Electric vehicle forecasts: a review of models and methods including diffusion and substitution effects

IF 9.5 1区 工程技术 Q1 TRANSPORTATION Transport Reviews Pub Date : 2023-01-01 DOI:10.1080/01441647.2023.2195687
Cristian Domarchi , Elisabetta Cherchi
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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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电动汽车预测:包括扩散和替代效应在内的模型和方法综述
摘要:世界各国政府都在投资创新交通技术,以促进其发展和广泛采用。由于准确的预测对于评估公共政策至关重要,因此我们致力于预测这些创新的潜在需求和采用时间。然而,这被证明是具有挑战性的,而且它往往无法提供准确的预测。吸取教训指导未来的工作至关重要,但很难,因为预测数字取决于建模方法和假设,并且在方法、数据和背景方面表现出很大的可变性。本文对文献中用于预测电动汽车需求的模型和方法进行了批判性回顾,重点是将选择行为纳入扩散模型的方法。该综述在三个方面补充和扩展了以前的工作:(1)它特别关注将燃料类型选择纳入扩散模型的方式,反之亦然;(2) 它包括对预测准确性的讨论,将预测与实际数据进行对比,并估计平均均方根误差。(3)比较了模型和方法的优势和局限性,以及它们在预测准确性方面的意义。在这样做的过程中,它也有助于讨论2019年至2021年间发表的文献。分析表明,电动汽车需求估计需要解决一个不平凡的问题,即联合建模导致社会网络中扩散的因素,以及考虑到可用的替代方案,可能有利于家庭采用的工具和心理因素。将分解微观模拟工具集成在一起以捕捉社会互动的混合模型和个人行为的离散选择模型似乎是一种有趣的方法,但与几乎所有分析的方法一样,即使使用复杂的建模技术,也无法提供令人满意的结果或准确的预测。仍然需要进一步改进各种组成部分,特别是输入数据,无论使用何种方法,输入数据都是任何预测工作准确性的关键。
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来源期刊
Transport Reviews
Transport Reviews TRANSPORTATION-
CiteScore
17.70
自引率
1.00%
发文量
32
期刊介绍: Transport Reviews is an international journal that comprehensively covers all aspects of transportation. It offers authoritative and current research-based reviews on transportation-related topics, catering to a knowledgeable audience while also being accessible to a wide readership. Encouraging submissions from diverse disciplinary perspectives such as economics and engineering, as well as various subject areas like social issues and the environment, Transport Reviews welcomes contributions employing different methodological approaches, including modeling, qualitative methods, or mixed-methods. The reviews typically introduce new methodologies, analyses, innovative viewpoints, and original data, although they are not limited to research-based content.
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