Detection of Anomaly State Caused by Unexpected Accident using Data of Smart Card for Public Transportation

Sakura Yamaki, Shou De Lin, Wataru Kameyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The railway is an indispensable means of transportation for people living in urban areas in Japan. However, unexpected accidents or disasters disturb the train operation. People usually check the operation status of trains on the official websites or Twitter of each railway company. However, it is still unclear whether such information is provided in realtime, when it is updated and which station is severely affected. Therefore, we tackle a real-world application of transportation big data using 8 months' data collected by smart cards for public transportation in Keikyu Line operating in Tokyo and Kanagawa Prefectures. We propose a method to detect the anomaly state by using the number of train users every 10 minutes in major 9 stations in Keikyu Line. In the method, outlier detections by interquartile range, interval estimation and Hotelling's theory are utilized to detect anomaly points. As the results, our proposal detects anomaly state better than the official announcement by Twitter on some points in terms of realtimeness, update frequency and geographic detail.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
EditorsChaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1693-1698
Number of pages6
ISBN (Electronic)9781728108582
DOIs
Publication statusPublished - 2019 Dec
Event2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States
Duration: 2019 Dec 92019 Dec 12

Publication series

NameProceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference2019 IEEE International Conference on Big Data, Big Data 2019
CountryUnited States
CityLos Angeles
Period19/12/919/12/12

Keywords

  • anomaly detection
  • smart card for public transportation
  • unexpected train accident

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

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  • Cite this

    Yamaki, S., Lin, S. D., & Kameyama, W. (2019). Detection of Anomaly State Caused by Unexpected Accident using Data of Smart Card for Public Transportation. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 1693-1698). [9005676] (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9005676