中文核心期刊
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中国科技核心期刊
RCCSE中国核心学术期刊
重庆交通大学学报（自然科学版）

重庆交通大学学报（自然科学版） ›› 2026, Vol. 45 ›› Issue (1): 53-62.DOI: 10.3969/j.issn.1674-0696.2026.01.08

• 交通运输+人工智能 • 上一篇    

建成环境对网约车-地铁组合出行的非线性影响分析

潘义勇,王聪伟,宋文超,贾熙来   

  1. (南京林业大学 汽车与交通工程学院，江苏 南京 210037)
  • 收稿日期:2025-03-06 修回日期:2025-06-16 发布日期:2026-01-15
  • 作者简介:潘义勇（1980—），男，安徽安庆人，副教授，博士，主要从事交通运输规划与管理方面的研究。E-mail：uoupanyg@njfu.edu.cn
  • 基金资助:
    国家自然科学基金项目（51508280）

Nonlinear Impact of Built Environment on the Combined Travel Mode of Ride-Hailing and Metro

PAN Yiyong, WANG Congwei, SONG Wenchao, JIA Xilai   

  1. (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, Jiangsu, China)
  • Received:2025-03-06 Revised:2025-06-16 Published:2026-01-15

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摘要： 为探究建成环境对网约车-地铁组合出行需求的非线性影响，首先，基于网约车订单轨迹数据，识别、筛选出地铁站500 m缓冲区内接入、接出的网约车订单数据；其次，建立网约车-地铁组合出行聚集缓冲区，根据“5D”原则构建建成环境变量指标，并将组合出行数据和变量数据对应至网格；最后，利用XGBoost模型分析建成环境对网约车-地铁组合出行的非线性影响，并识别出关键因素。研究结果表明：XGBoost模型的拟合效果整体优于选用的其他模型；到达市中心距离和人口密度是影响网约车-地铁组合出行的关键因素，它们的相对重要性在接入组合出行中超过45%，在接出组合出行中达到40%；建成环境对网约车-地铁不同组合出行模式的影响表现出显著的非线性特征，并展现出相应的阈值效应，到达市中心距离、人口密度和路网密度对组合出行表现出负向影响，商业设施对组合出行呈现出正向影响，金融设施的数量在16个时为关键阈值点，低于该值表现为正向影响，高于该值表现为负向影响，此外，不同时段公交站对组合出行表现出的非线性特征，表明网约车与城市公交之间具有复杂动态关系。

关键词: 交通工程；组合出行；XGBoost模型；非线性关系；建成环境；网约车聚集缓冲区

Abstract: To explore the nonlinear impact of the built environment on the demand for ride-hailing and metro combined travel, firstly, based on the trajectory data of ride-hailing orders, the data of ride-hailing orders that were picked up and dropped off within a 500-meter buffer zone of metro stations were identified and screened out. Secondly, a gathering buffer zone for the ride-hailing and metro combined travel was established. According to the “5D” principle, built environment variable indicators were constructed, and the combined travel data and variable data were mapped to grids. Finally, the XGBoost model was used to analyze the nonlinear impact of the built environment on ride-hailing and metro combined travel and identify the key factors. The research results show that the fitting effect of the XGBoost model is overall superior to that of the other selected models. The distance to the city center and population density are the key factors affecting ride-hailing and metro combined travel, whose relative importance exceeds 45% in the case of pick-up combined travel and reaches 40% in the case of drop-off combined travel. The impact of the built environment on different combined travel modes of ride-hailing and metro shows significant nonlinear characteristics and corresponding threshold effects. The distance to the city center, population density, and road network density have negative impacts on combined travel, while commercial facility has a positive impact. The number of financial facilities is at a critial threshold of 16. Values below this threshold indicate a positive impact, while values above this threshold indicate a negative impact. In addition, the nonlinear characteristics exhibited by bus stops during different time periods for combined travel indicate a complex dynamic relationship between ride-hailing services and urban buses.

Key words: traffic engineering; combined travel; XGBoost model; nonlinear relationship; built environment; ride-hailing vehicle gathering buffer zone

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