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之间的多元分析


网约车（RH）作为一种新兴的出行服务模式对公共交通（PT）系统的影响已经得到证实。然而，目前的研究仅基于宏观统计和出行时间差异将PT和RH之间的关系视为竞争或互补关系。事实上，这种关系超越了二元关系，以行程时间差作为唯一的分类因素是偏颇的。利用西安市的RH数据构建高斯混合模型（GMM），并利用出行时间、成本和服务质量差异三个指标对RH与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、经济因素和出行目的构建了多项逻辑模型（MNL）。结果表明，RH-PT关系大致可分为四类：竞争性（26.5%）、RH优势性（47.7%）、PT优势性（13.6%）和不相关性（12.2%）。竞争主要发生在地铁站周边，RH优势主要集中在外城区上班时间，PT优势分布最广的是早高峰。 POI密度和公交线路数量与竞争度、RH优势和PT优势正相关。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）模型进行分析。我们发现空间异质性可能源于空间自相关和回归系数分布的空间差异。因此，政策制定者应从多个角度制定转变竞争的政策。