将私人出行过度集中到高乘座率的公交系统中,如极具吸引力的共享乘车服务

Rafał Kucharski, Oded Cats
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引用次数: 0

摘要

与出行匹配问题相关的求解空间之大,使得寻找高阶合乘问题变得困难重重。我们引入了超级合乘,以及在城市需求模式中识别超级合乘的方法。超级合乘的乘客步行到共同的上车点,乘坐合乘车辆沿一系列站点行驶,然后在站点下车,再步行前往目的地。虽然超级拼车与传统的公共交通非常相似,但它纯粹是由需求驱动的,其行程(停靠站点、顺序、时间安排)是为所有共同出行的人优化的。在阿姆斯特丹的 2000 次旅行中,该算法生成了 40 次超级拼车,共运送 225 名乘客。单人出行需要 52.5 个车时,而超级拼车的多站搭乘则减少了六倍,仅需 9 个车时。这种效率的提高得益于平均载客量达到 5.8 人(最多 14 人),同时对所有共乘者保持吸引力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Hyper pooling private trips into high occupancy transit like attractive shared rides

Hyper pooling private trips into high occupancy transit like attractive shared rides
The size of the solution space associated with the trip-matching problem has made the search for high-order ride-pooling prohibitive. We introduce hyper-pooled rides along with a method to identify them within urban demand patterns. Travellers of hyper-pooled rides walk to common pick-up points, travel with a shared vehicle along a sequence of stops and are dropped off at stops from which they walk to their destinations. While closely resembling classical mass transit, hyper-pooled rides are purely demand-driven, with itineraries (stop locations, sequences, timings) optimised for all co-travellers. For 2000 trips in Amsterdam the algorithm generated 40 hyper-pooled rides transporting 225 travellers. They would require 52.5 vehicle hours to travel solo, whereas in the hyper-pooled multi-stop rides, it is reduced sixfold to 9 vehicle hours only. This efficiency gain is made possible by achieving an average occupancy of 5.8 (and a maximum of 14) while remaining attractive for all co-travellers.
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