01882003

Component

https://doi.org/10.1177/03611981231169533

To address the problem of climate change emissions from the transport sector, many countries are promoting electric vehicles (EVs). To support such efforts, it is essential to know what influences the choice of an EV over a traditional internal combustion engine vehicle (ICEV). To study this, a discrete choice experiment was developed, and 2,015 valid responses were gathered from Canadian adults with a driver’s license. In place of a more traditional analysis, a machine learning approach, XGBoost, was applied. However, two key issues were addressed with respect to its application. First, a practical question related to how best to split the training and testing data was examined. A new technique based on the Coyote optimization algorithm (COA) is developed that automatically determines the split that leads to the greatest prediction accuracy. The policy-relevant results of the analysis found that an individual’s Climate Change-Stage of Change (CC-SoC) and the price ratio of EVs to ICEVs are the most important direct influences. The interaction effect of the first two (CC-SoC and price ratio) is also influential. However, this leads to the second key issue: interpretability. Although high prediction accuracy (87.1%) was achieved, the black-box nature of the approach limits its policy relevance. As such, this research applied a technique, Accumulated Local Effects (ALE), that can determine the strength and direction of influence of the variable. This research demonstrates how machine learning can be applied to a policy-relevant question and provide information that is useful to policy decision makers.

E.O.D. Waygood https://orcid.org/0000-0002-7848-3191© National Academy of Sciences: Transportation Research Board 2023.

English

0000-0002-7848-3191

0000-0003-4437-2490

0000-0001-8878-7845

pp 704-717

2023-12

Transportation Research Record: Journal of the Transportation Research Board

Web

Figures; Illustrations; References (48) ; Tables

Choice models; Decision making; Electric vehicles; Machine learning; Purchasing

XGBoost (Software)

Canada

Data and Information Technology; Highways; Vehicles and Equipment

TRIS, TRB, ATRI

May 12 2023 3:01PM