A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application
A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application
复制标题
具有动态网络加载和动态乘车共享应用程序的共享自动驾驶车辆建模通用框架
DOI：
10.1016/j.compenvurbsys.2017.04.006
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Tianxin Li
中科院分区：
文献类型：
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作者：
Michael W. Levin;Kara Kockelman;S. Boyles;Tianxin Li
共享自动驾驶汽车（SAV）可以为旅行者提供低成本服务，并可能取代对个人车辆的需求。以前的研究发现，每个SAV可以服务多个旅客，但许多使用不切实际的拥塞模型，网络，和/或旅行需求。本文的目的是为未来的研究提供一种方法，使用现实的流量模型，以获得更准确的预测SAV的好处。本文提出了一个基于事件的框架，实现SAV行为在现有的交通仿真模型。我们证明了这个框架中的细胞传输模型为基础的动态网络负载模拟器。我们还研究了一个启发式的方法，动态共乘。我们比较了奥斯汀市中心城市网络上的个人车辆和SAV场景。在没有动态共乘的情况下，SAV进行的额外空重定位行程增加了拥堵和旅行时间。然而，动态共乘导致旅行时间与个人车辆相当，因为共乘减少了车辆需求。总体而言，结果表明，使用现实的交通流模型极大地影响了SAV将如何影响交通拥堵和出行模式的预测。未来的工作应该使用一个框架，如本文中的一个集成SAVs与建立交通流模拟器。
Shared autonomous vehicles (SAVs) could provide low-cost service to travelers and possibly replace the need for personal vehicles. Previous studies found that each SAV could service multiple travelers, but many used unrealistic congestion models, networks, and/or travel demands. The purpose of this paper is to provide a method for future research to use realistic flow models to obtain more accurate predictions about SAV benefits. This paper presents an event-based framework for implementing SAV behavior in existing traffic simulation models. We demonstrate this framework in a cell transmission model-based dynamic network loading simulator. We also study a heuristic approach for dynamic ride-sharing. We compared personal vehicles and SAV scenarios on the downtown Austin city network. Without dynamic ride-sharing, the additional empty repositioning trips made by SAVs increased congestion and travel times. However, dynamic ride-sharing resulted in travel times comparable to those of personal vehicles because ride-sharing reduced vehicular demand. Overall, the results show that using realistic traffic flow models greatly affects the predictions of how SAVs will affect traffic congestion and travel patterns. Future work should use a framework such as the one in this paper to integrate SAVs with established traffic flow simulators.