Exploring The Elastic Effects Of The Built Environment On Ride-Hailing Travel Demand

Understanding the relationship between the built environment and the ride-hailing ridership is crucial to the prediction of the demand for ride-hailing and the formulation of the strategy for upgrading the built environment.However,the existing studies on ride-hailing ignore the scale effect and zone effect of the modifiable area unit problem(MAUP),and show a lack of consideration for the elastic relationship with spatial heterogeneity between built environment variables and ride-hailing ridership.Considering modifiable area unit problem,the urban area within the third ring road of Chengdu city is divided into 15 types of spatial units,including 300m～1500m grids,Thiessen polygons,and traffic zones.The built environment dataset is constructed using the ride-hailing data provided by Didi Chuxing and other related built environment data.The built environment dataset is used as the independent variable,and the density of ride-hailing pick-ups and drop-offs are used as dependent variables.Compare the average fit quality of multi-scale geographically weighted regression(MGWR)models for morning,noon,and evening during weekdays and weekends,and determine the spatial unit with the best fit quality as the traffic analysis zones.Under the division of traffic analysis zones,spatial analysis methods such as spatial econometric modeling and data visualization were used to compare the spatial scale differences and elastic effects of built environment on the demand for ride-hailing services during different time periods.The spatial heterogeneity of the elasticity coefficient of the built environment is also explored.In terms of the spatial scale of the built environment,floor area ratio,parking density,and the density of office and public service POIs show a global scale in all time periods,while the distance to the CBD shows a local scale in all time periods.The spatial scale of other variables varies greatly at different time periods.In terms of the elastic effects of the built environment,the density of commercial POIs has the greatest effect on the pick-up passenger flow of ride-hailing,while the distance to the CBD has a greater effect on the drop-off passenger flow.In terms of spatial heterogeneity of the built environment,the spatial distribution pattern of the elasticity coefficient mainly shows gradient,circular,and multi-core distribution patterns.Among them,the density of commercial POIs and floor area ratio always show a positive circular distribution pattern.The density of office POIs and the distance to the CBD are always negatively correlated with ride-hailing travel demand,and the density of office POIs mainly shows a gradient distribution pattern,while the distance to the CBD mainly shows a circular distribution pattern.The average housing price has both positive and negative effects,and mainly shows a multicore distribution pattern.Comparing the spatial similarity of the elasticity coefficient of the built environment,it is found that land use mix entropy,distance to the nearest subway station,and the density of residential,office,and commercial POIs have greater spatial heterogeneity in their elastic effects on ride-hailing travel demand.Based on the results of the built environment elastic effects,this study examines the current issues and main causes of ride-hailing travel demand from five perspectives:residential facilities,commercial service facilities,urban land use,important transportation hubs,and urban road systems.Combining multiple perspectives such as urban traffic hotspots,ride-hailing operation management,and relevant policies,this study proposes strategies to optimize ride-hailing travel demand,advocating for green travel to reduce traffic congestion and improve ride-hailing efficiency.