Promoting the integrated use of bikeshare and metro: A focus on the nonlinearity of built environment effects

Cheng, Long; Jin, Tanhua; Wang, Kailai; Lee, Yong-Sung; Witlox, Frank

doi:10.1016/j.multra.2022.100004

Cited by 56 publications

(10 citation statements)

References 34 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%

“…Our finding indicates that areas with highly mixed land use are less likely to use cars as the main mode. This is probably because diverse land use promotes the use of active modes (e.g., walking, cycling; Cheng, Jin, et al, 2022), which, in turn, will decrease the use of private cars. Such restraint is not observed in areas with relatively lower land use mix.…”

Section: Relative Importance Of Independent Variablesmentioning

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: Relative Importance Of Independent Variablesmentioning

confidence: 99%

Investigating the Nonlinear Relationship Between Car Dependency and the Built Environment

Jun

Jin²,

Shou

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…Anomalous data mainly include abnormal longitude and latitude, abnormal travel distance, and abnormal travel time. The abnormal data for FFBS and public bike are orders with riding distance over 5 km and riding time over 2 h ( Cheng et al, 2022a ). For the metro, the abnormal data are those with the same entry and exit stations and travel times that exceed the full running time of the normal line.…”

Section: Datamentioning

confidence: 99%

Sustainable response strategy for COVID-19: Pandemic zoning with urban multimodal transport data

Wang

Hua

Chen

et al. 2023

Journal of Transport Geography

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“…This algorithm improves the model's predictive performance by adjusting two parameters: the decision-tree number and the number of random variables. Random forest learning is highly accurate, good at managing higher-order relationships between variables, and effective in mining nonlinear relationships [45]. The working process of the random forest method is illustrated in Figure 6.…”

Section: Random Forest Modelmentioning

confidence: 99%

Influence of the Built Environment on Older Adults’ Travel Time: Evidence from the Nanjing Metropolitan Area, China

Sun

Zhu

Han

et al. 2023

Land

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The built environment is among the critical factors in older adults’ travel behavior, and a favorable built environment can encourage them to travel and engage in various activities. Existing studies have mostly focused on exploring the correlation between the built environment and travel behavior, ignoring the heterogeneity between the two at different times of the day. In this study, we conducted structured, face-to-face interviews in the Nanjing (China) metropolitan area to investigate the time consumed per trip by older adults using various travel modes and used the structural equation and random forest models to explore the relationship between the built environment and older adults’ travel time. The results demonstrated that older adults had different perspectives on travel during different time periods. Different environments and the convenience of destinations affected their overall satisfaction during travel. We found a nonlinear relationship between the built environment and travel time. Metropolitan street connectivity initially had a positive effect on travel time until a certain threshold or peak, whereafter a gradual decline ensued. This nonlinear relationship also existed between the proportion of green space and the distance to subway stations. These results can guide the retrofitting and construction of age-friendly metropolitan infrastructure facilities that promote older adults’ mobility.

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Promoting the integrated use of bikeshare and metro: A focus on the nonlinearity of built environment effects

Cited by 56 publications

References 34 publications

Investigating the Nonlinear Relationship Between Car Dependency and the Built Environment

Investigating the Nonlinear Relationship Between Car Dependency and the Built Environment

Sustainable response strategy for COVID-19: Pandemic zoning with urban multimodal transport data

Influence of the Built Environment on Older Adults’ Travel Time: Evidence from the Nanjing Metropolitan Area, China

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