Multiscale spatiotemporal heterogeneity analysis of bike-sharing system's self-loop phenomenon: Evidence from Shanghai

TL;DR

This paper investigates the self-loop phenomenon in bike-sharing systems in Shanghai, analyzing its spatial and socioeconomic factors at different scales to inform better redistribution strategies.

Contribution

It introduces a multiscale analysis combining spatial autoregressive and double machine learning models to understand self-loop dynamics in bike-sharing.

Findings

Self-loop intensity shows significant spatial lag effects at street scale.

Residential land use positively correlates with self-loop intensity.

Public transit conditions influence self-loop behavior across scales.

Abstract

Bike-sharing is an environmentally friendly shared mobility mode, but its self-loop phenomenon, where bikes are returned to the same station after several time usage, significantly impacts equity in accessing its services. Therefore, this study conducts a multiscale analysis with a spatial autoregressive model and double machine learning framework to assess socioeconomic features and geospatial location's impact on the self-loop phenomenon at metro stations and street scales. The results reveal that bike-sharing self-loop intensity exhibits significant spatial lag effect at street scale and is positively associated with residential land use. Marginal treatment effects of residential land use is higher on streets with middle-aged residents, high fixed employment, and low car ownership. The multimodal public transit condition reveals significant positive marginal treatment effects at both scales. To enhance bike-sharing cooperation, we advocate augmenting bicycle availability in areas with high metro usage and low bus coverage, alongside implementing adaptable redistribution strategies.