论文标题
骑车乘车对过境乘车率的影响：芝加哥案例研究
Ride-hailing Impacts on Transit Ridership: Chicago Case Study
论文作者
论文摘要
现有关于乘车骑行（RH）和运输服务之间关系的文献仅限于缺乏实时空间环境的经验研究。为了填补这一空白，我们采用了一种新型的实时地理空间分析方法。借助伊利诺伊州芝加哥的乘车旅行的来源数据，我们在2019年6月为所有7,949,902次车辆旅行计算了实时交通当量旅行；我们样本的巨大尺寸与现有文献中研究的样本无与伦比。现有的多项式嵌套logit模型用于确定乘车手选择特定O-D对P（Transit | CTA）的乘车手的概率。我们发现31％的乘车旅行是可以更换的，而61％的旅行则无法替换。其余的8％位于缓冲区内。我们使用参数灵敏度分析测量了这种概率的鲁棒性，并进行了两尾t检验。我们的结果表明，在四个灵敏度参数中，概率对过境旅行的总旅行时间最敏感。我们研究的主要贡献是我们的详尽方法和一系列实时时空分析，研究了乘车旅行的替代性进行公共交通。结果和讨论旨在提供来自真实旅行的观点，我们预计本文将证明与录制和发布乘车数据相关的研究好处。
Existing literature on the relationship between ride-hailing (RH) and transit services is limited to empirical studies that lack real-time spatial contexts. To fill this gap, we took a novel real-time geospatial analysis approach. With source data on ride-hailing trips in Chicago, Illinois, we computed real-time transit-equivalent trips for all 7,949,902 ride-hailing trips in June 2019; the sheer size of our sample is incomparable to the samples studied in existing literature. An existing Multinomial Nested Logit Model was used to determine the probability of a ride-hailer selecting a transit alternative to serve the specific O-D pair, P(Transit|CTA). We find that 31% of ride-hailing trips are replaceable, whereas 61% of trips are not replaceable. The remaining 8% lie within a buffer zone. We measured the robustness of this probability using a parametric sensitivity analysis and performed a two-tailed t-test. Our results indicate that of the four sensitivity parameters, the probability was most sensitive to the total travel time of a transit trip. The main contribution of our research is our thorough approach and fine-tuned series of real-time spatiotemporal analyses that investigate the replaceability of ride-hailing trips for public transit. The results and discussion intend to provide perspective derived from real trips and we anticipate that this paper will demonstrate the research benefits associated with the recording and release of ride-hailing data.
