The influence of traffic management on emissions

Transcription

1 The influence of traffic management on emissions Guidebook for good practice RA-MOW B. Degraeuwe, B. De Coensel, B. Beusen, M. Madireddy, A. Can, I. De Vlieger Onderzoekslijn Duurzame mobiliteit DIEPENBEEK, STEUNPUNT MOBILITEIT & OPENBARE WERKEN SPOOR VERKEERSVEILIGHEID

2 Documentbeschrijving Rapportnummer: Titel: RA-MOW 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: Onderzoekslijn: Partner: Aantal pagina s: 60 Prof. dr. ir. Dick Botteldooren, ir. Ina De Vlieger Duurzame mobiliteit VITO en Universiteit Gent Projectnummer Steunpunt: 8.3 Projectinhoud: Invloed van verkeersmanagement op emissies, geluid en veiligheid Uitgave: Steunpunt Mobiliteit & Openbare Werken, april Steunpunt Mobiliteit & Openbare Werken Wetenschapspark 5 B 3590 Diepenbeek T F E info@steunpuntmowverkeersveiligheid.be I

3 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 CO 2 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 Steunpunt Mobiliteit & Openbare Werken 3 RA-MOW

4 limiet ligt. Het laagste verbruik (CO 2 ) 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 CO 2 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. Doorstroming Veiligheid Brandstof besparing CO 2 - reductie Locale emissiereductie (NO x, PM, HC) Geluid Kruispunten vervangen door ronde punten: 1. stedelijke gebieden hoofdwegen Snelheidsregeling op snelwegen: 1. verlaagde snelheid (80 km/h) 2. variabele snelheids-limieten 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. Steunpunt Mobiliteit & Openbare Werken 4 RA-MOW

5 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 CO 2 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 (CO 2 emission). The lowest average fuel consumption (CO 2 emission) is obtained for the 70 and 90 km/h speed limits. At highway speeds, engines are operating Steunpunt Mobiliteit & Openbare Werken 5 RA-MOW

6 very efficiently but the air resistance is increasing sharply, leading to higher fuel consumption, and CO 2 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 (CO 2 ) savings Local emissions reduction (NO x, PM, HC) Noise Replacing intersectio ns with roundabouts: 1. urban areas majors roads Highway speed management: 1. reduced speed (80 km/h) 2. variable speed limits 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 Steunpunt Mobiliteit & Openbare Werken 6 RA-MOW

7 Content 1. INTRODUCTION OVERVIEW OF EFFECTS OF TRAFFIC MANAGEMENT Replacement of the traditional signalized intersections with roundabouts Safety Traffic Flow Emissions and Fuel Usage Noise Emissions Freeway speed management Safety Traffic Flow Emissions and fuel consumption Speed reduction on local roads Safety Emissions and Fuel Consumption Noise Emissions Low emission zones (LEZ) Emissions Noise Emissions Effect of traffic lights synchronization Traffic Flow Emissions and Fuel Consumption Noise Emissions Speed humps/bumps Safety Emissions and Fuel Consumption Noise Emissions CASE STUDIES Applied simulation models Microscopic traffic simulation model Emission models for air pollutants and noise Validation of the integrated model Case Study A: Effect of reduced speed limits on emissions and noise in Zurenborg (Antwerp) Study area Policy measures Emissions of air pollutants and noise...31 Steunpunt Mobiliteit & Openbare Werken 7 RA-MOW

8 3.3 Case Study B: Effect of green wave on emissions and noise in Zurenborg (Antwerp) Study area Policy measures Emissions of air pollutants, CO 2 and noise Case Study C: Effect of variable speed limits on emissions and noise on the E Introduction: study area and scenarios The E313 model in Paramics Effect of VSL on air pollutant and noise emissions Evaluation of other measures on the E Conclusions ANALYSIS SPEED PROFILE VERSUS MAXIMUM ALLOWED SPEED Speed profile as a function of the speed limit Acceleration profiles as a function of the speed limit Fuel consumption as a function of the speed limit CONCLUSIONS REFFERENCES ANNEX OVERVIEW EVENTS AND INTERNATIONAL PUBLICATIONS Steunpunt Mobiliteit & Openbare Werken 8 RA-MOW

9 1. I N T R O D U C T I O N 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 CO 2 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, NO x, and carbon based particulate matter. Speed and acceleration also influence CO 2 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 ( 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. Steunpunt Mobiliteit & Openbare Werken 9 RA-MOW

10 2. O V E R V I E W O F E F F E C T S O F T R A F F I C M A N A G 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 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 Steunpunt Mobiliteit & Openbare Werken 10 RA-MOW

11 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). Steunpunt Mobiliteit & Openbare Werken 11 RA-MOW

12 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. Steunpunt Mobiliteit & Openbare Werken 12 RA-MOW

13 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 km/h. A study by Brude and Larsson (2000) on roundabout design concludes that singlelane 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): Steunpunt Mobiliteit & Openbare Werken 13 RA-MOW

14 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 Steunpunt Mobiliteit & Openbare Werken 14 RA-MOW

15 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 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 %. 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 Emissions and Fuel Usage Because roundabouts improve the efficiency of traffic flow, they also reduce vehicle emissions and fuel consumption. Steunpunt Mobiliteit & Openbare Werken 15 RA-MOW

16 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 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. Steunpunt Mobiliteit & Openbare Werken 16 RA-MOW

17 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. Steunpunt Mobiliteit & Openbare Werken 17 RA-MOW

18 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 standalone 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 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 NO x and by 5 25% for PM 10. 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 NO x and PM 10 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 NO x (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. Steunpunt Mobiliteit & Openbare Werken 18 RA-MOW

19 2.2.4 Noise Emissions 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. Steunpunt Mobiliteit & Openbare Werken 19 RA-MOW

20 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 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 Steunpunt Mobiliteit & Openbare Werken 20 RA-MOW

21 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 NO x, 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 mobilesource 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) Noise Emissions 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. Steunpunt Mobiliteit & Openbare Werken 21 RA-MOW

22 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 oneway 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 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. Steunpunt Mobiliteit & Openbare Werken 22 RA-MOW

23 These heavy duty trucks, even though they make a very small percentage of the total vehicles on the road, their overall contribution to NO x 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 NO x 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 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%), NO x (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 NO X. 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 NO x emitted in Greater London will reduce by about tons in 2008 and by 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 The reductions of NO x were predominantly expected in the roads with the greatest portion of heavy duty vehicles. However, future projections suggested that the greatest reductions in NO x and PM10 concentrations are expected to occur after 2012 when the Euro VI norms will be introduced Noise Emissions 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-noiseemission 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. Steunpunt Mobiliteit & Openbare Werken 23 RA-MOW

24 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 selforganize 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. Steunpunt Mobiliteit & Openbare Werken 24 RA-MOW

25 2.5.2 Emissions and Fuel Consumption 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 CO 2 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 NO x and CO 2 emissions can be reduced by 10% when the lights were synchronized Noise Emissions 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. Steunpunt Mobiliteit & Openbare Werken 25 RA-MOW

26 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) 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. Steunpunt Mobiliteit & Openbare Werken 26 RA-MOW

27 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) Emissions and Fuel Consumption 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 Noise Emissions 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. Steunpunt Mobiliteit & Openbare Werken 27 RA-MOW

28 3. C A S E S T U D I E 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 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) Emission models for air pollutants and noise The instantaneous CO 2 and NO X 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 Steunpunt Mobiliteit & Openbare Werken 28 RA-MOW

29 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 CO 2, NO x and PM 10. The PM 10 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 NO x and PM 10 will be underestimated and the emissions of CO 2 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, CO 2 and NO X 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 CO 2 and NO X 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 ± for CO 2 and 0.72 ± 0.10 for NO X for all test vehicles, indicating that the model is able to capture the dependencies on speed and acceleration well. The somewhat lower correlations for NO X 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 L W. When the noise emission of a vehicle trip through the network is considered, we further define L avg W 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 L tot W 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. Steunpunt Mobiliteit & Openbare Werken 29 RA-MOW

30 Fraction [normalized] Fraction [normalized] 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 secondby-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 simulated trips measured trips simulated trips measured trips CO [g/km] NO X [g/km] Figure 4: Normalized distributions of CO 2 and NO X emissions per km, for measured and simulated vehicle trips along the N Case Study A: Effect of reduced speed limits on emissions and noise in Zurenborg (Antwerp) 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. Steunpunt Mobiliteit & Openbare Werken 30 RA-MOW

31 N184 Railway N W E S m R10 R1 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 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 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. Steunpunt Mobiliteit & Openbare Werken 31 RA-MOW

32 Fraction [normalized] Fraction [normalized] Fraction [normalized] Fraction [normalized] original scenario reduced speed limits original scenario reduced speed limits Speed [km/h] Acceleration [m/s ] 2 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 CO 2 and NO X 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 CO 2 and NO X 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) original scenario reduced speed limits original scenario reduced speed limits CO [g/km] NO [g/km] X Figure 7: Normalized distributions of CO 2 and NO X 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) Study area For case study B, the same study area as for case study A is considered, and therefore we refer to section In particular, case study B focuses on the major arterial road, the N184 or Plantin-Moretuslei (Figure 5). Steunpunt Mobiliteit & Openbare Werken 32 RA-MOW

33 Number of trips Number of trips 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 Emissions of air pollutants, CO 2 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 CO 2 and NO X emissions increase by 9.5% and 8.7% because of the more interrupted traffic flow original scenario without green wave original scenario without green wave CO [g] NO [g] X Figure 8: Distributions of CO 2 and NO X 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 E 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. Steunpunt Mobiliteit & Openbare Werken 33 RA-MOW

34 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 vehicles per day. Because 23% are trucks, a passenger car equivalent of 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 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. Steunpunt Mobiliteit & Openbare Werken 34 RA-MOW

35 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 semiautomatic 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 (CO 2, NO X and PM 10 ) 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. Steunpunt Mobiliteit & Openbare Werken 35 RA-MOW

36 Figure 11: Control algorithm of the VSL gantries (Vlaams Verkeerscentrum, 2010) 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. Steunpunt Mobiliteit & Openbare Werken 36 RA-MOW

37 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 cloverleaf at Antwerpen-Oost from E313 to ring cloverleaf at Antwerpen-Oost from E313 to ring cloverleaf at Antwerpen-Oost exit Borgerhout Wommelgem approach Wommelgem Wommelgem exit Wommelgem junction of Ranst from E34 to E Massenhoven approach Massenhoven Massenhoven exit Massenhoven Herentals-West approach Herentals-West Herentals-West exit Herentals-West Herentals-Industrie approach Herentals-Industrie Herentals-Industrie exit Herentals-Industrie Herentals-Oost approach Herentals-Oost Herentals-Oost exit Herentals-Oost Geel-West approach Geel - West Geel-West exit Geel - West between Geel-West and Geel-Oost main road Steunpunt Mobiliteit & Openbare Werken 37 RA-MOW

38 Light vehicles per 5 min Trucks per 5 min CAR HDVr HDVa :00 6:30 7:00 7:30 8:00 8:30 9:00 9:30 10:00 Hour 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. Steunpunt Mobiliteit & Openbare Werken 38 RA-MOW

39 speed (km/h) Occupation [%] Figure 14: Variable Speed limit as a function of occupation and speed measured downstream the gantry 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 CO 2 and PM 10 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) CO 2 (ton) NO x (ton) PM 10 (ton) CO 2 (g/km) NO x (g/km) PM 10 (g/km) NOVSL VSL rel. diff % -3.27% -0.46% -0.60% -0.19% 0.39% 0.24% 0.66% p-value Steunpunt Mobiliteit & Openbare Werken 39 RA-MOW

40 Figure 15. Emissions of CO 2, NO x and PM 10 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 Steunpunt Mobiliteit & Openbare Werken 40 RA-MOW