Investigating the non-linear influence of the built environment on passengers’ travel distance within metro and bus networks using smart card data

Yang Liu, Donglin He, Jiayou Lei, Mingwei He, Zhuangbin Shi
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Abstract

Understanding the travel behavior of transit passengers and its influencing factors is crucial for promoting transit use and alleviating urban traffic congestion. However, limited studies have examined the determinants of spatial expansion in multimodal public transportation and overlooked the nonlinear influence between variables. To address these gaps, this study employs the travel distance indicator to portray the spatial expansion of transit passengers. Using smart card data collected from Beijing, China, we propose a comprehensive trip chain extraction method within the metro and bus network, considering transfer behaviors. From the extracted trip chain data, we calculate travel distances and observe significant variations across different transit networks: an average travel distance of 8.09 km in the bus network, 14.93 km in the metro network, and 23.10 km in the integrated network. Further, we explore the non-linear relationship between transit travel distance and the built environment by employing a Gradient Boosting Regression Tree (GBRT) model. The finding reveals that the built environment exerts the most significant influence on travel distance (46.80 %), particularly regarding the distance to the nearest metro station and the central business district (CBD). Additionally, all variables exhibit non-linear effects on travel distance, with many exhibiting relevance only within specific ranges. For instance, there is a noticeable decline in travel distance when the bus stop density falls within the range of 15 units/km² and the bus coverage rate within a range of 0.8. Beyond these threshold values, the decline in travel distance becomes gradual. These findings emphasize the significance of considering non-linear relationships and threshold effects in transit and urban planning. Finally, this study provides practicable recommendations regarding non-linearities for the government that could be beneficial in promoting transit usage.
利用智能卡数据研究了地铁和公交网络内建筑环境对乘客出行距离的非线性影响
了解公交乘客的出行行为及其影响因素,对于促进公交使用、缓解城市交通拥堵具有重要意义。然而,有限的研究考察了多式联运公共交通空间扩展的决定因素,忽视了变量之间的非线性影响。为了解决这些差距,本研究采用出行距离指标来描绘过境旅客的空间扩张。基于北京的智能卡数据,在考虑换乘行为的基础上,提出了一种综合的地铁和公交网络出行链提取方法。从提取的出行链数据中,我们计算了出行距离,并观察到不同交通网络之间的显著差异:公交网络的平均出行距离为8.09 km,地铁网络的平均出行距离为14.93 km,综合网络的平均出行距离为23.10 km。此外,我们采用梯度增强回归树(GBRT)模型探讨了交通出行距离与建成环境之间的非线性关系。研究结果表明,建筑环境对出行距离的影响最为显著(46.80%),尤其是到最近的地铁站和中央商务区(CBD)的距离。此外,所有变量对旅行距离都表现出非线性影响,其中许多变量仅在特定范围内表现出相关性。例如,当公交站点密度在15个单位/km²范围内,公交覆盖率在0.8范围内时,出行距离明显下降。超过这些阈值,行进距离的下降是逐渐的。这些发现强调了在交通和城市规划中考虑非线性关系和阈值效应的重要性。最后,本研究为政府提供了有关非线性的可行建议,有助于促进公共交通的使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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