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.

摘要

世界各国政府正在投资创新运输技术，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，因此人们付出了巨大的努力来预测这些创新的潜在需求和采用时间。然而，事实证明这具有挑战性，并且常常无法提供准确的预测。吸取教训来指导未来的工作至关重要但也很困难，因为预测数据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 (EV) 需求的模型和方法进行了批判性回顾，重点关注将选择行为纳入扩散建模的方法。该综述以三个方式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) 包括对预测准确性的讨论，将预测与可用的实际数据进行对比，并估计平均均方根误差；(3) 比较模型和方法的优点和局限性，以及它们对预测准确性的影响。在此过程中，它还有助于讨论 2019 年至 2021 年期间发表的文献。分析表明，电动汽车需求估计需要解决一个重要问题，即对在社交网络中引起扩散的因素以及考虑到可用替代方案可能有利于家庭采用的工具和心理因素进行联合建模。集成分解微观模拟工具来捕获社会互动和个体行为离散选择模型的混合模型似乎是一种有趣的方法，但就像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。仍需要对各个组成部分进行进一步改进，特别是在输入数据方面，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。集成分解微观模拟工具来捕获社会互动和个体行为离散选择模型的混合模型似乎是一种有趣的方法，但就像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。仍需要对各个组成部分进行进一步改进，特别是在输入数据方面，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。集成分解微观模拟工具来捕获社会互动和个体行为离散选择模型的混合模型似乎是一种有趣的方法，但就像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。仍需要对各个组成部分进行进一步改进，特别是在输入数据方面，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。