Urban street bicycle flow analysis based on multi-scale geographically weighted regression model
HUANG Yonghao1, YANG Xinmiao1, YUE Jintao2
1. Department of Civil Engineering, Tsinghua University, Beijing 100084, China; 2. China Urban Construction Design & Research Institute Co., Ltd., Beijing 100120, China
Abstract：Residents are demanding better conditions for urban non-motorized traffic. However, the classical forecasting model for non-motorized traffic has mainly focused on large city areas and existing roads, which cannot fully meet planning demands for the construction or extension of urban bicycle paths. This study used an improved multi-scale geographically weighted regression (MGWR) model to predict the flow of non-motorized traffic. The MGWR model includes the influence of land development and utilization, road network structure, and the nearby public transport on the bicycle flows. The area along the Beijing to Zhangjiakou Railway was used as an example for a case study using bike sharing data in this region. The results show that the MGWR model has better predictive ability with the geographically weighted regression model R2 increased by 0.087 and the multiple linear regression with R2 increased by 0.113. Finally, this paper gives an approach for non-motorized traffic planning and management based on key factors affecting non-motorized traffic with appropriate ranges of the independent variables.
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2022, Vol. 62 
