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Agent-based modelling for assessing hybrid and electric cars deployment policies in Luxembourg and Lorraine

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  • Querini, Florent
  • Benetto, Enrico

Abstract

Electric mobility is often presented as a way to tackle the environmental issues associated with individual mobility, provided that electric vehicles are adopted by drivers on a mass scale. In this paper, we propose an agent-based model (ABM) aiming at modelling the deployment of these vehicles. ABM is particularly indicated when modelling complex systems whose final results are the combination of the interactions between individuals and their environment and when the agents have partial information to take their decisions. We selected Luxembourg and its French neighbouring region, Lorraine, as the case study for our model, to test Luxembourg’s ambitious objective of deploying 40,000 electric vehicles by the year 2020. Model results show that the number of battery powered electric vehicles in Luxembourg (including vehicles from Lorraine’s commuters crossing the border every day) could be between 2000 and 21,000. A high number of commercial vehicles in Luxembourg, as well as an unlikely deployment in the neighbouring Belgium and Germany would therefore be required to meet the deployment objective. However, the deployment of plug-in hybrid vehicles could reach 60,000 cars by the end of 2020. To achieve this number, the deployment of charging points seems to be the more effective policy, along with actions aiming at increasing public awareness and acceptance of electric vehicles. The interest in using the ABM also lies in the identification of the main individuals’ characteristics affecting the deployment of electric vehicles (household size, commuting distances, etc.), which further support the setting of public policies.

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  • Querini, Florent & Benetto, Enrico, 2014. "Agent-based modelling for assessing hybrid and electric cars deployment policies in Luxembourg and Lorraine," Transportation Research Part A: Policy and Practice, Elsevier, vol. 70(C), pages 149-161.
    • Handle: RePEc:eee:transa:v:70:y:2014:i:c:p:149-161
    DOI: 10.1016/j.tra.2014.10.017
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    2. Shen, Max & Li, Meng & He , Fang & Jia, Yinghao, 2016. "Strategic Charging Infrastructure Deployment for Electric Vehicles," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6rp6n4sf, Institute of Transportation Studies, UC Berkeley.
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    6. Rugani, Benedetto & Marvuglia, Antonino & Pulselli, Federico Maria, 2018. "Predicting Sustainable Economic Welfare – Analysis and perspectives for Luxembourg based on energy policy scenarios," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 288-303.
    7. Li, Meng & Jia, Yinghao & Shen, Zuojun & He, Fang, 2017. "Improving the electrification rate of the vehicle miles traveled in Beijing: A data-driven approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 97(C), pages 106-120.
    8. Utomo, D.S. & Gripton, A. & Greening, P., 2021. "Analysing charging strategies for electric LGV in grocery delivery operation using agent-based modelling: An initial case study in the United Kingdom," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    9. Tobias Buchmann & Patrick Wolf & Stefan Fidaschek, 2021. "Stimulating E-Mobility Diffusion in Germany (EMOSIM): An Agent-Based Simulation Approach," Energies, MDPI, vol. 14(3), pages 1-25, January.
    10. Wang, Hua & Zhao, De & Meng, Qiang & Ong, Ghim Ping & Lee, Der-Horng, 2019. "A four-step method for electric-vehicle charging facility deployment in a dense city: An empirical study in Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 224-237.
    11. Albert Hiesl & Jasmine Ramsebner & Reinhard Haas, 2021. "Modelling Stochastic Electricity Demand of Electric Vehicles Based on Traffic Surveys—The Case of Austria," Energies, MDPI, vol. 14(6), pages 1-19, March.
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