Optimal curbside pricing for managing ride-hailing pick-ups and drop-offs
Optimal curbside pricing for managing ride-hailing pick-ups and drop-offs
复制标题
用于管理网约车上下车的最佳路边定价
DOI：
10.1016/j.trc.2022.103960
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Qian, Sean
中科院分区：
文献类型：
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作者：
Liu, Jiachao;Ma, Wei;Qian, Sean
近年来，由于无处不在的新兴技术，网约车出行服务兴起。路边空间作为公共基础设施的一类，除了送货和停车之外，还被私人叫车服务用来接送乘客。这在城市地区变得相当普遍，并导致行驶车道上的网约车、私人和公共交通车辆更加拥堵。各种交通方式的路缘石利用进一步改变了出行者对方式/路线的选择，堵塞了街道并污染了城市环境。然而，缺乏理论和模型来评估区域网络中路边网约车停靠站的影响，以及有效管理网约车上下车以实现系统优化。鉴于此，本文开发了一种双模态网络交通分配模型，考虑了私人驾驶和网约车模式在一般网络中争夺道路和路缘空间的情况。为了模拟有限的路边通行能力对通过交通的影响，利用路边排队模型来量化路边停车对路边和行驶车道的拥堵影响，包括等待时间和队列长度。旅客共同选择出行方式（驾车或叫车）、路边停车地点或停车地点。此外，本研究还探索了调节路边停车数量以提高系统性能的选项，具体方法是对使用路边上下车的网约车行程征收特定地点的停车费。路边定价将影响旅客的出行方式选择和停车地点选择。为了确定最佳路边定价，开发了一种基于敏感性分析的方法，以最小化所有行程中网络的总社会成本。所提出的方法在三个网络上进行了检查。我们发现，最优路边定价可以有效减少路边拥堵和交通系统的总社会成本，使网络中的所有出行受益。
Recent years have witnessed the rise of ride-hailing mobility services thanks to ubiquitous emerging technologies. Curbside spaces, as a category of public infrastructure, are being used by private ride-hailing services to pick up and drop off passengers, in addition to deliveries and parking access. This becomes quite common in urban areas and has led to additional congestion for ride-hailing, private and public transit vehicles on the driving lanes. Curb utilization by various traffic modes further alters travelers’ choices in modes/routes, clogging streets and polluting urban environment. However, there is a lack of theories and models to evaluate the effects of curbside ride-hailing stops in regional networks and to effectively manage ride-hailing pick-ups and drop-offs for system optimum. In view of this, this paper develops a bi-modal network traffic assignment model considering both private driving and ride-hailing modes who are competing for roads and curb spaces in general networks. To model the impact of limited curbside capacity to through traffic, a curbside queuing model is utilized to quantify the effect of congestion on both curbs and driving lanes induced by curbside stops in terms of waiting time and queue lengths. Travelers make joint choices of modes (driving or ride-hailing), curb stopping locations or parking locations. In addition, this study explores the option to regulate the amount of curbside stops to improve system performance, which is done by imposing a location-specific stopping fee on ride-hailing trips for using curbs to pick-up and drop-off. The curb pricing would influence travelers’ modal choices and parking location choices. To determine the optimal curbside pricing, a sensitivity analysis-based method is developed to minimize the total social cost of the network among all trips. The proposed methods are examined on three networks. We find that the optimal curbside pricing could effectively reduce curbside congestion and total social cost of the traffic system, benefiting all trips in the network.