Day-to-day market evaluation of modular autonomous vehicle fleet operations with en-route transfers
Day-to-day market evaluation of modular autonomous vehicle fleet operations with en-route transfers
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DOI：
10.1080/21680566.2020.1809549
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发表时间：
2021-01-01
影响因子：
2.8
通讯作者：
Chow, Joseph Y. J.
中科院分区：
文献类型：
--
作者：
Caros, Nicholas S.;Chow, Joseph Y. J.
本研究扩展了双边日常学习框架，以模拟使用模块化自动驾驶车辆（MAV）能够在途中运送乘客的移动服务的性能。插入启发式算法用于将行程分配给车队，并确定参与途中转移是否有利。运营商充当内生决策者，在每个模拟日之后更新路由算法内的运营商成本和用户成本的相对权重以优化利润。来自阿拉伯联合酋长国的真实的公交乘客量数据被用于三种运营策略的实证研究：门到门服务在城市核心，通勤第一/最后一英里服务和枢纽辐射服务。结果进行了比较，没有途中转移，以量化的优势，途中转移能力为每一个战略。
This study extends the two-sided day-to-day learning framework to simulate the performance of a mobility service using modular autonomous vehicles (MAVs) capable of en-route passenger transfers. An insertion heuristic is used to assign trips to a fleet of vehicles and to determine whether engaging in an en-route transfer is advantageous. The operator acts as an endogenous decision maker, updating the relative weight of the operator cost and user cost within the routing algorithm after each simulation day to optimize profit. Real transit ridership data from the United Arab Emirates are used for an empirical study of three operating strategies: door-to-door service within an urban core, commuter first/last mile service and a hub-and-spoke service. Results are compared with and without en-route transfers to quantify the advantage of the en-route transfer capability for each strategy.