Nonlinear effects of the built environment on metro-integrated ridesourcing usage

Jin, Tanhua; Cheng, Long; Zhang, Xucai; Jun, Cao; Qian, Xiaodong; Witlox, Frank

doi:10.1016/j.trd.2022.103426

Cited by 26 publications

(15 citation statements)

References 59 publications

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“…Second, an efficient road network and pedestrian-friendly street design are helpful to reduce car dependency. An effective road network can encourage people to use shared mobility services more (e.g., bike-sharing, ridesourcing) based on previous studies (e.g., Cheng, Jin, et al, 2022;Jin, Cheng, Zhang, et al, 2022). High pedestrian-oriented road density can also encourage active travel modes, which in turn, reduce car use.…”

Section: Discussionmentioning

confidence: 99%

“…Unlike exponential function as a traditional statistical method that follows a constrained statistical assumption and is usually pre-defined, machine learning methods, such as the XGBoost model used in this research, are data-driven and are not statistically constrained, which will provide more sophisticated results. Many other researchers have also attempted to uncover the nonlinear built effects on travel patterns using machine learning methods, including driving distance (Ding et al, 2018), metro ridership , usage of shared mobility services (Cheng et al, 2023;Jin, Cheng, Zhang, et al, 2022), and public transit ridership (Chen et al, 2021). Relaxing the assumption of linearity using a machine learning method has several advantages in travel behavior analysis (Cheng et al, 2019;Liu et al, 2021;Xu et al, 2021;Zhang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Nonlinear Relationship Between Car Dependency and the Built Environment

Jun

Jin²,

Shou

et al. 2023

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Car-dominated daily travel has caused many severe and urgent urban problems across the world, and such travel patterns have been found to be related to the built environment. However, few existing studies have uncovered the nonlinear relationship between the built environment and car dependency using a machine learning method, thus failing to provide policymakers with nuanced evidence-based guidance on reducing car dependency. Using data from Puget Sound regional household travel surveys, this study analyzes the complicated relationship between car dependency and the built environment using the gradient boost decision tree method. The results show that people living in high-density areas are less likely to rely on private cars than those living in low-density neighborhoods. Both threshold and nonlinear effects are observed in the relationships between the built environment and car dependency. Increasing road density promotes car usage when the road density is below 6 km/km<sup>2</sup>. However, the positive association between road density and car use is not observed in areas with high road density. Increasing pedestrian-oriented road density decreases the likelihood of using cars as the main mode. Such a negative effect is most effective when the pedestrian-oriented road density is over 14.5 km/km<sup>2</sup>. More diverse land use also discourages people’s car use, probably because those areas are more likely to promote active modes. Destination accessibility has an overall negative effect and a significant threshold effect on car dependency. These findings can help urban planners formulate tailored land-use interventions to reduce car dependency.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating the Nonlinear Relationship Between Car Dependency and the Built Environment

Jun

Jin²,

Shou

et al. 2023

Self Cite

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“…Metro stations near city centre, with higher land use mix, and more recreational and residential land, are more likely to generate more integrated bike-metro use [24,50,51]. Jin et al investigated the nonlinear relationship between the built environment and metro-ridesourcing integration, showing higher integrated usage in the suburban areas [7]. Huang et al revealed the relationship between multimodal transportation services and built environment [52].…”

Section: Taxi-metro Integrated Use and Its Relationship With Builtmentioning

confidence: 99%

“…Seven-day taxi trajectory data (April 5-11, 2021) are collected including taxi ID, pick-up/ drop-of time, pick-up/drop-of geolocations. According to some empirical studies, the features of taxi/ridesourcing-metro integrated use are relatively consistent over fve weekdays [7,15,39].…”

Section: Study Area and Datamentioning

confidence: 99%

“…Connections from the origin to the metro station (access trip) and from the metro station to the destination (egress trip) are required. Te trips for metro access and egress, which are the key components of metro travel and signifcantly afect the popularization of the metro system, can be multimodal, e.g., walking, bike-sharing, bus, ridesourcing, and taxi [5][6][7]. Due to zero emissions, fexibility, and low cost, walking and bicycling are suggested as friendly feeder modes to access/ egress metro system [8].…”

Section: Introductionmentioning

confidence: 99%

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Impacts of the Feeder-Related Built Environment on Taxi-Metro Integrated Use in Lanzhou, China

Chen

Hao

et al. 2023

Journal of Advanced Transportation

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It is very common that taxi is used as a feeder mode to/from a metro station especially in third-tier cities of China. But research on taxi-metro integrated use is rather limited. This paper investigates the relationship between feeder-related built environment and taxi-metro integrated use by using taxi trajectory data in Lanzhou, China. Firstly, regression models are developed to explore the transfer distance of access/egress trips during peak hours. Then, the catchment area is delineated for collecting feeder-related environment variables. Finally, several negative binomial regressions are employed to examine the relationships between feeder-related built environment and taxi-metro integrated use during peak hours on weekdays. The results reveal that (a) people prefer to use taxi as feeder mode for metro access during morning peak hours and for metro egress during evening peak hours; (b) the transfer distance of taxi-metro integrated use is about 3.8 km; (c) higher mixed land use generates more taxi-metro integrated use during evening peak hours. Higher proportion of residential land use attracts more taxi-metro integrated use for metro access during morning peak hours. Those findings will help transport planners to develop tailored land-use interventions to improve transit accessibility and promote the sustainable multimodal travel.

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Do built environment factors have different effects on ridesourcing usage before and after the COVID-19 pandemic?

Jin

Cheng

Wang

et al. 2023

Cities

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Nonlinear effects of the built environment on metro-integrated ridesourcing usage

Cited by 26 publications

References 59 publications

Investigating the Nonlinear Relationship Between Car Dependency and the Built Environment

Investigating the Nonlinear Relationship Between Car Dependency and the Built Environment

Impacts of the Feeder-Related Built Environment on Taxi-Metro Integrated Use in Lanzhou, China

Do built environment factors have different effects on ridesourcing usage before and after the COVID-19 pandemic?

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