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

Florent Querini and Enrico Benetto

Transportation Research Part A: Policy and Practice, 2014, vol. 70, issue C, 149-161

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.

Keywords: Electric mobility; Policy assessment; Agent-based modelling; Luxembourg; Alternative vehicles deployment (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (12)

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DOI: 10.1016/j.tra.2014.10.017

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