【摘要】 从活动的视角研究无人驾驶带来的通勤行为变化.将车内边际活动效用和车内时间利用效率纳入考虑，构建无人驾驶汽车通勤者基于活动的效用函数.对常数与线性边际活动效用情形下的出发与停车模式、最优动态瓶颈收费，以及模型性质进行推导，并与基于出行的模型结果进行比较.结果表明常数边际活动效用情形时，早到出发率是车内边际活动效用和车内时间利用效率的增函数，晚到出发率则反之；线性边际活动效用情形下，早到人数与车内时间损耗程度无关，与上班时间正相关.实施最优瓶颈收费后发现，基于出行的模型会高估总收费收入的上界，低估总收费收入的下界.从实现系统净效用最大化的角度来说，应结合停车社会成本函数和具体情境调整停车密度设置.更多还原

【Abstract】 This paper studies the variation of commute behaviors caused by unmanned vehicles from the perspective of activities. Considering the in-vehicle utility and the utilization efficiency of in-vehicle time, it formulates the activity-based utility function of commuters riding unmanned vehicles. Then it derives the departure and parking patterns, optimal dynamic bottleneck charge, and the model properties with constant and linear marginal-activity utility, respectively.These are compared with the corresponding results in the trip-based model.The results show that, with the constant marginal-activity utility, the early departure rate is an increasing function of in-vehicle activity utility and the utilization efficiency of the in-vehicle time, but the late departure rate reverses it. Meanwhile, with the linear marginal-activity utility, the number of early arriving commuters is not affected by the in-vehicle time loss. Still, it is positively related to the working start time. After implementing the optimal bottleneck toll, the trip-based model overestimates the upper bound of total toll revenue and underestimates the lower bound of total toll revenue. To maximize the net system utility, the government needs to adjust the parking density based on the parking social cost and the specific situation.更多还原