Spatial-Temporal Heterogeneity and Built Environment Nonlinearity in Inconsiderate Parking of Dockless Bike-Sharing

Yacan Wang · Jinɡjinɡ Li · Duan Su · Huiyu Zhou

21 citations0/0 files storedTransportation Research Part A Policy and Practice

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Urban Transport and Accessibility Transportation and Mobility Innovations Smart Parking Systems Research

Abstract

Although previous studies have shed light on the travel behaviour of dockless bike-sharing (DBS) users, little research focused on their inconsiderate parking behavior. Unlike the travelling behavior, the choice of parking location is closely linked to the different built environments surrounding the parking locations. Therefore, to improve the efficiency of governance, it is vital to explore the parking patterns and heterogeneous influences of the built environment on inconsiderate parking and formulate targeted measures. This paper measures the coordinates of prohibited parking areas in the field to identify inconsiderate parking. Based on big data from Mobike DBS and data on the built environment, the paper empirically analyzes the heterogeneous spatiotemporal distribution patterns of inconsiderate parking with clustering and decision trees. The influencing factors of inconsiderate parking and their nonlinear effects are further analyzed using random forest and partial dependence plots (PDP). The results show that there is significant spatiotemporal heterogeneity in inconsiderate parking, in which different clusters reflect various characteristics of the built environment. Furthermore, marginal effect analysis finds that influencing factors such as riding distance, catering service places, lifestyle services, sports and leisure places, and hotels and hostels have a strong effect on inconsiderate parking behavior, and show nonlinear effects with optimal allocation intervals. Therefore, targeted strategies should be carried out in terms of dynamic temporal adjustment, precise spatial layout, differential management according to time and zone, and cause-assisted administration. The paper’s results provide important decision-making support for inconsiderate parking.

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References

2015Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto 2018Environmental benefits of bike sharing: A big data-based analysis 2014How land-use and urban form impact bicycle flows: evidence from the bicycle-sharing system (BIXI) in Montreal 2018Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo 2020Built environment effects on the integration of dockless bike-sharing and the metro 2016Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan, China 2016Bikeshare trip generation in New York City 2020A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data

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2024Changes in visitor behaviour across COVID-19 pandemic: Unveiling urban visitation dynamics and non-linear relationships with the built environment using mobile big data 2024How does the built environment affect the usage efficiency of dockless-shared bicycle? An exploration of time-varying nonlinear relationships 2024Examining the impact of station location on dockless bikesharing-metro integration: Evidence from Beijing

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