Anomaly Detection of Passenger OD on Nanjing Metro Based on Smart Card Big Data

Abstract

Urban metro alleviates traffic pressure and also faces safety management problems. The metro AFC (Automatic Fare Collection System) records the OD (Origin-Destination) data of passengers' daily trips. Many researches often neglect the pretreatment of data cleaning based on smart card data. Anomaly OD records also reflect the safety problems. How to use OD to identify anomalous data and passengers' anomalous behavior is a research hotspot of metro big data. OD data of Nanjing metro were analyzed, and standard data cleaning processes were proposed including inbound records until the day before yesterday, inbound records of next days, negative records and overtime records. Then, using the data after cleaning, we analyze long-time records, short-time records, inbound and outbound records between the same stations, the swiping card records of more times, and carry out analysis. One day is chosen as an example to illustrate the analysis process, and then the OD records of several days are compared to summarize the classification of OD anomalies. Through analysis, OD anomalies can be classified into two categories: system anomalies and passenger behavior anomalies. System anomalies can be eliminated by upgrading. Abnormal passenger behavior reflects some potential safety problems. This research can effectively identify the abnormal behavior of passengers by tracking and comparing the appearing frequency of passenger cards. OD anomaly classification can be further refined, so that it has more practical value, can improve the level of metro safety management.

Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

“…Wei Y analyzed the temporal and spatial change rule of passenger flow based on the data of the Nanjing metro smart card [48]. Wei Y used smart card data to propose the data filtering process and exception recognition, and classified and explained exceptions [49]. Yu J used the field data of the Nanjing metro stations to establish an improved social force model and simulate the efficiency of passengers under different organizational modes [50].…”

“…Direction and stations of Nanjing metro lines 15,14,13,12,11,10,9,8,7,6,5,41,42,43,44,45,46,47,48,49,50,. 51, 52, 53, 54, 55] …”

Research on the Design of and Preference for Collection Modes of Reusable Takeaway Containers to Promote Sustainable Consumption

“…Previous studies have used questionnaires, field surveys, and interview surveys. Some have preferred to use mailing questionnaires to identify target respondents, but due to the low return rate (usually less than 40%), the results are always poor [ 47 , 48 , 49 ]. Some researchers choose to do their surveys online, which is more effective.…”

Exploring Equity in Public Transportation Planning Using Smart Card Data

“…The use of anonymous big data, e.g., PT Smart Card Fare Collection (PT-SCFC) data, has begun to figure more predominantly in the analysis of travel behavior and mobility patterns [ 12 , 13 , 14 ]. However, the misrepresentation and mis-modelling that characterizes the above in the form of using such large data can be found in a number of recently published studies [ 15 , 16 , 17 , 18 , 19 ].…”

“…PT smart card data benefits scholars and practitioners in understanding urban dynamics and human activities [ 12 , 33 ]. For instance, smart card data can be used to estimate the Origin-Destination (OD) of PT users, long-term network planning, demand forecasting, operational purposes like timetable and schedule adjustments as well as PT funding and investment decisions [ 13 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. PT smart card data includes information on boarding numbers, their times, and locations.…”