| Building and developing urban rail transit system has become an important means of alleviating traffic congestion and reducing air pollution in many cities.The urban rail transit system has developed rapidly in the past two decades and has become one of the crucial patterns of transportation for urban residents in China.As of the end of 2020,45 cities in China have opened urban rail transit system.In order to adapt to the rapid development of the urban rail transit system and formulate appropriate metro development and operation plans,it is particularly important to explore the factors that affect the metro ridership.Based on the existing research findings,this paper summarizes the factors that affect metro ridership.It makes a visual analysis of the travel characteristics such as the time-space distribution and travel distance of the ridership by processing the Chengdu Metro smart card data.This paper discusses the temporal and spatial differences of the impact of the built environment on the metro ridership by establishing regression models corresponding to the metro ridership and explanatory variables in different periods.The main research contents and findings are as follows:(1)The study area and study object are introduced,and buffer zones are established for each station site.The original data was cleaned and divided into corresponding weekdays,weekends,and holidays by using Em Editor software and Python.Meanwhile,sort out the factors that affect the metro ridership based on the existing research findings.This paper uses POI data to represent built environment factors,and tests for multicollinearity and spatial correlation of these variables.(2)Based on the processed ridership data,the travel characteristics of metro ridership in different periods are explored from four aspects: time distribution,spatial distribution,travel distance,and travel time.The results show that the ridership on weekdays has a significant morning and evening travel peak,while the ridership on weekends and holidays is mainly concentrated during 8:00-19:00.The ridership spatial distribution in the morning peak gradually increases from the city center to the suburbs,while the evening peak is just the opposite.The ridership spatial distribution on weekends and holidays is relatively similar,but some stations near famous scenic spots and transportation hubs have more ridership on holidays.The travel distance and travel time of the morning peak are longer than the evening peak,while the travel distance and travel time of weekends and holidays are almost similar.(3)Due to the metro ridership having significant spatial and temporal non-stationarity,it is established the geographically and temporally weighted regression(GTWR)models corresponding to weekdays,weekends,and holidays to explore the spatiotemporal heterogeneity of the impact of the built environment on metro ridership.The model evaluation indicators show that compared with the ordinary least squares(OLS)and geographically weighted regression(GWR)models,the GTWR model has the best fit,which verifies the superiority of the model in processing data with significant spatiotemporal characteristics.The visual analysis of the results of the GTWR model shows that the effects of explanatory variables on metro ridership in different periods have significant spatial and temporal differences.For example,commercial POI density promotes the growth of metro ridership at any time.In particular,the promotion effect is greater for the morning and afternoon peak hour and stations located in the city center and suburbs(western and southern branches)during weekdays,as well as for the 16:00-20:00 period and the stations located in the city center during weekends and holidays.This paper’s outcome can lay a certain foundation for the research on the characteristics of urban rail transit ridership and its influencing factors,and can provide theoretical support for operating companies to adjust their operational plans. |
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