Are travelers substituting between transportation network companies (TNC) and public buses? A case study in Pittsburgh
Are travelers substituting between transportation network companies (TNC) and public buses? A case study in Pittsburgh
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DOI：
10.1007/s11116-020-10081-4
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发表时间：
2020-02-10
期刊：
影响因子：
4.3
通讯作者：
Hendrickson, Chris
中科院分区：
文献类型：
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作者：
Grahn, Rick;Qian, Sean;Hendrickson, Chris
交通网络公司（TNC）提供的移动服务正在以未知的方式影响出行行为，因为TNC的出行数据有限。它们如何与其他交通方式互动，可产生直接的社会影响，促使采取适当的政策干预。本文概述了一种通过数据驱动方法为此类政策提供信息的方法，该方法专门分析了宾夕法尼亚州匹兹堡跨国公司与公交服务之间的互动。Uber激增乘数数据在6个月的时间段内被用来近似TNC的使用情况（即，需求超过供应比率）用于整个城市的十个预定义的兴趣点。利用每个关注点附近的公共汽车登车数据来说明跨国公司的使用情况。来自多个来源的数据（天气，交通速度数据，公共汽车服务水平）用于控制影响公共汽车乘客量的条件。我们发现，在公共汽车登机期间异常高的TNC的使用在四个地点在晚上的时间显着的变化。其余六个地点的巴士乘客人数没有显著变化。我们发现，一个专门的公交车站或附近的大学（或密集的商业区一般）的存在都影响跨国公司和公共交通之间的ad-hoc替代行为。我们还发现，这种行为因地点和时间而异。这一发现对于提高交通网络效率的有针对性的政策具有重要意义。
Transportation network companies (TNC) provide mobility services that are influencing travel behavior in unknown ways due to limited TNC trip-level data. How they interact with other modes of transportation can have direct societal impacts, prompting appropriate policy intervention. This paper outlines a method to inform such policies through a data-driven approach that specifically analyzes the interaction between TNCs and bus services in Pittsburgh, PA. Uber surge multiplier data is used over a 6-month time period to approximate TNC usage (i.e., demand over supply ratio) for ten predefined points of interest throughout the city. Bus boarding data near each point of interest is used to relate TNC usage. Data from multiple sources (weather, traffic speed data, bus levels of service) are used to control for conditions that influence bus ridership. We find significant changes in bus boardings during periods of unusually high TNC usage at four locations during the evening hours. The remaining six locations observe no significant change in bus boardings. We find that the presence of a dedicated bus way transit station or a nearby university (or dense commercial zones in general) both influence ad-hoc substitutional behavior between TNCs and public transit. We also find that this behavior varies by location and time of day. This finding is significant and important for targeted policies that improve transportation network efficiency.