Nonlinear model for impact of built environment on curb parking spaces occupancy
To finely grasp the curb parking demand pattern at a micro-spatial scale,quantile regression models for two typical scenarios,namely workdays and weekends were proposed,to explore the nonlinear effects of land utilization,curb parking spaces,traffic factors,and socioeconomic and population on curb parking space occupancy.The results show that quantile regression is superior to linear regression in capturing the complex relationship between variables.And the coefficients and significance levels of explanatory variables change as the quantile changes.At the same time,there is an obvious heterogeneity between workdays and weekends.For example,the number of bus stations around the parking sites is closely related to the low and high occupancies,but this variable has no statistical significance under the medium occupancy.The catering services POIs do not significantly influence curb parking space occupancy on workdays.
engineering of communication and transportation systemcurb parkingoccupancyquantile regressionnon-linear impact
万方数据
维普
