Research On The Identifying And Influencing Mechanism Of School Commute Behavior Based On IC Card Data Of Metro

In recent years,the number of car trips for parents to take students to and from school has gradually increased,resulting in a series of traffic problems,such as increased congestion during peak hours and increased potential safety hazards for students.Rail transit,as an efficient and large-volume mode of transportation,is the main mode of long-distance travel in cities.In order to guide students to travel by metro and reduce the use of family vehicles,this study is based on the data of IC card of metro to study the group of students commuting by metro.Firstly,according to the information of frequency and time of students' cards,distance between metro stations and school time,a method of recognizing students' commuting behavior based on metro IC card data is proposed by analyzing the law of students' travel.Taking Nanjing as an example,the regular students are identified from metro students' card data for three consecutive years(one month each year).Secondly,based on the 15 consecutive working days' commuting frequency of users,the commuting group is divided into three modes: high frequency mode,medium frequency mode and low frequency mode by clustering algorithm,and the characteristics of underground travel of students under different commuting modes are compared and analyzed.The study found that: Students in low-frequency mode have the longest commuting time;students in high-frequency mode have the most concentrated arrival time distribution;unlike most students in Mediumfrequency and low-frequency mode only take the metro from school,students in high-frequency mode tend to take the metro to and from school;students in low-frequency mode tend to travel more widely,while students in high-frequency mode tend to travel by metro only near schools or residential sites,etc.Furthermore,multiple Logit model(MNL model)and Ordered Logit model(ORL model)are selected to study the influencing factors of students' travel model from the aspects of personal attributes,attributes of travel characteristics,built environment near schools and residential sites.Based on the three-year metro cards swipe data,the impact mechanism model of metro commuting model is constructed respectively.By comparing and testing the two models,it is found that MNL model fits better.Finally,according to the MNL modeling results,the factors that have high sensitivity and stability to the metro commuting mode are selected for quantitative analysis.The research shows that senior students have stronger willingness to choose high-frequency mode;students with longer commuting hours or longer learning-living distance tend to choose low-frequency mode;the built environment near the subway station of their residence has little effect on the mode;and increase of the number of public bicycle stations near the school subway station,the quantity of primary and secondary schools and the shortening of the distance between the school subway station and the conventional bus station will be beneficial for the low-frequency mode students to turn to the medium and high-frequency mode.On this basis,the corresponding policy recommendations are put forward from two aspects of public facilities planning and operation and student travel demand management.