An investigation of the environmental impact of urban road capacity reductions POSTER P135973 INTRODUCTION ABSTRACT OBJECTIVES ROADWORKS MECHANISM OF IMPACT The road network is a vital medium for surface movements of goods and people, but also a conduit for the distribuRon of essenRal services such as gas and electricity below ground. CongesRon of the road network is caused by demand exceeding capacity. Various forms of traffic management, for example changes in speed limit, traffic calming measures or network design, can lead to a capacity reducRon on the network. Accidents, roadworks or vehicles infringing on parking restricRons can have a similar effect. CongesRon has an impact on vehicle emissions and the environment, both of which are becoming increasingly important to decision makers and road users due to their influence on air quality and human health. These issues are especially significant in densely populated urban areas. While methods exist to represent the relaRonships between capacity reducRon and vehicle delay, these are less well developed for urban networks, and the influence of capacity reducRon on vehicle emissions and how the locaRon or intensity of pollutant emission hotspots may change has not previously been considered. This poster proposes and demonstrates a methodology for assessing how localised capacity reducRons, focusing on roadworks, can affect vehicle dynamics and thus vehicle emissions and network performance indicators. Simple relaRonships between the characterisRcs of the roadworks and key traffic engineering parameters are proposed. The methodology is tested using a microsimulaRon model and a range of roadwork scenarios. Analysis focuses on an urban road network segment and suggests that a typical roadwork may increase emissions by 100%, 101% and 80% for CO 2 , NO X and PM 10 emissions respecRvely, with an associated 34% increase in delay. The importance of local vehicle acceleraRon paaerns in influencing the distribuRon of emissions is clearly seen. Further work to invesRgate the fidelity of acceleraRon simulaRon in traffic microsimulaRon is required to enable idenRficaRon of efficient traffic management intervenRons for management of temporary capacity reducRons. Road Network Required for the movement of good and people Required for the distribuRon of essenRal services The road network is primarily used for the movement of goods and people on both the carriageway and footway (sidewalk), which form the highway. However, the road network is also used for the distribuRon of essenRal services such as gas, electricity, water and communicaRon networks. The diagram on the right shows a typical cross secRon of a road. Image available from: hap://www.infovisual.info/05/025_en.html CongesRon When demand > capacity Impact on environment and network performance The demand is the number of vehicles desiring to travel along a parRcular link per unit Rme, and the capacity is the maximum number of vehicles that can pass through a link using all available road space per unit Rme (TransportaRon Research Board, 2010). When demand exceeds capacity, we expect congesRon. The stop start driving behaviour in a congested network will result in increased vehicle emissions compared to a smoother driving behaviour as would be expected in free flow condiRons. CongesRon will also have a negaRve impact on network performance by increasing travel Rme and reducing average speeds. Image available from: hap://society6.com/IkuannaStudios/CongesRonAheadExpectDelaysHighwaySign_Print Capacity reducRon Physical reducRon in available road space Can be planned or unplanned A capacity reducRon is an event, acRvity or process that results in the physical loss of road space. A capacity reducRon can be temporary, for example a broken down vehicle blocking a lane or permanent, for example a reduced speed limit. A capacity reducRon can also be termed planned or unplanned. A planned capacity reducRon could be the closing of a lane to carry out rouRne maintenance, where as an unplanned capacity reducRon could be emergency roadworks. Image available from: hap://news.bbcimg.co.uk/media/images/55503000/jpg/_55503555_55503554.jpg Significant economic costs The Department for Transport (2011) esRmates that the 1.2 million roadworks in England each year result in a cost to the economy of over £4 billion due to the delay caused. This figure fails to consider the addiRonal costs of congested traffic as highlighted by the Greater London Authority (2012), for example frustraRon to road users and the environmental impact. 1.2M roadworks in England each year cost economy £4B The World Health OrganisaRon (2011) states that 40 million people in the 115 largest ciRes in the European Union are exposed to air that exceeds WHO air quality guideline values for at least one pollutant. Roadworks, a typical capacity reducRon, can cause congesRon in a saturated network and this is expect to increase vehicle emissions. 40M people in 115 largest EU ciRes at risk due to poor air quality Roadworks, also commonly referred to as workzones, are becoming increasingly important, and are the focus of many pieces of legislaRon and guidance documentaRon. In London, UK, there is now a formal procedure that contractors have to follow to gain access to the highway, known as the London Permit Scheme (LoPS 2009). Other schemes such as the Lane Rental Scheme (TLRS 2012) force contractors to ‘rent’ secRons of the carriageway. Other key documents include the Design Manual for Roads and Bridges (DMRB 2012), New Roads and Street Works Act (NRSWA 1991), Traffic Management Act (TMA 2004) and the Mayor’s Code of Conduct (2009). New legislaRon and guidance documentaRon in London Health impacts Policy implicaRons Roadworks are an example of a capacity reducRon and can have a significant impact on network performance in a saturated network. If there is insufficient pracRcal reserve capacity, the introducRon of a set of roadworks will result in congesRon. Roadworks, which can be planned or unplanned, are a form of nonrecurrent congesRon. Nonrecurrent congesRon is the build up of traffic due to an incident and is unexpected, the opposite of recurrent congesRon which is predictable, for example during the AM peak. The management of roadworks varies greatly depending on whether the roadworks are planned or unplanned, but also based on the severity of the roadworks. With planned roadworks, the contractor needs to noRfy the relevant highway authority between 3 days and 3 months in advance of the works. The contractor and highway authority will then work together to put in the necessary traffic management and ensure the duraRon of the works and the space required is appropriate for the works to be conducted. With unplanned roadworks, the contractor informs the highway authority of the works up to 5 hours aper the works have commenced. The highway authority may then ask the contractor to stop and put in the necessary traffic management or conRnue. Unplanned roadworks have the potenRal to be more disrupRve as road users will not have been noRfied in advance and a traffic management plan will not be in effect. Planned v unplanned roadworks Are unplanned roadworks more disrupRve? CongesRon Management Stakeholders There are numerous stakeholders involved with roadworks, including local residents, road users, roadwork promoters, local authoriRes and central government each of whom may have different agendas and prioriRes. There is a clear need to invesRgate all of the cost components associated with roadworks, including the environmental impact, in order to support decisions about future roadwork management. Image available from: hap://27gen.files.wordpress.com/2011/09/sixthinkinghats1.jpg CongesRon Nonrecurrent congesRon (incident based) Recurrent congesRon Traffic calming measures Lack of capacity Roadworks Parked vehicles Pothole filling Many others Many others Accidents Repair gas leak Minor works Planned Unplanned Standard works Major works Immediate emergency works Immediate urgent works RouRne resurfacing Streetscape redevelopment Repair burst water main A localised reducRon in lane capacity will affect the dynamics of individual vehicle operaRon and therefore emissions and network performance indicators such as delay. Accurate assessment of emissions depends on analysis at this level (Smit et al., 2010). Above a certain degree of saturaRon, this capacity reducRon and the characterisRcs of the associated traffic management may lead to measurable changes in link performance. In principle, a change in link performance characterisRcs will have an impact on the route choice and behaviour across the network (Sheu (2006)). Furthermore, driver behaviour such as sensiRvity to informaRon and familiarity with the network will affect the level of rerouRng and thus the extent of the network that is affected by the capacity reducRon (e.g. Hu et al. (2007)). A key output, therefore, of a microscopic, linkbased analysis is to determine the extent to which a localised capacity reducRon affects the generalised cost of using different links and nodes in the network. Capacity Reduction Link Effect Network Effect E.g. – lower average speeds, increased delay, higher fuel usage, increased local polluRon E.g. – changes in demand and mode, reassignment of vehicles, re distribuRon of emissions If degree of saturaRon is sufficiently high If effects cause changes in assignment • In this study we focus on the simple link component of a roadwork in an urban network • In the scenarios explored, the presence of traffic management in the form of temporary traffic signals is required • The condiRons under which linklevel capacity reducRons influence adjacent links and nodes is idenRfied • A series of models built to simulate different roadwork scenarios • Underlying theoreRcal framework is based on basic traffic engineering concepts SCOPE THEORETICAL FRAMEWORK Key equaRons Blockingback The capacity is defined to be the maximum throughput of a parRcular segment of the network. The capacity can be calculated as a funcRon of the green raRo and saturaRon flow. The green raRo is the raRo of effecRve green Rme g to the traffic signal cycle length c. The saturaRon flow, also commonly referred to as the queue discharge rate, is denoted by s. ! = ! ! ∗ ! By manipulaRng the equaRon above, an expression for the criRcal green Rme g crit can be formed. q is the number of vehicles aaempRng to enter the capacity restrained link and the other variables are as defined above. ! !"#$ = ! ∗ ! ! Using the diagram below, it is possible to define an equaRon to esRmate the criRcal length of the platoon of the vehicles aaempRng to enter the capacity restrained link that will result in blockingback into the adjacent nodes and juncRons. As shown in the diagram below, d is the distance between the juncRon and the stop line of the temporary traffic management. z is the length of the queue that forms due to the temporary traffic signals and x is the length of the platoon of vehicles aaempRng to enter the capacity restrained link. !" ! → ! − ! !"#$%&'( !"#$ !"#$ !ℎ! !"#$%&'( !"#$%&'# !""#$% ! !"#$ ≈ ! − ! d x 1 z Stop line Back of queue x 2 x 3 x=x 1 +x 2 +x 3 = Length of platoon of vehicles entering capacity reduced link during analysis timeframe Aravinth Thiyagarajah ([email protected]) Dr Robin North ([email protected]) This poster describes doctoral work supported by the RJRF and supervised by Dr Robin North, Professor Michael Bell and Professor John Polak at the Centre for Transport Studies, Imperial College London Centre for Transport Studies www.imperial.ac.uk/cts
2
Embed
Investigation of the Environmental Impact of Urban Road Capacity Reductions
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
An investigation of the environmental impact of urban road capacity reductions
POSTER P13-‐5973
INTRODUCTION
ABSTRACT OBJECTIVES
ROADWORKS
MECHANISM OF IMPACT The road network is a vital medium for surface movements of goods and people, but also a conduit for the distribuRon of essenRal services such as gas and electricity below ground. CongesRon of the road network is caused by demand exceeding capacity. Various forms of traffic management, for example changes in speed limit, traffic calming measures or network design, can lead to a capacity reducRon on the network. Accidents, roadworks or vehicles infringing on parking restricRons can have a similar effect. CongesRon has an impact on vehicle emissions and the environment, both of which are becoming increasingly important to decision makers and road users due to their influence on air quality and human health. These issues are especially significant in densely populated urban areas. While methods exist to represent the relaRonships between capacity reducRon and vehicle delay, these are less well developed for urban networks, and the influence of capacity reducRon on vehicle emissions and how the locaRon or intensity of pollutant emission hotspots may change has not previously been considered. This poster proposes and demonstrates a methodology for assessing how localised capacity reducRons, focusing on roadworks, can affect vehicle dynamics and thus vehicle emissions and network performance indicators. Simple relaRonships between the characterisRcs of the roadworks and key traffic engineering parameters are proposed. The methodology is tested using a microsimulaRon model and a range of roadwork scenarios. Analysis focuses on an urban road network segment and suggests that a typical roadwork may increase emissions by 100%, 101% and 80% for CO2, NOX and PM10 emissions respecRvely, with an associated 34% increase in delay. The importance of local vehicle acceleraRon paaerns in influencing the distribuRon of emissions is clearly seen. Further work to invesRgate the fidelity of acceleraRon simulaRon in traffic microsimulaRon is required to enable idenRficaRon of efficient traffic management intervenRons for management of temporary capacity reducRons.
Road Network Required for the movement of good and people
Required for the distribuRon of essenRal services
The road network is primarily used for the movement of goods and people on both the carriageway and footway (sidewalk), which form the highway. However, the road network is also used for the distribuRon of essenRal services such as gas, electricity, water and communicaRon networks. The diagram on the right shows a typical cross secRon of a road.
Image available from: hap://www.infovisual.info/05/025_en.html
CongesRon When demand > capacity
Impact on environment and network performance
The demand is the number of vehicles desiring to travel along a parRcular link per unit Rme, and the capacity is the maximum number of vehicles that can pass through a link using all available road space per unit Rme (TransportaRon Research Board, 2010). When demand exceeds capacity, we expect congesRon. The stop-‐start driving behaviour in a congested network will result in increased vehicle emissions compared to a smoother driving behaviour as would be expected in free flow condiRons. CongesRon will also have a negaRve impact on network performance by increasing travel Rme and reducing average speeds.
Image available from: hap://society6.com/IkuannaStudios/CongesRon-‐Ahead-‐Expect-‐Delays-‐Highway-‐Sign_Print
Capacity reducRon Physical reducRon in available road space
Can be planned or unplanned
A capacity reducRon is an event, acRvity or process that results in the physical loss of road space. A capacity reducRon can be temporary, for example a broken down vehicle blocking a lane or permanent, for example a reduced speed limit. A capacity reducRon can also be termed planned or unplanned. A planned capacity reducRon could be the closing of a lane to carry out rouRne maintenance, where as an unplanned capacity reducRon could be emergency roadworks.
Image available from: hap://news.bbcimg.co.uk/media/images/55503000/jpg/_55503555_55503554.jpg
Significant economic costs
The Department for Transport (2011) esRmates that the 1.2 million roadworks in England each year result in a cost to the economy of over £4 billion due to the delay caused. This figure fails to consider the addiRonal costs of congested traffic as highlighted by the Greater London Authority (2012), for example frustraRon to road users and the environmental impact.
1.2M roadworks in England each year cost economy £4B
The World Health OrganisaRon (2011) states that 40 million people in the 115 largest ciRes in the European Union are exposed to air that exceeds WHO air quality guideline values for at least one pollutant. Roadworks, a typical capacity reducRon, can cause congesRon in a saturated network and this is expect to increase vehicle emissions.
40M people in 115 largest EU ciRes at risk due to poor air quality
Roadworks, also commonly referred to as workzones, are becoming increasingly important, and are the focus of many pieces of legislaRon and guidance documentaRon. In London, UK, there is now a formal procedure that contractors have to follow to gain access to the highway, known as the London Permit Scheme (LoPS 2009). Other schemes such as the Lane Rental Scheme (TLRS 2012) force contractors to ‘rent’ secRons of the carriageway. Other key documents include the Design Manual for Roads and Bridges (DMRB 2012), New Roads and Street Works Act (NRSWA 1991), Traffic Management Act (TMA 2004) and the Mayor’s Code of Conduct (2009).
New legislaRon and guidance documentaRon in London
Health impacts
Policy implicaRons
Roadworks are an example of a capacity reducRon and can have a significant impact on network performance in a saturated network. If there is insufficient pracRcal reserve capacity, the introducRon of a set of roadworks will result in congesRon. Roadworks, which can be planned or unplanned, are a form of non-‐recurrent congesRon. Non-‐recurrent congesRon is the build up of traffic due to an incident and is unexpected, the opposite of recurrent congesRon which is predictable, for example during the AM peak.
The management of roadworks varies greatly depending on whether the roadworks are planned or unplanned, but also based on the severity of the roadworks. With planned roadworks, the contractor needs to noRfy the relevant highway authority between 3 days and 3 months in advance of the works. The contractor and highway authority will then work together to put in the necessary traffic management and ensure the duraRon of the works and the space required is appropriate for the works to be conducted. With unplanned roadworks, the contractor informs the highway authority of the works up to 5 hours aper the works have commenced. The highway authority may then ask the contractor to stop and put in the necessary traffic management or conRnue. Unplanned roadworks have the potenRal to be more disrupRve as road users will not have been noRfied in advance and a traffic management plan will not be in effect.
Planned v unplanned roadworks
Are unplanned roadworks more disrupRve?
CongesRon
Management
Stakeholders
There are numerous stakeholders involved with roadworks, including local residents, road users, roadwork promoters, local authoriRes and central government each of whom may have different agendas and prioriRes. There is a clear need to invesRgate all of the cost components associated with roadworks, including the environmental impact, in order to support decisions about future roadwork management.
Image available from: hap://27gen.files.wordpress.com/2011/09/sixthinkinghats1.jpg
Standard works Major works Immediate emergency works
Immediate urgent works
RouRne resurfacing
Streetscape redevelopment Repair burst
water main
A localised reducRon in lane capacity will affect the dynamics of individual vehicle operaRon and therefore emissions and network performance indicators such as delay. Accurate assessment of emissions depends on analysis at this level (Smit et al., 2010). Above a certain degree of saturaRon, this capacity reducRon and the characterisRcs of the associated traffic management may lead to measurable changes in link performance. In principle, a change in link performance characterisRcs will have an impact on the route choice and behaviour across the network (Sheu (2006)). Furthermore, driver behaviour such as sensiRvity to informaRon and familiarity with the network will affect the level of rerouRng and thus the extent of the network that is affected by the capacity reducRon (e.g. Hu et al. (2007)). A key output, therefore, of a microscopic, link-‐based analysis is to determine the extent to which a localised capacity reducRon affects the generalised cost of using different links and nodes in the network.
Capacity Reduction
Link Effect
Network Effect
E.g. – lower average speeds, increased delay, higher fuel usage, increased local
polluRon
E.g. – changes in demand and mode, reassignment of vehicles, re-‐distribuRon of emissions
If degree of saturaRon is sufficiently high
If effects cause changes in assignment
• In this study we focus on the simple link component of a roadwork in an urban network
• In the scenarios explored, the presence of traffic management in the form of temporary traffic signals is required
• The condiRons under which link-‐level capacity reducRons influence adjacent links and nodes is idenRfied • A series of models built to simulate different roadwork scenarios
• Underlying theoreRcal framework is based on basic traffic engineering concepts
SCOPE
THEORETICAL FRAMEWORK
Key equaRons
Blocking-‐back
The capacity is defined to be the maximum throughput of a parRcular segment of the network. The capacity can be calculated as a funcRon of the green raRo and saturaRon flow. The green raRo is the raRo of effecRve green Rme g to the traffic signal cycle length c. The saturaRon flow, also commonly referred to as the queue discharge rate, is denoted by s.
! = !! ∗ !
By manipulaRng the equaRon above, an expression for the criRcal green Rme gcrit can be formed. q is the number of vehicles aaempRng to enter the capacity restrained link and the other variables are as defined above.
!!"#$ = ! ∗ !!
Using the diagram below, it is possible to define an equaRon to esRmate the criRcal length of the platoon of the vehicles aaempRng to enter the capacity restrained link that will result in blocking-‐back into the adjacent nodes and juncRons. As shown in the diagram below, d is the distance between the juncRon and the stop line of the temporary traffic management. z is the length of the queue that forms due to the temporary traffic signals and x is the length of the platoon of vehicles aaempRng to enter the capacity restrained link.
This poster describes doctoral work supported by the RJRF and supervised by Dr Robin North, Professor Michael Bell and
Professor John Polak at the Centre for Transport Studies, Imperial College London
Centre for Transport Studies www.imperial.ac.uk/cts
MODELLING
MODELLING FRAMEWORK RESULTS CONCLUSION
FURTHER WORK
REFERENCES
In order to assess the impact of urban capacity reducRons, VISSIM (Verkehr In Stadten – SIMulaRonsmodell), a mulR-‐modal microscopic traffic simulaRon sopware was used (PTV AG, 2012). VISSIM has its limitaRons as highlighted by Treiber et al. (2006) and Jie et al. (2012), however it is the microsimulaRon tool recommended for use in several modelling guidelines, for example Transport for London (2010). To esRmate the vehicle emissions, the individual vehicle records from VISSIM were exported into EnViVer, an instantaneous emissions modelling tool created by TNO (The Netherlands OrganisaRon for Applied ScienRfic Research) (TNO, 2012). The emissions are calculated by assigning each VISSIM vehicle type to an emissions class in EnViVer and applying a polynomial based on acceleraRon behaviour.
Traffic model
Emission model
Traffic data
Network data
Roadwork data
Vehicle fleet data
VISSIM
EnViVer
The modelling process, adapted from North et al. (2009) is shown to the right. Traffic, network and roadwork data support the building and configuraRon of the traffic model, and then the outputs of the traffic model are combined with the vehicle fleet data for emissions predicRon in the emission model.
Model structure
A simple model has been created where a parRal closure of a link is required and the introducRon of a signalised contra-‐flow to maintain the flow of traffic. The model, denoted ‘A’ is composed of a 300m link, typical of an urban city centre, and 100m entry links to control the behaviour of the vehicles as they enter the network. The image denoted ‘B’ shows how the contraflow has been implemented with a 10m buffer zone on either side to allow for vehicles to manoeuvre around the works. The temporary traffic signals that are present on the entrances to the contraflow have been programmed with a cycle Rme of 90 seconds, typical of urban environments. The green Rme has been set to minimise the queuing of vehicles but ensure sufficient inter-‐green Rme to allow vehicles to safely leave the contraflow.
A
B
100m Entry link 100m Entry link 300m Link (50kph)
600 veh/hr 600 veh/hr
100m Entry link 100m Entry link
600 veh/hr 600 veh/hr
115m (50kph)
50m contraflow (30kph)
10m buffer zone (20kph)
115m (50kph)
Temporary traffic signal Temporary traffic signal
Scenarios
100m Entry link
600 veh/hr (as on all entry links)
Traffic signals on each arm of junction
50m link “Yellow box” junction
115m (50kph)
50m contraflow (30kph)
10m buffer zone (20kph)
115m (50kph)
Temporary traffic signal Temporary traffic signal
C In order to invesRgate a range of levels of degradaRon of network performance, the length of the roadwork was varied between 30m-‐120m, represenRng a 10-‐40% reducRon in eastbound lane area. As links do not appear in isolaRon, addiRonal models were created with the presence of juncRons adjacent to the capacity restrained link. The signalised crossroads were programmed with a two-‐stage signal plan that allows for the same vehicle flow of 600 veh/hr. As with the link models, the length of the roadwork was varied between 30m-‐120m.
SimulaRon
In total 5 link models and 5 juncRon models were built in VISSIM. Over 100 simulaRons were carried out, with mulRple runs for each scenario. Various parameters in VISSIM such as delay, average speed and journey Rme were output. Other vehicle specific characterisRcs such as speed, posiRon and Rme in network were output from VISSIM and used as an input into EnViVer to esRmate the vehicle emissions. The outputs from EnViVer and VISSIM were post-‐processed in MATLAB and Excel in order to average across mulRple seeds and to calculate the total mass of pollutant emiaed from the capacity restrained link only.
From the research presented in this poster, the following conclusions can be drawn: • Link level capacity reducRons can have a significant impact on vehicle emissions and Rme-‐related network performance variables
• The length of the capacity reducRon and its proximity to adjacent juncRons is criRcal for determining whether just the capacity restrained link or the wider network needs to be taken into consideraRon when assessing the impact of a capacity reducRon
• The posiRon of the stop line for temporary traffic management and the effecRve green Rme on the temporary traffic signals are important, especially when the queue that forms during the inter-‐green Rme extends beyond the link
• The highest emissions are observed with zones of high acceleraRon and this is something pracRRoners should avoid when configuring roadworks and workzones
The research presented in this poster forms part of a wider invesRgaRon that will feed into Mr. Thiyagarajah’s PhD thesis. Further work to address the following will be conducted in due course: • Assessment of the suitability of exisRng modelling tools
• Improving the realism of the modelling procedure by increasing the complexity and including re-‐rouRng of traffic • CalibraRon and validaRon of the modelling procedure using real-‐world data
• TranslaRng the impact of capacity reducRons on the environment and network performance into a generalised cost which can be used to support decision making and feed into future policy
CASCETTA, E. 2001. Transporta)on Systems Engineering: Theory and Methods, Kluwer Academic Pub. CHUNG, Y. 2011. Assessment of non-‐recurrent traffic congesRon caused by freeway work zones and its staRsRcal analysis with unobserved heterogeneity. Transport Policy, 18, 587-‐594. DEPARTMENT FOR TRANSPORT. 2011. Lane Rental Schemes in England -‐ A Consulta)on [Online]. Available: hap://assets.dp.gov.uk/consultaRons/dp-‐2011-‐25/consultaRondocument.pdf [Accessed 14th January 2012]. DEPARTMENT FOR TRANSPORT. 2012a. Design Manual for Roads and Bridges [Online]. Available: hap://www.dp.gov.uk/ha/standards/dmrb/ [Accessed 6th January 2012]. DEPARTMENT FOR TRANSPORT. 2012b. Manual of Contract Documents for Highway Works [Online]. Available: hap://www.dp.gov.uk/ha/standards/mchw/index.htm [Accessed 3rd January 2012]. GONZÁLEZ, V. & ECHAVEGUREN, T. 2012. Exploring the environmental modeling of road construcRon operaRons using discrete-‐event simulaRon. Automa)on in Construc)on, 24, 100-‐110. GREATER LONDON AUTHORITY. 2012. Tackling road works [Online]. Available: hap://www.london.gov.uk/prioriRes/transport/smoothing-‐traffic-‐flow/tackling-‐road-‐works [Accessed 18th November 2012]. HAWTHORN, I. 2011. London Permit Scheme, First Year Evalua)on Report [Online]. Available: hap://www.njug.org.uk/uploads/1106_Oneroadnetwork_lops_review.pdf. [Accessed 15th June 2011]. HU, J., KAPARIAS, I. & BELL, M. G. H. IdenRficaRon of link congesRon dependence paaerns for dynamic route guidance. 14th World Congress of Intelligent TransportaRon Systems, 2007 Beijing, China. HUANG, Y., BIRD, R. & BELL, M. 2009. A comparaRve study of the emissions by road maintenance works and the disrupted traffic using life cycle assessment and micro-‐simulaRon. Transporta)on Research Part D: Transport and Environment, 14, 197-‐204. HUNT, J. G. & YOUSIF, S. Y. 1994. Traffic Capacity at Motorway Roadworks -‐ Effects of Layout, Incidents and Driver Behaviour. Proceedings of the 2nd Interna)onal Symposium on Highway Capacity. JIE, L., VAN ZUYLEN, H., CHEN, Y., VITI, F. & WILMINK, I. 2012. CalibraRon of a microscopic simulaRon model for emission calculaRon. Transporta)on Research Part C: Emerging Technologies. LEPERT, P. & BRILLET, F. 2009. The overall effects of road works on global warming gas emissions. Transporta)on Research Part D: Transport and Environment, 14, 576-‐584. LONDON FIRST. 2010. Road Sense -‐ Balancing the cost and benefit of roadworks [Online]. Available: hap://www.londonfirst.co.uk/documents/Road_Sense_WEB_FINAL.pdf [Accessed 3rd February 2012]. NORTH, R. J., HOOSE, N., POLAK, J. W., VAN BAALEN, J. & COHEN, J. 2009. On-‐Demand EvaluaRon of AlternaRve Strategies for Environmental Traffic Management. ITS World Congress 2009. OBER-‐SUNDERMEIER, A. & ZACKOR, H. PredicRon of congesRon due to road works on freeways. Intelligent TransportaRon Systems, 2001. Proceedings. 2001 IEEE, 2001 2001. 240-‐244. PARK, J. Y., NOLAND, R. & POLAK, J. 2007. A Microscopic Model of Air Pollutant ConcentraRons: Comparison of Simulated Results with Measured and Macroscpic EsRmates. Transporta)on Research Record: Journal of the Transporta)on Research Board. PTV AG. 2012. VISSIM -‐ Mul)-‐Modal Traffic Flow Modelling [Online]. Available: hap://www.ptvag.com/sopware/transportaRon-‐planning-‐traffic-‐engineering/sopware-‐system-‐soluRons/vissim/ [Accessed 10th May 2012]. SHEU, J.-‐B. 2006. A composite traffic flow modeling approach for incident-‐responsive network traffic assignment. Physica A: Sta)s)cal Mechanics and its Applica)ons, 367, 461-‐478. SMIT, R., NTZIACHRISTOS, L. & BOULTER, P. 2010. ValidaRon of road vehicle and traffic emission models – A review and meta-‐analysis. Atmospheric Environment, 44, 2943-‐2953. TNO. 2012. EnViVer: model traffic flow and emissions [Online]. Available: hap://www.tno.nl/content.cfm?context=thema&content=prop_case&laag1=894&laag2=914&laag3=105&item_id=853&Taal=2 [Accessed 18th November 2012]. TRAFFIC MANAGEMENT ACT 2004. Traffic Management Act. Great Britain. TRANSPORT FOR LONDON. 2010. Traffic Modelling Guidelines [Online]. Available: hap://www.zl.gov.uk/assets/downloads/businessandpartners/traffic-‐modelling-‐guidelines.pdf [Accessed 10th May 2012]. TRANSPORT RESEARCH LABORATORY. 2008. A review of literature on the nature of the impact of roadworks on traffic movement and delay [Online]. Available: hap://www.trl.co.uk/online_store/reports_publicaRons/trl_reports/cat_traffic_and_transport_planning/report_A_review_of_literature_on_the_nature_of_the_impact_of_roadworks__on_traffic_movement_and_delay.htm [Accessed 13th May 2012]. TRANSPORT RESEARCH LABORATORY. 2012. Reducing Conges)on from Highway Works [Online]. Available: hap://www.trl.co.uk/reducingcongesRonfromhighwayworks/ [Accessed 12th April 2012]. TRANSPORTATION RESEARCH BOARD 2010. HCM 2010: The Highway Capacity Manual. TREIBER, M., KESTING, A. & HELBING, D. 2006. Delays, inaccuracies and anRcipaRon in microscopic traffic models. Physica A: Sta)s)cal Mechanics and its Applica)ons, 360, 71-‐88. WORLD HEALTH ORGANISATION. 2011. Air Quality: Facts and figures [Online]. Available: hap://www.euro.who.int/en/what-‐we-‐do/health-‐topics/environment-‐and-‐health/air-‐quality/facts-‐and-‐figures [Accessed 7th May 2012]. ZHANG, K., BATTERMAN, S. & DION, F. 2011. Vehicle emissions in congesRon: Comparison of work zone, rush hour and free-‐flow condiRons. Atmospheric Environment, 45, 1929-‐1939.
Trends
Intensity maps
Blocking-‐back
• 149%, 180% and 112% increase in CO2, NOX and PM10 emissions respecRvely between the no roadwork case and shortest (30m) roadwork case
• Comparing the no roadwork case for the link model and
juncRon model, we observe a 69%, 36% and 34% increase in CO2, NOX and PM10 emissions aaributed to increased queuing
• Comparing the juncRon model with no roadworks and a 30m roadwork, 100%, 101% and 80% increases in CO2, NOX and PM10 emissions respecRvely are observed
• A 25% reducRon in average speed was observed when a 30m roadwork was introduced into the link model and a 34% reducRon in average speed for the juncRon model. A similar effect on average vehicle delay was observed in each case
JuncRon model results
Link model results
EnViVer is able to esRmate and output the mass of pollutant emiaed for CO2, NOX and PM10 for each 5m grid square. Using a combinaRon of MATLAB and Excel, the outputs have been averaged across mulRple seeds and normalised between the different scenarios invesRgated. Emissions intensity maps have then been produced by plo{ng the total emissions for each grid square using a linear grey scale, where black represents the maximum emissions.
Length of disruption (m) 0 30 50 70 120 CO2 (kg) 57.70 143.50 151.10 157.70 171.00 NOX (g) 154.00 430.50 454.50 475.10 536.60 PM10 (g) 13.99 29.70 31.21 32.53 34.41 Average vehicle delay (s) 0.29 26.01 31.92 36.18 78.56 Average speed (kph) 52.60 25.73 23.10 21.36 12.83
Length of disruption (m) 0 30 40 70 120 CO2 (kg) 97.29 194.70 202.40 208.80 201.4 NOX (g) 299.00 602.70 661.80 674.40 641.90 PM10 (g) 21.17 38.18 39.06 40.29 39.01 Average vehicle delay (s) 25.42 38.45 39.90 49.46 82.25 Average speed (kph) 21.78 16.46 15.90 13.71 9.28
Intensity maps for CO2 (link model)
Increasing emissions
No roadwork
30m roadwork
50m roadwork
Intensity maps for CO2 (juncRon model)
No roadwork
30m roadwork
50m roadwork
The intensity map for the no roadwork case link model shows a conRnuous grey scale, unlike the maps for the 30m and 50m roadwork case where there is a peak in emissions around the roadwork. The emissions intensity maps for the juncRon model show a similar trend, however there are addiRonal zones of increased emissions on the exits from the capacity restrained link. Comparing the 30m and 50m roadwork cases, we see a more dispersed map for the 50m case, this is likely to be due to vehicles travelling in a constant queue rather than acceleraRng between queues.
!!"#$ = 25.96! for$simulated$network$
!"!! < !!"#$
!!"#$%!!"#$%&'()!!"!!"#$
Focusing on the juncRon model, we observe a reducRon in emissions across all three pollutants between the 70m and 120m roadwork cases. A possible explanaRon is that vehicles are blocking-‐back into the adjacent juncRons, resulRng in a flow reducRon through the network. This can be confirmed by calculaRng the criRcal green Rme, gcrit for this network. For the 70m roadwork, the temporary traffic signals have a green Rme of 30s, which is higher than gcrit, however for the 120m roadwork, the green Rme is 25s, less than the criRcal green Rme. For the 120m roadwork, the queue that forms at the temporary traffic signals during the inter-‐green is not fully served and the queue becomes a funcRon of Rme. Eventually z=d and no new vehicles can enter the capacity restrained link.