The Model And Facter Analysis Of Effect On The Urban Taxi Ridership Based On Geographically Weighted Regression

By virtue of its unique advantage,taxi,a vital component of urban transit system,has always occupied a position in passenger transportation market.Reliable estimate of taxi ridership can help traffic management departments and transport service enterprises make reasonable control toward taxi market.On one hand,it can improve rational resources distribution and increase profitability.On the other hand,residents travel choice will be more flexible and free.However,with the cities' multi-level and multi-point development as well as the regional differences,the distribution pattern of taxi ridership varies in space.In this paper,we understand the spatial variation of urban taxi ridership using large scale New York City(NYC)taxi data.The taxi ridership is analyzed by relating it to various spatially explicit socio-demographic and built-environment variables.The Geographically Weighted Regression(GWR)is implemented to model the spatial heterogeneity of the taxi ridership and visualize the spatial distributions of parameter estimations.The main research contents are as follows:1)Conduct relative basic theoretical research according to the characteristics of urban taxi ridership.2)Focus on the Geographically Weighted Regression model structure and estimation method of parameters in GWR.3)Use database and GIS software to filter large data conditions.4)Use processed data to establish the analysis model of influencing factors in taxi ridership based on GWR.5)Visualize the model's spatial distributions of parameter estimations and analyze the influence imposed on the taxi ridership by each factor in specific area.The results suggest that the GWR model outperforms the ordinary least square model in both goodness of model fit and explanatory accuracy.The urban form is revealed to have significant impact on urban taxi ridership and strong spatial variability for parameter estimations is observed.The results provide valuable insights for predicting taxi demand as a function of spatially explicit variables which may have implications on taxi pricing,taxi industry regulation and urban planning.