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


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.


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


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.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
    World Urbanization Prospects (2014) The 2014 revision population database, vol ST/ESA/SE.A/352. United NationsGoogle Scholar
  9. 9.
    Ardon S, Bagchi A, Mahanti A, Ruhela A, Seth A, Tripathy RM, Triukose S (2013) Spatio-temporal and events based analysis of topic popularity in twitter. In: Proceedings of the 22nd ACM international conference on information & knowledge management, CIKM ’13, pp 219–228.
  10. 10.
    Auxilia R, Gandhi M (2016) Earthquake reporting system development by tweet analysis with approach earthquake alarm systems. European Journal of Applied Sciences 8(3):176–180. Google Scholar
  11. 11.
    Carvalho J, Marques M, Costeira JP (2017) Understanding people flow in transportation hubs. IEEE Trans Intell Transp Syst 19(10):1–10Google Scholar
  12. 12.
    Daly EM, Lecue F, Bicer V (2013) Westland row why so slow?: Fusing social media and linked data sources for understanding real-time traffic conditions. In: Proceedings of the 2013 international conference on intelligent user interfaces, IUI ’13, pp 203–212.
  13. 13.
    D’Andrea E, Ducange P, Lazzerini B, Marcelloni F (2015) Real-time detection of traffic from twitter stream analysis. IEEE Trans Intell Transp Syst 16(4):2269–2283. CrossRefGoogle Scholar
  14. 14.
    Dong G, Yang W, Zhu F, Wang W (2017) Discovering burst patterns of burst topic in twitter. Comput Electr Eng 58(C):551–559. CrossRefGoogle Scholar
  15. 15.
    Eleta I, Golbeck J (2014) Multilingual use of twitter: social networks at the language frontier. Comput Hum Behav 41:424–432CrossRefGoogle Scholar
  16. 16.
    Endarnoto SK, Pradipta S, Nugroho AS, Purnama J (2011) Traffic condition information extraction & visualization from social media twitter for android mobile application. In: Proceedings of the international conference on electronics engineering and informatics, ICEEI ’11, pp 1–4.
  17. 17.
    Goonetilleke O, Sellis T, Zhang X, Sathe S (2014) Twitter analytics: a big data management perspective. ACM SIGKDD Explorations Newsletter 16(1):11–20. CrossRefGoogle Scholar
  18. 18.
    Günnemann N, Pfeffer J (2015) Finding non-redundant multi-word events on twitter. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, ASONAM ’15, pp 520–525.
  19. 19.
    Gutierrez C, Figueiras P, Oliveira P, Costa R, Jardim-Goncalves R (2015) Twitter mining for traffic events detection. In: IEEE science and information conference 2015, SAI 2015.
  20. 20.
    Itoh M, Yoshinaga N, Toyoda M (2016) Spatio-temporal event visualization from a geo-parsed microblog stream. In: Companion publication of the 21st international conference on intelligent user interfaces, IUI ’16 Companion, pp 58–61.
  21. 21.
    Kabalan B, Leurent F, Christoforou Z, Dubroca-Voisin M (2017) Framework for centralized and dynamic pedestrian management in railway stations. Transportation Research Procedia 27:712–719. CrossRefGoogle Scholar
  22. 22.
    Kalloubi F, Nfaoui EH, El Beqqali O (2017) Harnessing semantic features for large-scale content-based hashtag recommendations on microblogging platforms. International Journal on Semantic Web & Information Systems 13(1):48–67. CrossRefGoogle Scholar
  23. 23.
    Lee R, Sumiya K (2010) Measuring geographical regularities of crowd behaviors for twitter-based geo-social event detection. In: Proceedings of the 2nd ACM SIGSPATIAL international workshop on location based social networks, LBSN ’10. ACM, New York, pp 1–10.
  24. 24.
    Lee R, Wakamiya S, Sumiya K (2011) Discovery of unusual regional social activities using geo-tagged microblogs. World Wide Web 14(4):321–349. CrossRefGoogle Scholar
  25. 25.
    Liu M, Fu K, Lu CT, Chen G, Wang H (2014) A search and summary application for traffic events detection based on twitter data. In: Proceedings of the 22nd ACM SIGSPATIAL international conference on advances in geographic information systems, SIGSPATIAL ’14, pp 549–552.
  26. 26.
    Mallela D, Ahlers D, Pera MS (2017) Mining twitter features for event summarization and rating. In: Proceedings of the international conference on web intelligence, WI ’17, pp 615–622.
  27. 27.
    Morioka M, Kuramochi K, Mishina Y, Akiyama T, Taniguchi N (2015) City management platform using big data from people and traffic flows. Hitachi Review 64(1):53Google Scholar
  28. 28.
    Nugroho R, Zhao W, Yang J, Paris C, Nepal S (2017) Using time-sensitive interactions to improve topic derivation in twitter. World Wide Web 20(1):61–87. CrossRefGoogle Scholar
  29. 29.
    Ozkurt C, Camci F (2009) Automatic traffic density estimation and vehicle classification for traffic surveillance systems using neural networks. Mathematical and Computational Application 14(3):187–196. CrossRefGoogle Scholar
  30. 30.
    Pla F, Hurtado LF (2016) Language identification of multilingual posts from twitter: a case study. Knowl Inf Syst 51(3):1–25. Google Scholar
  31. 31.
    Raghavi KC, Chinnakotla MK, Shrivastava M (2015) “answer ka type kya he?”: Learning to classify questions in code-mixed language. In: Proceedings of the 24th international conference on World Wide Web, WWW ’15 companion. ACM, New York, pp 853–858.
  32. 32.
    Ritter A, Mausam, Etzioni O, Clark S (2012) Open domain event extraction from twitter. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’12, pp 1104–1112.
  33. 33.
    Sakaki T, Okazaki M, Matsuo Y (2013) Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Trans Knowl Data Eng 25(4):919–931. CrossRefGoogle Scholar
  34. 34.
    Stilo G, Velardi P (2014) Time makes sense: event discovery in twitter using temporal similarity. In: Proceeidngs of the 2014 IEEE/WIC/ACM international joint conferences on Web Intelligence (WI) and intelligent agent technologies (IAT) - Volume 02, WI-IAT ’14, pp 186–193.
  35. 35.
    Sureesha B, Priyadarshini V (2016) Monitoring and analysis of dynamic traffic analyzer using twitter. IEEE Trans Intell Transp Syst 7(4):136–139Google Scholar
  36. 36.
    Wakamiya S, Lee R, Sumiya K (2011) Crowd-powered tv viewing rates: measuring relevancy between tweets and tv programs. In: International conference on database systems for advanced applications. Springer, pp 390–401Google Scholar
  37. 37.
    Wakamiya S, Lee R, Sumiya K (2011) Towards better tv viewing rates: Exploiting crowd’s media life logs over twitter for tv rating. In: Proceedings of the 5th international conference on ubiquitous information management and communication, ICUIMC ’11. ACM, New York, pp 39:1–39:10.
  38. 38.
    Wang S, Zhang X, Cao J, He L, Stenneth L, Yu PS, Li Z, Huang Z (2017) Computing urban traffic congestions by incorporating sparse gps probe data and social media data. ACM Trans Inf Syst (TOIS) 35 (4):40:1–40:30. Google Scholar
  39. 39.
    Wang Y, Yasui G, Hosokawa Y, Kawai Y, Akiyama T, Sumiya K (2014) Location-based microblog viewing system synchronized with web pages. In: 2014 IEEE 33rd international symposium on reliable distributed systems workshops (SRDSW). IEEE, pp 70–75.
  40. 40.
    Wang Y, Yasui G, Kawai Y, Akiyama T, Sumiya K, Ishikawa Y (2016) Dynamic mapping of dense geo-tweets and web pages based on spatio-temporal analysis. In: Proceedings of the 31st annual ACM symposium on applied computing, SAC ’16, pp 1170–1173.
  41. 41.
    Yuan Y, Lint HV, Wageningen-Kessels FV, Hoogendoorn S (2014) Network-wide traffic state estimation using loop detector and floating car data. J Intell Transp Syst Technol Plann Oper 18(1):41–50. CrossRefGoogle Scholar
  42. 42.
    Zhao F, Zhu Y, Jin H, Yang LT (2016) A personalized hashtag recommendation approach using lda-based topic model in microblog environment, vol 65, pp 196–206.
  43. 43.
    Zheng Y (2015) Methodologies for cross-domain data fusion: an overview. IEEE Transactions on Big Data 1 (1):16–34. CrossRefGoogle Scholar

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

Personalised recommendations