TrafficManagementSchemes

WETENSCHAPSPARK 5
B 3590 DIEPENBEEK
T ► 011 26 91 12
F ► 011 26 91 99
E ► info@steunpuntmowverkeersveiligheid.be
I ► www.steunpuntmowverkeersveiligheid.be
PROMOTOR ► Prof. dr. ir Dick Botteldooren, ir. Ina De Vlieger
ONDERZOEKSLIJN ► Duurzame mobiliteit
ONDERZOEKSGROEP ► Ugent, Vito
RAPPORTNUMMER ► ?
Steunpunt Mobiliteit & Openbare Werken
S p o o r V e r k e e r s v e i l i g h ei 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 info@steunpuntmowverkeersveiligheid.be
I www.steunpuntmowverkeersveiligheid.be

Steunpunt Mobiliteit & Openbare Werken 5 R-00-2002-01
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 D U C T I O N
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. LI T E R A T U R E S T U D Y : TR A F F I C M A N A G E M E N T
S C H E M E 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.

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.

 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

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.

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 S E S T U D I E 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.
50 100 150 200 250 300 350 400 450 500 550
0
5
10
15
20
25
Time (s)
CO2(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 12
0
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 14
0
10
20
30
...for the suburban driving
PredictedCO2(g/s)
(Rsq = 0.92)
0 2 4 6 8 10 12 14
0
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. CO N C L U S I O N S A N D RE C O M M E N D A T I O N 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.

LI T E R A T U R E L I S T
1. Trachet, B., Madireddy, M., Botteldooren, D., De Vlieger, I., 2010. The influence of
traffic management on Emissions: Literature study of existing emission models and initial
tests with micro traffic simulation. Duurzame Mobiliteit, Steunpunt Mobiliteit & Openbare
Werken – Spoor Verkeersveiligheid, Report No. RA-MOW-2010-001.
2. Mandavilli, S., McCartt, A., Retting, R.A., 2009. Crash patterns and potential
engineering counter measures at Maryland roundabouts. Traffic Injury Prevention,
vol.10, 44-50.
3. GER-Sécurité Routière – "Routes départementales – Efficacité des opérations de
sécurité", DDE44 & Conseil Général – Loire Atlantique, July 2002, Nantes
4. Jorgensen, E., Jorgensen, N.O., 1996. Traffic Safety at Danish Roundabouts -
Constructed after 1985. VTI Konferens, 4A, Linköping, Sweden.
5. Schoon, C., Minnen, J., 1994. The Safety of Roundabouts in The Netherlands", Traffic
Engineering and Control, vol. 35, n° 3, 142-148, London.
6. Persaud, B. N., Retting, R. A., Garder, P. E., Lord, D., 2000. Crash reduction following
installation ofroundabouts in the United States, Insurance Institute for Highway Safety,
7. Roundaboutsusa.com
8. Brabander, B., Nuyts, E., Vereck, A., 2005. Road safety effects of roundabouts in
Flanders, Journal of Safety Research, Vol. 36, 289-295.
9. Rodegerdts, L., Blogg, M., Wemple, E., Myers, E., Kyte, M., Dixon, M., List, G.,
Flannery, A., Troutbeck, R., Brilon, W., Wu, N., Persaud, B., Lyon, C., Harkey, D., Carter,
D. 2007. Roundabouts in the United States. National Cooperative Highway Research
Program Report no. 572. Washington, DC: Transportation Research Board.
10. Daniels, S., Brijs, T., Nuyts. E., Wets, G., 2011. Extended prediction models for
crashes at roundabouts. Safety Science, Vol. 49, 198-207.
11. Brude, U., Larsson, J., 2000. What roundabout design provides the highest possible
safety, Nordic Road & Transport Research, Vol. 2, 17-21.
12. Daniels, S., Brijs, T., Nuyts. E., Wets, G., 2010. Explaining variation in safety
performance of roundabouts. Accident Analysis and Prevention, Vol. 42, 393-402.

13. Daniels, S., Nuyts. E., Wets, G., 2010. Effects of roundabouts on traffic safety for
bicyclists. Accident Analysis and Prevention, Vol.42, 518-526.
14. Brilon, W., Stuwe, B., Drews, O., 1993. Sicherheit und Leistungsfahigkeit von
Kreisverkehrsplatzen. FE Nr 77359/91. Bochum, Germany: Lehrstuhl fur Verkehrswesen,
Ruhr-Universitat Bochum. Cited by: Elvik R. Effects on road safety of converting
intersections to roundabouts: a review of evidence from non-US studies. Transportation
Research Record 1847:1-10.
15. Schoon, C., Minnen, J. V., 1994. The safety of roundabouts in the Netherlands.
Traffic Engineering and Control, Vol. 35,142-48.
16. Hyden, C., Varhelyi, A., 2000. The effects on safety, time consumption and
environment of large scale use of roundabouts in an urban area: a case study, Accident
Analysis & Prevention, Vol. 32, 11-23.
17. Robinson, B. W., Rodegerdts, L., Scarborough W, Kittelson, W., 2000. Roundabouts:
An Informational Guide, Federal Highway Administration.
18. Retting, R. A., Luttrell, G., Russell, E. R., 2002. Public opinion and traffic flow impacts
of newly installed modern roundabouts in the United States. ITE Journal, Vol. 72, 30-32,
37.
19. Retting, R. A., Mandavilli, S., Russell, E. R., McCartt, A. T., 2006. Roundabouts traffic
flow and public opinion. Traffic Engineering and Control, Vol. 47, 268-72.
20. Russell, E. R., Mandavilli, S., Rys, M. J., 2004. Operational performance of Kansas
roundabouts: phase II. Report no. K-TRAN KSU-02-04, Final Report 01-04. Manhattan,
KS: Department of Civil Engineering, Kansas State University.
21. Bergh, C., Retting, R. A., Myers, E. J., 2005. Continued reliance on traffic signals the
cost of missed opportunities to improve traffic flow and safety at urban intersections.
Arlington, VA: Insurance Institute for Highway Safety.
22. Rodegerdts, L., Blogg, M., Wemple, E., Myers, E., Kyte, M., Dixon, M., List, G.,
Flannery, A., Troutbeck, R., Brilon, W., Wu, N., Persaud, B., Lyon, C., Harkey, D., Carter,
D. 2007. Roundabouts in the United States. National Cooperative Highway Research
Program Report no. 572. Washington, DC: Transportation Research Board.
23. Brude, U., Larsson, J., 2000. What roundabout design provides the highest possible
safety, Nordic Road & Transport Research, Vol. 2, 17-21.

24. Mishra, S., 2010. Traffic Flow Characteristics Comparison between Modern
Roundabouts and Intersections, ITE Student Paper, retrieved from the Worldwide web on
at http:// www.smartgrowth.umd.edu/pdf/TrafficFlowRoundaboutsIntersections_ Mishra_
3-9-09.pdf
25. Várhelyi, A., 2002. The effects of small roundabouts on emissions and fuel
consumption: a case study. Transportation Research Part D: Transport and Environment,
Vol.7, 65-71.
26. Mandavilli, S., Russell, E. R., and Rys, M., 2004. Modern roundabouts in United
States: an efficient intersection alternative for reducing vehicular emissions. Poster
presentation at the 83rd Annual Meeting of the Transportation Research Board,
Washington DC.
27. Niittymäki, J., Höglund P. G., 1999. Estimating vehicle emissions and air pollution
related to driving patterns and traffic calming. Presented at the Urban Transport Systems
Conference, Lund, Sweden.
28. Hogland, G., 1993. Alternative intersection design–a possible way of reducing air
pollutant emissions from road and street traffic. Swedish Transport Research Board,
Stockholm.
29. Akcelik , Associates, 1999. SIDRA software (Victoria,Australia). Burlington (2000)
30. Hagring, O., 1997. CAPCAL 2–a new version of the SNRA capacity, delay, and VOC
software. 115-123. Proc 3rd
International Symposium on Intersections without Traffic
Signals (University ofIdaho, Moscow, Idaho).
31. Staatsen, B. A. M., Nijland, H.A., van Kempen, E. E. M., De Hollander, A. E. M.,
Franssen, A. E. M., van Kamp, I., 2004. Assessment of health impacts and policy options
in relation to transport-related noise exposures : Topic paper on noise Bilthoven : RIVM,
32. Rosenhall, U., Pedersen, K., Svenborg, A., 1990. Presbycusis and Noise-induced
hearing loss, In: Ear & Hearing, Vol.11(4):257-263,
33. Tsukui, K., Oshino, Y., 2001. Basic Study of the Increase in Road Traffic Noise
Caused by the Installation of Traffic Lights. Japan Automobile Research Institute Journal,
Vol. 23, (10).

34. El-Fadel, M., Sbayti, H., 2000. Noise control at congested urban intersections:
Sensitivity analysis of traffic management alternatives. Noise Control Engineering
Journal, Vol. 48(6):206-213, 2000.
35. Objectified environmental impacts of roundabouts in residential areas of towns. In:
Proceedings of 10. International Conference Q- 2008. Žilina 15.-16.5.2008, ISBN 978-
80-969681-5-2, p.152-157.
36. Golebiewski, R., 2007. Makarewicz, Modeling of the Roundabout Noise Impact.
Journal of the Acoustical Society of America, R, 860- 868.
37. World Health Organization Report, (2004) , 1-34.
38. Synthesis of Safety Research Related to Speed and Speed Limits. Federal Highway
Administration.1998.http://safety.fhwa.dot.gov/speedmgt/ref_mats/fhwasa09028/resour
ces/Synthesis%20of%20Safety%20Researc...pdf. Retrieved 2009-09-17.
39. Cejun, L., Chou-Lin, C., 2009. An Analysis of Speeding-Related Crashes: Definitions
and the Effects of Road Environments. National Highway Traffic Safety Administration.
40. Lee, C., Saccomanno, F., 2006. Evaluation of variable speed limits to
improve traffic safety, Transportation Research Part C, Vol.14, 213-228.
41. Nes. V., Brandenburg, G., Twisk, D., 2010. Improving homogeneity by dynamic
speed limit systems, Accident Analysis and Prevention, Vol. 42, 944-952.
42. Boel, R., Mihaylova, L., 2006. A compositional stochastic model for real time
freeway traffic simulation, Transportation Research Part B, Vol. 40. 319-334.
43. Abdel, M., Dilmore, J., Dhindsa, A., 2005. Evaluation of variable speed limits for real-
time freeway safety improvement, Accident Analysis and Prevention, Vol.38. 335-345.
44. Carlson, R. C., Papamichail, I., Papageorgiou, M., Messmer, A., 2010. Optimal
mainstream traffic flow control of large-scale motorway networks Transportation
Research Part C: Emerging Technologies, Vol. 18, 193-212.
45. Negrenti, E., 1999. Corrected Average speed‟ approach in ENEA's TEE model: an
innovative solution for the evaluation of the energetic and environmental impacts of
urban transport policies. Science of the Total Environment, Vol. 235, 411–413.

46. Smit, R., Brown, A. L., Chan, Y. C., 2008. Do air pollution emissions and fuel
consumption models for roadways include the effects of congestion in the roadway traffic
flow? Environmental Modeling & Software, Vol. 23, 1262–1270.
47. Coelho, M, C., Farias, T. L., Rouphail, N. M., 2009. A numerical tool for estimating
pollutant corridors. International Journal of Sustainable Transportation Vol. 3(4), 246–
262.
48. Sucharov, L. J., Brebbia, C. A., Benitez, F., Veurman, J., Gense, N., Wilmink, I., R.,
2002. Emissions at different conditions of traffic flow. Urban Transport VIII - Urban
Transport and the Environment of the 21st
Century, Vol. 39, 571-80.
49. Dijkema, M., VanderZee, S., Brunekreef, B., Strien, V. R., 2008. Air Quality Effects
of an Urban Highway Speed Limit Reduction. Atmospheric Environment, Vol. 42, 9098–
9105.
50. Olde, M., Vanbeek, P., Stemerding, M., Havermans, P., 2005. Reducing Speed Limits
on Highways. Dutch Experiences and Impact on Air Pollution, Noise-Level, Traffic Safety
and Traffic Flow Proceedings of ETC, Strasbourg, France.
51. Keuken, M. P., Jonkers, S., Wilmink, I. R., Wesseling, J., 2010. Reduced NOx and
PM10 emissions on urban motorways in The Netherlands by 80km/h speed management.
Science of the Total Environment, Vol. 408, 2517–2526.
52. Zhong, W., Michael, W, C., 2006. An Investigation on the Environmental Benefits of a
Variable Speed Control Strategy. U.S. Department of Transportation, University
Transportation Centers Program, Report 473700-00072-1.
53. Emissions Impact of Elimination of the National 55 mph Speed Limit, Information
from the EPA Office of Mobile Sources, EPA Memo reproduced by Drive 55 Conservation
Project, 1996.
54. Keller, J., Andreani, A. S., Tinguely, M., Flemming, J., Heldstab, J., Keller, M.,
Zbinden, R., Prevot, A. S. H., 2008. The Impact of Reducing the Maximum Speed Limit
on Motorways in Switzerland to 80kmh on Emissions and Peak Ozone. Environmental
Modeling and Software Vol. 23, 322–332.
55. Coelho, M. C., Farias, T. L., Rouphail, N. M., 2005. A methodology for Modeling and
Measuring Traffic and Emission Performance of Speed Control Traffic Signals.
Atmospheric Environment, Vol. 39 (13), 2383–2392.

56. Coelho, M. C., Farias, T. L., Rouphail, N. M., 2005. Impact of speed control traffic
signals on pollutant emissions. Transportation Research,Vol. 10 D, 323–340.
57. Desarnaulds, V., Monay, G., Carvalho, A., Noise Reduction by urban traffic
management, In; Proc, ICA, Kyoto, Japsan, 2004.
58. Making Sound decisions about highway noise abatement, Pennsylvania Department
of Transportation. Retrieved
from……ftp://ftp.dot.state.pa.us/public/bureaus/BEQ/pub21.pdf
59. Wernsperger, F., Sammer, G., 1995. Results of the Scientific Investigation
Accompanying the Pilot Trial of 30 Kph Limit in Side Streets and 50 Km/hr Limit In
Priority Streets. Traffic Management and Road Safety Proceedings, Vol. 394, 27-38.
60. Taylor, M., 2001. Network modeling of the traffic, environmental and energy effects
of lower urban speed limits. Road and Transport Research, Vol. 9 (4) 48-57.
61. Archer, J., Fotheringhan, N., Symmons, M., Corben, B., 2008. The Impact of Lowered
Speed Limits in Urban/Metropolitan Areas. Monash University Accident Research Centre.
Report, Vol. 276. Victoria, Australia.
62. Kloeden, C. N., McLean, A. J., Moore, V. M., Ponte, G., 1997. Travelling speed and
the risk of crash involvement. NHMRC Road Accident Research Unit, The University of
Adelaide, Adelaide.
63. Nilsson, G., 1982. The effects of speed limits on traffic accidents in Sweden. VTI
Sartryck, Vol. 68, 1-10.
64. Nairn R. J., Partners 1999. Effects of urban speed management on travel time:
Simulation of the effects of maximum cruise speed changes in Melbourne. Federal Office
of Road Safety. SMEC Australia.
65. Elvik, R., Vaa, T., 2004. Handbook of Road Safety Measures. Elsevier Science,
Oxford.
66. Panis, L., Broekx, S., Liu, R., 2006. Modeling Instantaneous Traffic Emission and the
Influence of Traffic Speed Limits, Science of the Total Environment, Vol. 371, 270-285.
67. Pelkmans, L., Verhaeven, E., Spleesters, G., Kumra, S., Schaerf, A., 2005.
Simulations of Fuel Consumption and Emissions in Typical Traffic Circumstances. Brasil
Fuels and Lubricants Meeting and Exhibition, Vol. 2159.

68. Sucharov, L. J., Brebbia, C. A., Benitez, F., Veurman, J., Gense, N., Wilmink, I., R.,
2002. Emissions at different conditions of traffic flow. Urban Transport VIII - Urban
Transport and the Environment of the 21st
Century, Vol. 39, 571-80.
69. Ihab, E., Kyoungho, A., Rakha, H., 2005. Comparative Field Evaluation of Vehicle
Cruise Speed and Acceleration Level Impacts on Hot Stabilized Emissions. Transportation
Research Part D: Transport and Environment, Vol. 10 (1), 13-30.
70. Owen, B., 2005. Air quality impacts of speed restriction zones for road traffic.
Science of the Total Environment, Vol. 340, 13–22.
71. Gonçalves, P. M., Guerrero, J., López, E., Baldasano, J. M., 2008. Air quality models
sensitivity to on-road traffic speed representation: effects on air quality of 80 km h speed
limit in the Barcelona metropolitan area. Atmospheric Environment, Vol. 42, 8389–8402.
72. Wesseling, J. P., Hollander, J. C. Th., Teeuwisse, S., Keuken, M. P., Gense, R., E.
Burgwal, V.D., Hermans, L., Voerman, J. W. T., Kummu P. J., Elshout, J. V. D., 2003.
Research on the effects of 80 km/h on air quality in Overschie near the A13 motorway.
(in Dutch), TNO Report 2003-R0258, TNO, Utrecht, The Netherlands.
73. Desarnaulds, V., Monay, G., Carvalho, A., 2004. Noise Reduction by urban traffic
management. In; Proc, ICA, Kyoto, Japsan.
74. Berengier, M. C., Picaut, J., 2008. Methods for noise control by traffic management:
Impact of speed reducing equipments. Deliverable H.R2; SILENCE project, sixth
framework programme, European Commission, Brussels, Belgium.
75. Hedstrom, R., 1999. Miljoeffekter av 30 km/h I tatortmed avgasutslapp och buller.
Swedish National Road and Transport Research Institute, Linkoping Report 869,
76. Umweltbericht des landkreises starnberg Landratamt Starnberg, Starnberg, 2002.
77. Feychting, A., Jacobsson, M., Nilsson, A., Ryan, D., 2002. Stockholm Environmental
Zones in Europe. Stockholm Real Estate and Traffic Administration, SwedenTrendsetter
Report No 2002:1, Stockholm.
78. Johansson et al., 2007 C. Johansson, M. Norman and L. Gidhagen, Spatial and
temporal variations of PM10 and particle number concentrations in urban air,
Environmental Monitoring and Assessment, Vol. 127, 477–487.

79. Johansson et al., 1999 Johansson, C., Hadenius, A., Johansson, P. A., Jonson, T.,
1999. The Stockholm study on Health effects of Air Pollution and its Economic
Consequences. Part I. NO2 and Particulate Matter in Stockholm. AQMA Report 6:98.
80. Assessment of Environmental Zone in Goteborg, A report for the traffic and public
transit authority of the city of Goteborg. Document retrived from world wibe web at
http://www2.trafikkontoret.goteborg.se/resourcelibrary/Utv%C3%A4rdering%20MZ%20
10% 20%C3%A5r%20ENG.pdf
81. Bush, C., 2006.London Low Emission Zone: Environmental Appraisal, Environmental
Report, Proposed London Low Emission Zone Summary of the Environmental Report (PDF
183KB) – Nov.
82. Brunner, P., and Lang, J., 1995. Uberarbeitung des Rechenerfahrens der RVS 3.114
Larmschutz, Bundesministerium fur Wirtschaftliche Angelegenheiten, Strassenforschung,
Wien. Report 443,
83. Bayer, O., et al., 1995. Larmbekampfung – Ruheschutz Landesantalt fur
Umweltschutz Baden wurttemberg (D) Karlsruhe.
84. London Ambient Noise Strategy, Assembly and Functional Bodies Consultation Draft,
London, 2002.
85. Matson, T.M., Smith, W.S., and Hurd, F.W., 1955. Traffic Engineering, McGraw Hill,
New York.
86. Grerhenson, C., 2008. Self-Organizing Traffic Lights. Centrum Leo Apostel, Vrije
Universiteit Brussels, Belgium.
87. Hewage, K.N., Ruwanpura, J.Y., 2004. Optimization of traffic signal light timing using
simulation. WSC‟04 Proceedings of the 36th conference on winter simulation.
88. Huang, D., Huang, W., 2003. Traffic Light Synchronization. Journal of American
Physical Society, Vol. 6 7(5).
89. Brockfeld, E., Barlovic, R., Schadschneider, A., Schreckenberg, M., 2001.
Optimizing traffic lights in a cellular automaton model for city traffic, Phys. Rev. E 64,
056132.
90. Newell, G.F., 1964. Synchronizing Traffic Signals for High Flow, Quart. Appl. Math.
21, 315-325.

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