Investigating Urban Residents' Spatial And Temporal Travel Characteristics Based On Metro Smart Card Data

The trip of urban residents affects urban life.The analysis and exploration of residents' spatiotemporal travel characteristics is a hot issue in the study of urban geography and traffic engineering.On one hand,various forms of massive transportation data provide data for comprehensive analysis of spatiotemporal travel characteristics.Urban railway system can reduce environmental footprints and alleviate traffic congestion in mega-cities.Smart card data collected by automated fare collection systems record massive amounts of travel information.Processing and analyzing these data open new opportunities in travel behavior research.This paper aims to study the spatial and temporal travel characteristics of urban residents based on smart card data from Nanjing metro system and weather data.The influence mechanism of weather factors on urban residents' subway travel is focused on in particular.The main results of this paper are as follows:(1)This paper studies basic spatial and temporal travel characteristics of urban residents.It is found that: there are two peak periods(7:00-9:00 a.m.and 17:00-19:00 p.m.)in working days,and there is no obvious peak period on weekends.Passenger flows on Friday are larger than that on the other six days.Passenger flows carried by line 2 are the largest,and its fluctuation is also the most obvious.In the morning and evening peaks of working days,passenger flows of each subway station in central area are larger than that in old town.In morning peaks,and CBD area such as Xinjiekou is the main source of passenger flow attraction.Stations like Maigaoqiao station,Youfangqiao station and Liuzhoudonglu station are the main source of passenger flow generation in morning peaks and main source of passenger flow attraction in evening peaks.The old town shows stronger ability of passenger flow attraction or generation in morning and evening peaks than central area.The old town has stronger ability of passenger flow generation on weekends and central area has stronger ability of passenger flow attraction on working days.Passenger flows generated from Xinjiekou station are always the largest and the largest passenger flows generated from O-D pairs are Maigaoqiao station-Xinjiekou station.Passenger flows between large residential area and commercial center are also large.(2)Four relevant indicators related to shopping and leisure activity choice behavior are adopted to classify commuters,i.e.activity duration,weekly frequency,time preference and the stability of activity location.In results,commuters are divided into three categories through cluster analysis.They are negtive shoppers with unstable activity locations preferred working days,occasional shoppers with stable activity locations preferred weekends and active shoppers with stable activity locations preferred weekends.Among them,it is found that the category who rarely conducts shopping activities with unstable activity locations and preferred activity time are the most,which accounts for 64% of commuters.A Markov model is introduced to predict leisure activity choice behavior for each type of commuters.The results show that the leisure activity choice behavior of commuters who have long activity duration,stable activity location and strong time preference can be predicted with high accuracy.(3)A time-series model,i.e.seasonal autoregressive integrated moving average with explanatory variables(SARIMAX)model is developed to investigate the impact of weather conditions on metro passenger flows.The results show that some weather variables such as rainfall have significant influence on metro passenger flows.Except for some special sites(large residential areas and large transportation hubs),the influence of weather variables on passenger flows reduces gradually from the city center to suburban areas.The effects of weather conditions on regular metro passengers and irregular metro passengers are explicitly compared in this study.Irregular metro passengers are found more vulnerable to adverse weather conditions than regular metro passengers.