Characteristics of Urban Rail Transit Passenger Flow in Chongqing

2017 2nd International Conference on Computer Engineering, Information Science and Internet Technology (CII 2017) ISBN: 978-1-60595-504-9

RUILI ZHAO, ZHIZHONG ZHANG, FANG CHENG and HONGZHI TANG

ABSTRACT

Urban rail transit system in Chongqing is now in start-up stage and is catching up with the urbanization process. Based on the data of passenger flow of rail transit in 2015, this paper analyzes the characteristics of passenger flow in Chongqing rail transit, involving commuter identification, commuter station passenger flow statistics, commuter line passenger flow statistics, commuter site OD passenger flow statistics, and commuter line OD passenger flow statistics. According to the statistics of Chongqing commuter passenger flow and the peak time, this paper summarizes the relationship between the commuter bus and the peak period of Chongqing Municipality, and discusses the enlightenment to the planning and construction of Chongqing rail transit.

KEYWORDS Urban rail transit; Passenger flow characteristics; Chongqing.

INTRODUCTION

In the planning and design of urban rail transit projects, there are many important issues to judge and make decisions need to provide data support through passenger flow analysis. With the rapid development of urban rail transit in China, the analysis of passenger flow characteristics has been paid more and more attention. In the early stage of urban rail transit engineering, passenger flow characteristics analysis has been listed as a special study. Over the years, through the study of experts and scholars in China's traffic sector, we have gradually established a complete set of rail traffic passenger flow analysis model and method system, but also need to continue to accumulate and improve.

THE PRESENT SITUATION OF CHONGQING RAIL TRANSIT

By the end of 2016, Chongqing Rail Transit has four operating lines, including 1,2,3,6 line (including the EXPO line, airport line), covering the entire city of Chongqing, which contains 126 stations, eight transfer points, 213 km operating mileage, the highest daily passenger volume is 261.82 million times. The average daily passenger volume is more than 2 million times. As shown in Table 1, we can see the Chongqing city rail transit station map. ______

Ruili Zhao, Zhizhong Zhang, Fang Cheng, Hongzhi Tang, College of Communication and Information Technology, Chongqing University of posts and telecommunications, Chongqing, 400065 China.

452 TABLE 1. TRANSFER STATION INFORMATION TABLE. Site Line A Line B Transfer Form Xiaoshizi Line 1 Line 1 Channel transfer Jiaochangkou Line 1 Line 1 Channel transfer Lianglukou Line 1 Line 1 Cross transfer Daping Line 1 Line 1 Channel transfer Niujiaotuo Line 1 Line 1 Channel transfer Yudong Line 1 Line 1 Station transfer Hongqihegou Line 1 Line 1 Cross transfer Lijia Line 1 Line 1 On the same stage transfer

COMMUTER IDENTIFICATION

Data Preparation (OD Match)

Preprocessing data

Delete this data marked as 22 Grouped by user

Sorted by time N Check next record whether the entry/ exit flag is 21

Check the first record whether N Y the entry/ exit flag is 21

N Delete the former data Check next record whether the marked as 21 entry/ exit flag is 22

Y Match N Check next record whether the Y Check next record whether the N successfully, entry/ exit flag is 22 entry/ exit flag is 22 output data

Delete the former data marked as 21and later two data marked as 22 Figure 1. The flow chart of OD match.

Collect the data of the IC card of the rail transit passengers. The original data is sorted and processed into the preprocessing data. Then use the OD match for the preprocessing data, mainly by time series. Users were sorted by time, combined with the entry and exit identification to determine the user's each pair of OD, the specific implementation process of OD match is shown in Figure 1. First, number the various stations of Chongqing Rail Station, as shown in Figure 2, part data of Chongqing Rail stations. Where NO is the number of the station, CODE means the line number, NAME is the station name, TS is the transfer identifier (0 for

453 the normal station, 1 for the transfer station), and TS_TIME means the average transfer time (Unit: second). Second, describe and pretreat the collected data to meet the needs of our scheme. Chongqing Rail Transit AFC system currently only accept bus and light rail single ticket, therefore, this intelligent traffic card has a market share of 100%, so the data is comprehensive. This paper selects the data from October 19 to 24, 2015, during which there is no holiday and special circumstances, the collected data can more accurately reflect the daily travel situation of Chongqing passengers. The data collected certainly contains some abnormal data, how we treat these data will directly affect the accuracy of the results, therefore, an appropriate treat of exception data is significant. For the exception data processing, the steps are as follows: (1) Remove data which only has the entry records or only outbound records. (2) Inbound and outbound identification is opposite to the time series, and we correct the data to match the last entry time from the records, and delete the data if it cannot be corrected. For example, a user's records are sorted by time as shown in Table 2. Through the program to correct the middle of the two data, after correcting, the data is shown in Table 3. (3) Data with more than once entry record or exit record, match the data by the closest time. (4) In and out the station twice, which means, the record shows a single user get in and out station twice, according to the last pit stop time information of the outbound record to correct the match, the data will be deleted if it cannot be matched. (5) Every day before 0:00 and after the data to be combined after the day (4:00) before the data to match, if still no match is removed. (6) Remove the match of the OD pair in which the entry and exit station are same.

Figure 2. Part data of Chongqing Light Rail transit.

TABLE 2. USER’S CHECK TIME SEQUENCE TABLE. Card number Entry and exit identification Time 00000008888 21 2015/10/20 08:39:24 00000008888 22 2015/10/20 09:22:24 00000008888 22 2015/10/20 10:23:24 00000008888 21 2015/10/20 10:44:24 00000008888 21 2015/10/20 17:33:24 00000008888 22 2015/10/20 18:26:24

454 TABLE 3. CORRECTION TABLE OF USER’S CHECK TIME SERIES. Card number Entry and exit identification Time 00000008888 21 2015/10/20 08:39:24 00000008888 22 2015/10/20 09:22:24 00000008888 21 2015/10/20 10:23:24 00000008888 22 2015/10/20 10:44:24 00000008888 21 2015/10/20 17:33:24 00000008888 22 2015/10/20 18:26:24

Figure 3. OD match results graph.

OD matching, mainly aims at the time series, for each user were sorted by time, combined with the entry and exit identification to determine the user's each pair of OD. Successful matched data has to be output as an intermediate result, the output data is shown in Figure 3, the output line’s structure is as follows: card number; card type; entry station identification; entry time; entry station number; exit station identification; exit time; exit station number; amount; entry gate number; exit gate number; transfer sign; entry and exit time difference (Unit: second).

Time Training

Do the OD time training according to the matching OD data, obtain the average training time from departure place to the destination, the specific operation process is shown in Figure 4. To ensure the effectiveness of time training, the input should contain at least one month data to match the OD result data. The output is OD and the training time: entry station number; exit station number; Time (second). The results of the time training are shown in Figure 5.

Take the median of it Output

Y Get group numbers The number of time samples of by square and take each OD is > 50? the upper bound

Range divide group number, get the class interval OD data(delete data whose time Take the maximum below 120 seconds or longer than value of each OD 9000 seconds),delete staff data Take the median of the group Take the range of each OD pair

Take the minimum Choose the value of each OD Divide into groups group which has by range the most sample Figure 4. OD time training flow chart.

455 Commuter Identification

Enter the data of more than one working day, group by user, and judge the commuting rule of each user in multiple working days: when the user's same inbound and outbound site days reach 50% of the total number of days (configurable), it can be determined that the user is commuting. Input: card number; Card type; Inbound site number; Inbound time; Outbound site number; Time of departure; Amount; Gate number; Exit gate number; Change of sign; After the number of sites; The length of the track path; Transfer times; Path time; After line _ route length; The final choice of a path ((site 1, time 1) (site 2, time 2)... ). The output: card number; Card type; Commuter station number; Station number of the commuter station; Commuting days; the total number of days. The commuter identification results are shown in figure 6.

Figure 5. Time training results.

Figure 6. Commuter identification results.

456 COMMUTER TRAFFIC STATISTICS

Commute Site Passenger Flow Statistics

Statistics output by day: site name varchar; time DATE (day); stop passenger number; outbound passenger number. The results are shown in figure 7.

Commuter Line Passenger Flow

Statistics output by day: site name varchar; time DATE (day); line number varchar; time DATE (days); line passenger number. The results are shown in figure 8.

Commuter Site OD Passenger Flow

Statistics output by day: O varchar (site name); D varchar (site name); time DATE (day); OD traffic number. The results are shown in figure 9.

Figure 7. Commute site passenger flow statistics.

Figure 8. Commuter line passenger flow.

Figure 9. Commute site OD passenger flow statistics. Figure 10. Commuter line OD passenger. flow statistics.

457 Commuter Line OD Passenger Flow

Statistics output by day: O line varchar; D line varchar; time DATE (days); OD passenger number. The results are shown in figure 10.

SUMMARY

Through the commuter identification, commuter passenger flow analysis can be obtained, the normal Monday to Friday, morning and evening peak hour’s passenger flow was significantly higher than other peak time passenger flow, early peak hours in the 7:00-9:00, and late peak at 17:30 - 18:30. In the morning and evening peak passenger flow, commuter flow accounted for a large, therefore, for the traffic management department, need to do the morning and evening peak traffic management, timely to clear the working hours to work groups, to avoid crowded stampede and other dangerous accidents.

ACKNOWLEDGEMENTS

Ruili Zhao Email:[email protected]; this work is partially supported by Innovation Team Building Program at Institutions of Higher Education in Chongqing (KJTD201312).

REFERENCES

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