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A Route Choice Model for Capturing Driver Preferences When Driving Electric and Conventional Vehicles

机译:驱动电气和传统车辆时捕获驱动器偏好的路径选择模型

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摘要

Battery Electric Vehicles (BEVs) play an important role in the needed transition away from fossil fuels and Internal Combustion Engine Vehicles (ICEVs). Although transport planning models and routing problem solutions exist for BEVs, the assumption that BEV drivers search for the shortest path while constraining energy consumption does not have any empirical basis. This study presents a study of actual route choice behavior of drivers from 107 Danish households participating in a large-scale experiment with BEVs and at the same time driving their ICEVs. GPS traces from 8968 BEV and 6678 ICEV routes were map matched to a detailed road network to construct observed routes, and a route choice model was specified and estimated to capture behavioral differences related to the vehicle type. The results reveal that drivers had a higher sensitivity to travel time and trip length when driving BEVs, and to route directness after receiving the BEV, regardless of vehicle type. The results suggest the need to revise the assumptions of transport planning models and routing problems for BEVs in order not to fail to predict what drivers will do by ignoring differences and similarities related to vehicle type.
机译:电池电动车(BEV)在远离化石燃料和内燃机车辆(ICEV)的所需过渡中起重要作用。虽然BEV存在运输计划模型和路由问题解决方案,但是BEV驱动程序搜索最短路径的假设,同时约束能量消耗没有任何经验基础。本研究提出了从参加大规模实验的107家丹麦家庭的司机的实际路线选​​择行为研究,同时驾驶其ICEV。来自8968 BEV和6678 ICEV路线的GPS迹线与详细的道路网络匹配以构建观察到的路线,并指定了路由选择模型,估计与车辆类型相关的行为差异。结果表明,在驾驶BEV时,驾驶员对旅行时间和行程长度具有更高的灵敏度,并且在收到BEV后,无论车辆类型如何,都要在接收到BEV后进行直接。结果表明,需要修改运输计划模型的假设和BEV的路由问题,以便不可预测通过忽略与车辆类型相关的差异和相似性来做什么司机。

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