etc. from the locality. The questionnaire was prepared on the various factors causing road accidents in the North-Eastern region of India. The various factors are:

1. Unskilled drivers.

2. Drunken drivers.

3. Improper Traffic Management System (proper marking on road & bumps).

4. Non-compliance & lack of awareness regarding traffic rules.

5. Poor road condition.6. Unfit vehicles.7. Negligence & careless attitude of pedestrians.8. High vehicle density.9. Over-loaded vehicles.10. Limited Road Network.11. Fog & rainy weather condition.The response from the target group is represented in the histogram as follows:

Fig. 1 Response of Target Group Regarding ‘Unskilled Driver ‘as a Cause for Road Accidents

Fig. 2 Response of Target Group Regarding ‘Drunken Driver ‘as a Cause for Road Accidents

Fig. 3 Response of Target Group Regarding ‘Non-Compliance & Lack of Awareness Regarding Traffic Rules ‘as a

Cause for Road Accidents

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3.2 Analysis of primary Data: After observing the response from the survey it is understood that all the above mentioned factors are responsible to a large extent for the occurrence of road accidents in the North-Eastern region of India. But among these various factors, few factors are commonly responsible for road accidents in this region (for e.g. drunken driving), while some other factors are specifically responsible for road accidents in certain states of this region. This research paper will highlight the alarming

Fig. 4 Response of Target Group Regarding ‘Road Condition ‘as a Cause for Road Accidents

Fig. 5 Response of Target Group Regarding ‘Unfit Vehicles‘as a Cause for Road Accidents

Fig. 6 Response of Target Group Regarding ‘Negligence & Careless Attitude of Pedestrians‘ as a Cause for Road Accidents

Fig. 7 Response of Target Group Regarding ‘High Vehicle Density ‘as a Cause for Road Accidents

Fig. 8 Response of Target Group Regarding ‘Over-Loaded Vehicles ‘as a Cause for Road Accidents

Fig. 9 Response of Target Group Regarding ‘Limited Road Network’ as a Cause for Road Accidents

Fig. 10 Response of Target Group Regarding ‘Weather Condition’ as a Cause for Road Accidents

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factors/causes resulting in road accidents in North-East India.

secondary Data:

(Source: National Crime Records Bureau (NCRB) data bank, Ministry of Home Affairs)

Assam :

Similar, trend regarding the vehicle population and road accidents can also be seen in the States of Tripura & Manipur (ref. to Figs. No. 13, 14, 15 & 16). Compared to the vehicle population the road accidents occurring in Tripura is quite high, ultimately resulting in the increase in the number of fatalities (ref. to Fig. No. 4).

In case of Manipur, the vehicle population too grew at a linear rate, but some fluctuations can be seen in the number of road accidents and fatalities during the last decade (2000-2009) (ref. to Figs. No. 15 & 16). It is suggested to take appropriate measures & road safety initiatives to keep the road accident rate on the decreasing trend.

Tripura :Fig. 11 Growth in the Vehicle Population of Assam (2000-2010)

Fig. 12 Linear Growth in the no. of Road Accidents & Fatalities at Assam (2000-2009)

3.3 Analysis of secondary Data

(for States of Assam, Tripura & Manipur):

With reference to Figs. No. 11&12, in case of Assam, the vehicle population is too high and has grown linearly at a substantial high rate during the last decade (2000-2010). The high growth in the vehicle population has also resulted in large number of road accidents. With the increase in the number of road accidents (year-wise) the number of fatalities has also increased in the roads of Assam, making the situation highly critical and alarming.

Fig. 13 Growth in the Vehicle Population of Tripura (2000-2009)

Fig. 14 Linear Growth in the no. of Road Accidents & Fatalities at Tripura (2000-2009)

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manipur :

Fig. 15 Growth in the Vehicle Population of Manipur (2000-2009)

Fig. 16 Graphical Representation of the no. of Road Accidents & Fatalities at Manipur (2000-2009)

3.4 Analysis of secondary Data

(for States of Meghalaya, Mizoram & Nagaland):

In these 3 states (Meghalaya, Mizoram, Nagaland) the number of road accidents occurred during the last decade (2000-2009) is comparatively less than those of Assam, Tripura & Manipur. With reference to Figs No. 17 & 18, it is observed and understood that the vehicle population at Meghalaya grew continuously. But the number of road accidents increased substantially during 2003, 2005 & 2009 (Source: NCRB data bank). Continuous attention & road safety initiatives are required to minimize the number of road accidents each year.

In case of Mizoram (with ref. to Figs. No. 19 & 20) linear growth is observed regarding the vehicle population during 2000-2009. The number of road accidents at Mizoram increased marginally from 2000 to 2003, then for few consecutive years the number of road accidents decreased, but increased suddenly during 2008 & 2009.

meghalaya :

Fig. 17 Growth in the Vehicle Population of Meghalaya (2000-2009)

Fig. 18 Graphical Representation of the no. of Road Accidents & Fatalities at Meghalaya (2000-2009)

mizoram :

Fig. 19 Growth in the Vehicle Population of Mizoram (2000-2009)

Fig. 20 Graphical Representation of the no. of Road Accidents & Fatalities at Mizoram (2000-2009)

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Nagaland :

Fig. 21 Growth in the Vehicle Population of Nagaland (2000-2009)

Fig. 22 Graphical Representation of the no. of Road Accidents & Fatalities at Nagaland (2000-2009)

Nagaland, though being one of the smallest states of the North-Eastern region (area: 16,579 km2), records the second highest vehicle population (ref. to Fig. No. 21) of the North-Eastern region (in terms of number of vehicles registered). Compared to the vehicle population, the number of road accidents and fatalities occurred at Nagaland during the last decade (2000-2009) is marginal.

The data collected from the survey conducted at Silchar (Assam) is represented in a tabular format (Table-2). The factors/causes of road accidents are rated on a likert scale questionnaire by giving rating from 1 to 5, (explained at the bottom of Table-2). The average of each of the causes of road accidents in the North-Eastern region of India is calculated to identify & understand the final result/ output of the survey. The higher the average value the more responsible the factor is for the occurrence of road accidents in this region. Therefore, on this basis, among the ten factors (mentioned in Table-2), the top five factors highly responsible for road accidents in this region, chronologically are- 1. Limited Road Network, 2. Road Condition, 3 Non-compliance & lack of awareness regarding traffic rules, 4. Drunken Driving & 5. Over-loaded vehicles.

Table 2 Data Collection Through survey

sl. No. unskilled Driver

Drunken Driver

Non-Compliance

of Traffic Rules

Road Condition

Unfit Vehicles

Negligence & Careless Attitude of pedestrians

High Vehicle Density

over-loaded Vehicles

limited Road

Network

Weather Condition

1 4 5 4 4 5 4 3 4 4 3

2 3 4 4 5 3 4 3 3 3 3

3 5 5 5 5 4 4 4 5 5 2

4 5 5 5 5 5 4 4 5 5 3

5 5 4 5 4 2 4 4 5 4 3

6 3 4 5 5 2 5 2 5 5 1

7 5 5 4 4 4 4 2 3 4 2

8 5 5 5 5 4 3 2 3 4 2

9 2 4 5 4 4 4 4 2 3 2

10 5 5 5 5 3 2 3 4 4 1

11 3 5 5 5 3 4 4 5 5 2

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sl. No. unskilled Driver

Drunken Driver

Non-Compliance

of Traffic Rules

Road Condition

Unfit Vehicles

Negligence & Careless Attitude of pedestrians

High Vehicle Density

over-loaded Vehicles

limited Road

Network

Weather Condition

12 5 5 4 5 5 4 4 4 5 5

13 5 5 4 5 5 4 4 5 5 3

14 5 5 5 3 5 5 3 4 4 2

15 5 5 5 5 3 5 4 5 5 3

16 5 4 4 4 4 2 2 4 5 5

17 5 4 5 5 4 4 4 4 5 3

18 5 4 4 4 4 3 4 4 4 2

19 3 3 4 4 5 4 5 4 5 4

20 4 4 5 5 4 2 5 5 5 2

21 4 4 4 5 5 5 5 5 5 5

22 5 5 5 4 4 4 3 3 5 2

23 5 5 5 4 5 5 5 5 5 5

24 3 4 5 5 5 4 3 5 5 1

25 2 4 1 5 1 5 2 5 5 1

26 4 5 5 5 4 5 5 5 5 2

27 5 5 5 5 5 5 4 5 4 4

28 5 5 5 5 5 5 4 5 5 4

29 5 3 4 5 3 5 5 5 5 4

30 5 5 5 5 5 4 5 5 5 1

31 5 5 5 5 4 5 5 5 5 5

32 4 4 4 4 4 4 4 4 4 2

33 4 4 5 5 4 5 5 5 5 4

34 4 5 5 5 5 5 2 5 5 3

35 4 4 4 3 2 4 4 4 5 1

36 5 5 5 5 5 4 5 5 5 4

37 5 5 4 4 4 4 4 5 3 3

38 4 5 4 4 3 3 4 4 5 4

39 5 5 5 5 5 4 4 5 5 5

40 5 4 5 5 5 5 5 5 5 3

Avg: 4.375 4.525 4.55 4.6 4.025 4.125 3.825 4.45 4.625 2.9

5-Completely Agree, 4-Agree, 3-No opinion, 2-Disagree, 1-Completely disagree

Or, 5-Highly responsible, 4-Responsible, 3-Moderate, 2-Not generally, 1-Not responsible at all

Table 2 Contd ...

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Table 3 Correlation matrix unskilled

DriverDrunken

DriverNon-

compliance & lack of awareness regarding

traffic rules

Road Condition

Unfit vehicles

Negligence of

pedestrians

High vehicle density

over-loaded

vehicles

limited road

network

Weather condition

Unskilled Driver

1

Drunken Driver

0.4358 1

Non-compliance & lack of awareness regarding

traffic rules

0.3720 0.3115 1

Road Condition

0.0000 0.1016 0.1622 1

Unfit vehicles 0.3711 0.3050 0.4058 0.0993 1

Negligence of pedestrians

-0.0964 0.0188 0.0100 0.1527 0.0823 1

High vehicles density

0.1875 -0.1410 0.2660 0.1376 0.2945 0.2046 1

Over-loaded vehicles

0.1917 0.0848 0.0918 0.4548 0.0796 0.3744 0.3941 1

Limited road network

0.1195 -0.0085 0.0681 0.3458 0.0924 0.1855 0.2578 0.5608 1

Weather condition

0.3417 0.0363 0.0580 0.1138 0.4162 0.1738 0.3778 0.1970 0.2047 1

The data from the Table-2 is further used to prepare the correlation matrix by the application of data analysis tool in MS excel. A correlation is a single number that describes the degree of relationship between two variables. By using the correlation matrix, correlation among two factors can be established. Coefficients having higher value (close to one) will establish high correlation among the corresponding two factors. With reference to Table-3, considering coefficients whose value is more than 0.5, it is observed that only four coefficients have value higher than 0.5. Hence their corresponding two factors are highly correlated. Those factors are:

1. Limited road network vs Over-loaded vehicles (correlation coeff. 0.5608)

2. Over-loaded vehicles vs Road condition (cor. coeff 0.4548)

3. Drunken Driving vs Unskilled Drivers (correlation coeff. 0.4358)

4. Weather Condition vs Unfit vehicles (cor. coeff. 0.4162)

5. Unfit vehicles vs Non-compliance & lack of awareness regarding traffic rules (cor. coeff. 0.4058)

The above mentioned two factors (no. 1 & 2) are highly correlated and in many situations are combinedly responsible for occurrence of road accidents in the North-Eastern region of India.

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Data Analysis of Table-4 (shown in Annexure-I):

From Table-2, for each factor/variable the average of four consecutive numbers are calculated. For normalisation, those values are then divided by 5 and then by 10 (since 5 is the maximum value of Table-2 and by taking average of four consecutive numbers, 10 values are obtained for each factor/ variable). Also for each factor/variable the maximum value is obtained from the correlation matrix (Table-3). Then for calculating the weightage for each factor/variable, the summation of the product of those normalised values (from Table-4) and the maximum value of the correlation coefficient of those respective factors/variables is done, which is shown as follows:-

Calculation of weightage from normalised data: [Wi] = [Normalised survey data] [Max. value of correln coeff. for respective factors]W1 = (0.085 x 0.4358) + (0.095 x 0.3115) + (0.09 x

0.4058) + (0.095 x 0.4548) + (0.085 x 0.4162) + (0.08 x 0.3744) + (0.07 x 0.3941) + (0.085 x 0.5608) + (0.085 x 0.3458) + (0.055 x 0.3778) = 0.332

W2 = (0.09 x 0.4358) + (0.09 x 0.3115) + (0.095 x 0.4058) + (0.09 x 0.4548) + (0.06 x 0.4162) + (0.08 x 0.3744) + (0.05 x 0.3941) + (0.08 x 0.5608) + (0.085 x 0.3458) + (0.04 x 0.3778) = 0.305

and so on..

Therefore, 1. Limited road network (Weightage, W9 = 0.358)2. Over-loaded vehicles (Weightage, W8 = 0.356)3. Weather condition (Weightage, W10 = 0.355)4. Negligence & careless attitude of pedestrians

(Weightage, W6 = 0.351)5. High vehicle density (Weightage, W7 = 0.345)6. Road condition (Weightage, W4 = 0.341)7. Unskilled Driver (Weightage, W1 = 0.332)8. Unfit vehicles (Weightage, W5 = 0.33)9. Non-compliance & lack of awareness regarding

traffic rules (Weightage, W3 = 0.317)10. Drunken Driving (Weightage, W2 = 0.305)

Photo from Assam Times News Paper (Assam), Dated 13th Aug’2010

Haflong-Silchar road in dilapidated condition13 August, 2010, Anup Biswas

Photo From Seven Sisters Post Newspaper (Assam), Dated 7th May’ 2012

Haflong-Silchar road

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Factors with higher value of weightage are more responsible for the occurrence of road accidents in the North-Eastern region of India (i.e. limited road network, over-loaded vehicles, weather condition and so on).

The general objective function of road accidents can be related with the identified factor (higher the weightage value, higher is the contribution towards road accidents) as,

Road Accident (y) = i = 1 10Σ Wi xi, i = no. of factors

(Chance cause or probability)

Where,

x1 = unskilled driver

x2 = drunken driver

x3 = Non-compliance & lack of awareness regarding traffic rules

x4 = Road condition

x5 = Unfit vehicles

x6 = Negligence & careless attitude of pedestrians

x7 = High vehicle density

x8 = Over-loaded vehicles

x9 = Limited road network

x10 = Weather condition

The data for the various factors/variables are obtained from the survey (in Table-2) conducted at Silchar (Assam) is specific for the North-Eastern region of India. Therefore, the above mentioned equation of road accident is also specific for this region.

5 CoNClusIoN & ReCommeNDATIoN:

As shown in Table-2, ‘limited road network’ appears to be the most significant factor resulting in road accidents in the North-Eastern region of India, followed by ‘road condition’, ‘non-compliance & lack of awareness with respect to traffic rules’, ‘drunken driving’ and ‘over-loaded vehicles’. But apart from these individual causes responsible for occurrence

of road accidents in this region, it is more essential to find out a correlation between two factors/causes mainly resulting in road accidents in this region. From Table-3 (considering correlation between two factors), ‘limited road network’ vs ‘over-loaded vehicles’ shows the maximum correlation coefficient (i.e. 0.5608), followed by ‘over-loaded vehicles’ vs ‘road condition’ (correlation coefficient 0.4548), which means that those combinations result in maximum road accidents and also those respective factors need immediate and serious attention. Therefore, it is recommended that running of over-loaded vehicles on the poor & limited roads of North-East India is very risky & has higher probability of road accidents.

But, based on the response from the target group during this survey & the correlation matrix (Table-3), the correlation coefficient between ‘drunken driving’ vs ‘unskilled drivers’ is obtained (i.e. 0.4358), which means that the target group perceives that the chance of occurrence of road accidents with this combination is very less and therefore the correlation coefficient is also relatively less (i.e. 0.4358). Similar conclusion can also be drawn for the remaining correlations (between two factors) with lesser correlation coefficients (mentioned in Table-3). Moreover, it is also recommended that the drivers of this region should be properly trained and educated to change their basic mentality & behaviour and also to make them aware while driving on the road.

ReFeReNCes1. http://ncrb.gov.in/adsi/data/ADSI2000/accidental-deaths-

00.pdf

2. http://ncrb.gov.in/adsi/data/ADSI2001/Accidental.htm

3. http://ncrb.gov.in/adsi/data/ADSI2003/accident03.pdf

4. http://ncrb.gov.in/adsi/data/ADSI2005/accident05.pdf

5. http://ncrb.gov.in/adsi/data/ADSI2006/Accident06.pdf

6. http://ncrb.gov.in/adsi/data/ADSI2007/Accident07.pdf

7. http://ncrb.gov.in/ADSI2008/accidental-deaths-08.pdf

8. http://ncrb.gov.in/CD-ADSI2009/accidental-deaths-09.pdf

9. http://ncrb.gov.in/ADSI2010/accidental-deaths-10.pdf

10. http://www.theshillongtimes.com/2012/06/

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Annexure-ITable 4 matrix for Calculation of Weightage for Various Factors

Variables 1 2 3 4 5 6 7 8 9 10 Correlation coeff. (max.

value)Unskilled

Driver0.085 0.095 0.09 0.095 0.085 0.08 0.07 0.085 0.085 0.055 0.4358

Drunken Driver

0.09 0.09 0.095 0.09 0.06 0.08 0.05 0.08 0.085 0.04 0.3115

Non-compliance & lack of awareness regarding

traffic rules

0.075 0.095 0.095 0.095 0.075 0.07 0.075 0.075 0.085 0.05 0.4058

Road Condition

0.1 0.095 0.09 0.085 0.085 0.08 0.065 0.09 0.095 0.065 0.4548

Unfit vehicles

0.085 0.075 0.09 0.09 0.085 0.065 0.09 0.085 0.095 0.055 0.4162

Negligence & careless attitude of pedestrians

0.085 0.09 0.095 0.09 0.095 0.09 0.08 0.09 0.1 0.065 0.3744

High vehicle density

0.08 0.095 0.08 0.1 0.075 0.1 0.075 0.1 0.095 0.055 0.3941

Over-loaded vehicles

0.095 0.085 0.09 0.095 0.08 0.09 0.095 0.095 0.095 0.06 0.5608

Limited road

network

0.085 0.09 0.095 0.09 0.08 0.09 0.08 0.095 0.1 0.08 0.3458

Weather condition

0.095 0.095 0.09 0.09 0.085 0.08 0.085 0.095 0.09 0.075 0.3778

obITuARY

The Indian Roads Congress express their profound sorrow on the sad demise of Dr. Vijay Trimbak Ganpule resident of F. No.101 & 101-A, Laxmikant Apt-A, Shree Hanuman Chs., Opp. Kakad Ind. Estate, Off. T.H. Kataria Marg, Sitaram Keer Marg, Mumbai on 14th March 2013. He was an active member of the Indian Roads Congress.

May his soul rest in peace.

The Indian Roads Congress express their profound sorrow on the sad demise of Shri M. Amirthalingam, Joint Chief Engineer (Highways) & Officer on Special Duty (Retd.), 21, Gopal Pillaiyar Koil Street, Thiruvannamalai (Tamil Nadu). He was an active member of the Indian Roads Congress.

May his soul rest in peace.


AbsTRACTCinder is a waste material generated as coal residues from the blast furnace of power plant at TATA Steel Industries, Jharkhand, India. Another waste material, Slag is generated as a byproduct during the manufacturing of molten iron. Both cinder and slag were dumped together at the dumping site (80% cinder and 20% slag) and is commonly called “cinder” as it mainly contains cinder. The material has very limited applications. Cinder material was collected from the dump area and was investigated for its feasibility in the road embankment and sub grade. The paper discusses the physical, chemical and geotechnical properties of cinder and results of typical stability analysis of cinder embankment. The paper also discusses the suitability of cinder waste for embankment and sub grade considering the MORTH Specifications. It was concluded that the material has potential for the construction of embankment while it is unsuitable for sub grade.

1 INTRoDuCTIoN

Cinder and slag are waste materials mainly consists of oxides of silica, aluminum and iron (97%). Both cinder and slag were dumped together at the dumping site, outside the plant, shown in the Fig.1. The dump shown was formed over a period of 70 years. The slope of the dump is about 450 and the height is varying from 10 m to 45 m. Presently, it has no applications and occupying costly land near the plant. The material was collected and investigated for its physical, chemical and geotechnical characteristics to determine its suitability for embankment and sub grade.

2 mATeRIAl

Cinder sample was collected from the existing dump yard commonly known as “Jugasalai dumping yard”, outside the TATA industries, Jamshedpur, Jharkhand. The material was observed to be coarse grained and light weight (as compared to soil).

CINDeR WAsTe mATeRIAl FoR THe CoNsTRuCTIoN oF RoAD

v.G. havanaGi*, a.k. Sinha*, v. k. kanaujia**, a. ranjan* anD S. Mathur*

3 pHYsICAl AND CHemICAl CHARACTeRIsTICs oF CINDeR

Cinder samples were investigated for their physical and chemical characteristics. Different physical tests which were carried out include (a) Natural moisture content (b) Specific gravity test and (c) Free swelling index test.

3.1 Natural moisture Content

Natural moisture content of cinder was determined by oven drying at 105ºC. The natural moisture content was observed to be 0.4%. Cinder was observed to be dry in the site condition.

3.2 Specific Gravity Test (G)

Specific gravity test was carried out as per Indian Standard method. The value of specific gravity was observed to be 2.15. The specific gravity of cinder is low due to presence of unburnt carbon.

3.3 Free swelling Index Test

Free swelling index test was carried out as per Indian Standard method and observed to be non swelling in nature.

* Scientists, CRRI, New Delhi** Sr. Technical Officer, CRRI, New Delhi,

Fig. 1 Pictorial View of Cinder Dump Yard

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3.4 Chemical Analysis

Chemical analysis was carried out of cinder sample. Results of chemical analysis of dump material are given in Table 1. Cinder mainly contains oxides of Silica, Alumina and Iron amounting to about 97%.Table 1 Chemical Characteristics of Cinder material

Chemical parameters ValueSilica, SiO2 62.01%Alumina, Al2O3 29.2%Iron oxide, Fe2O3 5.7%Magnesium oxide, MgO 1.42%CaO 1.67%pH 6

* Report TATA (2011) [1]

4 GeoTeCHNICAl CHARACTeRIsTICs oF CINDeR

Different geotechnical tests which were carried out include (a) Grain size analysis (b) Atterberg limit test (c) Proctor compaction test (d) California Bearing Ratio test (e) Hydraulic conductivity test (f) Consolidation test, and (g) Direct shear test. The results have been briefly discussed below.

4.1 Grain size Analysis

The grain size analysis was carried out as per Indian Standard method. It was observed that cinder is a coarse grained material having gravel (52%), sand (38%) and silt (10%).

4.2 Atterberg limit Test

The plasticity characteristics were determined as per Indian Standard method. Cinder was observed to be non-plastic in nature. According to BIS classification [2], cinder is classified as GP i.e. poorly graded gravel.

4.3 proctor Compaction Test

Modified Proctor compaction test was carried out as per Indian Standard method. The Maximum Dry

Density (MDD) and Optimum Moisture Content (OMC) were observed to be 12.7 kN/m3 and 27% respectively. The compaction curve is observed to be flat indicating in-sensitiveness of dry density with moisture content. The low MDD of cinder is due to its low specific gravity value.

4.4 California bearing Ratio Test

California Bearing Ratio test was carried out as per Indian Standard method. Three specimens were prepared by compacting the samples at 97% of their respective MDD/OMC. The specimens were then soaked for 4 days in potable water before testing. The specimens were then sheared at the rate of 1.25 mm/min. The average value of soaked CBR was observed to be 108%. Higher value of CBR may be due to high shear strength of compacted non cohesive granular particles.

4.5 Consolidation Test

Consolidation test was carried out as per Indian Standard method. The value of coefficient of consolidation Cv was observed to be 4×10-4 cm2/s and compression index (Cc) was observed to be 0.04. The value of Cc indicates that material is low compressible in nature.

4.6 Hydraulic Conductivity Test

Hydraulic conductivity test was carried out on compacted cinder samples as per Indian Standard method. Remolded samples were prepared at Modified Maximum Dry Density (MDD). The coefficient of permeability is determined as 6.3×10-5 cm/s. The value indicates that it is a free draining material and has the potential for utilization as an embankment fill, sub grade.

4.7 Direct shear Test

Direct shear test was carried out as per Indian Standard method. The sample was oven dried and passed through 4.75 mm sieve. Three specimens of size 60x60x25mm

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were prepared at MDD/OMC. The specimens were saturated and Consolidated Drained (CD) test was carried out. The specimens were sheared at the rate of 1.25 mm/min. Cinder samples were observed to be non cohesive with angle of internal friction (φ) determined as 45º. The value indicates that cinder samples have high shear strength.

5 suITAbIlITY oF CINDeR FoR embANKmeNT AND sub GRADe

Results of detailed laboratory investigation were analyzed to arrive at conclusions regarding the suitability of cinder waste material for embankment and sub grade layers of road pavement.

5.1 As an embankment material

Cinder is cohesionless, non plastic, non swelling material having high shear strength characteristics. The material is less compressible (Cc=0.04) and has good drainage property. The material has the potential for construction of road embankment. As there is a possibility of erosion, side cover of 2 m thick on either side with good earth may be provided on cinder embankment slope.

5.2 As a sub Grade material

The dry density of cinder material (Yd = 12.7 kN/m3) did not satisfy the maximum density requirement (17.5 kN/m3 or 16.5 kN/m3) as per MORTH Specifications [3]. Also it is observed that 20% cinder material (>2.36mm) is crushable in nature. Hence, cinder is concluded to be not feasible for subgrade construction.

6 DesIGN AND sTAbIlITY ANAlYsIs oF CINDeR embANKmeNT

Considering the potential of cinder for embankment construction, a typical 3m high cinder embankment

was analyzed for stability analysis. Details of the analysis are given below:

6.1 Design parameters

The geotechnical parameters required for the stability analysis viz. Bulk density and shear strength characteristics were arrived by detailed laboratory investigation. Sub soil parameters were assumed as dry density = 19.7 k/m3, OMC = 10 %, angle of internal friction = 29º and Cohesion = 5 kNm2. The slope of the embankment is considered as 1V:2H, side cover of thickness 2 m (Horizontal) of soil, surcharge loading on the embankment due to pavement crust thickness and traffic loading is assumed as 24 kN/m2.

6.2 stability Analysis

It was proposed to use 100% cinder for the construction of embankment. Stability analysis was carried out under different moisture conditions i.e. partially saturated, fully saturated and sudden draw down. Seismic factors viz. αh = 0.05 and αv = 0.025 were also considered for the analysis. Stability was checked using computer software based on classical theory with limit state approach. A typical critical slip surface of partially saturated cinder embankment is shown in the Fig.2. The results of factor of safety have been summarized in Table 2.

Fig. 2 A Typical Stability Analysis of Cinder Embankment

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Table 2 Results of Factor of safety of Cinder embankment

saturated Condition earthquake condition

Without With

Partially saturated 1.61 1.592m HFL 1.39 1.16Sudden draw down 1.34 1.14

It is observed that factor of safety values varied in the range 1.61 to 1.14 under different saturation conditions. The cinder embankment is observed to be safe even under the worst draw down condition. The present design is a typical case, detailed design needs to be carried out for a site specific case considering local traffic, environmental and seismic conditions.

7 CoNClusIoNs

Cinder waste material was collected from TATA industries, Jamshedpur. The material was evaluated for its physical, chemical and geotechnical characteristics. Different laboratory tests which were carried out included: Grain size analysis, Atterberg limit test, Free swelling index, Specific gravity, Proctor compaction test, CBR test, Consolidation test, Hydraulic conductivity test, and Direct shear test. Brief summary of conclusions have been given below:

● Cinder was observed to be coarse grained and non-plastic material. According to BIS classification, Cinder was classified as GP i.e. poorly graded gravel.

● The value of specific gravity was observed to be 2.15. The specific gravity of cinder is low as compared to soil. This may be due to unburnt carbon content present in the cinder waste.

● Compaction characteristics viz. Maximum Dry Density (MDD) and Optimum Moisture Content (OMC) were observed to be 12.7 kN/m3 and 27% respectively. The compaction curve is observed to be flat indicating in-sensitiveness of dry density with moisture content.

● The value of compression index (Cc) was observed to be 0.04. Cinder material was observed to be non cohesive with angle of internal friction (φ) determined as 450. The geotechnical parameters indicated the potential of cinder material for embankment construction.

● It was concluded that cinder material is not feasible for the construction of sub grade as the material is crushable and may result in failure of subgrade.

ReFeReNCes1. Tata Report (2011). Chemical analysis of cinder waste

material.

2. IS 1498 - 1970. Classification and identification of soils for general engineering purposes. Published by Bureau of Indian standard, New Delhi.

3. MORTH (2001). Specifications for Road and Bridge Works, Published by Indian Roads Congress.

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