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Twitter-based traffic delay detection based on topic propagation analysis using railway network topology

  • Yuanyuan WangEmail author
  • Panote Siriaraya
  • Yukiko Kawai
  • Toyokazu Akiyama
Original Article
  • 11 Downloads

Abstract

Twitter has become one of the most popular social media platforms, evidently stirred by a very popular trend of event detection with many applications, including delay detection and traffic congestion on the public transport network. In this paper, we propose a Twitter-based railway delay detection method based on topic propagation analysis of geo-tagged tweets between railway stations. In particular, we aim to discover delay events and to predict train delays due to traffic accidents by analyzing topic propagation using railway network topology of real space. To realize this, first, we construct the topology of the railway network (the physical space) as a graph in which nodes are railway stations and edges are represented as routes between them. Then, we extract the topology of the social network that is mapped on the railway network, based on topic propagation analysis of accident delays between stations and by analyzing geo-tagged tweets of each station with a neural network. This allows us to observe the influence of delays on railway stations even if there are a few tweets on them and to predict stations affected by delays with the tweets which contain indirect topics about delays such as “crowded!” and “raining!”. Overall, this paper proposes the method which enables us to analyze the topic propagation of geo-tagged tweets in order to predict accident delays by considering the railway topology of real space. In addition, we also evaluate the performance of the proposed method on datasets derived from Twitter with the actual delay information from 488 stations of 62 routes in Tokyo area in Japan.

Keywords

Railway network topology Delay detection Topic propagation analysis Twitter Location-based tweets 

Notes

Funding information

This work was partially supported by SCOPE of the Ministry of Internal Affairs and Communications of Japan (#171507010), JSPS KAKENHI Grant Numbers 16H01722, 17K12686, 15K00162, and 17H01822.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Graduate School of Sciences and Technology for InnovationYamaguchi UniversityUbeJapan
  2. 2.Faculty of Information Science and EngineeringKyoto Sangyo UniversityKyotoJapan
  3. 3.Cybermedia CenterOsaka UniversityIbarakiJapan

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