@article{ref1,
title="Optimal purchase subsidy design for human-driven electric vehicles and autonomous electric vehicles",
journal="Transportation research part C: emerging technologies",
year="2020",
author="Meng, Qiang and Wang, Hua and Chen, Shukai",
volume="116",
number="",
pages="e102641-e102641",
abstract="This paper studies the purchase subsidy design problem for human-driven electric vehicles (HDEVs) and autonomous electric vehicles (AEVs). The proposed range- and mode-specific purchase subsidy aims to maximize the social benefits from vehicle electrification and automation. In this study, we first classify electric vehicles (EVs) into several classes based on electric driving ranges. Each EV class contains two driving modes, i.e., human driving and automated driving. We provide a simplified model to estimate the greenhouse gas (GHG) emission and the inconvenience costs of vehicle charging. The nested logit model is used to characterize users' vehicle choice behaviors. A mixed integer nonlinear programming (MINLP) model is formulated for the purchase subsidy design problem. A customized branch-and-bound (B&B) method is developed to seek a globally optimal solution to the formulated MINLP model. The numerical examples show that the developed solution method can effectively solve the proposed problem in a reasonable time. The local search strategy embedded in the customized B&B method helps reduce 7% computation time on average. Some managerial insights obtained from the numerical experiments are discussed, which can help the government agency to achieve a reasonable budget allocation between HDEVs and AEVs with different electric driving ranges.<p /> <p>Language: en</p>",
language="en",
issn="0968-090X",
doi="10.1016/j.trc.2020.102641",
url="http://dx.doi.org/10.1016/j.trc.2020.102641"
}