Exploring the Interplay between Shared E-Bikes and Public Transportation: Evidence from Huai’an, Jiangsu

Shared e-bikes are becoming a prominent shared transportation mode in Chinese cities. This study explores their interplay with traditional public transportation using Huai’an, China, as a case study. Analyzing transaction data, we determine whether shared e-bikes substitute for or complement public transport. Spatial and temporal data analysis is employed, alongside Generalized Additive Models (GAMs) to understand the influencing factors. Our findings reveal that 32.67% of shared e-bike trips are alternative, 21.93% are complementary, and 45.40% fall in the intermediate category. These usage patterns display spatial and temporal heterogeneity. Factors such as facility densities, gradients, and the number of public transportation stops have nonlinear and differential effects on different shared e-bike usage types. This research provides valuable insights for operational and planning strategies to enhance the integration of shared e-bikes with public transportation, supporting sustainable urban mobility.

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Publication Details

Published
2024-12-11
DOI
https://doi.org/10.1061/9780784485484.269
Primary Topic
Urban Transport and Accessibility
Type
article
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article

Exploring the Interplay between Shared E-Bikes and Public Transportation: Evidence from Huai’an, Jiangsu

Ke Ma, Min Yang, Yuyuan Yang
Urban Transport and Accessibility
article

Exploring the Interplay between Shared E-Bikes and Public Transportation: Evidence from Huai’an, Jiangsu

Ke Ma, Min Yang, Yuyuan Yang
article en

Abstract

Shared e-bikes are becoming a prominent shared transportation mode in Chinese cities. This study explores their interplay with traditional public transportation using Huai’an, China, as a case study. Analyzing transaction data, we determine whether shared e-bikes substitute for or complement public transport. Spatial and temporal data analysis is employed, alongside Generalized Additive Models (GAMs) to understand the influencing factors. Our findings reveal that 32.67% of shared e-bike trips are alternative, 21.93% are complementary, and 45.40% fall in the intermediate category. These usage patterns display spatial and temporal heterogeneity. Factors such as facility densities, gradients, and the number of public transportation stops have nonlinear and differential effects on different shared e-bike usage types. This research provides valuable insights for operational and planning strategies to enhance the integration of shared e-bikes with public transportation, supporting sustainable urban mobility.

Southeast University (BD)
Sustainable cities and communities
Openalex Percentile: Top 80%
Urban Transport and Accessibility
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