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Linton, CL; Grant-Muller, S; Gale, WF (2015)
Publisher: Taylor & Francis (Routledge)
Languages: English
Types: Article
Transport accounts for around a quarter of CO2 emissions globally. Transport modelling provides a useful means to explore the dynamics, scale and magnitude of transport related emissions. This paper explores the modelling tools available for analysing the emissions of CO2 from transport. Covering a range of techniques from transport microsimulation to global techno-economic models, this review provides insight into the various advantages and shortcomings of these tools. The paper also examines the value of having a broad range of perspectives for analysing emissions from transport. The paper concludes by suggesting that the broad range of models creates a rich environment for exploring a spectrum of policy questions around the emissions from transport, and the potential for combining modelling approaches further enhances the understanding that can be attained.
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    • Anable, J., Brand, C., Tran, M., & Eyre, N. (2012). Modelling transport energy demand: A socio-technical approach. Energy Policy, 41, 125 - 138.
    • Atkins-ITS. (2013). SATURN manual. Retrieved from http://www.saturnsoftware.co.uk/ saturnmanual/chapters.html: Atkins and Institute for Transport Studies
    • Axhausen, K. W., & Ga¨rling, T. (1992). Activity-based approaches to travel analysis: Conceptual frameworks, models, and research problems. Transport Reviews, 12, 323 - 341.
    • Brand, C., Tran, M., & Anable, J. (2012). The UK transport carbon model: An integrated life cycle approach to explore low carbon futures. Energy Policy, 41, 107 - 124.
    • Br o¨mmelstroet, M. T., & Bertolini, L. (2011). The role of transport-related models in urban planning practice. Transport Reviews, 31, 139 - 143.
    • Brun, B., & Hansen, S. (2009). TRANS-TOOLS user guide version 2.0. Seville: EC Joint Research Centre.
    • Davidson, W., Donnelly, R., Vovsha, P., Freedman, J., Ruegg, S., Hicks, J., . . . Picado, R. (2007). Synthesis of first practices and operational research approaches in activity-based travel demand modeling. Transportation Research Part A: Policy and Practice, 41, 464 - 488.
    • DECC. 2013. 2012 UK greenhouse gas emissions, provisional figures. UK greenhouse gas emissions statistics. London: Author.
    • De Dios Ort u´zar, J., & Willumsen, L. G. (2011). Modelling transport. Chichester: Wiley.
    • Dougherty, M., Fox, K., Cullip, M., & Boero, M. (2000). Technological advances that impact on microsimulation modelling. Transport Reviews, 20, 145 - 171.
    • Dowling, R., Holland, J., & Huang, A. (2002). Guidelines for applying traffic microsimulation modelling software. Oakland, CA: Dowling Associates and California Department of Transportation.
    • Ferreira, L. (1982). Car fuel consumption in urban traffic: The results of a survey in Leeds using instrumented vehicles (Working Paper No. 162). Leeds: Institute of Transport Studies, University of Leeds.
    • Fiorello, D., Schade, W., Akkermans, L., Krail, M., Schade, B., & Shepherd, S. P. (2012). Results of the technoeconomic analysis of the R&D and transport policy packages for the time horizons 2020 and 2050. In GHG-TransPoRD (Reducing greenhouse-gas emissions of transport beyond 2020: Linking R&D, transport policies and reduction targets). Milan: European Commission.
    • Fishwick, P. (2007). Handbook of dynamic system modelling. London: Chapman & Hall.
    • Garcia, R. (2005). Uses of agent-based modeling in innovation/new product development research∗. Journal of Product Innovation Management, 22, 380 - 398.
    • Gipps, P. G. (1981). A behavioural car-following model for computer simulation. Transportation Research Part B: Methodological, 15, 105 - 111.
    • Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., . . . Deangelis, D. L. (2006). A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 198, 115 - 126.
    • Gudmundsson, H. (2011). Analysing models as a knowledge technology in transport planning. Transport Reviews, 31, 145 - 159.
    • Hatzopoulou, M., Hao, J., & Miller, E. (2011). Simulating the impacts of household travel on greenhouse gas emissions, urban air quality, and population exposure. Transportation, 38, 871 - 887.
    • Hensher, D. A., & Button, K. J. (2008). Handbook of transport modelling. Oxford: Elsevier.
    • Hensher, D. A., Rose, J. M., Leong, W., Tirachini, A., & Li, Z. (2013). Choosing public transport - incorporating Richer behavioural elements in modal choice models. Transport Reviews, 33, 92 - 106.
    • Intergovernmental Panel on Climate Change. (2007). Technical summary. In S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, . . . H. L. Miller (Eds.), Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 19 - 91). Cambridge: Cambridge University Press.
    • International Council for Clean Transportation. (2012). Global transportation roadmap - model documentation and user guide. San Francisco: Author.
    • International Energy Agency. 2009. Transport energy and CO2: Moving towards sustainability. Paris: OECD.
    • Kim, S. H., Edmonds, J., Lurz, J., Smith, S. J., & Wise, M. (2012). The ObjECTS framework for integrated assessment: Hybrid modeling of transportation. The Energy Journal, (Special Issue 2), 63 - 92.
    • K o¨hler, J., Whitmarsh, L., Nykvist, B., Schilperoord, M., Bergman, N., & Haxeltine, A. (2009). A transitions model for sustainable mobility. Ecological Economics, 68, 2985 - 2995.
    • Kyle, P., & Kim, S. H. (2011). Long-term implications of alternative light-duty vehicle technologies for global greenhouse gas emissions and primary energy demands. Energy Policy, 39, 3012 - 3024.
    • Liu, R. (2005). The DRACULA dynamic network microsimulation model. In R. Kitamura & M. Kuwahara (Eds.), Simulation approaches in transportation analysis (pp. 23 - 56). New York: Springer.
    • Liu, R. (2007). DRACULA 2.4 user manual. Leeds: Institute for Transport Studies.
    • Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4, 151- 162.
    • Mcnally, M. G. (2008). The four-step model. In D. A. Hensher & K. J. Button (Eds.), Handbook of transport modelling (pp. 35 - 52). Oxford: Elsevier.
    • Mcnally, M. G., & Rindt, C. R. (2008). The activity-based approach. In D. A. Hensher & K. J. Button (Eds.), Handbook of transport modelling (2nd ed., pp. 55 - 72). Oxford: Elsevier.
    • Paltsev, S., Reilly, J. M., Jacoby, H. D., Eckaus, R. S., Mcfarland, J. R., Sarofim, M. C., . . . Babiker, M. H. (2005). The MIT emissions prediction and policy analysis (EPPA) model: Version 4. Cambridge, MA: MIT Joint Program on the Science and Policy of Global Change.
    • Pfaffenbichler, P. (2003). The strategic, dynamic and integrated urban land use and transport model MARS (Metropolitan Activity Relocation Simulator) (Doctoral thesis). Vienna University of Technology.
    • Pfaffenbichler, P. (2011). Modelling with systems dynamics as a method to bridge the gap between politics, planning and science? lessons learnt from the development of the land use and transport model MARS. Transport Reviews, 31, 267- 289.
    • Pfaffenbichler, P., Emberger, G., & Shepherd, S. (2010). A system dynamics approach to land use transport interaction modelling: The strategic model MARS and its application. System Dynamics Review, 26, 262- 282.
    • Proost, S., & Van Dender, K. (2011). What long-term road transport future? Trends and policy options. Review of Environmental Economics and Policy, 5, 44 - 65.
    • PTV Group. (2015). Emissions modelling. Retrieved January 16, 2015, from http://vision-traffic. ptvgroup.com/en-us/products/ptv-vissim/use-cases/emissions-modelling/
    • Quadstone Paramics. (2013). Paramics full feature list. Retrieved July 5, 2013, from http://www. paramics-online.com/paramics-features.php
    • Research Centre for Transport Planning and Traffic Engineering. (2009). MARS (Metropolitan Activity Relocation Simulator). Retrieved September 2013, from http://www.ivv.tuwien.ac.at/forschung/ mars-metropolitan-activity-relocation-simulator.html
    • Rothengatter, W., Schade, W., Martino, A., Roda, M., Davies, A., Devereux, L., & Williams, I. (2000). ASTRA - assessment of transport strategies. Karlsruhe: Commission of the European Communities.
    • Samaras, Z., & Ntziachristos, L. (1998). Average hot emission factors for passenger cars and light duty trucks. LAT report.
    • Samaras, Z., Ntziachristos, L., Burzio, G., Toffolo, S., Tatschl, R., Mertz, J., & Monzon, A. (2012). Development of a methodology and tool to evaluate the impact of ICT measures on road transport emissions. In P. Papaioannou (Ed.), Transport research arena 2012 (pp. 3418 - 3427). Amsterdam: Elsevier Science.
    • Schaap, N. T. W., & van de Riet, O. (2012). Behavioral insights model: Overarching framework for applying behavioral insights in transport policy analysis. Transportation Research Record, (2322), 42 - 50.
    • Schade, W., & Krail, M. (2012). Aligned R&D and transport policy to meet EU GHG reduction targets. In GHG-TransPoRD (Reducing greenhouse-gas emissions of transport beyond 2020: Linking R&D, transport policies and reduction targets). Karlshrue: European Commission.
    • Schafer, A. (2012). Introducing behavioral change in transportation into energy/economy/environment models. World Bank Policy Research Working Paper.
    • Shafiei, E., Stefansson, H., Asgeirsson, E. I., Davidsdottir, B., & Raberto, M. (2012). Integrated agentbased and system dynamics modelling for simulation of sustainable mobility. Transport Reviews, 33, 44 - 70.
    • Shafiei, E., Thorkelsson, H., A´ sgeirsson, E. I., Davidsdottir, B., Raberto, M., & Stefansson, H. (2012). An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland. Technological Forecasting and Social Change, 79, 1638- 1653.
    • Shepherd, S., Bonsall, P., & Harrison, G. (2012). Factors affecting future demand for electric vehicles: A model based study. Transport Policy, 20, 62- 74.
    • Shepherd, S. P. (2014). A review of system dynamics models applied in transportation. Transportmetrica B: Transport Dynamics, 2, 1 - 23.
    • Smit, R., Smokers, R., Schoen, E., & Hensema, A. (2006). A new modelling approach for road traffic emissions: VERSIT + LD - background and methodology. Delft: TNO.
    • Stern, E., & Richardson, H. W. (2005). Behavioural modelling of road users: Current research and future needs. Transport Reviews, 25, 159- 180.
    • Stern, N. (2007). The economics of climate change: The stern review. Cambridge: Cambridge University Press.
    • Sternman, J. (2000). Business dynamics: Systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill.
    • US Energy Information Administration. (2011). Transportation model of the World Energy Projection System Plus: Model documentation 2011. Washington, DC: US Department of Energy, Office of Energy Analysis.
    • US EPA. (2003). User's guide to MOBILE6.1 and MOBILE6.2, mobile source emission factor model. Washington, DC: Author.
    • Verkehr, P. T. (2011). VISSIM 5.30- 05 user manual. Karlsruhe: Germany.
    • Waisman, H.-D., Guivarch, C., & Lecocq, F. (2013). The transportation sector and low-carbon growth pathways: Modelling urban, infrastructure, and spatial determinants of mobility. Climate Policy, 13, 106- 129.
    • Wegener, M. (2011). From macro to micro - how much micro is too much? Transport Reviews, 31, 161- 177.
    • Willumsen, L. G. (2008). Travel networks. In D. A. Hensher & K. J. Button (Eds.), Handbook of transport modelling (pp. 203- 219). Oxford: Elesvier.
    • Wismans, L., Van Berkum, E., & Bliemer, M. (2011). Modelling externalities using dynamic traffic assignment models: A review. Transport Reviews, 31, 521- 545.
    • Young, W., & Weng, T. (2005). Data and parking simulation models. In R. Kitamura & M. Kuwahara (Eds.), Simulation approaches in transportation analysis (pp. 235- 268). New York: Springer.
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