International Journal of Technical Innovation in Modern ...raised median to permit vehicles to reach...

International Journal of Technical Innovation in Modern

Engineering & Science (IJTIMES) Impact Factor: 5.22 (SJIF-2017), e-ISSN: 2455-2585

Volume 5, Issue 03, March-2019

IJTIMES-2019@All rights reserved 236

DEVELOPMENTOF CAPACITY AND DELAY MODEL FOR

UNCONTROLLED MEDIAN OPENINGS UNDER MIXED TRAFFIC

CONDITION

R. Srinivasa Kumar1, V. Priyanka2, K. Shashi kumar3

1Associate Professor, Dept. of Civil Engineering, University College of Engineering, Osmania University. 2PG Student, Dept. of Civil Engineering, University College of Engineering, Osmania University.

3Research Scholar, Dept. of Civil Engineering, University College of Engineering, Osmania University.

Abstract—Establishment of un-signalized median openings have expanded in numerous urban districts of cities in India.

The thought process behind this establishment is to take out issues connected with illicit U-turns occurring at crossing

points and other transportation facilities near these median openings on multi-lane urban streets. Most of the median

openings in India are uncontrolled and un-signalized. The goal of this study is to develop a procedure for developing the

capacity of U-turn movement at median openings on multilane highways. Data were collected at five selected median

openings in the city Hyderabad. Data were collected in peak hours using video recording technique at the selected

sections, which are free from gradient, curves, on street parking, pedestrian movement and effect non-motorized vehicles.

Normally, the speed of conflicting traffic stream is relatively high and the turning vehicle must wait for accepted gap and

then turn under low speed level. Therefore, the turning vehicle needs large gap in the conflicting stream before

performing the U-turn. In fact, the little studies, which contain procedures and models for estimating capacity and delay

for different movements at un-signalized intersections, do not provide specific guidelines for estimating capacity and delay

of U-turn movement at median openings. For this reason, an effort was made to estimate capacity and delay at U-turn

median openings. In this study, empirical approach is used to estimate capacity of U-turn movement at median openings

of divided arterials. The empirical approach using regression analysis was adopted to estimate the best form of the

predictive equation for the U-turn capacity and investigate the effect of different relevant factors that might affect the

estimated capacity. The results of the approach are presented in this study. A linear model was also recommended as a

relationship between the average total delay of the U- turning vehicles and the conflicting traffic flow

Keywords: Traffic engineering, Median opening, U-turn, Capacity, Delay and Conflicting Traffic Flow.

I. INTRODUCTION

In line with the worldwide trend, India is experiencing rapid urbanization and has witnessed tremendous growth, resulting in

a rapid increase in vehicular traffic, which has imposed a burden on transportation infrastructure.Openings are provided in the

raised median to permit vehicles to reach abutting property or reverse their direction of travel. Vehicles make U-turns at these

openings and merge with the opposing through traffic. A U-turning vehicle requires a suitable gap in the opposing through

traffic to merge, and during this process the possibility of merging conflict develops. This conflict will not occur in two

situations. One, if there is no vehicle attempting a U-turn when a through vehicle arrives in the opposing lane; and two, if

there is no arrival of through vehicles in the opposing lane when a turning vehicle arrives to make a U-turn.

1.1 Features Related to Mixed Traffic Conditions in India

Estimation of critical gaps under mixed traffic situation is more perplexing than that of homogeneous movement conditions.

The traffic movements in India is very heterogeneous comprising of a mixture of quick moving vehicles, for example, cars,

bus, truck, scooter(motorized bike), auto rickshaw (mechanized three-wheeler) and moderate moving vehicle, for example,

bicycle and pedal rickshaw. The static and element qualities of these vehicles differ fundamentally. Without path discipline

and wide variety in sizes of distinctive sorts of vehicles, they are found to queue side by side in the minor road approach.

More diminutive size vehicles regularly press through any accessible gap between vast size vehicles and move into the

crossing point zone in aimless way. This forced gap acceptance which happens because of non- adherence to priority,

altogether influences the passage limit of the lower priority stream vehicles and causes substantial delay to higher priority

movements. These situations make gap acceptance an extremely intricate procedure. All these circumstances oblige a re-

investigate the idea of critical gap, conflict area at the U-turn median openings and strategy for video data extraction.

1.2 Research Objectives and Scope

The Objective of the research work is to develop a capacity and delay model for few uncontrolled median openings under

mixed traffic condition.The Scope of the study is

International Journal of Technical Innovation in Modern Engineering & Science (IJTIMES)

Volume 5, Issue 03, March-2019, e-ISSN: 2455-2585, Impact Factor: 5.22 (SJIF-2017)


1. To obtain the relation between capacity and conflicting traffic flow.

2. To obtain the relation between delay and conflicting traffic flow.

3. To develop a model for estimating the capacity and Delay at U-turn median opening.

II. LITERATURE REVIEW

Majority of the literatures are available for the estimation of critical gap and follow-up time at different transportation

facilities such as rotaries, un-signalized intersections and median openings. However, limited research has been carried out to

estimate the capacity of U-turn vehicles at median openings.The U-turning movement at a median opening is highly complex

and risky when compared with turning movements at intersections. It is because of the high speed and heavy traffic volume

of opposite flow and also the turning vehicle has to make a movement and merge with the opposing traffic stream in

which it is seeking an acceptable gap (Aldian and Taylor, 2001). In developed countries, un-signalized intersections are

usually controlled by signs which decide the priority of various movements. Enforcement of priority rules makes it possible

to cross the intersections with minimum conflicts. However, the situation is totally different in India. Most of the un-

signalized intersections do not have a stop or yield sign, and even if they have, the driver does not follow rules of priority and

attempts to enter intersections with a risk of collision.

2.1 Theoretical Background

Aldian and Taylor, 2001 The U-turning movement at a median opening is highly complex and risky when compared with

turning movements at intersections. It is because of the high speed and heavy traffic volume of opposite flow and also the

turning vehicle has to make an l80o movement and merge with the opposing traffic stream in which it is seeking an

acceptable gap.

Kim et al, (2006) performed some simulation studies for three different cases of superstreet which is similar to median U-

turn. In the first case one left turn lane and two through lanes on the major road was considered, the second case considered

one left turn lane and three through lanes on the major road and the third case considered two left lanes and three through

lanes on the major road. For each case microscopic traffic simulations were conducted for various traffic volumes and their

performance was compared to the conventional design option. The first case was simulated for high, medium and low traffic

scenarios and the remaining two cases were studied for high volumes as their application was mainly intended for sites

operating under heavy traffic conditionsThe Simulation Surrogate Safety Assessment tool was used to perform some safety

evaluations.

Liu et al. (2007, 2008a, 2008b, 2009) have conducted a detailed research relating to capacity estimation of U-turn vehicles at

median opening. The author estimated the parameters (critical headway and follow-up headway) of U-turn movements from

the field data and validated the capacity estimation from the model with the field capacity. The model provides reasonable

estimated capacity for U-turn movement at median openings. But the disadvantage of this model is that the author did not

consider mixed traffic conditions and developed a model only on 4-lane roads.

Cooner, 2008 To meet the demand of vehicular traffic, most of the urban roads are now constructed as multilane roads or

existing two lane roads are being widened to multilane roads. The multilane roads are generally constructed with raised

median in order to segregate the opposing traffic movements.

Al-Taei (2010) Conducted empirical study on eight U-turn locations in Iraq under different flow and speed conditions. He

found that these locations were characterized by high delay and accident rates. The author also investigated the gap

acceptance for left turning vehicles. However, this study has several limitations such as absence of clear methodology of

collecting field data and the information of these data.

Highway Capacity Manual (HCM 2010) includes the major-street U-turn movements in the methodology for two-way

stop-controlled (TWSC) intersections (TRB, 2010). The gap-acceptance theory defines the method for capacity estimation.

Three basic elements are gap availability, gap usefulness, and relative priority of subjected movements. The potential

capacity equation assumes random arrival process of vehicles on the major street. The model also assumes consistent and

homogeneous driving behaviour.

(Shelby, 2011) The presence of congestion or zone of conflict at U-turning area is defined as a hypothetical area within

which two or more vehicles try to share the same space at the same time and create a possibility of conflict.

Tupper et al. (2011) Studied different driver groups, specifically different age and gender groups, displayed different gap

acceptance behaviour. The factors that had the greatest affect on gap acceptance behaviour were the presence of a queue

behind the driver, driver wait time, and number of gaps rejected. These factors, relating to drivers feeling pressure or simply

impatience, resulted in drivers accepting shorter gaps and sacrificing a degree of safety to execute their turn.




Pirdavani et al. (2011) proposed some crucial differences to the other types of median openings. These developed U-turn

facilities are built on main roads, both sides of the intersection, and used as a complete replacement of signalized

intersections. It means that all the movements on the intersection will be one by U-turn and the signalized intersection is fully

blocked; while all types of U-turn facilities reviewed in the literature were used just for left turns.

Different parts of U-turn as defined:

A. Channelizing Island

B. Deceleration lane

C. U-turn raised island and

D. Acceleration lane.

Fig 2.1: Protected U-turn (RTUT design) (Pirdavani et al., 2011).

(Sugiarto et al., 2012) The maneuverity of U-turning vehicle at an uncontrolled median opening creates conflict with

approaching through traffic movement and causes traffic congestion, resulting in reduction in roadway capacity. As the two

road users attempt to occupy same space at the same time conflict arises. The U-turn creates interruption to smooth traffic

flow and causes congestion, which results in incremental delay, air pollution, driver stress, vehicle operating costs, additional

energy consumption, and driver’s frustration.

Balaji et al. (2013) reported that as the vehicle shifts towards the centre of a two-lane road there is an increase in the speed of

the vehicle. All the previous studies on placement of vehicles were carried out for straight moving traffic. No study has been

carried out to know the lateral placement of U-turning vehicles which is very essential for identification of conflict zone at

the median opening.

Highway Capacity Manual (HCM 2016)

Quantity of travel, the magnitude of use of a transportation facility or service;

Quality of travel, users’ perceptions of travel on a transportation facility or service with respect to their expectations;

Accessibility, the ease with which travellers can engage in desired activities; and

Capacity, the ability of a transportation facility or service to meet the quantity of travel demanded of it.

2.2 Concept of Level of Service at Median openings

A term closely related to capacity and often confused with it is service volume. When capacity gives a quantitative measure

of traffic, level of service or LOS tries to give a qualitative measure. A service volume is the maximum number of vehicles,

passengers, or the like, which can be accommodated by a given facility or system under given conditions at a given level of

service. For a given road or facility, capacity could be constant. But actual flow will be different for different days and

different times in a day itself. The intention of LOS is to relate the traffic service quality to a given flow rate of traffic. It is a

term that designates a range of operating conditions on a particular type of facility. Highway capacity manual (HCM)

developed by the transportation research board of USA provides some procedure to determine level of service. It divides the

quality of traffic into six levels ranging from level A to level F. Level A represents the best quality of traffic where the driver

has the freedom to drive with free flow speed and level F represents the worst quality of traffic. Level of service is defined

based on the measure of effectiveness or (MOE). Typically three parameters are used under this and they are speed and travel

time, density, and delay. One of the important measures of service quality is the amount of time spent in travel. Therefore,

speed and travel time are considered to be more effective in defining LOS of a facility. Density gives the proximity of other

vehicles in the stream. Since it affects the ability of drivers to manoeuvre in the traffic stream, it is also used to describe LOS.




Delay is a term that describes excess or unexpected time spent in travel. Many specific delay measures are defined and used

as MOE’s in the highway capacity manual.

Table 2.1 Delay ranges for LOS categories of uncontrolled median openings (HCM)

LOS category Service Delay range

(s/Vehicle)

Control Delay ranges

(s/Vehicle) for TWSC (HCM-

2010)

A 0-5 0-5

B >5-8 >10-15

C >8-13 >15-25

D >13-20 >25-35

E >20-33 >35-50

F >33 >50

III. STUDY AREA AND METHODOLOGY

The study methodology consists of following phases, through which the corridor management study would be completed for

the selected corridor. The phases are listed as below.

Fig 3.1: Flow chart of study methodology

3.1 Identification of the study Area.

To accomplish the objective of this study, five median openings located in HyderabadCity was selected. These median

openings are located along divided suburban arterials and operated at capacity during peak periods.

a) Afzalgunj

b) MJ Market

c) Nampally

d) Kachiguda

e) Narayanaguda

Identification of the study

area Traffic data collection

Developing Capacity model for

heterogeneous traffic flow

Developing Delay model for

heterogeneous traffic flow

Collect Video

records Multi –linear

regression

Results




Fig 3.2 Google map and median opening at Afzalgunj stretch

Fig 3.3 Google map and median opening at MJ market stretch

Fig 3.4 Google map and median opening at Nampally stretch

Fig 3.5 Google map and median opening at Kachiguda stretch




Fig 3.6 Google map and median opening at Narayanaguda stretch

IV. DATA COLLECTION AND ANALYSIS

The required traffic data were collected at different uncontrolled median openings of four-lane divided urban roads in

Hyderabad. At all test sections, there were no signals, no police personnel, and no traffic signs to regulate traffic movement.

All the median openings were exclusive median openings where only U-turns were possible, and the sections were free from

the effects of nearby intersections, bus stops, parked vehicles, curvature, pedestrian movements, and any side friction. Data

were collected in the field, both manually and by video recording techniques.

Table 4.1: Composition of Traffic Count at Different Sections for one hour count

Location

Proportions

of

U-turning

vehicles

(veh/hr)

Proportions

of through

vehicles

(veh/hr)

Composition of U-turning

vehicles

Composition of through

vehicles

2W 3W 4W HV Otr 2W 3W 4W HV Otr

Afzalgunj 2002 3599 789 478 512 198 25 1598 1113 698 169 21

Nampally 3162 5167 1489 807 577 252 37 3122 1896 987 198 41

Narayanaguda 590 2611 287 122 98 54 29 1265 657 485 188 16

Kachiguda 1207 3661 352 289 351 184 31 1611 1189 710 132 19

MJ Market 406 6260 202 74 55 61 14 3511 1504 998 447 59

4.1 Analysis of the data

4.1.1 Development of Capacity at Afzalgunj stretch

The capacity of U –turning vehicles is denoted by ‘C’, Conflicting Traffic is represented as ‘q’.

Table 4.7: Analysis of capacity data at a median opening at Afzalgunj stretch

C

(veh/hr) q

(veh/hr) C×q

960 921600 4680 21902400 4492800

1500 2250000 3900 15210000 5850000

1800 3240000 3240 10497600 5832000

1980 3920400 3360 11289600 6652800

3540 12531600 2040 4161600 7221600

1200 1440000 4620 21344400 5544000

1200 1440000 3720 13838400 4464000

1740 3027600 3180 10112400 5533200

1800 3240000 3360 11289600 6048000

3300 10890000 2400 5760000 7920000

1860 3459600 3420 11696400 6361200




1800 3240000 4140 17139600 7452000

2700 7290000 2700 7290000 7290000

2820 7952400 2820 7952400 7952400

2100 4410000 3660 13395600 7686000

1860 3459600 3420 11696400 6361200

1800 3240000 3720 13838400 6696000

1680 2822400 4020 16160400 6753600

2040 4161600 3180 10112400 6487200

1860 3459600 3300 10890000 6138000

3180 10112400 2700 7290000 8586000

1860 3459600 3660 13395600 6807600

2100 4410000 4020 16160400 8442000

720 518400 4980 24800400 3585600

840 705600 4800 23040000 4032000

1080 1166400 4500 20250000 4860000

2040 4161600 3600 12960000 7344000

2040 4161600 3780 14288400 7711200

2460 6051600 3540 12531600 8708400

2940 8643600 2520 6350400 7408800

2100 4410000 4020 16160400 8442000

2100 4410000 3600 12960000 7560000

2580 6656400 3060 9363600 7894800

2220 4928400 3300 10890000 7326000

1680 2822400 3720 13838400 6249600

1560 2433600 4020 16160400 6271200

1140 1299600 4320 18662400 4924800

1740 3027600 3900 15210000 6786000

1440 2073600 4320 18662400 6220800

2100 4410000 3900 15210000 8190000

2100 4410000 3780 14288400 7938000

2460 6051600 3120 9734400 7675200

2460 6051600 3540 12531600 8708400

2040 4161600 3660 13395600 7466400

2040 4161600 4020 16160400 8200800

2160 4665600 3420 11696400 7387200

2040 4161600 3720 13838400 7588800

2700 7290000 3120 9734400 8424000

2040 4161600 3300 10890000 6732000

2340 5475600 3060 9363600 7160400

2160 4665600 3360 11289600 7257600

1860 3459600 3720 13838400 6919200

2280 5198400 3900 15210000 8892000

1260 1587600 4200 17640000 5292000

1380 1904400 4140 17139600 5713200

3000 9000000 2940 8643600 8820000

1380 1904400 3540 12531600 4885200

2460 6051600 3180 10112400 7822800

2400 5760000 3300 10890000 7920000

2100 4410000 3780 14288400 7938000

From the above table

∑C = 120120

∑C2 = 260791200

Mean of capacity =

=

= 2002veh/hr




Standard Deviation =

=

S.D = =581.8213


= 215940 = 796978800

Mean of conflicting traffic flow =

=

= 3599 veh/hr

Standard deviation =

=

S.D = =1574.612

= 414828000

Cov (c,q) =

=

(414828000) (2002 3599)

= -291398

Where

Cov(c,q)=capacity and conflicting

r =

=

= -0.871

Where

r = coefficient of correlation (lies between -1 and +1)

Regression equation is given by

c - = bcq (q - )

bcq = r.

C = 5858.9e-3E-04q

Fig 4.1: Relationship between Capacity and Conflicting Flow

4.1.2 Development of Delay model


∑D = 375.9

C = 5858.9e-3E-04q

R² = 0.7602

0

1000

2000

3000

4000

5000

6000

0 500 1000 1500 2000 2500 3000 3500 4000

Cap

aci

ty (

veh

/hr)

Conflicting Traffic Flow (veh/hr)

Afzalgunj Median




∑ = 2417.55

Mean of delay =

=

= 6.275 sec/veh


=

S.D = =0.385


= 215940 = 796978800

Mean of conflicting traffic flow =

=

= 3599 veh/hr


=

S.D = = 574.612

= 9107236.8

Cov(D,q) =

∑ (D×q)-( . )

=

=128843.65

Where

Cov(D,q) = covariance of delay and conflicting flow

r =

r =

r = 0.673

Where

R = coefficient of correlation lies between -1and +1

Regression equation is given by

D-

bDq = r.

D = 0.0009q + 3.187

Fig 4.2: Relationship between Delay and Conflicting Flow

D = 0.0009q + 3.187 R² = 0.6726

0

4

8

12

0 1000 2000 3000 4000 5000 6000

Del

ay (

veh

/se

c)

Conflicting Traffic flow (veh/hr)

Afzalgunj Median




D= 0.0011q + 3.6471 R² = 0.8321

0

3

6

9

12

0 5000

De

lay

(ve

h/s

ec)


Narayanaguda Median

Similary the above analysis has been donefor capacity and delay and following graphs has been extracted.

Fig.4.3 graphs showing relationship between capacity, Delay and conflicting flow.

C = 12913e-3E-04x R² = 0.7

0

1000

2000

3000

4000

5000

6000

0 2000 4000 6000 8000

Cap

acit

y (v

eh/h

r)


Nampally Median D = 0.0013q - 0.0604

R² = 0.7278

0

2

4

6

8

10

0 3000 6000 9000

Del

ay (

veh

/se

c)


Nampally Median

C = 58.168e-0.018q R² = 0.8738

0

10

20

30

40

0 50 100

Cap

acit

y (v

eh/h

r)


Kacheguda Median

C = 1802.2e-5E-04q R² = 0.7013

0

200

400

600

800

1000

1200

1400

0 2000 4000 6000

Cap

acit

y (v

eh/h

r)


Narayanaguda Median

D = -0.0591q + 9.9983 R² = 0.874

0

2

4

6

8

10

0 30 60 90 120

Dea

ly (

veh

/se

c)

Conflicting Traffic Flow(veh/hr)

Kacheguda Median




V. Conclusions

5.1 Conclusions obtained using Multi-Linear Regression

Based on the results of this study, the following points were concluded:

1. Capacity and average total delay models for U-turn movements at median openings were found to be significantly

influenced by the conflicting traffic flow.

2. For Afzalgunj stretch, Capacity of the U-turning vehicles is inversely proportional to the Conflicting Traffic Flow.

3. Capacity model at Afzalgunj stretch has an R² value of 0.760 i.e., it explains a high percentage of relation between

Capacity of U-turning vehicles and conflicting traffic flow.

4. The Delay model has linear relationship obtained between the average total delay and the conflicting traffic flow at U-turn

median openings.

5. Capacity model at Kachiguda stretch has an R² value of 0.8738 i.e., the capacity is correlated with conflicting traffic flow.

6. Capacity model at Narayanaguda stretch has an R² value of 0.7013 i.e., the capacity is correlated with conflicting traffic

flow.

7. Capacity model at Nampally stretch has an R² value of 0.7 i.e., the capacity is correlated with conflicting traffic flow.

8. Capacity model at MJ market stretch has an R² value of 0.7164 i.e., the capacity is correlated with conflicting traffic flow.

5.2 Conclusions using Regression

1. At Afzalgunj stretch Delay model has a regression value of 0.6726 i.e., delay is correlated with conflicting traffic flow.

2. At Kachiguda stretch Delay model has a regression value of 0.874 i.e., delay is correlated with conflicting traffic flow.

3. At Narayanaguda stretch Delay model has a regression value of 0.8321and i.e., delay is correlated with conflicting traffic

flow.

4. At Nampally stretch Delay model has a regression value of 0.914and i.e., delay is correlated with conflicting traffic flow.

5. At MJ Market stretch Delay model has a regression value of 0.866and i.e., delay is correlated with conflicting traffic flow.

5.3 Scope for Future Work

For better understanding of the model, the data should be considered from morning to evening. The model developed in this

study should be validated from a completely different set of observations to understand the field applicability of the model.

Besides, linear regression methodology, other models should also be modified under the heterogeneous road traffic

conditions to estimate the Capacity and Delay for Over saturated conditions.

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