The spatially varying effects of built environment characteristics on the integrated usage of dockless bike-sharing and public transport

Nonlinear and threshold effects of traffic condition and built environment on dockless bike sharing at street level

Although ridesourcing has served as an emerging feeder mode to the metro system to solve the first/last-mile issue, research on metro-integrated ridesourcing usage is rather limited. This paper applies a gradient boosting decision tree (GBDT) method to investigate the nonlinear relationship between the built environment and metro-originated and metro-destinated usage, using ridesourcing trip record data. The results show that built environment factors (i.e., density, diversity, and destination accessibility) have significant nonlinear and threshold effects on the integrated usage, which differentiate between weekdays and weekends. Different patterns are also observed between metro-originated and metro-destinated usage. Employment density has a more significantly positive effect on weekday metro-originated usage than metro-destinated usage. The distance to metro stations does exist an effective range. These findings could help not only transportation network companies optimize ridesourcing services but also transportation planners formulate tailor-made land use interventions to facilitate intermodal mobility. 

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

Concordance between Regional Functions Identified from Bike-sharing and Land-use near Metro Stations

E-bike, characterized as a low-carbon and health-beneficial active travel mode, is gradually becoming popular in China. Although built environment factors are considered to be key parameters that can facilitate or hinder active transportation, such as cycling or walking, few studies have explored the impact of built environment on e-bikes. To fill this gap, this study was the first to explore the relationship between e-bike usage and built environment factors based on population level travel survey in central Jinan, China. Both macro and micro levels of built environment were measured using multi-source data. We employed ordinary least squares (OLS) and geographically weighted regression (GWR) models to explore the aggregation patterns of e-bike trips. Besides, the local Moran's I was employed to classify the aggregation patterns of e-bike trips into four types. The results from OLS model showed that eye-level greenery, building floor area, road density and public service POI were positive significantly related to e-bike trips, while open sky index and NDVI had negative association with e-bike trips. The usage of GWR model provided more subtle results, which revealed significant spatial heterogeneity on the impacts of different built environment parameters. Road density and public service POI posed positive effects on e-bike travel while NDVI and open sky index were found mainly pose negative impacts on e-bike travel. Moreover, we found similar coefficient distribution patterns of eye-level greenery, building floor area and distance to bus stop. Therefore, tailored planning interventions and policies can be developed to facilitate e-bike travel and promote individual's health level.

Exploring built environment factors on e-bike travel behavior in urban China: A case study of Jinan

To better understand dockless bike-sharing (DBS) usage and advance the knowledge on shared bicycle service, this study empirically investigated the riding behavior in the time and space dimensions based on multisource datasets. Taking Central Business District (CBD) and Beijing West Railway Station (BWRS) as study areas, this study analyzed and compared the DBS usage based on the traffic grid between the two study areas. Furthermore, the random forest (RF) model was applied to investigate the contribution of influencing factors on origin/ destination and origin–destination pair trip volume. Partial Dependence Plots (PDP) analysis was conducted to explore the nonlinear effects of influencing factors. Results show considerable variation across different scenarios. Variables such as government agencies, restaurants, bus stop distance, and metro distance show nonlinear and threshold effects on DBS usage. The findings offer valuable insights for urban infrastructure development and bike rebalancing strategies, and the formulation of green and sustainable transportation policies. 

Zilin Zhan

Papers

Nonlinear effects of factors on dockless bike-sharing usage considering grid-based spatiotemporal heterogeneity