Bike-Sharing Travel Demand Forecasting via Travel Environment-Based Modeling
Source abstract
Abstract
This research aims to address the limited consideration given to non-motorized transport facilities in current studies on shared bike travel demand forecasting. This study is the first to propose a method that applies complete citywide non-motorized facility data to predict bike-sharing demand. This study employs a multiscale geographically weighted regression (MGWR) model to examine the effects of non-motorized transport facility conditions, quantity of intersections, and land use per unit area on riding demand at various spatial scales. The results of comparison experiments reveal that riding demand is substantially affected by non-motorized transport facilities and the quantity of intersections.
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Research context
OpenAlex topics: Urban Transport and Accessibility · Transportation Planning and Optimization · Human Mobility and Location-Based Analysis
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Affiliations
Beijing Key Lab of Urban Intelligent Traffic Control Technology, North China University of Technology, Beijing 100144, China
Funding
Yuxiu Innovation Project of NCUT · Beijing Natural Science Foundation
References (28)
Show source-provided references
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