ANNUAL CONGRESS 2025

Are All Movements of Shared Bicycles Low-Carbon? A Spatial Heterogeneity Analysis and Optimizing Strategies of Green Movement Rate of Shared Bicycles in Shanghai

Not scheduled

20m

Yildiz Technical University, Istanbul

Poster Track 03 | MOBILITY

Speaker

Mr Yifei Wang (Tongji University)

Description

Shared bicycle has become one of the most popular modes of transportation in China, contributing positively to reducing traffic congestion, improving air quality and promoting physical health, etc. However, the mismatch between supply and demand caused by uneven spatial distribution and different operational strategies has led to scenarios where not all bicycle movements are user-driven. In many cases, redistribution vehicles are employed to relocate shared bicycles, resulting in "passive movements". This redistribution process generates carbon emissions, potentially offsetting the environmental benefits of using shared bicycles and even transforming it into a "high-carbon" transportation mode.

This study focuses on Shanghai, leveraging GPS data of shared bicycles to differentiate between active movements (user-driven riding trajectories) and passive movements (relocations by redistribution vehicles). It examines the spatial heterogeneity of green movement rate (the ratio of active to passive movement) and identifies "high-carbon zones" along with their influencing factors. The study offers optimization strategies for municipal authorities and shared bicycle operators to achieve a balance between ecological sustainability and operational efficiency.

(1) Spatial Distribution Characteristics of Green Movement Rate of Shared Bicycles:
The study analyzes trip data from April to June 2023 (60 days). Each grid cell is defined as the origin (O-point) of shared bicycle trips. A complete trip begins with a bicycle’s continuous trajectory and ends when a redistribution event occurs (indicating spatial displacement). For each grid cell, the total distance of active movements and passive movements is calculated to derive green movement rate.
Using global and local spatial autocorrelation analyses, the study reveals significant spatial clustering in shared bicycle green movement rate across Shanghai. High-efficiency zones are concentrated in central areas such as Jing’an Temple, Nanjing Road, and Suzhou Creek, while low-efficiency zones are found around universities, office districts, and metro stations. These low-efficiency areas exhibit pronounced supply-demand imbalances due to high commuting and campus activity demands.

(2) Influencing Factors of Green Movement Rate of Shared Bicycles:
A geographically weighted regression (GWR) model is employed to investigate the influence of built environment characteristics and regulatory policies on green movement rate. Results indicate that land-use types and public transport accessibility are the most significant factors. Mono-functional areas with strong commuting attributes (e.g., university towns and industrial parks) exhibit frequent passive movements due to supply-demand mismatches.
Notably, the Wujiaochang area, where Tongji University, Fudan University, and numerous internet companies locate, has the highest frequency of redistribution, resulting in the lowest green movement rate. The carbon emissions from redistribution vehicles in this area exceed the carbon reduction benefits of shared bicycles, making Wujiaochang area the largest "high-carbon zone" in Shanghai.
Furthermore, Shanghai’s non-motorized vehicle restrictions significantly impact green movement rate. Mismatches between restricted roads and residents' travel demands lead to detours, increasing shared bicycle usage and parking in bypass areas. Subsequent redistribution activities further reduce efficiency in such zones.

(3) Optimization Strategies for Shared Bicycle Ecology and Efficiency:
Spatial Optimization: Enhancing spatial layout by promoting job-housing balance and reducing the need for redistribution. Strengthening connectivity between shared bicycles and public transportations through additional docking stations and integrated information platforms to improve transfer efficiency.
Policy insurances: Implementing refined management strategies for "high-carbon zones," such as increasing shared bicycle availability during peak hours to optimize resource allocation. Adjusting non-motorized vehicle regulations by introducing tidal bike lanes on restricted roads, balancing traffic efficiency with actual travel demands.

By analyzing the spatial heterogeneity of green movement rate of shared bicycles and its influencing factors, this study identifies spatial distribution patterns, "high-carbon zones", and corresponding optimization strategies. The findings provide valuable insights for municipal authorities to regulate shared bicycle operations and achieve a balance between traffic and operational efficiency.