TrafficManagementSchemes

WETENSCHAPSPARK 5

B 3590 DIEPENBEEK T ► 011 26 91 12

F ► 011 26 91 99 E ► [email protected]

I ► www.steunpuntmowverkeersveiligheid.be

PROMOTOR ► Prof. dr. ir Dick Botteldooren, ir. Ina De Vlieger

ONDERZOEKSLIJN ► Duurzame mobiliteit

ONDERZOEKSGROEP ► Ugent, Vito

RAPPORTNUMMER ► ?

Steunpunt Mob i l i te i t & Openbare Werken Spoo r V e rkee rsv e i l i ghe i d

Feasible Traffic Management Schemes and

their Effects

Literature study of existing traffic management schemes and how these schemes affect safety, traffic flow,

emissions, fuel usage and noise.

M. Madireddy, B. De Coensel, A, Can, B. Degraeuwe, B. Beusen,

I. De Vlieger, D. Botteldooren

DIEPENBEEK, 2011.

STEUNPUNT MOBILITEIT & OPENBARE WERKEN

SPOOR VERKEERSVEILIGHEID

Feasible Traffic Management Schemes and their Effects

Literature study of existing traffic management schemes and how these schemes affect safety, traffic flow, emissions, fuel usage and noise

M. Madireddy, B. De Coensel, A, Can, B. Degraeuwe, B. Beusen,

I. De Vlieger, D. Botteldooren

Onderzoekslijn: Duurzame mobiliteit

Documentbeschrijving

Rapportnummer:

Titel: Feasible Traffic Management Schemes and their Effects

Ondertitel: Literature study of existing traffic management

schemes and how these schemes affect safety, traffic

flow, emissions, fuel usage and noise

Auteur(s): M. Madireddy, B. De Coensel, A, Can, B. Degraeuwe,

B. Beusen, I. De Vlieger, D. Botteldooren

Promotor: Prof. dr. ir Dick Botteldooren, ir. Ina de Vlieger

Onderzoekslijn: Duurzame mobiliteit

Partner: VITO en UGent

Aantal pagina‟s: 60

Projectnummer Steunpunt: 8.3

Projectinhoud: Verkeersmanagement en milieu

Uitgave: Steunpunt Mobiliteit & Openbare Werken – Spoor

Verkeersveiligheid,Februari 2010.

Steunpunt Mobiliteit & Openbare Werken Spoor Verkeersveiligheid

Wetenschapspark 5 B 3590 Diepenbeek T 011 26 91 12

F 011 26 91 99 E [email protected] I www.steunpuntmowverkeersveiligheid.be

Steunpunt Mobiliteit & Openbare Werken 5 R-00-2002-01 Spoor Verkeersveiligheid

Samenvatting


English summary

Title: Feasible Traffic Management Schemes and their Effects

Subtitle: Literature study of existing traffic management schemes and how these

schemes affect safety, traffic flow, emissions, fuel usage and noise

Abstract

With the ever increasing number of vehicles on the Flemish roads, the need for effective

traffic management is well understood. Urban congestion is well known problem and to

tackle this problem, several ideas are put forth, several changes in infrastructure are

currently taking place and the traffic centers are constantly working to keep the traffic in

check. While employing a particular traffic management scheme could solve one of the

problems, say congestion, it might not be the most effective in reducing fuel

consumption. Hence a total understanding of all the effects of a feasible traffic

management scheme is necessary.

This report examines the traffic management schemes that are usually employed in

various countries (including Belgium) and how successful they were in combating

congestion, urban air quality problems and noise. Moreover, the effect of each such

measure on fuel consumption, total GHG emissions and safety will also be investigated.

The traffic management schemes that were investigated are

1. Replacement of the traditional signalized intersections with roundabouts.

2. Highway speed management.

3. Introduction of environmental zones or Low Emission Zones.

4. Speed reduction on local roads.

5. Traffic lights synchronization.

6. Introduction of speed humps.

Some of these measures were tested using some case studies. These case studies were

conducted in the preselected regions where the congestion problems exist. These studies

were simulated using a traffic simulation model, Paramics. The traffic counts (obtained

from the Verkeerscentrum) and signal light timings (obtained from the Antwerp Police

Department) were also incorporated to accurately represent the real world traffic.


Table of contents

Dutch Summary

English Summary

List of Abbrevations

1. INTRODUCTION ........................................................................10

2. LTERATURE STUDY: TRAFFIC MANAGEMENT SCHEMES..............................11

2.1 Background.............................................................................................11

2.2 Literature of traffic management measured and their effects.........................12

2.2.1 Replacements of the traditional signalized intersections with

roundabouts.................................................................................12

2.2.1.1 Safety...............................................................................13

2.2.1.2 Traffic flow.........................................................................15

2.2.1.3 Emessions and fuel usage.....................................................16

2.2.1.4 Noise emissions...................................................................1

2.2.2 Highway speed management. 18

2.2.2.1 Safety................................................................................18

2.2.2.2 Traffic flow.........................................................................19

2.2.2.3 Emissions and fuel usage.......................................................20

2.2.2.4 Noise Emissions...................................................................21

2.2.3 Lowered speed limits. 22

2.2.3.1 Safety...............................................................................23

2.2.3.2 Emissions and fuel consumption.............................................24

2.2.3.3 Noise Emissions...................................................................25

2.2.4 LEZ (Low Emission Zone). 26

2.2.4.1 Local Emessions..................................................................27

2.2.4.2 New Emessions...................................................................28

2.2.5 Effect of traffic lights synchronization. 28

2.2.5.1 Traffic Flow........................................................................28

2.2.5.2 Emissions and fuel usage......................................................31

2.2.5.3 Noise Emissions...................................................................31

2.2.6 Speed Humps/Bumps. 32

2.2.6.1 Safety...............................................................................32

2.2.6.2 Emissions and Fuel Consumption............................................33

2.2.6.3 Noise Emissions...................................................................33

3. CASE STUDY ........................................................................... 35

3.1 Introduction 35


3.2 Validation of the models used (Versit + Paramics) ...........................................35

3.2.1 Validation of the Emission model ...............................................................35

3.2.2 Validation of the integrated model ............................................................39

3.3 Case Studies integrated using the integrated model .........................................40

3.3.1 Case Study-A: Effect of reduced speed limits on emissions and noise in

Zurborg............................................................................................................41

3.3.1.1 Methodology .............................................................................41

3.3.1.2 Results .....................................................................................42

3.3.2 Case Study-B: Effect of green wave on emissions and noise in Zureborg,

Antwerp..........................................................................................................44

3.3.2.2 Results .....................................................................................44

3.3.3 Case Study-C: Effect of......... on ........ along Grotesteensweg in Zurenborg,

Antwerp..........................................................................................................45

3.3.3.1 Methodology .............................................................................45

3.3.4 Comparing roundabouts with signalized intersections ....................................46

4. CONCLUSIONS AND RECOMMENDATIONS ........................................... 48


List of Abbrevations

CO Carbon Monoxide

CO2 Carbon Dioxide

dB(A) Decibels

GHG Green House Gases

GPS Global Positioning System

LEZ Low Emission Zone

NOx Nitrogen Oxides

PM Particulate Matter

SPS Special Purpose Simulation

TMM Traffic Management Measure

VEDETT Vehicle Device For Tracking And Tracing

VSL Variable Speed Limits


1. IN T R O DU C T ION

The mission of work package 8.3 is to investigate possible traffic management schemes

that could be applicable in the Flemish region and to present their advantages and

disadvantages.

Traffic congestion causes travel delays, and thus imposes a substantial cost on society.

With the increasing number of road vehicles in urban areas in the last few decades,

controlling congestion and vehicle related pollution have become major challenges for

city planners. Congestion increases travel time and idling because of which urban regions

are facing increasing concentrations of carbon monoxide (CO), nitrogen oxides (NOx) and

particulate matter (PM10). Apart from these emissions, the rise of atmospheric carbon

dioxide (CO2), which is a major greenhouse gas, has become a matter of concern.

Urban traffic management solutions, such as introducing variable speed limits, installing

express lanes or optimizing traffic signal timing, are commonly used to moderate

congestion in urban areas, where expanding the road network is not feasible. For

example, roundabouts are gaining a lot of attention due to their traffic smoothening

ability and reduction in accident frequency. This is prompting lot of cities to replace their

traditional intersections with modern roundabouts. Highway speed management is

another aspect with lot of positive effects of reducing total travel time and avoiding

congestion. Some cities are building tunnels to divert the traffic from a busy lane to ease

congestion and regulate the traffic flow. To prevent the residential areas from harmful

effects of noise and emissions, Low emission zones (LEZ) are being introduced and lower

speed limits are imposed. This has a proven benefit of reduced concentration of harmful

emissions and accidents in highly populated residential areas or school zones.

Although the potential of traffic management to reduce travel delays is widely accepted,

the side-effects on noise and air quality are much less clear. Improving traffic conditions

does not necessarily mean that there is less noise or air pollutant emission. The objective

of this report is to clarify all the effects of an isolated traffic management measure. In

other words, how each measure could have influenced traffic flow, noise, emissions,

safety and fuel consumption.


2. L IT E R A T U R E ST U DY : TR A F F IC MA N AG E M E N T

S C H E ME S

2.1 Background

This section contains all the possible traffic management schemes that were investigated.

The effect of each measure on safety, traffic flow, emissions and fuel usage and noise.

The traffic management measures (TMM) that are investigated in this report are:

1. Replacement of the traditional signalized intersections with roundabouts.

2. Highway speed management.

3. Introduction of environmental zones or Low Emission Zones.

4. Speed reduction on local roads.

5. Traffic lights synchronization.

6. Introduction of speed humps.

7. Introduction of tunnels.

The objective of this section is to fill in this table with how advantageous each TMM is. It

has to be noted that various literature review studies for the same measure indicate

different results. For example, an LEZ might give a 10% reduction of NOx concentrations

in one study and only 5% in another study. Moreover the road conditions differ from

study to study. Hence a merit is allotted for each measure and based on the overall

understanding of the results presented by the studies, the blocks are to be filled with =

(no improvement), + (slight improvement), ++ (good improvement) or +++ (very good

improvement). These merits can be altered with further studies/ more literature review

that enriches the scope and validity of the subjective conclusions.


Table 2.1 Different Traffic Management Measures and their influences

Traffic

Flow

Safety

Fuel

Conservation

(CO2) savings

Local Emissions

Reduction(NOx,

PM, HC, etc)

Noise

Abatement

Replacing

Intersections with

Roundabouts

Highway speed

Management.

Low Emisison

Zones

Speed Reduction

GreenWave

Speed Bumps

Tunnels

2.2 Literature of Traffic Management Measures and their Effects

2.2.1 Replacement of the traditional signalized intersections with roundabouts

A roundabout is a circular intersection where the vehicles enter an intersection and go

around in a circular path before exiting into their destination lanes. The flow of traffic will

be unidirectional along the roundabout. The vehicles entering the roundabout will yield to

the vehicles already travelling in the roundabout. These are a recent innovation, not

newer than 15 years.


2.2.1.1. Safety

Roundabouts are believed to improve safety by reducing injury crashes at the

intersections. This can be attributed to the following reasons.

a. With the signalized intersections, the vehicles cross at right angles and the

collisions are usually fatal. In a roundabout, the vehicles travel in the same

direction and the crashes are side on and potentially less dangerous. Previous

research indicates that this could potentially reduce severe crash types that

commonly occur at traditional intersections [2].

b. Roundabouts can also reduce the likelihood and intensity of rear-end crashes

by removing the incentive for drivers to speed up as they approach green

lights and by reducing abrupt stops at red lights. This could be anticipated to

have a significant reduction of serious injury collisions.

c. The vehicle-to-vehicle conflicts that occur at roundabouts generally involve a

vehicle merging into the circular roadway, with both vehicles traveling at low

speeds. This is less dangerous. This is in stark contrast with the scenario

where vehicles try to speed up along their path often in perpendicular direction

to each other.

Safety Research Results from different studies

Vehicle to Vehicle crashes

Roundabouts are shown to reduce the fatal accidents as much as 76% in USA,

75% in Australia and 86% in Great Britain.


In France, a study concerning 55 roundabouts that were constructed between

1979 and 2000 is found to reduce the physical accidents by 88% [3].

In Denmark, there is a reduction of 53% of the bodily accidents in urban areas

and 84% in the rural areas [4].

In Netherlands, when 181 crossroads were converted to roundabouts, there was a

71% reduction in bodily accidents [5].

In a study by the Insurance Institute for Highway Safety, roundabouts were

associated with large reductions in crashes and injuries (Persaud et al. 2000,

Status Report, May 13, 2000) [6]. The results were attributed to the reduced

speeds and reduced number of conflict points [7].

While these are overwhelmingly positive results, slightly moderate, but still

significant improvements were found in studies related to Flemish traffic.

A comprehensive study conducted on roundabouts in Flanders region in Belgium

concludes that a reduction of 34% in the total number of injury accidents is

possible by replacement of signalized intersections with roundabouts. The study

also predicts an average 30% reduction for light injury accidents, and 38% for

serious injury accidents [8].

The study further indicated that the severity and frequency of accidents at the

roundabouts is significantly dependent on the speed limits of the approaching

roads. The study concluded that the roundabouts are the best replacement for

signalized intersections where there the main road with speed limits of 90 kmph

intersects with minor roads with speed limits of 50-70 kmph. This is an important

observation since it cannot be misunderstood that the roundabouts are solution

for all injury crashes.

Also the number of lanes in the roundabouts is a determining factor in crash

intensities. Fewer traffic conflicts and crashes are typically seen at single lane

roundabouts compared with multi-lane roundabouts; additional lanes allow for

more points of contact between vehicles [9]. Another comprehensive study [10]

deduced that the three-leg roundabouts tend to perform worse than roundabouts

with four or more legs and that crashes occur frequently at roundabouts with

bypasses for traffic in some direction. Larger central islands correlate with more

single-vehicle crashes. Another study concludes that single-lane roundabouts, in

particular, have been reported to involve substantially lower pedestrian crash

rates than comparable intersections with traffic signals and multi-lane

roundabouts [11].

Vehicle to Pedestrian/cyclist crashes


Further studies conducted in Flanders, Belgium concluded that the replacement of

intersections with roundabouts is unsafe for the pedestrians and cyclists. The

results can be safely concluded from the observation that the vulnerable road

users are more frequently than expected involved in crashes at roundabouts and

roundabouts with cycle lanes are clearly performing worse than roundabouts with

cycle paths [12].

The conversion of intersections into roundabouts resulted in 27% increase in the

number of injury accidents involving bicyclists on or closer to the roundabouts.

While this in itself is an alarmingly high figure, the increase is even higher (43%)

for accidents involving fatal or serious injuries [13].

In stark contrast to the above conclusions, some studies indicate otherwise that

on average, converting conventional intersections to roundabouts can reduce

pedestrian crashes by about 75% [14, 15].

Mixed results are available for who benefits the most from replacing the

intersections with roundabouts. While Hyden and Varhelyi [16] (2000) argued that

replacing intersections with roundabouts reduced risk for bicyclists and

pedestrians significantly, but not for cars. In contrast to this conclusion, studies

cited by Robinson et al claimed that crash reductions were most pronounced for

motor vehicles, and smaller for pedestrians [17].

For any kind of crash at a roundabout, it is generally accepted that unsafe speeds

is significant factor. It is possible that some drivers may not be aware of the

roundabout ahead. This is fatal and measures need to be taken to alert drivers to

slow down. This can be done by posting the signs of the roundabout on the

downstream of the roundabout and by increasing the conspicuity of roundabouts

by the elevated height of the center islands and by marking the pavement with

reflectors.

2.2.1.2. Traffic Flow

While there is some disagreement on the safety issues of roundabouts in the

research community, there is little disagreement that the roundabouts usually

improve traffic flow. All the studies agree with the improved traffic flow at the

roundabouts and this is the major reason why city planners are leaning towards

roundabouts in the design of sustainable road transport systems. The results

from various studies are as follows.


In a study of three intersections in Kansas, Maryland, and Nevada, where

roundabouts replaced the previously present stop signs, it was found that vehicle

delays were reduced 13-23 percent and the proportion of vehicles that stopped

was reduced 14-37 percent [18].

A similar study where roundabouts replaced traffic signals found vehicle delays

were reduced by 89% and average vehicle stops by 56% [19].

Another roundabout replacement of 11 intersections in Kansas produced on an

average 65% reduction in delays and a 52% average reduction in vehicle stops

after roundabouts were installed [20].

A 2005 Institute study documented missed opportunities to improve traffic flow

and safety at 10 urban intersections suitable for roundabouts where either traffic

signals were installed or major modifications were made to signalized

intersections [21]. It was estimated that the use of roundabouts instead of traffic

signals at these 10 intersections would have reduced vehicle delays by 62-74 %.

The traffic flow can be improved by adding more lanes to the roundabout, but that

might compromise safety as suggested above [22, 23]. The dependence of the

traffic flow as a function of number of legs, number of lanes and traffic condition

is presented extensively by Mishra [24].

While these are individual and isolated studies that were dependent heavily on

several factors and landscape and width of lanes, traffic speed variation,

awareness of the people about the roundabout, etc, the general conclusion can be

drawn that the traffic flow can be improved with roundabouts. Improving the

traffic flow due to roundabouts is a widely accepted and tested concept and this is

accounting for the increasing replacement of traditional intersections with

roundabouts in areas of high urban traffic.

2.2.1.3. Emissions and Fuel Usage

Because roundabouts improve the efficiency of traffic flow, they also reduce

vehicle emissions and fuel consumption.

In one study, replacing a signalized intersection with a roundabout reduced carbon

monoxide emissions by 29 percent and nitrous oxide emissions by 21 percent

[25].

In another study, replacing traffic signals and stop signs with roundabouts

reduced carbon monoxide emissions by 32 percent, nitrous oxide emissions by 34

percent, carbon dioxide emissions by 37 percent, and hydrocarbon emissions by

42 percent [26].


According to some studies, constructing roundabouts in place of traffic signals can

reduce fuel consumption by about 30 percent [25, 27]. This was attributed to the

fact that the smoother traffic flow avoided the wait time at the signal reducing the

fuel usage while the vehicle is idling.

Hoglund et al suggested that roundabouts perform significantly better with fuel

conservation compared to traditional traffic signals by limiting the stop and go

traffic [28].

The GHG savings for replacing intersections with roundabouts can be modeled by

a software tool such as SIDRA [29]. SIDRA models intersection performance of

pollutant emissions, delay and energy consumption. Another traffic model,

CAPCAL 2, released in 1996 calculates performance measures, including vehicle

costs and emissions, for all intersection types (Hagring, 1997) [30]. These could

be effective tools to use in future if the Flemish government wanted to explore the

possibility of roundabouts replacing some of the troubled intersections.

2.2.1.4. Noise Emissions

Traffic noise frequently exceeds the guideline values published by the WHO and

those exposed to traffic noise consequently suffer an array of adverse health

effects. These include socio-psychological responses like annoyance and sleep

disturbance, and physiological effects such as cardiovascular diseases (heart and

circulatory problems) and impacts on mental health (RIVM, 2004) [31]. In

addition, traffic noise may also affect children‟s learning progress. Finally,

prolonged, cumulative exposure to noise levels above 70 dB(A), common along

major roads, may lead to irreversible loss of hearing (Rosenhall et al., 1990) [32].

Hence this document presents the issues of traffic management and how each of

these issues affect the noise levels.

Roundabouts are not specifically designed for reduced noise. However some

studies indicate that the traditional signalized intersections cause an unacceptable

level of noise and these levels can be brought down when these intersections are

replaced with roundabouts. This can be expected since roundabouts smoothen the

traffic flow at the intersections, they could reduce noise related to stop-and-go

traffic. The noise increases depend significantly on the traffic volume, street

layout and driving behavior and is very difficult to draw general conclusions from

one unique intersection scenario. Tsukui et al [33] presents the noise problems


with the traditional signalized intersections. El-Fadel et al [34] presents a

comparative study of different types of intersections and concludes that noise is

predominantly a factor of how the intersections are designed and several minor

details of road design affects the noise levels at the intersections. But noise

emissions from a given intersection can be quantified and put into a general

theory [35, 36].

2.2.2. Highway Speed Management

Road speed limits are used to regulate the speed of the vehicles. Speed limits may define

maximum which may be variable, minimum or no speed limit and are normally indicated

using a traffic sign. Speed limits are set primarily to improve road traffic safety.

However, it has added benefits of fuel conservation and reduced emissions.

2.2.2.1. Safety

According to a 2004 report from the World Health Organization a total of 22% of

all 'injury mortality' worldwide were from road traffic injuries in 2002 and without

'increased efforts and new initiatives' casualty rates would increase by 65%

between 2000 and 2020 [37]. The report identified that the speed of vehicles was

the most significant problem and that speed limits should be set appropriately for

the road function. The report further suggests that the road design ( physical

measures related to the road) are to be complementary to the speed enforcement

by the police.

http://en.wikipedia.org/wiki/Vehicles

http://en.wikipedia.org/wiki/Traffic_sign

http://en.wikipedia.org/wiki/Road_traffic_safety

http://en.wikipedia.org/wiki/World_Health_Organisation


It should be expected that in most cases maximum speed limits place an upper

limit on speed choice. If they are obeyed by majority of the drivers, they can

reduce the differences in vehicle speeds by drivers. It is widely accepted among

the traffic managers that the likelihood of a crash is significantly higher if vehicles

are traveling at speeds „different‟ from the mean speed of traffic. This means the

speed difference is a bigger factor than the mean speed of the vehicles. When the

crash severity is taken into account the risk is lowest for those traveling at or

below the median speed and is believed to increase exponentially for motorists

driving faster. This is because the kinetic energy involved in a motor vehicle

collision is proportional to the square of the speed at impact. However, it is

interestingly suggested [38] that the probability of a fatality is, for typical collision

speeds, empirically correlated to the fourth power of the speed difference at

impact, rising much faster than kinetic energy. The 2009 technical report by the

National Highway Traffic Safety Administration showed that a 55 percent of all

speeding-related crashes in fatal crashes were due to exceeding posted speed

limits and 45 percent were due to driving too fast for conditions [39]. Highway

speed management can effectively bring down these crash fatalities. The

objectives should be limiting the maximum speed and limiting the differential

speeds between vehicles.

Variable speed limits are currently being employed along many urban highways to

ensure smoother traffic flow and avoid congestion during peak hours. Several

studies showed improvement. It was indicated that variable speed limits could

reduce crash potential by 5–17%, by temporarily reducing speed limits during

risky traffic conditions [40].

Homogeneity of driving speeds is an important variable in determining road

safety. A study conducted by Nes et al indicated that the homogeneity of

individual speeds, defined as the variation in driving speed for an individual

subject along a particular road section, was higher with the dynamic speed limit

system than with the static speed limit system [41].


Freeway traffic flow is especially complex and can be modeled only with great

details of inputs such as complex interactions between vehicles, routing and ramp

metering, etc [42]. Variable speed limits (VSL) can be effectively employed to

improve traffic flow.

http://en.wikipedia.org/wiki/Mean

http://en.wikipedia.org/wiki/Median

http://en.wikipedia.org/wiki/Kinetic_energy

http://en.wikipedia.org/wiki/Motor-vehicle_collision

http://en.wikipedia.org/wiki/Motor-vehicle_collision

http://en.wikipedia.org/wiki/Collision

http://en.wikipedia.org/wiki/National_Highway_Traffic_Safety_Administration


VSL implementation produced safety improvement by simultaneously

implementing lower speed limits upstream and higher speed limits downstream of

the location where crash likelihood is observed in real-time [43]. The study

suggests to gradually introduce speed limit changes over time (5 mph every

10 min), reduce the speed limits upstream and increase speed limits downstream

of location of interest. However, the speed limit changes upstream and

downstream should be large in magnitude (15 mph) and implemented within

short distances (2 miles) of the location of interest.

Another study proposed a traffic management tool suitable for highways. It can

influence the traffic flow efficiency [44]. A variable speed limit, suitably operated

and enforced, is considered as a stand-alone measure or in combination with

ramp metering. A previously developed, computationally efficient software tool for

optimal integrated motorway network traffic control including is applied to a large-

scale motorway ring-road. It is demonstrated via several investigated control

scenarios that traffic flow can be substantially improved using VSL schemes even

without the aid of ramp metering.

2.2.2.3. Emissions and fuel usage

In general, traffic management was mainly aimed at smoothening the traffic flow.

However, besides the maximum allowed speed, exhaust emissions are

significantly increased by accelerating and decelerating traffic, i.e., stop-and-go

traffic, compared to traffic driving at an equivalent constant speed, i.e. free-

flowing traffic [45, 46]. Therefore, traffic flows can be characterized by both mean

average speed and speed variation. Traffic with high dynamics (more stop and go

traffic) is expected to have higher emissions than smooth traffic [47]. Hence, it

can be expected that the emissions can be decreased if the highway traffic is

effectively managed.

Several studies demonstrate that reduced freeway speeds can reduce fuel

consumption and related emissions [48, 49]. This indicates that the engines of the

vehicles are not typically designed for highest efficiency at those speeds. While

cruising in general could reduce the total fuel consumption due to decrease of

inertial load, the higher speed limits allows the driver to „try‟ to drive at maximum

allowable speed, but in fact he will be driving at variable speed with average


speed below the maximum speed limit and this results in sudden bursts of fuel

demands and higher emissions due to incomplete combustion.

Traffic management studies conducted on Dutch freeways suggested that the

current freeway speed limit could be reduced to 80 km/hr and this can produce

the most desirable combined effects of reducing energy use, emissions and

accidents [50].

In a similar study conducted by Keuken et al [51] on urban motorways in

Netherlands, when the maximum speed limit of 80 kmph is imposed and tested,

emission reduced by 5–30% for NOx and 5–25% for PM10. Actual emission

reductions by speed management at a specific motorway mainly depended on the

ratio of congested traffic prior and after implementation of speed management.

The larger this ratio, the larger is the relative emission reduction. Moreover, the

impact on air quality of 80 km/h for NOx and PM10 is largest on motorways with a

high fraction of heavy-duty vehicles.

Apart from the reduced speed limits, variable speed limits are also suggested to

improve mobility and reduce emissions simultaneously. Significant reduction in

NOx is possible by effective variation of speed limits [52].

Apart from the real time studies, simulation studies for speed limit reductions on

highways predicted congruent reductions in total highway distance travelled, fuel

consumption and total emissions [53, 54].

Speed control traffic signals are proved to be very effective tool in reduction of

pollutant emissions [55]. One concern about this type of signals is that while they

may be effective in reducing high speed crashes, they not only stop traffic that is

exceeding the speed limit, but other traffic on the approach that is not. As a

result, vehicle emissions are likely to increase, because of the existence of

excessive delays, queue formation and speed change cycles for approaching

traffic. On the other hand, if the speed control traffic signals modify drivers‟

behavior by inducing speed reduction, they will also result in a decrease in relative

pollutant emissions [56].


The level of highway traffic noise depends mainly on three factors. They could be

listed as follows

a. The volume of the traffic

b. The speed of the traffic/ traffic flow.


c. Numbers of heavy duty (usually vehicles with large diesel engines)

Besides these factors, the loudness of traffic noise can also be increased by

defective mufflers or other faulty equipment on vehicles. Any condition (such as a

steep incline) that causes heavy laboring of motor vehicle engines will also

increase traffic noise levels. In addition, there are other more complicated factors

that affect the loudness of traffic noise. For example, as a person moves away

from a highway, traffic noise levels are reduced by distance, terrain, vegetation,

and natural and manmade obstacles.

Desarnaulds et al [57] argued that a free flowing interrupted traffic can locally

reduce the noise from 1 to 2dB (A). Hence, highway noise problem can be solved

with traffic flow management, speed management, land use control, and highway

planning and design. It is traditional to meet the noise problems on highways by

constructing the highway at a different location farther to the residential areas, by

increasing the number of traffic lanes or remodeling the highway for its acoustics

[58].

2.2.3. Lowered Speed Limits

Speed reduction in residential neighborhoods rank among the most common schemes to

improve traffic safety. Traffic mangers understand very well that lower speeds reduce the

number of serious injuries, but they are forced to deal with drivers expressing their

dissent with reducing speed limits further and further for safety. However, in order to

protect residential areas from the impacts of high speed traffic, city planners devise

several methods to divert traffic away from these small networks. Zones of 30 km/hr are

becoming popular in some member states [59, 60]. These are sometimes referred to as

„Zone 30‟. These are popular in busy city centers, highly dense residential neighborhood,

near the parks where the children are expected to run across the streets, etc.


2.2.3.1. Safety

Several studies present the possible safety benefits of driving at lower and uniform

speeds.

Archer‟s study [61] suggested that reduced speed is likely to bring about a

reduction in average travel speed and have a positive impact on both the number

of accidents and accident outcome severity. Besides, secondary benefits

suggested by the study included reduced fuel and vehicle operating costs, and

reduced vehicle emissions and noise.

Kloeden et al proposes (from his experiments), a rule of thumb: In a 60 km/h

speed limit area, the risk of involvement in a casualty crash doubles with each 5

km/h increase in travelling speed above 60 km/h”[62]. According to his analysis,

a uniform 10 km/h reduction in the travelling speeds of the case vehicles offered

the greatest reduction in the number of crashes (42%) and persons injured (35%)

and also offered the greatest reduction in crash energy experienced by injured

parties in crashes that would still have taken place (39%). The 5 km/h reduction

scenario had much less effect on the elimination of crashes (15%) but still

reduced the average crash energy level experienced by the injured parties in

those crashes that still would have occurred by 24 per cent.

Nilsson (1982), by using a number of evaluations of speed limit changes in

Sweden, developed a model that established power relationships between crashes

and proportional change in mean speed. The exponent ranged from 2 for injury


crashes to 4 for fatal crashes i.e., the risk of getting involved in a crash increases

two to four times faster with an increase in speed [63].

In another study, a 10 kmph reduction of speed limits for all the roads in

Melbourne suggested an increase in travel time by 5% in the short run and 1% in

the long run, while the accidents decrease by 13.5%.{SMEC and Nairn (1999)

[64].

2.2.3.2. Emissions and Fuel Consumption

It is widely acknowledged within the scientific community that if traffic is allowed

to flow at a uniform speed, the reduction in acceleration and deceleration events

associated with stop-and-go traffic will result in increased fuel efficiency and

reduced emissions. This calls for constant lower speeds.

But setting an ideal speed-limit for every road in a network is challenging because

several factors such as the temporal variation in traffic intensity, the direction of

flow of traffic, the amount of estimated exposure, etc. need to be considered.

Hence, an optimal approach is required since the speed reduction simultaneously

influences traffic delays and waiting times as well. However, a review of the

literature indicated that the relationship between speed and fuel consumption or

emissions is quite complex [65]. Efforts to reduce congestion and traffic dynamics

(by traffic management measures) should be concentrated on specific routes or

sections with frequent occurrence of heavy congestion and a large share of heavy

duty traffic. [66]

Some findings relating speed limits with emissions and fuel use are as follows.

Model predictions by Pelkmans et al [67] demonstrated that when average speed

is reduced from speeds above 100km/h down to 80 or 60km/h, fuel consumption

can be expected to decrease. However, when the average speed drops below 30

or 40km/h, fuel consumption increases significantly. Emissions of NOx, CO and HC

also increase in this case. So, according to Pelkmans, it is necessary to prevent

traffic jams and promote slow moving traffic for reduced fuel usage.

The study by Panis et al [68] suggests that the analysis of the environmental

impacts of any traffic management and control policies is a complex issue and

requires detailed analysis of not only their impact on average speeds but also on

other aspects of vehicle operation such as acceleration and deceleration.


According to the study, there is a huge dependency of emissions on average

speed and speed variation.

Ihab et al [69] argued that the acceleration (reflective of traffic dynamics) is key

factor in determining emissions. The study predicted that when emissions are

gathered over a sufficiently long fixed distance, fuel-consumption and mobile-

source emissions rates per-unit distance increase as the level of acceleration

increases because of the rich-mode engine operations.

Road authorities in various countries (e.g. the United Kingdom, Spain, Switzerland

and Netherlands) have employed reduced speeds in their traffic management

schemes to improve air quality near heavy-traffic roads [70, 71].

Similarly, a 2003 pilot study in Rotterdam concluded that reducing traffic

dynamics (i.e. uniform traffic flow) is especially important for effective reduction

of traffic exhaust emissions [72].


Traffic noise is the combination of engine, exhaust system and transmission noise,

and noise generated from the interaction of the tyres with the road surface. The

engine noise is predominantly associated with speed and can thus be controlled by

reducing traffic dynamics.

Desarnaulds et al [73] argues that speed limitation (from 50 to 30 km/h) induces

a noise reduction of 2 to 4 dB(A) for passenger cars and 0 to 2dB (A) for heavy

vehicles (and 2 dBA more for the maximum noise level). Speed reduction induced

by diminution of road width can lead to a noise reduction of 1 to 3 dB (A)

especially if it is combined with other traffic management measures.

In another study, Berengier et al [74] studied the impacts of speed reducing

equipments and suggested that the noise can be mitigated though the speed

reduction and smoothening of the traffic flow.

In a study conducted by Hedstrom et al [75] noise reductions of speeds from 50

kmph to 30 knph can have a noise reduction of 2 to 4 dB(A) for cars and 0 to 2

dB(A) for heavy vehicles. The reduction was also found to be dependent on

driving behavior after lowering the speed limits.

In Germany [76], the introduction of 30 kmph speed limit in certain busy

residential streets brought up a significant 3 dB(A) reduction in the average noise

levels.


Model predictions by OFEFP, a road noise simplified model preditcted that with

every 5 kmph reduction in speed levels of the vehicles, the noise subsided by 0.5

dB(A).

2.2.4. LEZ (Low Emission Zones)

A low emission zone is a geographical zone within which special regulations and

restrictions for car and heavy vehicle traffic apply aimed at reducing air pollution.

Environmental zone is another name for Low Emission Zone (LEZ). Environmental zones

are getting increasingly popular in most European cities.

The environmental zone introduced in Stockholm, the capital city of Sweeden was

extremely successful in improving the local air quality [77].

London has worked with reducing the accessibility for traffic in the city by

reducing the number of Entry points and by closing streets (or making one-way

streets). This measure requires very little work for the authorities, since the

restriction is based on physical measures as signs, bollards etc.

In Prague, the restriction in the zone holds for heavy vehicles with a weight over a

special limit.

In Barcelona, the city is closed for traffic during a special time of the day.

German cities, under a law passed in 2006, are acquiring environmental zones,

areas into which you can't drive your car unless it bears a windshield sticker

certifying that it has an acceptable emission level.

There are currently 11 cities (Amsterdam, Utrecht, Rotterdam, Den Haag,

Eindhoven, Breda, Den Bosch, Tilburg, Delft, Leiden and Maastricht) in the

Netherlands that have introduced environmental zones in their city centers.. Only

clean lorries, defined by the Euro norm (Euro 3 or higher) may enter

environmental zones.


2.2.4.1. Local Emissions

The major purpose of the LEZ is to reduce local emissions. This can be done by

simply restraining the high polluting population of the vehicle traffic, namely

heavy duty trucks. These heavy duty trucks, even though they make a very small

percentage of the total vehicles on the road, are biggest contributors to NOx and

PM emissions. This emissions are compounded when the vehicles have to

overcome high inertial load during the acceleration and deceleration phases that

are a significant part of the city driving. Hence banning the heavy duty vehicles

from the LEZ is expected to improve the local air quality. This technique of

restricting high polluting vehicles or vehicles with lower euro norms from city

centers and residential neighborhoods is getting increasingly common in European

cities.

In Stockholm, the environmental zone covers around 30% of the total population

of the city. An assessment of the air quality benefits of that emission of NOX

from heavy vehicles within this zone revealed that the emissions were reduced by

10% and emissions of particulates by 40% [78]. In a related study, the health

benefits were also presented by the author [79].

In Goteborg, another city in Sweeden, the introduction of Environmental zone for

heavy duty vehicles was posted in 1996 [80]. All the diesel powered vehicles over

3.5 tons were banned from the zone. Owing to this, there were significant

reductions in CO (3.6%), HC (6.1%), NOx (7.8%) and PM10 (33.2%). While some

of these reductions can be partially attributed to the technological improvements,

the underlying cause is the introduction of Environmental zone.


In London, road transport is the single biggest source of Particulate Matter (PM)

and Oxides of Nitrogen(NOX). LEZs introduced in Greater London were

successfully able to reduce traffic pollution by deterring the most polluting diesel-

engine lorries, buses, coaches, minibuses and large vans from driving within the

city[81]. A simulation study projected that the total tonnes of NOx emitted in

Greater London will reduce by about 1,100 tons in 2008 and by 1,200 in 2010

while the PM10 (which include exhaust and tire and brake wear) will reduce by

100 tons in 2008 and by 200 tons in 2010. The reductions of NOx were

predominantly expected in the roads with the greatest portion of heavy duty

vehicles. However, future projections suggested that the greatest reductions in

NOx and PM10 concentrations are expected to occur after 2012 when the Euro IV

norms will be introduced.


The noise emissions can also be reduced if the LEZ is introduced. This is because

the most noisy of the vehicles are the high emitting trucks. Hence a significant

drop in noise levels could be expected. Several of the LEZs in major cities

experienced a noise reduction.

In Austria [82], measures such as limiting the trucks from busy areas have found

to reduce the noise levels.

In Berlin [83], night time noise is limited and is decreased by an amazing 6 dB(A)

when low emission zone is introduced, which limited the number of heavy

vehicles.

In Hongkong, when high emitting vehicles are banned during the night time, noise

subsided by 2 dB (A).

In London [84], the noise levels reduced drastically when the urban toll was

introduced during nights.

2.2.5. Effect of traffic lights synchronization.


To regulate traffic flow along major roads, city planners also employ

synchronization of traffic lights (green wave) on busy major roads in urban


locations. A green wave is an intentionally induced phenomenon in which a

series of traffic lights (usually three or more) are coordinated to allow continuous

traffic flow over several intersections in one main direction.

The coordination of the signals is either done dynamically by using the sensor

data of currently existing traffic flows or statistically by the use of timers. A

vehicle encountering a green wave, if travelling at the suggested road speed, will

see a progressive cascade of green lights, and not have to stop at intersections.

This allows higher traffic loads, and reduces noise and energy use (because less

acceleration and braking is needed).

Green wave will be useful for only a set of vehicles through the intersections

before the flow is interrupted to give way to other traffic flows (usually

perpendicular) through the intersections. This problem is compounded if there is

an equally higher traffic flow from all the legs to the intersection. If it is one main

arterial road with small minor roads, signal light timings can be timed to maximize

the total flow through the main road. Matson et al [85] presents how the main

street delays and side street delays can be optimized using a set of offsets and

cycle times.

Grerhenson et al [86] proposed a scheme in which traffic lights self-organize to

improve traffic flow. Using simple rules and no direct communication, traffic lights

are able to self-organize and adapt to changing traffic conditions, reducing waiting

times, number of stopped cars, and increasing average speeds.

Kasun et al [87] discusses a special-purpose simulation (SPS) tool for optimize

traffic signal light timing. The simulation model is capable of optimizing signal

light timing at a single junction as well as an actual road network with multiple

http://en.wikipedia.org/wiki/Traffic_light

http://en.wikipedia.org/wiki/Intersection_(road)


junctions. It also provides signal light timing for certain time periods according to

traffic demand.

Huang et al [88] argued that the green-light wave solutions can be realized only

for under-saturated traffic. However, for saturated traffic, the correlation among

the traffic signals has no effect on the throughput. While coordinating of the traffic

lights is simple enough to implement, the bigger challenge comes when the traffic

volume is near saturation. A green wave has a disadvantage that slow drivers

may reach a red signal at the traffic lights, with a queue of traffic may build up

behind them, thus ending the wave. In general, stopping and then starting at a

red light will require more time to reach the speed of the wave coming from

behind when the traffic light turns to green.

This saturation limit of traffic at which green wave is no longer effective was

addressed by Brockfeld et al [89]. The study concluded that the capacity of the

network strongly depends on the cycle times of the traffic lights and that the

optimal time periods are determined by the geometric characteristics of the

network, i.e., the distance between the intersections. The study proposed that

when the lights were synchronized, the derivation of the optimal cycle times in the

network can be obtained through flow optimization of a single street with one

traffic light operating as a bottleneck.

Newell [90] argued that a particular offset between the coordinated signal lights

along the arterial road could minimize the number of stops and total delay, that

offset might not be the one that maximizes traffic flow. These studies presented

models in which the emphasis was laid on maximizing the flow rate through the

arterial road.

Morgan et al [91] argues that it is simple to improve traffic flow through signal

synchronization in one direction; several factors need to be considered and

optimized if the traffic flow is on the other direction as well. He addresses these

difficulties and presents an optimized approach. According to him, green waves

are most effective with one-way traffic. A green wave in both directions may be

possible with different speed recommendations for each direction; otherwise

traffic coming from one direction may reach the traffic light faster than from the

other direction if the distance from the previous traffic light is not mathematically

a multiple of the opposite direction.

http://en.wikipedia.org/wiki/One-way_traffic


2.2.5.2. Emissions and Fuel Usage

Traffic light synchronization is employed basically to maximize traffic flow while

minimizing stops for a given traffic volume, but the useful added benefits could be

realized in reduction of fuel consumption and improvement of air quality around

the intersections.

Madireddy et al [92] suggested that on a major urban road, the emissions can be

reduced by at least 10% when the lights were synchronized.

In a more extensive study conducted by Unal et al [93], the relationship between

the signal coordination and emissions is presented. For the selected test vehicles,

the emissions rates were highest during acceleration and tend to decrease for

cruise, deceleration, and idle. The study also concluded that the emissions were

lower at the congested conditions than uncongested conditions.

Li et al [94] proposed a signal timing model, in which a performance index

function for optimization is defined to reduce vehicle delays, fuel consumption and

emissions at intersections. This model optimizes the signal cycle length and green

time by considering the constraint of a minimum green time to allow pedestrians

to cross.

The concept of optimizing signal timings to reduce fuel consumption and

emissions was also addressed in this study [95] by linking emissions models to

optimize signal timings. This had minimized fuel consumption, local and CO2

emissions. Based on this study, when estimated fuel consumption is used as an

objective function, fuel savings of 1.5% were estimated.


In a study conducted in Belgium [96], it was suggested that the synchronization

of traffic lights helps reduce noise emissions.

A study conducted in Geneva[97] showed that by adapting the traffic lights to

vehicle speeds, noise levels can be reduced by 2 dB (A).

These results completely agree with another similar study by Nelson et al [98]

which also suggested that if the traffic flow was smoothened, noise levels could be

brought down by 2 dB (A).

De Coensel et al [99] examines the effects of traffic management on noise

emissions. From their observations, they argued that while there can be a

reduction of up to 1 dBA in the noise levels near the intersections when there is a


coordination of traffic lights along an arterial road, there can be an increase in the

noise level by 1.5 dBA along the road between the intersections. This study

suggests that the net effect of synchronizing traffic lights is negative in noise

perspective.

2.2.6. Speed Humps/Bumps

A speed bump is a bump in a roadway with heights typically ranging between 3

and 4 inches (7.6 and 10 cm). The length of speed bumps are typically less than

or near to 1 foot (30 cm); whereas speed humps are longer and are typically 10

to 14 feet (3.0 to 4.3 m) in length [100].

Speed humps are fundamentally designed to slow traffic in residential areas. They

are usually referred to as sleeping police. They should be placed in series of about

300 to 500 foot intervals. These will reduce vehicle speed both upstream and

downstream of the humps, besides a significant speed reduction at the humps. In

an extensive study conducted by Hallmark et al, the impact of speed bumps on

vehicle speeds and speed profiles is investigated. The speed reduction devices are

found to be effective in reducing the mean vehicle speeds and also the number of

vehicles that exceed the speed limit [101].

2.2.6.1. Safety

Traffic calming is typically implemented to address speeding and external traffic

concerns. It is intuitively recognized that successful traffic calming would

therefore result in safety benefits. The magnitude of these benefits varied among


the projects, with an average 40 percent reduction in collision frequency and 38

percent reduction in the annual claims costs.

A total of 85 case studies from Europe, Australia, and North America were

reviewed to determine the safety benefits of traffic calming as measured by other

jurisdictions. The international case studies in which more than five pre-calming

collisions per year occurred were analyzed separately. In this group of 15 studies,

the decrease in collision frequency ranged from 8 percent to 95 percent [103].

A multivariate conditional logistic regression analysis showed that speed humps

were associated with lower odds of children being injured within their

neighborhood (adjusted odds ratio [OR] = 0.47) and being struck in front of their

home (adjusted OR = 0.40) [104].

Beckman and Kuch [105] investigated the effects of road bumps on speed. Their

research suggested that the bump could be a limiting factor in concerning speed

and that it could adversely affect the dynamics of a car.

2.2.6.2. Emissions and Fuel Consumption

It should be expected that the speed bumps increase the traffic dynamics of the

vehicles by creating acceleration and deceleration and this could result in low fuel

efficiency. This was usually the biggest criticism towards building road bumps. If

they were built permanently, they could cause slowing traffic even during the off

peak hours, which would be unnecessary. Moreover they increase vehicle wear

and tear. Moreover speed bumps are often a hindrance to emergency vehicles.


Besides improving safety, speed humps can also reduce traffic noise. A significant

noise level reductions were obtained with a speed bumps of a height of 75 to 100

mm [106].

Speed humps, are found effective in reduction of noise levels for cars [107].

However the effectiveness of speed humps depends primarily on the distance

between the humps and speed levels.


A study conducted in the towns of Slough and York showed that when the speed

reductions in the range of 10 kmph, speed humps can bring about a noise level

reduction 10 dB(A) for the cars and 4 dB(A) for busses.

Speed humps, when introduced in Denmark showed that the overall noise is

reduced, but the braking associated with vehicles approaching the speed humps

resulted in a slight increase in noise before and after the hump.

Revisiting the table and filling in the blocks based on the observations.

Traffic

Flow

Safety

Fuel

Conservati

on

(CO2)

savings

Local

Emissions

Reduction(NO

x, PM, HC,

etc)

Noise

Abatement

Replacing

Intersections with

Roundabouts

+++ ++ + + +

Highway speed

Management.

+++ + + + ++

Low Emisison

Zones

= ++ ++ ++ ++

Speed Reduction

+ +++ +++ +++ ++

GreenWave ++ = + + -

Speed Bumps +++ + +++ +++ -

Tunnels ++ + ++ ++ ++


3. CA SE ST U D IE S

3.1 Introduction

The study integrates a traffic model and an emission model to assess the impact of

implementing these two measures on emissions in a selected area in the city of Antwerp

in Belgium. The microscopic traffic simulation model Paramics is used to simulate

different traffic scenarios with a given fleet composition, road characteristics, traffic

movement, speed limits on the roads, etc. The output of the model consists of second by

second position, speed and acceleration of each vehicle. This output served as an input to

Versit+, an emission prediction model. The model is capable of generating emissions of

CO2 and NOx on a spatial basis as well as on a per-trip basis.

To obtain reliable predictions that enable investigation on the influence of traffic

management on emissions, two important conditions regarding the used model need to

be fulfilled.

1. The emission model, Versit+ should approximate reality as good as possible.

2. The combination of traffic simulation software and emission model should be validated

to be accurate enough to investigate a traffic management scheme for emissions.

These conditions are met by the validation results of Verist+ and the integrated model

(Paramics and Versit+) in the previous work by the author. First the validation of Versit+

is presented and then the validation of the integrated model is presented. Then the case

studies that were performed using the integrated model were presented.

3.2 Validation of the Models used (Versit+ and Paramics)

3.2.1 Validation of the emission model

A validation study for Versit+ is performed using the data obtained by VITO‟s On Road

Emissions and Energy Measurement (VOEM) system. This system is embedded on the

vehicle and is capable of measuring real-time instantaneous emissions of CO2, CO, THC,

PM and NOx. The VOEM data for the validation study are collected from the tests

conducted on four diesel vehicles presented in Table 3.1. The drive cycle used to collect

these data is Mol_30, a 30 minute cycle which is a combination of ten minutes of city

driving, ten minutes of suburban driving and ten minutes of highway driving. The speed


profiles of each of these vehicle tests are inputted into the emission model, which

generates the continuous emission predictions for CO2 and NOx. Then for every test, the

results from the measurements are compared with those predicted by the emission

model.

Table 3.1.Vehicles equipped and tested with VOEM

Vehicle Tested

Model/Year Fuel

Type

Engine

Displacement

Max.

Power

Euro

Norm

Citroen Berlingo 2007 Diesel 1560 cc 90 hp Euro IV

Citroen C4 2007 Diesel 1560 cc 80 kW Euro IV

Nissan Patrol 2000 Diesel 2953 cc 116 kW Euro III

Opel Vivaro 2007 Diesel 2000 cc 66 kW Euro III

Temporal plots of the measured data for all vehicles, alongside the model predictions are

obtained. The first 600 seconds of instantaneous data is shown in Fig. 3.1. The „cyan‟

colored clouds of measurements obtained from vehicles have their peaks coinciding with

the red peaks predicted by the model. This indicates that the model is able to capture the

dependencies on speed and acceleration.

50 100 150 200 250 300 350 400 450 500 5500

5

10

15

20

25

Time (s)

CO

2 (

g/s

)

Opel Vivaro

Berlingo

NissanPatrol

Citroen C4

Versit

Fig.3.1. Comparison of measured instantaneous CO2 emissions

(for different vehicles) with model predictions.


Fig. 3.2 presents the results predicted by the emission model for a test conducted on the

vehicle Citroen Berlingo, a small diesel van tested on Mol_30 drive cycle. This vehicle is

defined in the traffic model as a light duty diesel car. For each of the 10-minute parts of

the test cycle, the model predictions are compared against the measurements. The

predicted CO2 is highly correlated with the measured CO2 for all the three parts of the

test cycle. From Table 2, it can be inferred that CO2 predictions are very good for all the

vehicles and for all the parts of the drive cycle. The correlations are only slightly lower in

the city-driving conditions, possibly because of the high fraction of idling and stop-and-go

traffic which are more difficult to be translated by the model into corresponding

emissions than those measured at steady speeds. The correlation of NOx is not as high as

that of CO2, but is reasonable. This is because Exhaust Gas Recirculation (EGR) methods

are employed only in part of the vehicles in the model, which represents the average

Dutch vehicle fleet vehicle.

In all the subplots of Fig. 3.2 there exists an over-prediction of total CO2 emissions by

the model for all the three parts of the cycle. From Table 3.2, for this particular vehicle,

the total emissions per cycle are almost twice that of the real values. Similar over-

predictions can be noted for other vehicles as well. This is because the vehicles used for

testing are not representative of an average diesel car that is represented by the model.

In other words, the database of the emissions model consists of several old vehicles

which have higher fuel consumption and related emissions. Hence the diesel car based on

Dutch fleet represented by the model has more emissions than the relatively modern

vehicles used for the emissions measurement.

The distributions of measured and predicted emission values for each of the three parts

(city, suburban and highway) of the Mol_30 drive cycle are shown in Fig. 3.3 The model

predictions of both CO2 and NOx tend to be much closer to the measured values

especially at lower speeds (in city driving). In highway driving, the model predictions are

slightly higher than majority of the measurements from the vehicles, with the exception

of predictions of NOx from Nissan Patrol. Since the emissions model is shown to be

sensitive enough to capture the speed and acceleration fluctuations, the network level

emissions predicted by the model are considered accurate enough for the analysis in this

study.


0 2 4 6 8 10 120

10

20

30

Correlation between measured and predicted CO2 for Citroen Berlingo for the city driving

(Rsq = 0.9)

0 2 4 6 8 10 12 140

10

20

30...for the suburban driving

Pre

dic

ted C

O2 (

g/s

)

(Rsq = 0.92)

0 2 4 6 8 10 12 140

10

20

30...for the highway driving

VOEM measured CO2 (g/s)

(Rsq = 0.93)

Fig.3.2. Correlation of the model-predicted CO2 with measured CO2 for Citroen Berlingo

tested on Mol_30 cycle. The correlation is shown separately for city, suburban and

highway parts of the cycle.

Table 3.2. Comparison of predicted emissions with the measured emissions for three

parts of the Mol_30 cycle. The average of predicted emissions and measured emissions is

shown along with respective R2 values

City Suburban Highway

R2 Pred./Meas. R2 Pred./Meas. R2 Pred./Meas.

Opel

Vivaro

CO2 0.89 1.64 0.93 1.50 0.91 1.42

NOx 0.70 0.84 0.77 0.97 0.84 0.93

Citroen

Berlingo

CO2 0.90 2.13 0.92 2.03 0.93 1.94

NOx 0.78 0.72 0.79 0.84 0.83 1.16

Nissan

Patrol

CO2 0.85 1.32 0.92 1.15 0.94 1.08

NOx 0.57 0.29 0.62 0.34 0.68 0.36

Citroen

C4

CO2 0.83 2.60 0.90 2.48 0.88 2.12

NOx 0.55 0.87 0.72 1.24 0.82 1.48


Fig. 3.3. Distribution of CO2 and NOx emissions predicted by the model and those

measured from four diesel vehicles for city, suburban and highway driving.

3.2.2. Validation of the integrated model

After the emission model is externally validated using real-time emission measurements,

the accuracy of the integrated model (combination of the traffic and emission model) is

examined using real vehicle trip data. A vehicle is equipped with a data logging device

and is driven along Plantijn and Moretuslei (P&M) on a typical working day. While the

CAN bus interface provides instantaneous engine speed, throttle and fuel consumption,


the GPS logging device provides speed and position. The speeds of the vehicle are

recorded on a second by second basis. This speed profile is used as input to the emission

model and the generated emissions are compared with the emissions predicted by the

integrated model for chosen trips along P&M (Fig. 3.4). The distribution of distance based

emissions obtained by the emissions model for the real-time vehicle trips is similar to

that predicted by the integrated model. This suggests that the accuracy of the integrated

model is sufficient to estimate the effect of a given traffic management measure on

emissions.

Fig.3.4. Comparison of the emissions distributions along P&M obtained from simulated

speeds from the traffic model with those obtained from the measured speeds by the real

vehicle trips along P&M.

3.3 Case studies investigated using the integrated model

Four case studies are presented in this study using the model predictions and the

measurements obtained from the emissions measurement and GPS logging devices. The

studies are:

Case Study-A: Effect of Reduced speed limits on emissions and noise in Zurenborg


Case Study-B: Effect of green wave on emissions and noise in Zurenborg

Case Study-C: Effect of improved signal control in Grotesteensweg, Antwerp????

Case Study-D: Effect of …..

3.3.1. Case Study-A: Effect of Reduced speed limits on emissions and noise in

Zurenborg

3.3.1.1. Methodology

A micro-simulation network was constructed for the area of Zurenborg, part of the

19th century city belt of Antwerp, Belgium (Fig. 3.5). The network was coded on

the basis of Geographic Information Systems (GIS) data which comprises of

roads, buildings, and aerial photographs, and traffic light timing data, supplied by

the Antwerp police department. Further, traffic counts, supplied by the Flemish

Department of Mobility and Public Works, were used to calibrate the traffic flows

during morning rush hour. These traffic flows were inputted into Paramics by

defining „zones‟, from which traffic flows in and out to another zone.

Fig. 3.5. Case study in zurenborg.

Two scenarios are created using the traffic model. The first is the original scenario

with current speeds limits of 50, 70 and 100 km/hr in the residential part of the

network, P&M and highway respectively. In the second scenario, a 30 km/hr

speed zone is simulated in the residential part of the study area shaded in maroon

in Fig. 3.5. This zone encompasses all the minor roads, but not any of the major


roads. Simultaneously, the speed limits on P&M and on the highway are reduced

to 50 km/hr and 70 km/hr respectively. It should be noted that the applied

microscopic simulation model uses dynamic traffic assignment: routes are chosen

according to the instantaneous congestion conditions.

3.3.1.2 Results

The distribution of the speeds, accelerations and emissions for the two scenarios

are presented in Fig.3.6 (a). Within the residential area, the reduction of speed

limits brought about lower average speeds. Moreover, a large fraction of the

speeds measured are within a narrow range indicating that majority of the

vehicles are driving at speed between 25 and 35 km/hr. The occurrences of

maximum and minimum accelerations are reduced indicating lesser braking and

slower pick up. Hence, reducing the speed limits to 30 km/hr ensures uniform

speeds.

The speed limit reduction has also brought a reduction of all the gaseous

emissions (Table 3.3). The reduction in total emissions is about 27%, which is

more than the reduction in distance travelled. Hence, majority of the reduction of

emissions should be attributed to speed reduction. This suggests that lower speed

limits not only limit the traffic via the residential area, but also reduce distance

based emissions and fuel consumption

Table 3.3.Effect of reduced network speed on emissions

In the residential area Along the P&M

Vehicle

Km

CO2 (g) NOx (g) Vehicle

Km

CO2 (g) NOx (g)

Original Speeds 911.7 346, 350 1258 791.1 362, 500 1464

Reduced

Speeds

782.7 253, 670 922 789.2 326, 670 1311

% Reduction 14.14% 26.8% 26.7% 0.24% 9.9% 10.4%

Reduction of speed limits also had effect along P&M. While the total distance

travelled along P&M is not significantly changed, there is about 10% reduction in

total emissions. When the vehicle trips that travelled along P&M are isolated, the

emissions shifted towards the lower limits at reduced speeds (Fig. 3.6 (b)). This


indicates that on the busy major roads, speed limit reduction could lead to lower

emissions.

Fig. 3.6 (a). The effect of speed limit reduction on speed distributions, acceleration

distributions and the distance based emissions in the residential area.


Fig. 3.6 (b). The effect of speed limit reduction on the emissions for trips along P&M.

3.3.2 Case Study-B: Effect of Green wave on emissions and noise in Zurenborg,

Antwerp

3.3.2.1 Methodology

To understand the influence of synchronization of traffic lights along a road on

emissions, the following scenarios are examined on N184 road or Plantijn &

Moretuslei (P&M). The original scenario is the one with the green wave, where all

the traffic signals are coordinated. A second scenario is created by removing the

synchronization. In order to desynchronize the signals, a small but random

number of seconds is added to or subtracted from the cycle times. This way, a

wide range of waiting times at each intersection is encountered over the course of

the simulation run (1 hour).

3.3.2.2 Results

The corresponding emissions in both scenarios are compared (see Table 3.4). For

both these scenarios, the only vehicle trips selected are those that drove along the

P&M and completed their trip during the simulation. For a typical light duty car

that travelled along P&M, the CO2 emissions increased by 9.5%, while the NOx

emissions are increased by 8.7%. There is a slight increase of 2.8% in travel time

for same trip. From Fig. 3.7, it can be seen that the overall trip emissions shift

towards the higher values when the green wave is removed. The maximum value

of all the emissions is also higher if the green wave is removed.

Table 3.4.Effect of Green Wave along P&M on the travel times and emissions

Original Scenario Without Green

wave

Percent difference

Avg. travel time (sec) 293.15 301.36 2.8%

Avg. CO2 (g) 658.36 720.99 9.5%

Avg. NOx (g) 1.547 1.681 8.7%


The effect of a green wave is positive because of the smoother traffic flow, at least

in the short run. In the long run however, care has to be taken that the effect of

smoother traffic flow does not attract more traffic volume, which could negate the

positive effect.

Fig. 3.7. Effect of green wave along P&M on total trip emissions.

----------------------------------------------------------------------------------------------

3.3.3 Case Study-C: Effect of---------- on -------------along

Grotesteensweg in Zurenborg, Antwerp

3.3.3.1 Methodology

VEDETT (Vehicle Device for Tracking and Tracing) was installed in 20

Volkswagen Polos that will be driven mainly in the area of Antwerpen, Mol and on

the highway between Antwerp and Mol. The device measurements were noted

along the Grotesteensweg in Zurenborg. The VEDETT system records time, speed,

position and fuel consumption of the vehicle on a second by second basis. The

traffic counts for different times of the day are necessary. They have to be


obtained from the Verkeercentrum. VEDETT provides data at a frequency of 1 HZ.

Because of low speeds in city traffic, the 1 Hz data collection is expected to

provide enough detail to accurately analyze the speed profiles.

Initially, the intersection is modelled in Paramics by the use of traffic counts and

signal light timings. The modeled network is shown in Fig. ….

However, the speed profiles are based on the measurements obtained from

Axotec. But total speed profiles cannot be obtained because the vehicles cross the

intersection predominantly in one UNIQUE direction and the reverse. The

remaining cells in the demand (O D matrix) has to be filled up with conjecture.

1. For the selected vehicle trips, obtain the speed profiles and input these speed

profiles into Versit+ to obtain emissions.

2. For the crossing, obtain the following parameters for every travel direction

a. The total wait time, or idle time.

b. Time consumed by the vehicle to pass the crossing

c. The average speed for each road approaching and leaving the intersection.

d. The average value of maximum acceleration and maximum deceleration.

e. The average of the relative positive acceleration

3. Repeat this procedure for a NEW configuration( replace the intersection totally

with a roundabout).

The fuel consumption (and correlated CO2 emissions) from each of the individual

vehicles can be recorded and these results can be double checked by the results

obtained from Versit+, an emissions model. This will be done by using the real

speed data as input for the Versit+ runs. If the number of observations are quite

large (more than 30), then the average speed profiles can be constructed for the

data around every intersection and this can be fed into Versit+ to generate the

fuel consumption and CO2emissions.

4. Coasting and eco-driving of the vehicles around the crossings can be

examined. Some literature studies can be put together and our observations

can be compared against them.

5. Now the dependencies on driving behavior (based on the four parameters)

around the intersection and the fuel consumption and emission results can be

investigated. These results can provide us with feedback on interesting

observations such as how much additional fuel is consumed if the driver

chooses to strongly decelerate and accelerate or how much more PM is

resulted from long wait times around the intersection, etc.


Similar Case Study Scenario Examinations and the parameters to be used:

3.3.4 Comparing roundabouts with signalized intersections.

With the same traffic counts, the environmental impacts and fuel consumption of these

„possible‟ crossings can be investigated in detail. The following sets of analyses can be

done:

a. Which crossing has the least average wait time?

b. What is the average speed around each of the crossing?

c. For which crossing the RPA is minimum?

d. What are the aggregate emissions and fuel consumptions for each

crossing?

A weightage can be given to each of these parameters, (of course this is

subject to discussion as which is more significant than the other) and pros

and cons for each of the crossings can be evaluated.

The optimized speed limits for the network and especially in the roads closer to the

intersections can be obtained. In this case, the optimization parameters include average

wait times, fuel consumption, CO2 and emissions of NOx and PM obtained from Versit+.

Again, a weightage can be provided to each of these parameters and an optimized speed

limit reduction or increase can be suggested around the intersection. For example, a

result such as “A reduction of speed limits on the approaching roads by 10% can reduce

the fuel consumption by x %, NOx emissions by y% and so on” can be interesting.

Recommended Further Case Studies:

Further traffic management measures such as lane additions to a busy road or replacing

a signalized intersection with a roundabout can be investigated. It could be interesting to

study the effect of increasing the number of lanes on a major road while simultaneously

reducing the speed limit. This could have the added benefits of emissions reduction and

smoother traffic flow.


4. CON C L U S I ON S AN D RE C OMM E N D AT ION S

Based on the literature review and the case study results, the following recommendations

can be made from policy stand point.

1. Several troubled intersections in Flanders have to be considered for a total

replacement with a modern roundabout. This will ensure smoother traffic flow

despite some slight inconveniences in adjusting to the change.

2. Highway speed management can be achieved using variable speed controls along

all the major highways and the use of intelligent transport systems could further

enhance the effectiveness of such a drastic scheme. Highway speed limits could

be reduced by 10 to 20 kmph wherever possible.

3. Major roads are to synchronized for traffic flow using green-wave. But identifying

these roads could be a challenge because green wave is proven effective only in

conditions when the traffic is unsaturated. This could be a vital step in reducing

congestion and air quality near the intersections.

4. More residential zones need to be defined and marked as low emissions zone. This

should be a top priority in all the city centers and areas with high population

density. Banning all the automobiles (not just high emitters) in a zone during the

evening hours could also be considered seriously.

5. Speeds are to be reduced along the major roads in cities. Slow and uniform speed

vehicles could ensure safety, smoother traffic, lower fuel usage and reduced

emissions. Most of the residential urban roads need to be a maximum of 40 kmph

instead of traditional 50 kmph.

6. Speed humps should be avoided (except in school zones where safety is major

criteria). The slighter benefit of increased safety is more than overshadowed by

the increased fuel usage associated with stop and go traffic. Existing speed bumps

should be replaced with reduced speed limit signs if safety on a particular road is

compromised.

7. Road side parking near the city centers and busy roads should be avoided and the

cities should be cleared of slow moving traffic. Large parking lots need to be

constructed in the outskirts of the cities to free the heart of city for non motorized

transport and walking. This will increase the available space that could be used for

planting trees and building bike paths or foot paths for the pedestrians. This is

already being considered in New York.


8. Most importantly, travel demand management need to be given number one

priority. Encouraging people to shift to a public transport or take a bike to work

could be very rewarding. The policy makers should work with employers to

provide the right incentives for their employees to get them out of their cars. This

will reduce the stress for the traffic management centers and the need for major

infrastructural changes.


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