A systematic review of the agent-based modelling/simulation paradigm in mobility transition

Milad Mehdizadeh, Trond Nordfjaern and Christian A. Klöckner

Technological Forecasting and Social Change, 2022, vol. 184, issue C

Abstract: Simulations and agent-based modelling (ABM) have gained momentum as techniques in the transport, energy, and technology diffusion literature to analyse the mobility transition as a complex emergent phenomenon. This study systematically reviews the application of the ABM paradigm in 86 mobility transition studies. The study reveals several research gaps and proposes avenues for future research. Our review highlights that (i) the field has considerably matured in studying the diffusion of electric vehicles, (ii) both price-based and preference-based scenarios for mobility transition should be considered in future research, (iii) most of the empirical model calibrations have been confined to Western countries. Not only will the literature benefit from similar research in other Western regions, but also from non-Western nations with their unique mobility transition pathways, (iv) the conceptual modelling framework of studies can be divided into the two categories of theory-driven and heuristic models. The theory-driven models, which include psychological and non-psychological (e.g., activity-based travel) models, tend to use well-established behavioural rules, (v) most of the models have used the random utility maximization concept, social psychological models, and other simple assumptions/thresholds for the decision-making process of agents, (vi) half of the studies did not validate their models, and (vii) two-thirds of studies omitted to discuss interaction topology among agents. Major remaining challenges and gaps are identified in the review.

Keywords: Agent-based model; Mobility transition; Electric vehicle; Simulation; Diffusion of innovation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:184:y:2022:i:c:s0040162522005327

DOI: 10.1016/j.techfore.2022.122011

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