the influence of traffic management on emissions · 2013-11-28 · steunpunt mobiliteit &...

DIEPENBEEK, 2013.

STEUNPUNT MOBILITEIT & OPENBARE WERKEN

SPOOR VERKEERSVEILIGHEID

The influence of traffic management on emissions

Guidebook for good practice

RA-MOW-2011-023

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

Onderzoekslijn Duurzame mobiliteit

Documentbeschrijving

Rapportnummer: RA-MOW-2011-023

Titel: The influence of traffic management on emissions

Ondertitel: Guidebook for good practice

Auteur(s): B. Degraeuwe, B. De Coensel, B. Beusen, M.

Madireddy, A. Can, I. De Vlieger

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

Onderzoekslijn: Duurzame mobiliteit

Partner: VITO en Universiteit Gent

Aantal pagina’s: 60

Projectnummer Steunpunt: 8.3

Projectinhoud: Invloed van verkeersmanagement op emissies, geluid

en veiligheid

Uitgave: Steunpunt Mobiliteit & Openbare Werken – Spoor Verkeersveiligheid, april 2012.

Steunpunt Mobiliteit & Openbare Werken Spoor Verkeersveiligheid

Wetenschapspark 5 B 3590 Diepenbeek T 011 26 91 12

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

Steunpunt Mobiliteit & Openbare Werken 3 RA-MOW-2011-023 Spoor Verkeersveiligheid

Samenvatting

Titel: Verkeersmanagement en milieu

Ondertitel: Gids goede praktijk

Het doel van werkpakket 8.3 was de invloed van verkeersmanagement te onderzoeken

op luchtvervuiling en geluid van wegverkeer. Hiervoor werden een emissiemodel en een

geluidsmodel verbonden aan het microscopisch verkeersmodel Paramics.

Een literatuurstudie die uitgevoerd werd in een voorafgaande fase van dit project,

onderzocht bestaande emissiemodellen en hun link met verkeersmodellen. De resultaten

hiervan werden neergeschreven in een apart rapport (zie Trachet et al., 2010). Hierin

werd aangetoond dat er geschikte modellen bestonden om de impact van

verkeersmaatregelen op emissies te berekenen. Zowel voor CO2 als geluid werd

aangetoond dat met microscopische verkeerssimulatie het effect van

verkeersmanagement bestudeerd kan worden nog voor deze praktisch geïmplementeerd

zijn.

Het huidige rapport gaat dieper in op de invloed van verkeersmanagement op emissies

en geluid. Het bestaat zowel uit een uitgebreide literatuurstudie als uit nieuwe simulaties

van specifieke verkeerssituaties. De onderstaande tabel vat de resultaten van de

literatuurstudie samen. Voor deze nieuwe simulaties werd rekening gehouden met de

suggesties van de stuurgroep. Zo werden de Antwerpse wijk Zurenborg en de E313

tussen Geel en Antwerpen als gevalstudie uitgekozen.

In de wijk Zurenborg werd het effect bestudeerd van een snelheidsverlaging in de week

van 50 naar 30 km/u, op de hoofdwegen (Singel) van 70 naar 50 km/h en op de Ring

van 100 naar 70 km/u. Dit leidt tot een vermindering van alle emissies en geluid.

Daarnaast werd het effect van een groene golf op de Plantin-Moretuslei onderzocht. De

aanwezigheid van een groene golf leidt tot een afname van de emissies maar tot een

toename van het geluid.

Uit de analyse van de E313 autosnelweg tussen Geel-Oost en Antwerpen kon afgeleid

worden dat variabele snelheidslimieten (VSL) slechts een zeer klein effect hebben op

luchtvervuiling en geluid. Er is waarschijnlijk een indirect effect door een vermindering

van het aantal ongevallen en de bijhorende congestie. Het VSL-systeem dient immers in

de eerste plaats voor filestaartbeveiliging. De emissies van wegverkeer zijn minimaal

rond 90 km/h en nemen vooral sterk toe bij lagere snelheden. De gemiddelde snelheid

tijdens de ochtendspits ligt tegenwoordig rond de 50 km/h op de E313. Meer verkeer zal

daarom leiden tot meer emissies, maar lagere geluidsniveaus op deze locatie. Aan de

andere kant zullen maatregelen die de doorstroming bevorderen zonder extra verkeer

aan te trekken leiden tot minder emissies en hogere geluidsniveaus.

In de literatuur vindt men soms verschillende effecten voor bepaalde maatregelen.

Specifieke infrastructuurkenmerken zoals de diameter van een rotonde, de heersende

snelheidslimieten of de breedte van de weg kunnen het effect van een maatregel sterk

beïnvloeden. Omwille van het belang van lokale kenmerken raden we aan om de impact

van verkeersmanagement op doorstroming, veiligheid, emissies en geluid daarom geval

per geval te bekijken. Prioriteiten moeten op voorhand gesteld worden om in iedere

situatie de meest geschikte oplossing te kiezen. In dit project werden enkele gevalstudies

uitgevoerd. De resultaten van een verkeerssimulatie geijkt met tellingen werd gebruikt in

een emissie- en geluidsmodel. Deze modellenketen bleek erg nuttig om beleidsvragen te

beantwoorden.

In het kader van de ‘vrije onderzoeksruimte’ werd met metingen in wagens onderzocht

hoe werkelijke rijsnelheden zich verhouden tot de heersende snelheidslimieten. Hieruit

bleek dat de gemiddelde werkelijke snelheid steeds iets onder de snelheidslimiet ligt. De

enige uitzondering hierop is de zone 30 waar de werkelijke gemiddelde snelheid boven de


limiet ligt. Het laagste verbruik (CO2) wordt bereikt bij snelheiden tussen 70 en 90 km/h.

Op snelwegen aan hoge snelheid werkt een verbrandingsmotor efficiënt maar stijgen het

verbruik en de CO2 emissies door de hogere luchtweerstand.

Voor een gedetailleerde samenvatting van de bevindingen verwijzen we naar de

conclusies in hoofdstuk 5. Meer informatie omtrent de literatuurstudie en de uitgevoerde

gevalstudies is beschikbaar in het rapport.

Door-

stroming

Veiligheid Brandstof

besparing

CO2-

reductie

Locale

emissie-

reductie

(NOx, PM,

HC)

Geluid

Kruispunten

vervangen door

ronde punten:

1. stedelijke

gebieden ++ - + + +

2. hoofdwegen ++ + + + +

Snelheidsregeling op

snelwegen:

1. verlaagde snelheid

(80 km/h) ≈ ++ ++ ++ ++

2. variabele

snelheids-limieten ≈ + + + +

3. snelheidscontrole ≈ + ≈ ≈ ≈

Snelheidsverlaging

op lokale wegen ≈ ++ ≈ ≈ ++

Lage emissie zones ≈ ≈ + ++ ++

Groene golf ++ ≈ + + -

verkeersdrempels - ++ -- -- ≈

Legende:

++ Grote verbetering - Klein negatief effect

+ Lichte verbetering -- Belangrijk negatief effect

≈ Geen of niet-significante verbetering

Dankwoord

We zouden de Antwerpse politie willen bedanken voor de verkeersdata van Zurenborg en

het Vlaams Verkeerscentrum voor de informatie over de rijstrooksignalisatie en de

verkeerstellingen op de E313.


English summary

Title: The influence of traffic management on emissions

Subtitle: Guidebook for good practice

The mission of work package 8.3 was to investigate the influence of traffic management

on the reduction of emissions of air pollution and noise from road traffic flows. This was

done by implementing an emission model and noise model as two external plug-ins into

the traffic simulation software Paramics.

A preceding literature study on emission models and initial tests with microscopic traffic

simulation, presented in a separate technical report (see Trachet et al., 2010),

demonstrated that suitable emission models are available for studying the effect of traffic

management. For CO2 and noise, it was shown that microscopic traffic simulation can be

used to study the effect of this traffic management prior to implementing.

The present report focuses on the influence of traffic management on emissions and

noise. It consists of both an elaborate literature study and the analysis of specific case

studies in separate model simulations. The table below gives an overview of the results

of the literature study. For these new simulations the suggestions of the steering

committee were taken into account. In this way the Zurenborg neighbourhood in Antwerp

and the E313 highway between Geel and Antwerp were selected as study areas.

In the Zurenborg neighbourhood the effect of speed reductions was studied. In the

proper neighbourhood the speed was reduced from 50 to 30 km/h, on the major roads

(Singel) from 70 to 50 km/h and on the freeway from 100 to 70 km/h. This results in a

decrease of emissions and noise. Also the effect of traffic light synchronization was

determined. The presence of a green wave results in a decrease of emissions but an

increase in traffic noise.

Specific analyses on the E313 freeway between Geel-Oost and Antwerpen indicate that

the introduction of variable speed limits (VSL) has only very little direct effect on air

pollutant and noise emissions. There may be an indirect effect through the reduction of

accidents and the corresponding congestion. Air pollutant emissions are minimal around

a speed of 90 km/h. Given that the current average speeds on the E313 during rush hour

are around 50 km/h, decreasing average speeds will lead to extra air pollution, but, on

the other hand, will lead to lower noise levels along the freeway. Furthermore, measures

that enhance traffic flow fluency without attracting more traffic can decrease air pollutant

emissions, but will increase noise emissions.

It has to be noted that various literature review studies indicate different results for the

same measure. Depending on the specific characteristics of the measure considered (e.g.

dimensions of the roundabout, magnitude of the speed reduction,...) or the features of

the local situation (e.g. dimensions of the road, applicable speed limits, amount of

traffic,...), the impact results might differ largely between locations/studies. Due to the

importance of local characteristics when assessing the impact of traffic management

schemes we therefore recommend to examine the impacts on traffic flow, emissions,...

case by case. Each situation should be examined thoroughly and priorities on the desired

outcome (e.g. on traffic flow, safety, air quality,...) need to be established in advance to

select the most valuable traffic management measure for each situation. The model chain

applied within the case studies of this project, combining information on vehicle

intensities, road characteristics and (noise)emission functions on a microscopic level,

appeared to be a very useful tool for examining this kind of policy questions.

In addition, on-the-field analyses were performed within a confined additional research

(‘vrije onderzoeksruimte’). Hereby we analysed on-the-road speed profiles (recorded by

an on-board logging device) to examine the link between speeds, speed limits and fuel

consumption (CO2 emission). The lowest average fuel consumption (CO2 emission) is

obtained for the 70 and 90 km/h speed limits. At highway speeds, engines are operating


very efficiently but the air resistance is increasing sharply, leading to higher fuel

consumption, and CO2 emissions.

For a more detailed summary of the findings we refer to Chapter 5 (Conclusions). More

detailed information on the literature study and the specific case studies can be found in

the current report.

Traffic

flow

Safety Fuel

(CO2)

savings

Local

emissions

reduction

(NOx, PM,

HC)

Noise

Replacing intersectio

ns with roundabouts:

1. urban areas ++ - + + +

2. majors roads ++ + + + +

Highway speed

management:

1. reduced speed

(80 km/h) ≈ ++ ++ ++ ++

2. variable speed

limits ≈ + + + +

3. speed control ≈ + ≈ ≈ ≈

Speed reduction on

local roads ≈ ++ ≈ ≈ ++

Low emission zones ≈ ≈ + ++ ++

Traffic lights

synchronisation ++ ≈ + + -

Speed bumps/humps - ++ -- -- ≈

Legend of scores:

++ Good improvement - Slightly negative effect

+ Slight improvement -- Important negative effect

≈ No or insignificant improvement


Content

1. INTRODUCTION .......................................................................... 9

2. OVERVIEW OF EFFECTS OF TRAFFIC MANAGEMENT ................................. 10

2.1 Replacement of the traditional signalized intersections with roundabouts 10

2.1.1 Safety ........................................................................................10

2.1.2 Traffic Flow .................................................................................15

2.1.3 Emissions and Fuel Usage .............................................................15

2.1.4 Noise Emissions ...........................................................................16

2.2 Freeway speed management 16

2.2.1 Safety ........................................................................................17

2.2.2 Traffic Flow .................................................................................18

2.2.3 Emissions and fuel consumption .....................................................18

2.3 Speed reduction on local roads 19

2.3.1 Safety ........................................................................................20

2.3.2 Emissions and Fuel Consumption ...................................................20

2.3.3 Noise Emissions ...........................................................................21

2.4 Low emission zones (LEZ) 22

2.4.1 Emissions ....................................................................................22

2.4.2 Noise Emissions ...........................................................................23

2.5 Effect of traffic lights synchronization 23

2.5.1 Traffic Flow .................................................................................24


2.5.3 Noise Emissions ...........................................................................25

2.6 Speed humps/bumps 26

2.6.1 Safety ........................................................................................26


2.6.3 Noise Emissions ...........................................................................27

3. CASE STUDIES ......................................................................... 28

3.1 Applied simulation models 28

3.1.1 Microscopic traffic simulation model ...............................................28

3.1.2 Emission models for air pollutants and noise ....................................28

3.1.3 Validation of the integrated model ..................................................30

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

Zurenborg (Antwerp) 30

3.2.1 Study area .................................................................................30

3.2.2 Policy measures ...........................................................................31

3.2.3 Emissions of air pollutants and noise ..............................................31


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

(Antwerp) 32

3.3.1 Study area .................................................................................32

3.3.2 Policy measures ...........................................................................33

3.3.3 Emissions of air pollutants, CO2 and noise .......................................33

3.4 Case Study C: Effect of variable speed limits on emissions and noise on the E313

33

3.4.1 Introduction: study area and scenarios ...........................................33

3.4.2 The E313 model in Paramics ..........................................................36

3.4.3 Effect of VSL on air pollutant and noise emissions ............................39

3.4.4 Evaluation of other measures on the E313 ......................................41

3.4.5 Conclusions .................................................................................43

4. ANALYSIS SPEED PROFILE VERSUS MAXIMUM ALLOWED SPEED ................... 45

4.1 Speed profile as a function of the speed limit 45

4.2 Acceleration profiles as a function of the speed limit 47

4.3 Fuel consumption as a function of the speed limit 49

5. CONCLUSIONS ......................................................................... 51

6. REFFERENCES .......................................................................... 54

7. ANNEX – OVERVIEW EVENTS AND INTERNATIONAL PUBLICATIONS .............. 60


1. IN T R O DU C T ION

Traffic affects liveability and the global environment, not only through objective and

subjective accident risks, but also through emissions of air pollutants and noise. The

global impact of CO2 emission via climate change is recognized by policy. Health impacts

of pollutants such as fine and ultrafine particles have been established and the strong

impact of local noise climate on liveability of a neighbourhood is obvious and explicated

in WP8.1 reports. Noise and air pollution thus have a global component that depends on

the overall emission only and a local component for which the location of the emission is

important.

Classical mitigation of traffic related air pollution focuses on vehicle fleet composition and

traffic volume. Classical assessment of traffic noise has to be more local by the nature of

the problem, but vehicle operation parameters are often abstracted by assuming that all

vehicles travel at a constant speed: the speed limit of the road segment. This classical

approach disregards the opportunities created by detailed traffic management and thus it

was decided to dedicate a study to it in the “Steunpunt Mobiliteit & Openbare werken”.

Traffic management can influence both the speed and acceleration and deceleration

patterns of vehicles. Speed is a main determinant in noise emissions. Acceleration and

deceleration result in incomplete combustion and emissions of CO, NOx, and carbon

based particulate matter. Speed and acceleration also influence CO2 emissions. The

relationship between operation parameters and emission of single vehicles used in this

study are obtained from previous work. After careful deliberation, the

Harmonoise/Imagine model was used for noise emission and the Versit+ model for air

pollutant emission (Trachet et al., 2010).

Relating traffic management and infrastructure to detailed operation characteristics of

single vehicles requires detailed traffic information, which can be obtained through so

called microscopic traffic simulation models. Paramics (www.paramics-online.com) was

chosen as a traffic simulation software package, and plugins for noise and air pollution

emission were developed at Ghent University (similar software was also developed

outside this project for the Aimsun microscopic traffic simulator). Over the years, the

combined simulation was thoroughly tested. The computational model together with field

measurements by VITO allows to perform detailed parameter studies that are used as

guidelines in this report.

The underlying report combines existing know-how, based on an extensive literature

search, with new simulations to produce a set of relationships between traffic

management and emissions of air pollution and noise. Although the report attempts to be

complete in its overview, specific focus is put on topics of interest suggested by the

steering committee members such as the effects of speed limits on the E313.

Also the results of the analyses of speed profiles and fuel consumption versus maximum

allowed speed are presented. This work is performed within a confined additional

research (vrije onderzoeksruimte) and is based on on-the-field measurements.


2. OV E R V IE W OF E F F E C T S OF T R A F F IC M A N AG E M E N T

In this section the results of a literature study on different traffic management schemes

are presented. The focus lies on the evaluation of the effect on safety, traffic flow, fuel

consumption, emissions and noise of the following traffic management measures:

1. Replacement of signalized intersections with roundabouts

2. Highway speed management

3. Speed reduction on local roads

4. Introduction of environmental zones or Low Emission Zones

5. Traffic lights synchronization

6. Introduction of speed humps.

In the following sections these six measures are discussed successively.

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.

Figure 1. Vehicle conflict points: conventional intersections versus roundabouts

2.1.1 Safety

Roundabouts are believed to improve traffic safety by reducing crashes with injuries at

the intersections. This can be attributed to the following reasons.

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

collisions are usually severe. 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.

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.

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

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

speeds. This is less dangerous. This is in strong contrast with the scenario where

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

other.

It has been proven that the conversion of intersections into roundabouts reduces the

number of crashes with injuries or fatalities, evaluation studies frequently showed

considerable individual differences in safety performance of roundabouts. In Daniels et al.

(2010a) crash data, traffic data and geometric data of a sample of 90 roundabouts in

Flanders-Belgium were used to examine the safety performance of roundabouts by

means of a state-of-the-art cross-sectional risk model. Without going into detail on the

methods applied and data sets used, results from this study can be listed as follows:

Vulnerable road users (moped riders, motorcyclists, bicyclists, pedestrians) are

more often involved in injury crashes at roundabouts then could be expected

based on their presence in traffic;

variations in crash rates at roundabouts are relatively small and mainly driven by

variations in traffic intensity;

roundabouts with cycle lanes are performing worse than roundabouts with cycle

paths;

there exists a safety-in-numbers-effects for bicyclists, moped riders and, with less

certainty, for pedestrians at roundabouts;

variables like the roundabout dimensions (circle diameter, road width, number of

lanes,...) are no meaningful predictors for the number of crashes.

However, the authors suggest to further explore the safety aspects of different

roundabout types and extend the study also to other countries. The following sections in

this report therefore give an overview of safety research results from other roundabout

studies performed in Belgium or in other countries. Since the impact on safety might

vary between different types of road users and different types of roundabouts, the safety

research results are classified into the following categories

Safety research results from studies on vehicle to vehicle crashes,

Safety results from studies on vehicle to pedestrian/bicyclist crashes,

Safety results as a function of speed and roundabout design

Safety Research Results from studies on Vehicle to Vehicle crashes

Motor vehicles can face several conflicts at roundabouts. However, the amount of

conflicts and severity of the impact can depend on different factors. The following results

and remarks are reported in national and international studies:

A study of roundabouts in Belgium by Antoine (2005) compared accident

frequencies between roundabouts and traffic lights. The study reports that, in

urban environments, traffic lights have a higher accidents frequency from 20 to

25% to the roundabouts. In open country, the accidents frequency at the traffic

lights is practically twice as high as on roundabouts.

Results reported by a Danish study mention a reduction of 53% of the bodily

accidents in urban areas and 84% in the rural areas when a signalised intersection

is replaced by a roundabout (Jorgensen and Jorgensen, 1996).


In The Netherlands, where in the past 181 crossroads were converted to

roundabouts, a 71% reduction in accidents with a physical injury was reported by

Schoon and Minnen (1994).

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

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

. The results were attributed to the reduced speeds and reduced number of

conflict points.

Safety research results from studies on Vehicle to Pedestrian/Cyclist crashes

While, in general, roundabouts might have favourable effects on traffic safety, this might

not be the case for particular types of road users, such as bicyclists or pedestrians.

Concerning the safety impact of roundabouts for these vulnerable road users, the

following relevant studies and safety results can be mentioned:

Daniels et al. (2008, 2010b) examined crash data involving bicyclists at

roundabouts and concluded that the construction of a roundabout raises in general

the number of severe injury crashes with bicyclists. Concerning the design type of

the roundabout, roundabouts with cycle lanes appeared to perform worse

compared to three other design types (mixed traffic, separate cycle paths and

grade-separated cycle paths).

According to Rodegerdts et al. (2007), the conversion of intersections into

roundabouts resulted in a 27% increase in the number of injury accidents

involving bicyclists on or close to the roundabouts. The increase is even higher

(43%) for accidents involving fatal or serious injuries.

Some other studies conducted on roundabouts indicate however that on average,

converting conventional intersections to roundabouts can reduce pedestrian

crashes by about 75% (Schoon and Minnen, 1994).

Hyden and Varhelyi (2000) also argued that replacing intersections with

roundabouts reduced risk for bicyclists and pedestrians significantly, but not for

cars. They found large reductions at roundabouts for bicyclists and pedestrians

(60 and 80%, respectively). The expected number of injury accidents for car

drivers, however, increased slightly (12%).

Hels and Bekkevold (2007) conducted a study for the high incidence of accidents

at Danish roundabouts. The study concluded that the injury accidents for bicyclists

depend on the traffic volume and vehicle speed limits at the intersection. Moller et

al. (2008) investigated the reason for the bicycle accidents at Danish

roundabouts. They concluded from the structured interviews conducted on 1019

bicyclists at 5 roundabouts that the cyclists preferred the road designs with clear

regulation of the road user behaviour, and hence there is a need for increasing the

awareness of the road rules at the roundabouts.

Studies cited by Robinson et al. (2000) claimed that crash reductions were most

pronounced for motor vehicles, and smaller for pedestrians

Recently Daniels et al. (2011) suggested that the effects of roundabouts on

bicycle accidents differ depending on whether these roundabouts are built inside

or outside built-up areas. When inside built-up areas, the construction of

roundabouts increased the number of injury accidents involving bicyclists by 48%.

For accidents causing fatal or serious injuries inside built-up areas, an average

increase of 77% was found.

There is a significant difference in conclusions from several studies because in all the

studies the road parameters, traffic volume and driving behaviours are predominantly

different and hence no general conclusions can be drawn. Mixed results are therefore

available on who benefits the most from replacing the intersections with roundabouts.


Safety as a function of speed and roundabout design

Several studies indicated that the safety impact of roundabouts can depend on the type

of roundabout constructed or on the speed of the adjacent roads. For any kind of crash at

a roundabout, it is generally accepted that unsafe speeds are 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. On the impact of speed and

roundabout design, the following study results can be noted:

A comprehensive study conducted on roundabouts in Flanders region in Belgium

concludes that a reduction of 34% in the total number of accidents with injury 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 (Daniels et al. 2010b). 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.

Research results from De Brabander et al. (2005) and Daniels et al. (2010b) both

concluded that the roundabouts are the best replacement for signalized

intersections where the main road with speed limits of 90 km/h intersects with

minor roads with speed limits of 50-70 km/h.

A study by Brude and Larsson (2000) on roundabout design 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 .

Concerning the number of lanes in the roundabout, Daniels et al. (2010b) report

this aspect as 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.

Elvik (2002) 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.

The safety effect is largely dependent on the original signalization situation.

Roundabouts replacing intersections without traffic lights reduce the number of

injury accidents by 44% compared to 32% for intersections initially designed with

traffic lights (De Brabander and Vereeck, 2007). The largest improvements are

observed on high speed roads without signalization on the original intersection (90

km/h×50 km/h and 90 km/h×90 km/h). The study also concludes that serious

injury accidents are estimated to increase by 117% on 70 km/h×50 km/h

intersections equipped with signalization before the roundabout. The number of

injury accidents involving vulnerable road users is also found to increase (28%) on

50 km/h×50 km/h junctions that were originally signalized. Moreover, the

vulnerable road user is more likely to get fatally or seriously injured. Therefore, it

is concluded that traffic lights protect vulnerable road users more effectively than

roundabouts, which, in turn, are superior to intersections without signalization.

On the ‘design’ aspect of roundabouts, Sakshaugh et al. (2010) made some interesting

discussions concerning the difference between a separated and an integrated roundabout

and the related safety aspects (especially for cyclists) (Figure 2):


Figure 2. Separated roundabout (left) versus integrated roundabout (right) (source: Sakshaugh et al., 2010)

In a separated roundabout, the cycle paths, together with the pedestrian paths, run

parallel to and outside the carriageway. Contact between cyclists and motor-vehicle

traffic occurs only when a cyclist has to cross the carriageway at a roundabout approach

or exit, interacting then with drivers entering or leaving the roundabout. Cycling in both

directions on the cycle paths is permitted, which means that drivers have to pay

attention to cyclists coming from the left and from the right at the same time.

In an integrated roundabout, the cycle paths are separated from motor-vehicle traffic

along the approach to the roundabout, but cyclists are led onto the carriageway and

merged with motor vehicles approximately 30m before the roundabout. The intention of

the design is for cyclists and motor vehicles to form one mixed flow and enter the

roundabout and circulate in it as if it was just one lane. However, the widths of the

approaches and the ring itself allow cyclists to move in parallel with the vehicles, i.e., two

informal lanes are formed. After the roundabout, cyclists are led away from the

carriageway again. Cycling is allowed in one direction only on cycle paths along all the

approaches, i.e., the cycle path on the right is for those coming towards the roundabout

and on the left for those leaving it.

Sakshaugh et al. (2010) conclude that the separated roundabout is safer than the

integrated roundabout for cyclists. The integrated roundabout is more complex with a

higher number of conflict and interaction types. Moreover, the yielding situation is clearer

in the integrated roundabout, leading to a higher yielding rate but also to a greater trust

in the other road user’s willingness to yield. Hence the motorist and the cyclist are less

prepared to act when either fails to yield. The most dangerous situations in the

integrated roundabout seem to be when the motorist enters while the cyclist is

circulating, and when the motorist exits while they are circulating in parallel. In the

separated roundabout the situations with the lowest yielding rate to cyclists occur when

the motor vehicles exit the roundabout at the same times as cyclists are riding in a

circulating direction and hence coming from the right. Still, most of the accidents in

separated roundabouts take place when motorists enter or exit the roundabouts while

cyclists are moving against the circulating direction.

All-over conclusions on safety of roundabouts

Broadly put, roundabouts are good when there is less pedestrian and bicycle traffic

crossing at the intersection. For Flanders, since the safety of pedestrians and bikers is a

top priority, it is advisable that the speeds in the vicinity of the roundabout are reduced


to safe levels and the drivers be made aware of the roundabout with signs. But

roundabouts are of good use if the traffic is unsaturated and when there is not a lot of

pedestrian traffic because this could reduce the frequency of vehicle to vehicle crashes.

In other words, residential neighbourhoods or school zones are not ideal candidates for

roundabouts, but major road crossings which are congestion prone can be selectively

replaced with roundabouts.

2.1.2 Traffic Flow

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

community in Flanders, 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% and the proportion of vehicles that stopped was

reduced 14-37% (Retting et al. 2002).

A similar study where roundabouts replaced traffic signals found vehicle delays

were reduced by 89% and average vehicle stops by 56% (Retting et al. 2006).

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 (Russel et al. 2004).

A 2005 study from Bergh et al. (2005) 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. 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. The dependence of the traffic flow

as a function of number of legs, number of lanes and traffic condition is presented

extensively in a study from Mishra (2010). According to the study, when the lane

is narrow (width of 4 meters), one lane and two lane approach roundabouts

perform better than the signalized intersections under only low volume traffic

conditions. However, when the lane is wide (5 meters), roundabouts show better

performance than signalized intersection under both low volume and high volume

traffic conditions. This is because the additional space provided by the lanes

facilitated easy movement in the central island.

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

factors such as the 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.

2.1.3 Emissions and Fuel Usage

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

emissions and fuel consumption.


In a case study examining the environmental impact of small roundabouts,

replacing a signalized intersection with a roundabout reduced nitrous oxide

emissions by 21 percent (Varhelyi, 2002).

Another study concluded that replacing traffic signals and stop signs with

roundabouts reduced nitrous oxide emissions by 34 percent and carbon dioxide

emissions by 37 percent (Mandavilli et al. 2004).

Concerning the impact on fuel consumption, constructing roundabouts in place of

traffic signals appeared to reduce fuel consumption by about 30 percent (Verhelyi,

2002; Niittymäki and Höglund, 1999). 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.

2.1.4 Noise Emissions

Roundabouts are not specifically designed to reduce 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 increase

depends significantly on the traffic volume, street layout and driving behaviour and it is

very difficult to draw general conclusions from one unique intersection scenario:

El-Fadel et al. (2000) 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 such as the

width of the road, distance between the road and the building, road surface, etc

affect the noise levels at the intersections.

Noise emissions from a given intersection can be modelled given parameters such

as the road dimensions, road texture, vehicle composition and traffic intensity

(Decky, 2009). These model predictions and some case studies support the idea

that the roundabout (if replacing a traditional intersection) is effective in reducing

the noise levels by 0.5 dBA or more depending on the specific parameters

(Makarewicz, 2007).

De Coensel et al. (2007) performed a comparative, computational study of

different intersection types, in which a wide range of operational parameters were

considered. They conclude that, when there is no congestion, replacing a

conventional intersection by a roundbout would reduce noise levels by no more

than 1 dBA. There are however more pronounced effects at close distance from

the roundabout due to the different spatial layout.

2.2 Freeway speed management

Concerning the highway speed management, the following three measures were

considered:

Reducing the speed limit

Introducing variable speed limits (VSL)

Performing speed controls

These measures are usually taken to regulate the speed of the vehicles, primarily to

improve road traffic safety. However, they can also have some benefits on fuel

consumption and reduced (noise) emissions.

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


2.2.1 Safety

According to a 2004 report from the WHO (World Health Organisation) 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 (World Health Organization, 2004). The report identified that the speed

of vehicles is 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) is to be complementary to the speed

enforcement by the police. It is widely accepted among the traffic managers that the

likelihood of a crash is significantly higher if vehicles are travelling 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 travelling at or below the median speed and is believed to increase

exponentially for motorists driving faster. The 2009 technical report by the National

Highway Traffic Safety Administration in USA showed that a 55% of all speeding-related

crashes in fatal crashes were due to exceeding posted speed limits and 45% were due to

driving too fast for conditions (Cejun and Chou-Lin, 2009). 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.

It was indicated that VSL could reduce crash potential by 5–17%, by temporarily

reducing speed limits during risky traffic conditions (Lee and Saccomanno, 2006). 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 (Abdel et al. 2005). The study suggests to gradually

introduce speed limit changes over time (8 km/h 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 (24 km/h)

and implemented within short distances (3.2 km) of the location of interest.

Homogeneity of driving speeds is an important variable in determining road safety. A

study conducted by Nes et al. (2010) 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.


2.2.2 Traffic Flow

Highway traffic flow is especially complex and can be modelled only with great details of

inputs such as complex interactions between vehicles, routing and ramp metering, etc

(Boel and Mihaylova, 2006).

A variable speed limit, suitably operated and enforced, is often considered as a stand-

alone measure or in combination with ramp metering. Carlson et al. (2010)

demonstrated via several investigated control scenarios (within a software tool) that

traffic flow can be substantially improved using VSL schemes even without the aid of

ramp metering.

2.2.3 Emissions and fuel consumption

In general, highway traffic management is mainly aimed at increasing traffic safety or

smoothening the traffic flow. However, also impacts on emissions and fuel consumption

can be noted. 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 (Smit et al. 2008). 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 (Coelho et al. 2009). Hence, it can be expected that the emissions can be

decreased if the highway traffic is effectively managed.

Several studies demonstrate that reduced highway speeds can reduce fuel

consumption and related emissions (e.g. Dijkema et al. 2008). Traffic management

studies conducted on Dutch highways suggested that the current highway speed limit

could be reduced to 80 km/h and this can produce the most desirable combined effects of

reducing energy use, emissions and accidents (Olde et al. 2005).

In a similar study conducted by Keuken et al. (2010) on highways in The Netherlands,

when the maximum speed limit of 80 km/h is imposed and tested, emissions were

reduced by 5–30% for NOx and by 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. According to Zhong and Michael

(2006) up to a 5% reduction in distance based NOx (g/km) is possible by effective

variation of speed limits. Apart from the real time studies, simulation studies for speed

limit reductions on highways predicted congruent reductions in fuel consumption and

total emissions (e.g. EPA, 1996; Keller et al. 2008).

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

high speed crashes and pollutant emissions (Coelho et al. 2005). One concern about this

type of signals is that while they may be effective in reducing high speed crashes, they

lead to stop and go traffic. The drivers are likely to press the brakes suddenly as they

notice the sign and this could be fatal. This might slow down the following vehicles. 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’ behaviour by inducing speed

reduction, they will also result in a decrease in relative pollutant emissions. This means

that the speed control signals are useful when the drivers know about the signals well in

advance and their driving behaviour is safe.



The level of highway traffic noise depends on several factors:

The volume of the traffic

The speed of the traffic and traffic flow characteristics.

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

The road surface

Besides these factors, traffic noise levels are also increased by defective mufflers or other

faulty equipment on vehicles. Any condition (such as a steep incline) that causes heavy

labouring of motor vehicle engines will also increase traffic noise levels.

Reducing traffic volume or the amount of heavy vehicles are obvious measures to reduce

highway noise problems, but these are not always feasible on the short term. Moreover,

the effect of reducing traffic volumes should not be overestimated. Reducing traffic

volume by 50% will only reduce noise levels by 3 dBA. In practice, the effect may even

be smaller, as a reduced traffic volume may be associated with an increase in average

travel speeds. Measures that target source power, such as stimulating the use of low

noise tires, installing low noise road surfaces, or reducing travel speeds, are more

effective. For example, reducing the average speed from 120 km/h to 90 km/h will

reduce sound pressure levels by about 4 dBA (Peeters & van Blokland, 2007). Variable

speed limits are usually implemented to smoothen traffic flow, and this may increase

average speeds. Therefore, the effect of this measure on noise is less clear. A

computational study to assess the impact of implementing variable speed limits is

discussed in Section 3.4 of this report.

Congested highway traffic results in reduced average driving speed and an increase of

acceleration and deceleration. The positive effect of speed reduction on rolling noise

generally outbalances the increase in engine noise due to acceleration. Traffic noise

emission on highways during congestion is strongly correlated to driving parameters and

air pollutant emission but the relationship is far from a simple linear regression as can be

concluded from measurements on the Antwerp Ring road (Can et al., 2011).

2.3 Speed reduction on local roads

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

improve traffic safety. Traffic managers 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 lower networks. Zones with 30 km/h

speed limit are becoming popular (Taylor, 2001). These are sometimes referred to as

‘Zone 30’. These are popular in busy city centres, highly dense residential

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

etc.


2.3.1 Safety

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

speeds at local roads.

Archer’s study (2008) suggested that reduced speed limits in urban areas 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. (1997) 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”. 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%.

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.

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

also calls for constant lower speeds.

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 and emissions is quite complex and is

presented in the Road Safety Handbook (Elvik and Vaa, 2004).

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

Model predictions by Pelkmans et al. (2005) demonstrated that when average

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

consumption can be expected to decrease. However, when the average speed

drops below 30 or 40 km/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 Int Panis et al. (2006) 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. (2005) 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 (Van Beek et al. 2007;

Gonçalves et al. 2008).

Similarly, a pilot study in Rotterdam concluded that reducing traffic dynamics (i.e.

uniform traffic flow) is especially important for effective reduction of traffic

exhaust Research results obtained from a case study on reduced speeds in a

residential area, report that reductions in CO2 and NOX emissions of the order of

25% were found if speed limits are lowered from 50 to 30 km/h (Madireddy et al.

2011).


The most important sources of noise in road vehicles are the engine (mainly emitting to

the environment via the exhaust but also through air inlet and frame vibration) the

transmission system, the tires (noise generated from the interaction of the tires with the

road surface) and the vehicle frame (aerodynamic noise). At lower travelling speeds, the

engine is the predominant source of noise, and the source power of the latter is directly

linked to the engine rpm and thus influenced by vehicle speed and acceleration. Thus,

reducing average speeds on local roads and in urban context can be expected to have a

direct reducing effect on sound pressure levels:

Desarnaulds et al. (2004) show 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).

In another study, Berengier et al. (2008) studied the impacts of speed reducing

equipments and suggested that the noise can be mitigated through speed

reduction and smoothening of the traffic flow. Uncarefully designed speed

reduction equipment might however increase noise levels locally.


Model predictions by OFEFP, a simplified road traffic noise model, showed that

with every 5 km/h reduction in speed levels of the vehicles, the noise subsided

by 0.5 dB(A) (Desarnaulds et al. 2004).

Care should be taken in mixed urban environments since reducing speed on the

minor roads may not have the expected effect if more distant higher level

roads contribute significantly to the overall noise level. This will be explicated in

case study A. Moreover speed limit reduction might change the temporal

structure of the overall sound climate: lower speed gives less distinct peaks

and more constant sound. This is expected to further reduce the effects on

people, in particular at night.

2.4 Low emission zones (LEZ)

A low emission zone is a geographical zone with 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 many European cities:

The environmental zone introduced in Stockholm, the capital city of Sweden

was extremely successful in improving the local air quality.

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).

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 at least 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 centres.

Only clean lorries, defined by the Euro norm may enter environmental zones.

2.4.1 Emissions

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

restraining the high polluting fraction of the vehicle fleet, namely heavy duty trucks.


These heavy duty trucks, even though they make a very small percentage of the total

vehicles on the road, their overall contribution to NOx and PM emissions is about 50%

(VMM, 2011). These 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 centres and residential neighbourhoods is

getting 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 within this zone revealed

that the NOx emissions were reduced by 10% and emissions of particulates by

40% within the LEZ (Johansson and Burman, 2006)

In Goteborg, another city in Sweden, the introduction of an environmental zone

for heavy duty vehicles was posted in 1996. The entire 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%) (Johansson,

2006). 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 PM and 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 (Bush, 2006). 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 VI norms will be introduced.


The noise emissions can also be reduced if a LEZ is introduced. Since heavy trucks, which

are normally banned from the LEZ, also produce higher noise power levels, in particular

at low frequencies, a significant drop in overall noise levels could be expected. Several of

the LEZs in major cities experienced a noise reduction, e.g. a network wide reduction by

0.3 dBA in the inner city of London (Barrowcliffe, 2006). In the future, noise reduction

could also become important if zones are restricted to hybrid and electric vehicles. At

urban driving speeds, where rolling noise is limited, these vehicles will produce

significantly lower noise levels. In addition they are often equipped with low-noise-

emission tires, which further reduce rolling noise. However, a potential side-effect could

be that through the implementation of a LEZ, heavy traffic is rerouted along arterial

access or ring roads, which could increase noise levels along these roads significantly.

2.5 Effect of traffic lights synchronization

To regulate traffic along major roads, city planners often employ synchronization of

traffic lights (the so-called ‘green wave’). 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.


2.5.1 Traffic Flow

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. The following

remarks or interesting studies can be mentioned on the impact of green waves on traffic

flow:

The green wave measure will be useful for only a set of vehicles through the

intersections before the flow is interrupted by 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.

Grerhenson et al. (2008) 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.

Hewage et al. (2004) discusses a special-purpose simulation tool that can be

effectively used to optimize signal light timing.

Huang et al. (2003) 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. (2001). 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.

Madireddy et al. (2011) however points out that, if traffic signal coordination

decreases travel times, the effect of facilitating traffic flow may, in the long

term, induce additional traffic with the potential side effect of offsetting some

of the beneficial environmental consequences of signal coordination.



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.

In a study conducted by Unal et al. (2003), 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. (2004) 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 addressed in a study by Stevanovic et al. (2009) 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.

Madireddy et al. (2010, 2011) reports that on a major urban road, the NOx and

CO2 emissions can be reduced by 10% when the lights were synchronized.


Research on the influence of traffic light coordination on noise emission is by no means

complete; literature on the effects of signal coordination on noise is sparse, as most

studies consider the noise emission at a single intersection at most. In general, most

recent studies show a mixed effect of signal coordination on noise levels, with increases

in noise level in between intersections, and decreases near intersections:

Based on a review of measurements performed in the UK and Switzerland,

Desarnaulds et al. (2004) found that coordination of traffic lights may lower the

sound pressure level near intersections by up to 2 dBA.

As part of the SILENCE project, simulations were carried out for a road with

three signalized intersections with 200m and 500m in between (Bérengier &

Picaut, 2008). Two situations with coordinated traffic lights were compared: a

green wave and a red wave, in which cars have to stop at all traffic lights.

Results indicated that, for the given traffic intensity of 1440 vehicles/hour,

sound pressure levels could be lowered by up to 4 dBA near the intersections

(comparing the green wave to the extreme case of the red wave), but could

increase by as much as 3 dBA in between intersections, due to higher average

speeds.

De Coensel et al. (2010) also examined the effects of traffic light coordination

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.


2.6 Speed humps/bumps

Speed humps/bumps are traffic calming devices intended to reduce vehicle speed. The

‘bumps’ usually refer to the shorter variants whereas the ‘humps’ are usually longer in

depth.

Figure 3. Speed humps and bumps on the road (picture: Kluwer)

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

usually referred to as “sleeping police”. 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 and Smith (2002) 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 (Hallmark and Smith, 2002).

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.

In Zein et al. (1997) 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.

http://en.wikipedia.org/wiki/File:Bremsschwelle.JPG


A multivariate conditional logistic regression analysis showed that speed humps

were associated with lower odds of children being injured within their

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

their home (adjusted OR = 0.40) (Tester et al., 2004).


Kyoungho and Hesham (2009) found that speed bumps and humps increase the traffic

dynamics of the vehicles by creating acceleration and deceleration and this results in low

fuel efficiency and higher emissions. 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 through speed

reduction. Traffic Advisory Leaflets for UK Roads (1996) mention significant noise level

reductions obtained with speed bumps of a height of 75 to 100 mm. Speed humps, are

found effective in reduction of noise levels for cars. However the effectiveness of speed

humps depends primarily on the distance between the humps and effective speed

reduction they introduce.

The report from Desnarnaulds et al. (2004) mentions also results for studies conducted

the UK and in Denmark. A study conducted in the towns of Slough and York showed that

when the speed reductions in the range of 10 km/h, speed humps can bring about a

noise level reduction 10 dB(A) for the cars and 4 dB(A) for busses (see also Abbott et al.

1997). Results from the study in Denmark, shows that humps lower the noise thanks to

the speed reduction they induce. There is however a slight increase in the noise before

and after the speed reducers due to braking followed by acceleration of the vehicles.

Speed humps and bumps should be designed carefully to avoid the local increase of noise

they may cause. Changing the road surface on the hump should be avoided since it

introduces additional noise. Speed bumps on roads that carry heavy traffic can introduce

significant amount of peak noise in case of loose cargo. Although this type of noise can

be the most important factor in noise annoyance and sleep disturbance, it does not

always show in the averaged noise level. For these reasons other infrastructure measures

such as lane narrowing and road axis displacement that effectively reduce driving speed

should be preferred over bumps and humps when it comes to reducing noise levels.


3. CA SE ST U D IE S

In this chapter, the effect of three different traffic management measures on air pollutant

and noise emissions is considered:

A. reducing speed limits in urban environment;

B. coordinating traffic signals on arterial roads;

C. introducing variable speed limits on freeways.

For each of these traffic management measures, an existing case study was selected

(Zurenborg in Antwerp for measures A and B, and the E313 freeway for measure C). A

computational approach has been adopted for each measure/case study, and in the next

section, we will briefly describe the common methodology.

3.1 Applied simulation models

3.1.1 Microscopic traffic simulation model

We use Quadstone Paramics, a commercially available microscopic traffic simulation tool,

to simulate traffic conditions. Case study networks are constructed on the basis of

geographic information system (GIS) data and aerial photographs, which supply the

detailed positions of all roads and buildings in the area. Network wide traffic demands are

calibrated for the morning rush-hour in all case studies, based on traffic counts made

available by the Flemish Department of Mobility and Public Works. For case study A and

B, traffic signal parameters (cycle times, signal offsets between intersections, etc.) were

set according to the actual situation, based on data obtained from the Antwerp police

department. In all case studies, light- and heavy-duty vehicles are considered, which

were linked to the respective emission classes of the emission model. For case study A

and B, the railway passing through the area was not modeled, as we mainly consider

emissions by road traffic in this work. The simulation period was 1h for case studies A

and B, and 4h for case study C, all with a timestep of 0.5s. Vehicles are loaded onto the

network at the edge roads along the sides of the network, according to the traffic

demand. During simulation, the position, speed and acceleration of each vehicle is

recorded at each timestep, for subsequent calculation of emissions.

Although the microscopic traffic model is able to take into account a wide range of

vehicle driving behavior, a number of factors that have an influence on vehicle speeds

and accelerations cannot be fully embraced. Among those are the influence of

pedestrians crossing the street in urban context, cars slowing down to park or cars

leaving a parking spot, or the full extent of the stochastic component in driver’s behavior.

Next to this, the traffic counts used to calibrate the model reflect the average situation

during morning rush hour. Therefore, traffic counts and speed distributions measured at

a single instant in time within the simulated region could significantly differ from those

that are simulated. Nevertheless, as only average trends are usually considered,

microscopic traffic simulation models are increasingly being applied for estimating the

emissions from traffic flows. Earlier work has shown that, for emission modeling

purposes, a reasonably good agreement between simulated and measured speeds and

accelerations can be achieved (De Coensel et al., 2005).

3.1.2 Emission models for air pollutants and noise

The instantaneous CO2 and NOX emission of each vehicle in the simulation is calculated

using the VERSIT+ vehicle exhaust emission model, based on the speeds and

accelerations extracted from the traffic model. The latter model (Smit et al., 2007), is

based on more than 12,500 measurements on vehicles of a wide range of makes and

models, fuel types, Euro class, fuel injection technology, types of transmission, etc. It

uses multivariate regression techniques to determine emission factors for different

vehicle classes. As the model requires actual driving pattern data as input, it is fully

capable of accounting for the effects of congestion on emission. A derived model was


recently developed by TNO (Ligterink and De Lange, 2009), specifically targeted at a

coupling with microscopic traffic simulation models. For this, emission parameters of

vehicles of varying age, fuel type, etc. are aggregated into a prototypical vehicle

emission model representing the average emission of the Dutch vehicle fleet. While there

may be differences between individual vehicles, the model aims at predicting aggregates

over a sufficiently large number of vehicles sampled from the Dutch vehicle fleet. Here

the VERSIT+ light and heavy-duty vehicle classes representing the fleet in Dutch urban

environments during 2009 are used. Finally, only overall emissions are considered; the

dispersion of air pollutants is not modeled.

When the layout of the network, the signal timing, road signs and origin-destination

matrices are defined, Paramics is able to simulate trips of individual vehicles on the

network. The result of these simulations is a file with a second by second record of

vehicle positions and speeds. These files are the input for the emission model Versit+,

which returns the instantaneous emission of CO2, NOx and PM10. The PM10 emissions are

the sum of exhaust and non-exhaust particulate matter. Because the emission functions

are valid for the Dutch fleet of 2009, and because in Belgium the share of Diesel cars is

higher than in the Netherlands, the emissions of NOx and PM10 will be underestimated and

the emissions of CO2 overestimated. However, the trends will be similar.

A small-scale validation of the dynamic properties of the emission model was carried out

using VOEM, VITO’s on-road emission and energy measurement system (De Vlieger,

1997). Measurements of instantaneous speed, acceleration, CO2 and NOX emissions were

carried out using four diesel vehicles subjected to the MOL30 driving cycle, which is

based on real driving behavior in urban, suburban and freeway traffic situations.

Subsequently, the emission model was used to estimate the CO2 and NOX emissions

based on measured speeds and accelerations. Finally, both measured and estimated

emission time series are compared. In general, a good dynamic agreement is found, with

temporal correlation factors of 0.90 ± 0.030 for CO2 and 0.72 ± 0.10 for NOX for all test

vehicles, indicating that the model is able to capture the dependencies on speed and

acceleration well. The somewhat lower correlations for NOX may be explained by the

presence of an exhaust gas recirculation system in some of the vehicles. Details of this

validation can be found in Trachet et al. (2010).

The instantaneous noise emission of each vehicle in the simulation is calculated using the

Imagine road traffic noise emission model (Peeters and van Blockland, 2007), which is in

itself an update of the earlier Harmonoise model. This model was specifically developed

with microscopic traffic simulation in mind, and has been validated widely on an

European scale, using measurements on a large number of vehicles, driven on a wide

range of road surface types. The model forms the basis for a potential future European

standard for road traffic noise prediction, and was calibrated to generate the average

noise emission in the European vehicle fleet. As with the air pollutant emission model,

the Imagine model aims to correctly predict measurements results aggregated over a

sufficiently large number of vehicles sampled from the European fleet.

The Imagine noise emission model produces point source sound power levels at a specific

height above the ground, on the basis of vehicle type, speed and acceleration. Two

sources of noise are considered: rolling noise generated by tire-road interaction, and

combined powertrain and exhaust noise. Emissions are calculated on a 1/3-octave band

basis; however, in this work we only considered the total (A-weighted) source sound

power level, noted as LW. When the noise emission of a vehicle trip through the network

is considered, we further define LWavg as the average source sound power level of the

particular vehicle over the course of its trip (energetically averaged). In a similar way,

the total source sound power level LWtot for the trip of a single vehicle can be defined,

which also takes into account the duration of the trip. The latter value is thus directly

related with sound pressure levels along the route of the vehicle.


3.1.3 Validation of the integrated model

The accuracy of the estimated air pollutant emissions using the combination of traffic and

emission models is examined using data from a series of vehicle trips through the study

area. A vehicle equipped with data logging devices was driven several times along the

N184 on a typical working day. Instantaneous speed, throttle position and fuel

consumption were gathered through the CAN-bus interface of the vehicle on a second-

by-second basis, while the vehicle location was logged using a GPS device. Trip data for

all light duty vehicles driving along the N184 is extracted from the microsimulation

model. In both cases, only the part of the trip along the N184 is considered.

Instantaneous emissions are calculated using the emission model, for both measured and

simulated vehicle trips (Figure 4). In general, a good agreement is found between them,

suggesting that the accuracy of the integrated model is sufficient for estimating the

effects of traffic management measures on emissions.

The integrated model for noise emissions (Paramics + Harmonoise/Imagine model) has

been validated with measurements already in the past for several other case study

networks (see e.g. De Coensel et al., 2005, for the case of Gentbrugge, Belgium), and in

general a good agreement was found between simulations and measurements. Thus, a

validation of the integrated noise emission model was not considered in this project.

Figure 4: Normalized distributions of CO2 and NOX emissions per km, for measured and simulated vehicle trips along the N184

3.2 Case Study A: Effect of reduced speed limits on emissions

and noise in Zurenborg (Antwerp)

3.2.1 Study area

The study area, ‘‘Zurenborg’’, is located in the southeastern part of the 19th century city

belt of Antwerp, Belgium. Figure 5 shows a map of the region. In the east, the area is

bounded by the R1 freeway that has a speed limit of 100 km/h, and a major road, the

R10 or ‘‘Singel’’, with a speed limit of 70 km/h. In the southwest, the area is bounded by

a railway track. In the north, the area is bounded by a major arterial road, the N184 or

‘‘Plantin-Moretuslei’’, which connects the city of Antwerp to the west side of the area with

suburban areas in the east. This road has two lanes in each direction, and implements

traffic signal coordination. More in particular, during morning rush hour, all signals along

this road operate at the same cycle time (60–90 s intervals, depending on the presence

of pedestrians or buses), and the temporal offset of the cycle of each intersection is set

such that vehicles traveling from east to west encounter only green lights, when driving

at the desired speed of 50 km/h. A similar traffic signal setting is applied in the reverse

direction during the evening rush hour. Traffic intensity during morning rush hour, from

east to west, varies between 700 and 1000 vehicles/hour, depending on the segment

that is considered (vehicles also enter along the side streets). The triangular area within

the eastern, southwestern and northern borders is mainly residential, with an overall

speed limit of 50 km/h.

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Figure 5: Study area of ‘‘Zurenborg’’ in Antwerp, Belgium

Note: The triangular area bounded by the R1, the N184 and the railway forms the outline of the traffic simulation network. The circles along the N184 mark signalized intersections with coordinated traffic lights.

3.2.2 Policy measures

As a first traffic management measure, the effect of a speed limit reduction is studied.

Based on measures being considered by the traffic planning authorities of the city of

Antwerp, speed limits are reduced from 100 to 70 km/h on the freeway, from 70 to 50

km/h on the Singel, and from 50 to 30 km/h on the other residential roads and the

N184. For the latter, the traffic signal coordination is recalibrated for the lower speed

limit to have a green wave as in the original scenario. The microscopic traffic simulation

model applies dynamic traffic assignment: routes are chosen according to the

instantaneous congestion conditions. Traffic demands are kept constant.

3.2.3 Emissions of air pollutants and noise

The changes in the distribution of instantaneous speeds and accelerations for vehicles

driving within the residential part of the network (excluding the N184, R10 and R1) are

seen in Figure 6. Next to a reduction in average speeds, the speed distribution becomes

narrower, coupled with a reduction in the occurrence of maximum acceleration events.

Hence, the speed limit reduction results in a smoother traffic flow in the residential area.

Maximum speeds are about 10% above the speed limits because the traffic model also

accounts for speeding to resemble the actual situation as closely as possible.

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Figure 6: Normalized distributions of instantaneous speed and acceleration, for vehicles

driving within the residential part of the network (original versus reduced speed limits)

Figure 7 shows the corresponding change in distribution of instantaneous distance-based

emissions for the light duty vehicles; the results for heavy-duty vehicles show a similar

trend. The distance travelled by all vehicles within the residential area fell by 14.1%

because of traffic rerouting, but CO2 and NOX emissions fell by 26.8% and 26.7%.

Consequently, a reduction in distance-based emissions is also seen in Figure 7. For the

vehicles moving along the N184, similar results are found. Although the distance

travelled by all vehicles along the N184 only falls by 0.2%, still, a reduction in CO2 and

NOX emissions by 9.9% and 10.4% is recorded. Considering noise emissions, reducing

speeds has a clear beneficial influence, with reductions in total noise emission from 1.2 to

1.9 dB(A).

Figure 7: Normalized distributions of CO2 and NOX emissions per km, for vehicles driving within the residential part of the network (original versus reduced speed limits)

It should be remarked that the speeds simulated by paramics are on the high side. In

reality in is not possible to drive at an average speed of about 50 km/h in a

neighbourhood like Zurenborg (see Figure 6). Many obstacles like crossing pedestrians,

parking cars, cyclists and crossroads with a poor view limit the speed considerably.

However, modelling these features is very time consuming and was not possible in this

project. This might influence the magnitude of the effects calculated above but not the

overall trends.

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

Zurenborg (Antwerp)

3.3.1 Study area

For case study B, the same study area as for case study A is considered, and therefore

we refer to section 3.2.1 . In particular, case study B focuses on the major arterial road,

the N184 or ‘‘Plantin-Moretuslei’’ (Figure 5).

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3.3.2 Policy measures

For the N184 also the effect of traffic signal coordination is studied. The original situation,

with implementation of a green wave from east to west, is compared to a scenario in

which coordination is removed. To desynchronize the traffic signals, a small but random

number of seconds (≤2) is added or subtracted from the cycle times of all lights along

the N184. This results in a wide range of waiting times and queue lengths at each

intersection being encountered over the course of the simulation run, with the results

representing the average over all possible schemes in which there is no signal

coordination. Again, traffic demands were kept constant.

3.3.3 Emissions of air pollutants, CO2 and noise

Figure 8 shows the changes in the distribution of trip emissions for the light duty vehicles

that drove along the N184, completing their trips during the simulation run; only that

part of the trip along the N184 is considered. When the signal coordination is removed,

the combined light and heavy-duty vehicles CO2 and NOX emissions increase by 9.5%

and 8.7% because of the more interrupted traffic flow.

Figure 8: Distributions of CO2 and NOX emissions, for light duty vehicle trips along the

N184 (original versus without green wave)

Considering noise, it was found that the average vehicle sound power would decrease by

0.6 dBA (for heavy duty vehicles) to 0.9 dBA (for light duty vehicles) when the green

wave is removed. However, because trips would take a longer time to complete, the

average total emission was found to still increase with 0.1 to 0.3 dBA. The effect of the

green wave on total noise emissions thus seemed to be negligible. However, there are

large spatial variations. Sound pressure levels, calculated using an ISO 9613 sound

propagation model, were found to increase by up to 1 dBA near the signalized

intersections, but to decrease by up to 1.5 dBA in between intersections, when the green

wave is removed.

3.4 Case Study C: Effect of variable speed limits on emissions and noise on the E313

3.4.1 Introduction: study area and scenarios

The E313 between the cities of Antwerp and Liege is a major link in the Belgian highway

network. Its primary functions are:

An east-west link for long distance traffic.

A link between the harbour of Antwerp and the Ruhrgebiet in Germany.

Providing access from the Kempen, in the Northeast of Belgium, to the capital

area of Brussels.

Providing access to the large industrial areas along the Albert Canal.

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Figure 9: Degree of congestion on the E313 highway between Liege and Antwerp (Vlaams Verkeerscentrum, 2009a).

On the stretch between Antwerpen-Oost and Wommelgem (on top of Figure 9) pass up to

150 000 vehicles per day. Because 23% are trucks, a passenger car equivalent of

180 000 vehicles per day is reached. All day long the traffic volume on this stretch of

highway lies over 90% of its maximum capacity. As a consequence traffic jams are a

daily problem. On a normal morning a traffic jam starts building up from the junction

with the Antwerp ring road in the direction of Liege (top of Figure 9). A second traffic jam

starts to form at the junction between the E34 and the E313. During the morning rush

hour these traffic jams grow together. Accidents are very frequent in both directions

(Vlaams Verkeerscentrum, 2009b). In the direction of Antwerp 595 accidents occurred in

the period 2006-2008 or almost 1 accident every 3 days. In the direction of Antwerp

24% of the accidents are related to traffic jams. Drivers do not notice the traffic jam in

time and crash into the queuing vehicles. Other causes are a wet road surface and sharp

turns on some approaches. Due to the high traffic density, each incident causes even

bigger traffic jams.


Figure 10: VSL gantries around Antwerp (Vlaams Verkeerscentrum, 2011).

To tackle the problem of accidents at the tail of traffic jams, the Vlaams Verkeerscentrum

(VVC - Flemish Traffic Centre) implemented variable speed limits (VSL) between Geel-

Oost and Antwerpen-Oost. In 2007 the system was finished and equipped with a semi-

automatic control. The infrastructure consists of gantries and loop detector about every

750 m over about 40 km (Figure 10). Each gantry has a digital panel above each lane.

This panel can display a speed limit or other traffic information. For example in the case

of an accident an evacuation arrow or a red cross will be shown above a blocked lane.

The loop detectors count vehicles and measure their speed. If a traffic jam is detected

(high vehicle density and low speed) the speed limits on the upstream panels will be

reduced gradually. Figure 11 shows the algorithm used to determine the speed limit as a

function of the speed and occupation measured by the loop detectors of the next

downstream gantry. The speed limit is updated each time the speed or occupation moves

out of the overlapping intervals. In a next step the speed limits are equalized over each

gantry. Finally it is checked if the difference between successive gantries is not bigger

than 20 km/h. In this case the speed limits are reduced in steps of 20 km/h.

The objective of this study is to evaluate the effect of VSL on tailpipe emissions (CO2,

NOX and PM10) and on noise levels along the freeway. The study area is the section

between Geel-Oost and Antwerp (Figure 9). The VSL system warns people in advance of

a traffic jam further ahead. Hence, they are able to slow down more gradually instead of

breaking at the last moment when they see the traffic jam. This may have a small effect

on emissons. The speed limits are compulsory. However, in reality the variable speed

limits are ignored by most car drivers and not enforced by the police. Hence, there

expected effect will be small. In Paramics, the speed limit reductions are adhered to, so

Paramics will overestimate the effects.

Besides VSL other measures will be studied:

The VSL signs can also be used to deliberately reduce speed limits to reduce air

pollution. The effect of a 90 km/h speed limit will be evaluated.

An extra lane between Ranst and Antwerp.

Reduction of the traffic flow. This could be the effect of road pricing, higher fuel

taxes or a modal shift to public transport or cycling.


Figure 11: Control algorithm of the VSL gantries (Vlaams Verkeerscentrum, 2010).

3.4.2 The E313 model in Paramics

A model of the E313 was constructed in Quadstone Paramics, as with case studies A and

B. The first step was to implement the road network. All relevant features of the highway

were taken into account:

Between Geel-Oost and the junction with the E34 highway in Ranst there are 2

lanes with a speed limit of 120 km/h.

After the E34 merges with the E313 there are 3 lanes with a speed limit of 120

km/h.

VSL gantries were placed as in Figure 12. The positions were provided by the VVC.

Three types of vehicles were used in the simulation: passenger cars, light trucks

and heavy trucks. These types correspond to the types for which traffic counts

were available and for which emission functions are defined in Versit+ and

Imagine, the air pollutant and noise emission models.

The second step is the definition of the traffic load. The VVC provided traffic counts of a

normal morning (Monday 28/2/2011) between 6 am and 10 am every 5 minutes. Counts

were available for the approaches, exits and some intermediate points. The counts were

split up in three vehicle types: light vehicles, light trucks and heavy trucks.

Table 1Table 1lists the counting stations used in the simulation. Counts at the junction at

Ranst were not available due to a technical failure. These data were calculated as the

difference between a counting station right before and right after the merging point of

the E34 with the E313. As an example Figure 13 shows the counts at the approach of

Wommelgem. One can see that the truck avoid a bit the peak of passenger traffic around

8 a.m.


Figure 12: Positions of the VSL gantries on the E313.

Table 1: Counting post used to define the traffic load

Locpost site description post description

106411 cloverleaf at Antwerpen-Oost from E313 to ring 1

106407 cloverleaf at Antwerpen-Oost from E313 to ring 2

106491 cloverleaf at Antwerpen-Oost exit Borgerhout

106105 Wommelgem approach Wommelgem

106107 Wommelgem exit Wommelgem

118505 junction of Ranst from E34 to E313

118305 Massenhoven approach Massenhoven

118307 Massenhoven exit Massenhoven

117905 Herentals-West approach Herentals-West

117907 Herentals-West exit Herentals-West

117805 Herentals-Industrie approach Herentals-Industrie

117807 Herentals-Industrie exit Herentals-Industrie

117605 Herentals-Oost approach Herentals-Oost

117607 Herentals-Oost exit Herentals-Oost

117305 Geel-West approach Geel - West

117307 Geel-West exit Geel - West

117101 between Geel-West and Geel-Oost main road


Figure 13: Traffic counts of light vehicles (CAR) and trucks (rigid and articulated) per 5

minutes on the approach of Wommelgem. (Source: Vlaams Verkeerscentrum)

Also the destination of the traffic entering the network has to be defined. There was no

information about the destinations of the incoming vehicles. To obtain such data, number

plate recognition on all entrance and exit points is needed. Hence, each entering flow was

distributed over all possible exits proportionally with the total vehicle count on each exit.

The flow on the exits was made to match the counts as much as possible. A perfect

match is not possible because during the whole simulation the total inflow does not

match the outflow. However, the imbalance is small (0.7% of the total number of cars

and 0.4% of the total number for trucks). Because the flows on approaches and exits

match, also the flows on intermediate points on the highway will match closely.

The VSL gantries were put in place but the control strategy had to be simplified. The

overlapping speed and occupancy bands could not be taken into account in the standard

implementation of VSL in Paramics. Figure 14 shows the decision matrix used to

determine the speed limit that will be shown on a gantry as a function of the speed and

occupancy measured at the next, downstream gantry. The measured speed is an average

over the lanes and over a period of time. The occupancy is the density of vehicles per

meter.

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Figure 14: Variable Speed limit as a function of occupation and speed measured downstream the gantry.

3.4.3 Effect of VSL on air pollutant and noise emissions

For each scenario, without and with VSL, 5 model runs with different seeds were done.

The seed determines the random release of vehicles within each time interval of five

minutes. Both scenarios use the same 5 seeds. The results are in Table 2. The application

of VSL reduces the average speed significantly from 42.0 to 40.7 km/h (paired t-test with

p-value=0.0004). As a consequence the distance travelled and the total emissions

decrease. These changes occur within the period from 6 to 10 am. A part of the trips is

moved outside this time interval. The change in emissions per km gives a better idea of

what VSL does with the emissions. All emissions increase a little and the increase of CO2

and PM10 is significant. This is further explained in Figure 15. This figure shows the

average trip emissions in g/km as a function of the average trip speed. Emissions are

minimal around 90 km/h and they rise sharply at lower speeds and a little at higher

speed. Both scenarios are represented on the graphs with red dots.

Because in reality the variable speed limits are not compulsory, we can conclude that

they will not have very limited influence on air pollutant emissions. The only indirect

effect on emissions is the prevention of accidents. Accidents cause traffic jams that

reduce the average trip speed and will increase the emissions per distance travelled as

shown in Figure 15.

Table 2: Average results of 5 runs without and with VSL, relative differences and p-values of a paired t-test.

Scenario Distance (km)

Speed (km/h)

CO2 (ton) NOx (ton) PM10 (ton)

CO2 (g/km)

NOx (g/km)

PM10 (g/km)

NOVSL 360280 42.0 170.8 0.7563 0.03573 474.0 2.099 0.09918

VSL 357234 40.7 170.8 0.7517 0.03566 475.9 2.104 0.09983

rel. diff. -0.85% -3.27% -0.46% -0.60% -0.19% 0.39% 0.24% 0.66%

p-value 0.0004 0.0112 0.0314 0.0982 0.2765 0.0185 0.3561 0.0050

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0 10 20 30 40 50 60 70 80 90 100

50

70 50spee

d (

km/h

)

Occupation [%]

90 70

90

90

120

120


Figure 15. Emissions of CO2, NOx and PM10 in g/km as a function of average trip speed for passenger cars.

Figure 16. Location of the receiver points along the E313 for noise calculations.

In order to assess the influence of the implementation of VSL on noise levels along the

freeway, a series of receiver points were placed along the freeway. The locations of these

receivers are shown in Figure 16. All receivers were placed north of the E313 freeway, at

a distance of 15m from the centre of the rightmost lane, and at a height of 1.5m.

Receivers 1 and 2 were placed near the ring road of Antwerp, receivers 3 and 4 in

between Antwerp and Ranst, and receivers 5 and 6 in between Ranst and Herentals. The

odd-numbered receivers were placed next to a VSL sign board, while the even-numbered


receivers were placed in between sign boards. Instantaneous noise levels at the location

of these receivers were calculated using the ISO 9613 sound propagation model, based

on the instantaneous sound power calculated for all vehicles on the freeway.

Subsequently, the equivalent sound pressure level for the 4-hour simulation period was

calculated. Table 3 summarizes the change in LAeq,4h (average over 5 runs) due to the

VSL implementation. A decrease in noise levels was found for all locations and for all

simulation runs. However, decreases were very small. The largest decrease in levels is

found along the stretch of freeway between Ranst and the Antwerp ring road, with a

maximal decrease of about 0.5 dBA at most, which could be expected based on the small

decrease in average speeds. Traffic jams build up in the upstream direction from

Antwerp, and therefore the VSL will be in effect first in this stretch of road. Nevertheless,

as with the case of air pollutant emissions, the implementation of VSL has only a small

influence on noise levels.

Table 3: Average changes in sound pressure level (LAeq,4h) at 15m from the E313

freeway, resulting from an implementation of VSL, and from a network wide speed limit reduction to 90 km/h.

Location No VSL Implementation of VSL Speed limit to 90 km/h

LAeq,4h (dBA) LAeq,4h (dBA) Change (dBA) LAeq,4h (dBA) Change (dBA)

1 73.21 73.12 -0.09 73.05 -0.16

2 73.55 73.03 -0.52 73.35 -0.20

3 74.48 74.13 -0.34 74.17 -0.30

4 74.57 74.27 -0.30 74.24 -0.34

5 75.15 75.11 -0.04 74.43 -0.71

6 75.41 75.38 -0.03 74.69 -0.73

3.4.4 Evaluation of other measures on the E313

The model that was developed to evaluate the effect of VSL can be used to assess the

effect of other measures that could be taken.

a. A general speed limit of 90 km/h

The VSL panels could be used to impose a permanent maximum speed of 90 km/h.

The reduction was implemented in the Paramics model of the E313. Figure 15 and

Table 4 show that a considerable reduction of CO2 and NOx emissions can be

achieved when the maximum speed is reduced from 120 km/h to 90 km/h.

Table 5 shows the results in real traffic. The effects are smaller than shown in Table 4

because most of the time vehicles are not able to drive more than 90 km/h. However,

significant reductions of CO2 and NOx are achieved.

Table 4: Effect of a reduction of the average trips speed from 120 to 90 km/h on emissions.

Veh. Type Avg . trip speed (km/h) CO2 (g/km) NOx (g/km) PM10 (g/km)

CAR 120 208.5 0.6876 0.04251

CAR 90 191.0 0.5027 0.04199

rel. diff. -25.0% -8.4% -26.9% -1.2%

Table 5: Effect of a general speed limit of 90 km/h on emissions

scenario Distance (km)

Speed (km/h)

CO2 (ton) NOx (ton) PM10 (ton)

CO2 (g/km)

NOx (g/km)

PM10 (g/km)


NOVSL 360280 42.0 170.8 0.7563 0.03573 474.0 2.099 0.09918

SPEED 90 360149 40.3 168.0 0.7374 0.03546 468.4 2.057 0.09890

rel. diff. -0.48% -4.25% -1.66% -2.50% -0.76% -1.18% -2.03% -0.27%

p-value 0.0088 0.0114 0.0017 0.0014 0.0271 0.0154 0.0045 0.4308

Table 3 also summarizes the results of the speed limit reduction on noise levels at the

receiver locations defined in the previous section. It is found that noise levels decrease

due to a speed limit reduction, up to 0.7 dBA for the stretch of freeway between Ranst

and Herentals. In contrast to the VSL implementation, the largest effect is found more

upstream of the E313 freeway, as this stretch is less affected by traffic jams and thus

initial average speeds are higher than downstream of the E313 freeway. It has to be

noted that here we only consider the morning rush hour with the associated traffic jams;

the effect of a network-wide speed limit reduction will obviously have a much larger

effect on noise levels during periods of the day with lower traffic volumes.

b. A fourth lane between Ranst and Antwerpen-Oost

A possible way to reduce congestion is increasing the capacity of existing roads. On the

28th September 2011 a 4th lanes was opened between Antwerpen-Oost and Ranst in the

direction of Liege (opposite direction of this study). This reduced travel times during the

evening rush hour for people leaving Antwerp. There are no plans to do the same

between Ranst and Antwerp but in the Paramics a 4th lane was added in the direction to

Antwerp. Nowadays in Ranst 2 highways with each 2 lanes come together on 3 lanes.

This capacity drop causes traffic jams. Also the approach of Wommelgem is responsible

for a big influx of vehicles. In Table 6 one can see that the average trip speed increases

from 42 to 75 km/h with a 4th lane. Emissions decrease considerably because at the new

trip average speeds emissions are lower (see Figure 15). It is important to mention that

the traffic demand was not changed in this simulation. In reality, the decrease in travel

time might attract more traffic. Thus, the total emissions on the highway with 4 lanes

might be higher than in the 3 lane situation.

Table 6: Effect of a 4th lane between Ranst and Antwerp on emissions

Scenario Distance (km)

Speed (km/h)

CO2 (ton)

NOx (ton)

PM10 (ton)

CO2 (g/km)

NOx (g/km)

PM10 (g/km)

NOVSL 360280 42.0 170.8 0.7563 0.03573 474.0 2.099 0.09918

LANES 385507 75.0 158.0 0.7116 0.03097 409.9 1.846 0.08033

rel. diff. +7.00% +78.5% -7.47% -5.91% -13.33% -13.53% -12.07% -19.00%

c. A Reduction of the traffic demand

Another way to reduce pollution and congestion is road pricing or a fuel tax. These

measures will reduce the traffic demand. Scenarios with other demands were simulated

with Paramics. Runs were done with flows between 20% and 120% of the baseline

scenario without VSL. Emissions were calculated with Versit+. Figure 17 shows what

happens with the average speed, the vehicle kilometres and the emissions with respect

to the reference situation without VSL. The total vehicle kilometres go through a

maximum near the baseline scenario (purple line). This means that even with higher

flows on the entries, the number of vehicle kilometres does not rise. The traffic in the

baseline situation is saturated. When the traffic load is increased the average trip speed

goes down (blue line) and the emissions per kilometre (red line) go up (see also Figure

15). The total emissions just stagnate, because queues build up outside the study area.

When the traffic flow is decreased the total emissions decrease faster than the vehicle


kilometres. The traffic gets more fluid, average trip speed rises and vehicles emit less per

kilometre. The picture for the emissions of NOx and PM10 is similar.

Figure 17. Average trip speed, emissions of CO2 (total and per km) and vehicle kilometers on the E313 for different traffic demands.

3.4.5 Conclusions

VSL has only very little direct effect on air pollutant and noise emissions. There

may be an indirect effect through the reduction of accidents and the

corresponding congestion.

Air pollutant emissions are minimal around a speed of 90 km/h. Given that the

current average speeds on the E313 during rush hour are so low, decreasing

average speeds will lead to extra air pollution, but, on the other hand, will lead to

lower noise levels along the freeway.

Measures that enhance traffic flow fluency without attracting more traffic can

decrease air pollutant emissions, but will increase noise emissions.

The model can be improved on several points:

o Put the real VSL control logic into the simulation.

o Incorporate the Antwerp Ring road into the simulation. Events on the ring

road have an influence on the E313.

It may be interesting to investigate the effect of adding extra lanes on existing

freeways. On the E40 between Leuven and Brussels the emergency lane was

converted to a permanent 4th lane.

As in Case study A some remarks have to be made about the Paramics simulations:

It was noticed that lane shifting is not well modelled in Paramics. Vehicles move

suddenly from one lane to another which results in a monentary high speed and

acceleration. However, leaving out these points showed that the effect on

emissions was negligible.

-90%

-80%

-70%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

0 20 40 60 80 100 120 140

Re

lati

ve c

han

ge t

o b

ase

lin

e

Ave

rage

tri

p s

pe

ed

(km

/h)

traffic flow (% from baseline)

avg. trip speed (km/h)

relative CO2 (g/km)

relative CO2 (ton)

relative vkm


The traffic on the E313 near the connection with the Antwerp ring is strongly

influenced by events on the ring. This could not be taken into account in this

simulation. Modelling the whole ring and the surrounding highways was out of the

scope of this study. It would be interesting to apply this methodology on available

models of the Flemish Traffic Centre.


4. AN AL Y S I S S PE E D P R O F IL E V E R SU S MA X I MU M

A L L OW E D SP E E D

In this section we report on the analysis of speed profile and fuel consumption (CO2)

versus maximum allowed speed. This task has been defined within a confined additional

research (vrije onderzoeksruimte).

A total of 19 passenger cars were equipped by VITO with an on-board logging device.

The logging device recorded vehicle position and speed through a GPS device, but at the

same time the device was connected to the electronic data network of the vehicle (“CAN-

bus”). The CAN connection provided information on instantaneous fuel consumption,

engine speed and throttle position. These parameters were logged at a frequency of 1Hz,

and were transmitted daily via GPRS (General Packet Radio Service) to a data server at

VITO. All vehicles were monitored for a period of minimum 6 months, and all vehicles

were registered in Flanders. Of the 19 vehicles that were monitored, 17 used diesel

engines and 2 used gasoline engines. Vehicle type ranged from small (e.g. Renault Clio)

to medium sized family cars (e.g. Ford Mondeo).

In a next step, the vehicle positions were mapped to a specific road segment specified by

the Tele Atlas Multinet. Every road segment of the Tele Atlas Multinet has a speed limit

associated with it. However, only a subset of the road segment speed limits were verified

by Tele Atlas, so only for this subset we were certain that the Multinet speed limit

conforms to the real world speed limit at that location (at a particular date). In the

analysis of the vehicle’s speed profiles as a function of the maximum allowed speed (or

“speed limits”), only data on road segments with a verified speed limit were taken into

account.

4.1 Speed profile as a function of the speed limit

For every vehicle we calculated the average speed for all road segments with the same

speed limit. In total 5 different speed limits are taken into account: 30, 50, 70, 90 and

120 km/h. For every speed limit category, we thus have a distribution of 19 values

indicating the average speed on that speed limit of the 19 different vehicles.

First, the average speed was calculated on a time basis:

average speed = sum(speed)*time/sum(time)

The results are displayed as a box plot in the Figure 18, below. On each box, the central

mark is the median, the edges of the box are the 25th and 75th percentiles, the whiskers

extend to the most extreme data points not considered outliers, and outliers are plotted

individually.


Figure 18: Box plot of the average speed (time-based) of all vehicles for each speed limit

Secondly, the average speed was also calculated on a distance basis:

average speed = sum(speed)*distance/sum(distance)

The distance-based average speed is a better approximation to the actual speeds at

which the vehicles drove. The values are presented in the Figure 19.

Figure 19: Box plot of the average speed (distance-based) of all vehicles for each speed limit

From the figures we can conclude that the average speeds (distance-based) correspond

closely to the speed limits, but are always somewhat lower than the speed limit itself.

The only exception is the average speed for the 30 km/h zones. The table below presents

the average over all 19 vehicles of the average speeds (distance-based) for every speed

limit.


Table 7: average over all 19 vehicles of the average speeds for every speed limit

Speed limit 30 50 70 90 120

av. speed (km/h)

38,1 44,7 62,0 79,0 108,8

4.2 Acceleration profiles as a function of the speed limit

The same methodology, as mentioned above for the speed profiles, is also applied to the

data on accelerations. Acceleration values are calculated as the first time-derivative of

the speed values. Average accelerations are calculated for positive values (acceleration)

and negative values (decelerations) separately.

For every vehicle, the average (positive) acceleration is calculated separately for each

speed limit. For every speed limit category (30, 50, 70, 90 and 120), we thus have a

distribution of 19 values indicating the average acceleration on that speed limit of the 19

different vehicles.

The average positive accelerations for every speed limit are displayed in the Figure 20.

Figure 20: Box plot of the average (positive) acceleration values of all vehicles for each speed limit

In the same way the average values for the decelerations were calculated, as presented

in the Figure 21.


Figure 21: Box plot of the average deceleration values of all vehicles for each speed limit

When driving at low speeds, a low gear is used. As a consequence, a lot of motor torque

is available on the wheels for acceleration. At higher speeds, less torque can be

transmitted to the wheels because of the higher gear ratio (“torque reduction”). Thus, at

low speeds higher acceleration values can be achieved. To judge the acceleration in a

speed-independent way, the RPA or “Relative Positive Acceleration” is used. RPA is

defined as:

RPA = sum(speed x acceleration (positive values) x time step)/sum(distance)

The values for the RPA are presented in the Figure 22.

Figure 22: Box plot of the RPA for every speed limit

In the same way, the “Relative Negative Acceleration” can be calculated (RNA)


RNA = sum(speed x acceleration(negative values) x time step)/sum(distance)

These values are presented in the Figure 23.

Figure 23: Box plot of the RNA (relative negative acceleration) for every speed limit

Overall the figures show a substantial decrease in average acceleration and deceleration

for increasing speed limits, indicating less variation in driving speeds at higher speed

limits.

4.3 Fuel consumption as a function of the speed limit

For every speed limit category, the relative average fuel consumption was calculated, i.e.

the average fuel consumption per vehicle and per speed limit divided by the average fuel

consumption for that vehicle over all speed limits. If a vehicle did not have a total

distance of more than 5 kilometres for a certain speed limit, the data for this vehicle was

discarded for that specific speed limit.

From Figure 24, it is clear that average fuel consumption in low speed regions (30 and

50) is high in comparison to overall average fuel consumption. This was to be expected

since low speed regions involve a lot of stop-and-go traffic and internal combustion

engines are not used efficiently at low speeds. However, the number of data points

available for the 30 km/h speed limit was low, decreasing the statistical power for this

speed limit. Of all 19 vehicles, only 3 vehicles had covered a distance of more than 5

kilometres in the 30 km/h speed zones. Only the fuel consumption values for these 3

vehicles are therefore taken into account for this speed limit.

The lowest average fuel consumption is obtained for the 70 and 90 km/h speed limits. At

highway speeds, engines are operating very efficiently but the air resistance is increasing

sharply, leading to higher fuel consumption. In addition, due to the high engine

temperature at high speeds, NOx emissions are high for freeway traffic.


Figure 24: Box plot of the relative fuel consumption for every speed limit

The averages over all 19 vehicles for the relative fuel consumption are presented in Table

8.

Table 8: average of the relative fuel consumptions over all 19 vehicles

Speed limit 30 50 70 90 120

Relative fuel consumption

1,32 1,13 0,92 0,94 0,97


5. CON C L U S IO N S

Based on an extensive literature review and case study results on a selection of traffic

management measures, conclusions on the effect on traffic flow, safety, fuel

consumption, and emissions of pollutants and noise of the following traffic management

schemes are drawn:

Replacement of traditional signalized intersections with roundabouts

Highway speed management

Speed reduction on local roads

Introduction of low emission zones

Traffic lights synchronization

Introduction of speed humps/bumps.

Results often differed between studies and/or locations. However, we tried to summarize

the main results in one table (Table 9) in order to give a clear overview of the general

effects.

Replacing traditional signalized intersections with roundabouts has, in general, positive

effects on traffic flow, fuel savings, emission reductions and noise abatement. In the

literature, little disagreement was found on the fact that the roundabouts usually improve

traffic flow. And, due to the general improvement in the efficiency of traffic flow,

roundabouts reduce vehicle fuel consumption and emissions of pollutants and noise.

Concerning the effect on safety, the effects depend on the location and on the type of

traffic passing by (see score +/- in Table 9). In general, safety at roundabouts is

considered good when there is not much pedestrian and bicycle traffic. Therefore,

residential neighbourhoods and school zones in urban areas are not ideal candidates for

roundabouts. On the other hand, major road crossings which are congestion prone can

better be replaced with roundabouts. Further, to reduce severity and frequency of

accidents at the roundabouts, it is advisable to reduce the speed in the vicinity of the

roundabouts to safe levels and to make drivers aware they are approaching a

roundabout. It seems that roundabouts are the best replacement for signalized

intersections where a main road with speed limits of 90 km/h intersects with minor roads

with speed limits of 50-70 km/h. Furthermore, for cyclists it is preferable to construct

cycle paths at the roundabout.

Concerning highway speed management, three different types of measures were

distinguished: reducing maximum speed (to 80 km/h), introducing variable speed limits

and applying speed control. Traffic safety is improved by all these measures. Reducing

the maximum speed to 80 km/h on highways could increase the traffic flow depending on

the configuration of the highway and the traffic density. It is clear that this lower speed

decreases the fuel consumptions and emissions of pollutants and noise. To a lesser

extent this will also be the case for variable speed limits. The impact of speed control on

highways is to raise drivers’ awareness, stimulating the to drive more safely. Here the

other aspects are of minor relevance.

Specific analyses on the E313 freeway between Geel-Oost and Antwerpen indicate VSL

has only very little direct effect on air pollutant and noise emissions. There may be an

indirect effect through the reduction of accidents and the corresponding congestion. Air

pollutant emissions per km are minimal around a speed of 90 km/h. Given that the

current average speeds on the E313 during rush hour are so low, decreasing average

speeds will lead to extra air pollution, but, on the other hand, will lead to lower noise

levels along the freeway. Furthermore, measures that enhance traffic flow fluency

without attracting more traffic can decrease air pollutant emissions, but will increase

noise emissions. However, in the literature was found that VSL can have a positive effect

on traffic flow and emissions (see Table 9). This was not seen on the E313 because on

this freeway the maximum capacity is reached during rush hours.


Table 9: Overview of the effect of different traffic management measures

Traffic

flow

Safety Fuel

(CO2)

savings

Local

emissions

reduction

(NOx, PM,

HC)

Noise

Replacing intersectio

ns with roundabouts:

1. urban areas ++ - + + +

2. majors roads ++ + + + +

Highway speed

management:

1. reduced speed

(80 km/h) ≈ ++ ++ ++ ++

2. variable speed

limits ≈ + + + +

3. speed control ≈ + ≈ ≈ ≈

Speed reduction on

local roads ≈ ++ ≈ ≈ ++

Low emission zones ≈ ≈ + ++ ++

Traffic lights

synchronisation ++ ≈ + + -

Speed bumps/humps - ++ -- -- ≈

Legend:

++ Good improvement - Slightly negative effect

+ Slight improvement -- Important negative effect

≈ No or insignificant improvement

Speed reduction on local roads generally results in less (severe) accidents and lower

noise emission. Speed reductions to speeds smaller than 30-40 km/h might however

result in increases of the fuel consumption and pollutant emissions. Furthermore, since

the engine noise is predominantly associated with speed, noise emissions can be

controlled by reducing the speed.

The major purpose of low emissions zones (LEZs) is to reduce local emissions by

means of banning certain vehicles in these zones (e.g. heavy duty vehicles, older

vehicles, …). In the future, noise reduction could also become important if zones are

restricted to hybrid and electric vehicles. The effect on traffic flow and safety strongly

depends on the avoided kilometres driven in the LEZ.

Improving traffic flow by means of installing green waves (traffic lights

synchronization) is most effective on main arterial roads with smaller minor roads.

Implementing green waves in these situations will result in fuel savings and less local

pollutions as they generate smooth traffic and less stop-and-go traffic. Noise emissions


lower at the intersections, but there could be an increase between two intersections,

resulting in an overall negative effect for noise. However, if traffic signal coordination

decreases travel times, the effect of facilitating traffic flow may, in the long term, induce

additional traffic with the potential side effect of offsetting some of the beneficial

environmental consequences of signal coordination.

Speed humps/bumps are designed to slow down traffic in residential areas. They

clearly result in safety benefits due to their traffic calming effect. However, speeds

humps have a negative effect on traffic flow, fuel consumptions and emissions of

pollutants. Also, effects on noise could be negative due to braking when approaching a

speed hump/bump.

It has to be noted that various literature review studies indicate different results for the

same measure. Depending on the specific characteristics of the measure considered (e.g.

dimensions of the roundabout, magnitude of the speed reduction,...) or the features of

the local situation (e.g. dimensions of the road, applicable speed limits, amount of

traffic,...), the impact results might differ largely between locations/studies.

Due to the importance of local characteristics when assessing the impact of traffic

management schemes we therefore recommend to examine the impacts on traffic flow,

emissions,... case by case. Each situation should be examined thoroughly and priorities

on the desired outcome (e.g. on traffic flow, safety, air quality,...) need to be established

in advance to select the most valuable traffic management measure for each situation.

The model chain applied within the case studies of this project, combining

information on vehicle intensities, road characteristics and (noise)emission functions on a

microscopic level, appeared to be a very useful tool for examining this kind of policy

questions.

Concerning the E313 freeway case study, the model can be improved on several points:

1° put the real VSL control logic into the simulation, 2° incorporate the Antwerp Ring

road into the simulation. Events on the ring road have an influence on the E313.

Furthermore, it may be interesting to investigate the effect of adding extra lanes on

existing freeways. On the E40 between Leuven and Brussels the emergency lane was

converted into a permanent 4th lane.

In addition, on-the-field analyses performed within the confined additional research

space (vrije onderzoeksruimte) show that measured average speeds correspond closely

to the speed limits, but are always somewhat lower than the speed limit itself. The only

exception is the average speed for the 30 km/h zones. The lowest average fuel

consumption (CO2) is obtained for the 70 and 90 km/h speed limits. At highway speeds,

engines are operating very efficiently but the air resistance is increasing sharply, leading

to higher fuel consumption, and CO2 and NOx emissions.


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7. AN N E X – OVE R V IE W E VE N T S A N D I N T E R N AT IO N AL

P U B L IC AT I ON S

During the project mid-term results of WP8.3 have been presented at national and

international events:

M. Madireddy and B. De Coensel (2009) “Modelleren van dynamische emissies en

geluid voor Vlaamse verkeerssituaties” op de “onderzoekersdag” van het

Steunpunt Mobiliteit op 23/10/2009 in Diepenbeek.

M. Madireddy, B. De Coensel, I. De Vlieger, D. Botteldooren, B. Beusen, B.

Degraeuwe, G. Lenaers, A, Can, A. Eijk (2010), Micro-Simulation of a Traffic Fleet

to Predict the Impact of Traffic Management on Exhaust Emissions.Conference

proceedings, 18th International Symposium 2010 ‘Transport and Air Pollution’,

Dübendorf, Switzerland, May 2010.

B. De Coensel, A. Can, M. Madireddy, I. De Vlieger and D. Botteldooren.

Combined assessment of noise and air pollution caused by road traffic. In

Proceedings of Noise in the Built Environment, Meeting of the Institute of

Acoustics and the Belgian Acoustical Society, Ghent, Belgium, Apr. 2010. (UK) en

de Belgische Akoestische Vereniging (2010).

Workshop Steunpunt – Presentation of Midterm results of WP8.3 to the steering

committee (Brussels, Februari 2011).

We also have used the noise model to compute the noise climate in the Antwerp urban

development project "New Zurenborg”.

Furthermore, some peer-reviewed publications have been achieved with the results of

Work Package 8.3:

Madhava Madireddy, Bert De Coensel, Arnaud Can, Bart Degraeuwe, Bart Beusen,

Ina De Vlieger, Dick Botteldooren (2011) Assessment of the impact of speed limit

reduction and traffic signal coordination on vehicle emissions using an integrated

approach, Transportation Research Part D: Transport and Environment, Volume

16, Issue 7, Pages 504-508. See ScienceDirect Alert.

Arnaud Can, Dick Botteldooren (2011). Towards traffic situation noise emission

models, Acta Acustica united with Acustica, Volume 97, Issue 5, Pages 900-903.

Bert De Coensel, Arnaud Can, Bart Degraeuwe, Ina De Vlieger, Dick Botteldooren,

(2011) Effects of traffic signal coordination on noise and air pollutant emissions,

Environmental Modelling and Software (accepted, publication 2012).

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