The impact of ride-hailing (RH) as an emerging mode of travel service on public transit (PT) systems has been confirmed. However, the current research only views the relationship between PT and RH as competition or complementation based on macro statistics and travel time differences. In fact, the relationship is beyond binary, and it is partial to take the travel time difference as the only classification factor. We constructed a Gaussian mixture model (GMM) using RH data in Xi’an, and three indicators of travel time, cost, and service quality difference were used to classify the relationship between RH and PT. To clarify the factors influencing the relationship classifications, a Multinomial logistic model (MNL) was constructed with the built environment, economic factors, and travel purpose. The results show that the RH-PT relationship can be generally classified into four classifications: Competition (26.5%), RH superiority (47.7%), PT superiority (13.6%), and Irrelevance (12.2%). Competition occurs mainly around metro stations, RH superiority mainly during working hours in outer urban areas, and PT superiority is most widely distributed in the morning peak. POI density and the number of bus lines are positively correlated with Competition, RH superiority, and PT superiority. In addition, there is significant spatial heterogeneity in the RH-PT relationship, for which we constructed a Geographically weighted regression (GWR) model to analyze it. We find that the spatial heterogeneity may stem from the spatial autocorrelation and the spatial disparities in the distribution of regression coefficients. Therefore, policymakers should formulate policies to transform competition from multiple perspectives.

中文翻译：

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超越二元关系：基于多源数据的网约车和公共交通多变量分析


网约车 （RH） 作为一种新兴的出行服务方式，对公共交通 （PT） 系统的影响已得到证实。然而，目前的研究仅根据宏观统计和旅行时间差异将 PT 和 RH 之间的关系视为竞争或互补。事实上，这种关系超越了二元，将旅行时间差异作为唯一的分类因素是片面的。我们利用习安的 RH 数据构建了高斯混合模型 （GMM），并使用出行时间、成本和服务质量差异三个指标对 RH 和 PT 之间的关系进行分类。为了阐明影响关系分类的因素，根据建筑环境、经济因素和旅行目的构建了一个多项式逻辑模型 （MNL）。结果表明，RH-PT 关系一般可分为四类：竞争 （26.5%）、RH 优胜性 （47.7%）、PT 优胜性 （13.6%） 和无关性 （12.2%）。竞争主要发生在地铁站周围，RH 优势主要发生在郊区的工作时间，PT 优势在早高峰分布最广。POI 密度和公交线路数量与 Competition、RH 优胜性和 PT 优胜率呈正相关。此外，RH-PT 关系存在显着的空间异质性，为此我们构建了地理加权回归 （GWR） 模型对其进行分析。我们发现空间异质性可能源于回归系数分布的空间自相关和空间差异。因此，政策制定者应该制定政策，从多个角度改变竞争。