Electric vehicle forecasts: a review of models and methods including diffusion and substitution effects
Electric vehicle forecasts: a review of models and methods including diffusion and substitution effects
Governments worldwide are investing in innovative transport technologies to foster their development and widespread adoptions. Since accurate predictions are essential for evaluating public policies, great efforts have been devoted to forecast the potential demand and adoption times of these innovations. However, this proves to be challenging, and it often fails to deliver accurate predictions. Learning a lesson to guide future work is critical but difficult because forecast figures depend on modelling methods and assumptions, and exhibit a great variability in methodologies, data and contexts. This paper provides a critical review of the models and methods employed in the literature to forecast the demand for electric vehicles (EVs), with a focus on the methods for incorporating choice behaviour into diffusion modelling. The review complements and extends previous works in three ways: (1) it focuses specifically on the ways in which fuel type choice has been incorporated into diffusion models or vice-versa; (2) it includes a discussion on forecast accuracy, contrasting the predictions with the actual figures available and estimating an average root mean square error and (3) it compares models and methods in terms of their strengths and limitations, and their implications in forecasting accuracy. In doing that, it also contributes discussing the literature published between 2019 and 2021. The analysis shows that EV demand estimation requires solving the non-trivial issue of jointly modelling the factors that induce diffusion in a social network and the instrumental and psychological elements that might favour household adoption considering the available alternatives. Mixed models that integrate disaggregate micro-simulation tools to capture social interaction and discrete choice models for individual behaviour appear as an interesting approach, but like almost all methods analysed failed to deliver satisfactory results or accurate predictions even when using sophisticated modelling techniques. Further improvement in various components is still needed, in particular in the input data, which regardless of the method used, is key to the accuracy of any forecasting exercise.
agent-based models, choice and diffusion models, electric vehicles, Innovation diffusion, system dynamics, willingness to consider
1118-1143
Domarchi, Cristian
12770dd9-ec99-4d57-acfc-4ca745b63f07
Cherchi, Elisabetta
0d564532-eaaf-459d-a2eb-7cdc76737f5b
2023
Domarchi, Cristian
12770dd9-ec99-4d57-acfc-4ca745b63f07
Cherchi, Elisabetta
0d564532-eaaf-459d-a2eb-7cdc76737f5b
Domarchi, Cristian and Cherchi, Elisabetta
(2023)
Electric vehicle forecasts: a review of models and methods including diffusion and substitution effects.
Transport Reviews, 43 (6), .
(doi:10.1080/01441647.2023.2195687).
Abstract
Governments worldwide are investing in innovative transport technologies to foster their development and widespread adoptions. Since accurate predictions are essential for evaluating public policies, great efforts have been devoted to forecast the potential demand and adoption times of these innovations. However, this proves to be challenging, and it often fails to deliver accurate predictions. Learning a lesson to guide future work is critical but difficult because forecast figures depend on modelling methods and assumptions, and exhibit a great variability in methodologies, data and contexts. This paper provides a critical review of the models and methods employed in the literature to forecast the demand for electric vehicles (EVs), with a focus on the methods for incorporating choice behaviour into diffusion modelling. The review complements and extends previous works in three ways: (1) it focuses specifically on the ways in which fuel type choice has been incorporated into diffusion models or vice-versa; (2) it includes a discussion on forecast accuracy, contrasting the predictions with the actual figures available and estimating an average root mean square error and (3) it compares models and methods in terms of their strengths and limitations, and their implications in forecasting accuracy. In doing that, it also contributes discussing the literature published between 2019 and 2021. The analysis shows that EV demand estimation requires solving the non-trivial issue of jointly modelling the factors that induce diffusion in a social network and the instrumental and psychological elements that might favour household adoption considering the available alternatives. Mixed models that integrate disaggregate micro-simulation tools to capture social interaction and discrete choice models for individual behaviour appear as an interesting approach, but like almost all methods analysed failed to deliver satisfactory results or accurate predictions even when using sophisticated modelling techniques. Further improvement in various components is still needed, in particular in the input data, which regardless of the method used, is key to the accuracy of any forecasting exercise.
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Electric vehicle forecasts a review of models and methods including diffusion and substitution effects
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More information
Accepted/In Press date: 17 March 2023
e-pub ahead of print date: 2 April 2023
Published date: 2023
Additional Information:
Funding Information: this work was supported by The Leverhulme Trust [Doctoral Scholarship in Behaviour Informatics, grant number DS-2017-015] and the multimodal study of behaviour, and by the Newcastle University [Overseas Scholarship]. We would like to thank the editors and reviewers for their valuable comments and suggestions, which helped us to improve the paper. All remaining errors are our responsibility.
Keywords:
agent-based models, choice and diffusion models, electric vehicles, Innovation diffusion, system dynamics, willingness to consider
Identifiers
Local EPrints ID: 487103
URI: http://eprints.soton.ac.uk/id/eprint/487103
ISSN: 0144-1647
PURE UUID: 5b4c6a34-4ad3-4b0c-8de3-3ed1af37aaba
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Date deposited: 13 Feb 2024 17:33
Last modified: 11 May 2024 02:13
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Author:
Cristian Domarchi
Author:
Elisabetta Cherchi
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