交通运输系统工程与信息

交通运输系统工程与信息 ›› 2023, Vol. 23 ›› Issue (4): 194-202.DOI: 10.16097/j.cnki.1009-6744.2023.04.020

• 系统工程理论与方法 • 上一篇    下一篇

建成环境对轨道交通客流的时空异质性影响分析

许心越*1，孔庆雪1，李建民1，刘军1，孙琦2   

  1. 1. 北京交通大学，轨道交通控制与安全国家重点实验室，北京 100044；2. 北京市轨道交通指挥中心，北京 100101
  • 收稿日期:2023-03-17 修回日期:2023-04-23 接受日期:2023-04-25 出版日期:2023-08-25 发布日期:2023-08-22
  • 作者简介:许心越(1983- )，男，河南信阳人，教授，博士
  • 基金资助:
    中央高校基本科研业务费专项资金(2022JBZY022)； 北京市自然科学基金(9212014)

Analysis of Spatio-temporal Heterogeneity Impact of Built Environment on Rail Transit Passenger Flow

XU Xin-yue*1, KONG Qing-xue1, LI Jian-min1, LIU Jun1, SUN Qi2   

  1. 1. State Key Laboratory of Rail Traffic Control & Safety, Beijing Jiaotong University, Beijing 100044, China; 2. Beijing Rail Transit Command Center, Beijing 100101, China
  • Received:2023-03-17 Revised:2023-04-23 Accepted:2023-04-25 Online:2023-08-25 Published:2023-08-22
  • Supported by:
    The Fundamental Research Funds for the Central Universities (2022JBZY022)； Natural Science Foundation of Beijing, China (9212014)

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摘要： 研究各类建成环境特征对客流的影响，对城市轨道交通网络规划和运营客流控制具有重要意义。本文考虑人口经济特征、车站特征、外部交通特征与土地利用特征这4类建成环境对客流的影响，提出一种融合时空地理加权回归(GTWR)和随机森林(RF)的时空地理加权随机森林模型(GTWR-RF)，以捕捉建成环境特征对客流影响的时空异质性与非线性。首先，利用多源数据对各建成环境的统计指标进行细化和完善，采用GTWR模型计算建成环境对客流的影响系数，捕捉并分析建成环境对客流影响的时空异质性。其次，将影响系数输入RF模型中进行训练，捕捉并分析建成环境对客流的非线性影响，实现客流预测并确定建成环境特征对客流预测影响的相对重要度。针对北京的案例研究表明：GTWR-RF模型能够同时捕捉建成环境特征对客流影响的时空异质性与非线性，在所有建成环境特征中，工作人口数量对客流预测影响最显著，其次为公交接驳量；与普通最小二乘法、RF、梯度提升回归树、极限梯度提升树和 GTWR 模型相比，GTWR-RF 模型具有更好的预测性能，在早高峰客流预测中决定系数较其他方法分别提升了5.7%，6.3%，0.5%，10.1%和7.3%。

关键词: 城市交通, 客流时空异质性, 建成环境, 随机森林, 时空地理加权回归

Abstract: It is of great significance to study the influence of various built environment characteristics on passenger flow for urban rail transit network planning and operational passenger flow control. This paper considers the influence of four types of built environment characteristics on rail transit passenger flow, including population economic characteristics, station characteristics, external traffic characteristics and land use characteristics. A hybrid model (GTWR-RF) is proposed, which combines the geographically and temporally weighted regression (GTWR) and random forest (RF). The model is used to capture the spatio-temporal heterogeneity and nonlinearity of the effects of built environment characteristics on passenger flow. First, the statistical indicators of built environment are refined and improved by collecting multi-source data. The GTWR was used to calculate the influence coefficient of built environment on rail transit passenger flow, and to analyze the spatio-temporal heterogeneity of the influence of built environment on passenger flow. Then, the influence coefficient is input into the RF model for training, to analyze the nonlinearity of the influence of the built environment on passenger flow. Using the GTWR-RF model, the study completed the passenger flow prediction and determined the mean relative importance of the built environment characteristics on passenger flow prediction. A case study in Beijing shows that the GTWR-RF model can describe both the spatio-temporal heterogeneity and nonlinearity of the impact of built environment characteristics on passenger flow. Of all the built environment features, the number of working population has the most significant influence on the forecast of passenger flow, followed by the number of bus connections. In the morning peak passenger flow forecast, the determination coefficient of GTWR-RF model is increased by 5.7% compared to the OLS method, increased by 6.3% compared to the RF method, increased by 0.5% compared to the GBRT method, increased by 10.1% compared to the XGBoost method, increased by 7.3% compared to the GTWR method.

Key words: urban traffic, spatio-temporal heterogeneity of passenger flow, built environment, random forest, geographically and temporally weighted regression

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