Willingness to pay for electric vehicle charging: A comparative study of machine learning and ordered logit models

IF 1.9

Asian Transport Studies Pub Date : 2026-01-01 Epub Date: 2026-03-01 DOI:10.1016/j.eastsj.2026.100179

Mahmoud Reza Nateghi, Amir Reza Mamdoohi

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Abstract

This study analyzes factors influencing users’ willingness to pay (WTP) for electric vehicle (EV) charging in Tehran, Iran, where cheap fossil fuels and limited infrastructure pose key challenges. Data were collected from a stated preference survey of 366 residents in summer 2024. An ordered logit model (OLM) was applied to identify influential variables, and machine learning models (Random Forest, XGBoost, Support Vector Machine) were tested for predictive accuracy. Results show that WTP is highest at commercial centers and workplaces or educational institutions, about 60% higher than cost parity with fossil fuels. Longer charging times reduce WTP, with each additional minute lowering the chance of choosing higher payment options by 0.4%. Environmental awareness and the presence of amenities increase WTP. Machine learning models outperform OLM, with Random Forest reaching 75.73% accuracy compared to 59.02%. Expanding charging facilities in workplaces and educational institutions could maximize WTP, improve accessibility, and support investment recovery.

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为电动汽车充电付费的意愿：机器学习和有序logit模型的比较研究

本研究分析了影响伊朗德黑兰电动汽车（EV）充电用户支付意愿的因素，在德黑兰，廉价的化石燃料和有限的基础设施构成了关键挑战。数据收集自2024年夏季对366名居民的既定偏好调查。应用有序logit模型（OLM）识别影响变量，并测试机器学习模型（Random Forest, XGBoost, Support Vector machine）的预测准确性。结果表明，商业中心、工作场所或教育机构的WTP最高，比化石燃料的成本平价高出约60%。更长的收费时间会降低WTP，每增加一分钟，选择更高付费选项的几率就会降低0.4%。环保意识和设施的存在增加了WTP。机器学习模型的表现优于OLM， Random Forest的准确率达到75.73%，而OLM的准确率为59.02%。在工作场所和教育机构扩大充电设施可以最大限度地提高WTP，改善可达性，并支持投资回收。

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来源期刊

Asian Transport Studies

CiteScore

2.10

自引率

0.00%

发文量