Spatio-temporal heterogeneous effects of built environment on urban motorized travel
To enhance the precise management of urban motor vehicle traffic,this study examines the impacts of demand management and regional spatial configuration on the spatiotemporal characteris-tics of vehicle travel.A model was developed based on 7D built environment factors to analyze how these elements influence the spatiotemporal distribution of motor vehicle movements.This study fo-cuses on Shanghai and divides its urban areas into 225 traffic zones.The dynamic operating data of 500,000 motor vehicles in Shanghai were collected to analyze the spatiotemporal characteristics of travel.Based on the built environment's 5D elements,rail transit data are incorporated to calculate station and line density indicators,which assess public transportation accessibility.To reflect dynamic changes in traffic demand and account for spatial heterogeneity,demand management factors and re-gional spatial shape are introduced,forming the 7D elements.Parking lot density and the spatial shape index of traffic zones are used as evaluation metrics.Motor vehicle travel volume is the depen-dent variable,and the 7D elements of the built environment are the independent variables.Correlation and multicollinearity analyses are conducted.The impacts of the built environment on urban motor ve-hicle travel were modeled using ordinary least squares(OLS),geographically weighted regression(GWR),and spatiotemporal geographically weighted regression(GTWR)models.Spatial visualiza-tion analyses of the regression coefficients for weekdays and non-working days were conducted to ex-plore the spatiotemporal heterogeneity of these impacts.These results indicate that the GTWR model achieves a better fit than do the OLS and GWR models.In most areas,the population density,public transport route density,station density,distance to the city CBD,parking lot density,and spatial shape index of traffic zones inhibit urban motor vehicle travel,whereas the land use mix entropy index and road network density promote urban motor vehicle travel.The optimization of urban motor vehicle travel patterns provides a robust foundation for refined traffic management in diverse urban regions.
urban trafficspatiotemporal heterogeneityspatiotemporal geographically weighted re-gression(GTWR)modelurban motor vehicle travelbuilt environment
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