ABSTRACT
Computing technologies have already established their place in various areas of public transport control in smart cities. While the analysis of signals coming from various sensors is executed at a very high level of sophistication, information expressed by humans in natural language is still not being used in a way that takes advantage of its full potential. Existing research on text mining in public transport monitoring is focused mainly on event detection. In this paper, we present a novel approach to vehicle delay prediction based on text data. The proposed method fuses information coming from standard sources (sensors) with text messages, to construct a regression model, that predicts delays for previously unseen messages describing road conditions. The method has been implemented based on an existing public transport monitoring system in Warsaw, Poland. In the paper, we discuss it briefly. Delay prediction based on information expressed in natural language will not replace standard methods for delay prediction that involve the use of vehicle sensors. However, it offers an attractive alternative to mine for knowledge from sources such as social media.
- P. Anantharam, P. Barnaghi, K. Thirunarayan, and A. Sheth. 2015. Extracting City Traffic Events from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4 (2015), 43:1--43:27. https://doi.org/10.1145/2717317Google ScholarDigital Library
- D. E. Brown. 2016. Text Mining the Contributors to Rail Accidents. IEEE Transactions on Intelligent Transportation Systems 17, 2 (2016), 346--355.Google ScholarDigital Library
- N. C. Chen. 2016. Urban data mining: social media data analysis as a complementary tool for urban design. PhD Thesis. Massachusetts Institute of Technology. https://dspace.mit.edu/handle/1721.1/106414Google Scholar
- F. Corman, A. D'Ariano, A. D. Marra, D. Pacciarelli, and M. Sama. 2017. Integrating train scheduling and delay management in real-time railway traffic control. Transportation Research Part E: Logistics and Transportation Review 105 (2017), 213--239. https://doi.org/10.1016/j.tre.2016.04.007Google ScholarCross Ref
- B. Du, Y. Cui, Y. Fu, R. Zhong, and H. Xiong. 2018. SmartTransfer: Modeling the Spatiotemporal Dynamics of Passenger Transfers for Crowdedness-Aware Route Recommendations. ACM Trans. Intell. Syst. Technol. 9, 6 (2018), 70:1--70:26.Google ScholarDigital Library
- A. Fernandez Vilas, R. P. Diaz Redondo, and M. Ben Khalifa. 2019. Analysis of crowds' movement using Twitter. Computational Intelligence 35, 2 (2019), 448--472. https://doi.org/10.1111/coin.12205Google ScholarCross Ref
- M. S. Gerber and L. Tang. 2013. Automatic Quality Control of Transportation Reports Using Statistical Language Processing. IEEE Transactions on Intelligent Transportation Systems 14, 4 (2013), 1681-=1689.Google ScholarDigital Library
- Y. Goldberg. 2017. Neural Network Methods for Natural Language Processing. Synthesis Lectures on Human Language Technologies 10, 1 (2017), 1--309.Google ScholarCross Ref
- F. Golpayegani, I. Dusparic, and S. Clarke. 2019. Using Social Dependence to Enable Neighbourly Behaviour in Open Multi-Agent Systems. ACM Trans. Intell. Syst. Technol. 10, 3 (2019), 31:1--31:31. https://doi.org/10.1145/3319402Google ScholarDigital Library
- B. Guo, Y. Liang, Z. Yu, M. Li, and X. Zhou. 2016. From Mobile Phone Sensing to Human Geo-Social Behavior Understanding. Computational Intelligence 32, 2 (2016), 240--258. https://doi.org/10.1111/coin.12050Google ScholarDigital Library
- R. Hadfi, S. Tokuda, and T. Ito. 2017. Traffic Simulation in Urban Networks Using Stochastic Cell Transmission Model. Computational Intelligence 33, 4 (2017), 826--842. https://doi.org/10.1111/coin.12115Google ScholarCross Ref
- F. Liu and S. Wang. 2021. Predicting subway incident delays using text analysis based accelerated failure time model. Journal of Transportation Safety & Security 13, 3 (2021), 340--356. https://doi.org/10.1080/19439962.2019.1638474Google ScholarCross Ref
- J. M. Lopez-Guede, B. Fernandez-Gauna, M. Grana, and E. Zulueta. 2015. Training Multiagent Systems by Q-Learning: Approaches and Empirical Results. Computational Intelligence 31, 3 (2015), 498--512. https://doi.org/10.1111/coin.12035Google ScholarDigital Library
- T. Ma, G. Motta, and K. Liu. 2017. Delivering Real-Time Information Services on Public Transit: A Framework. IEEE Transactions on Intelligent Transportation Systems 18, 10 (2017), 2642--2656. https://doi.org/10.1109/TITS.2017.2656387Google ScholarDigital Library
- A. D. Marra and F. Corman. 2020. From Delay to Disruption: Impact of Service Degradation on Public Transport Networks. Transportation Research Record 2674, 10 (2020), 886--897. https://doi.org/10.1177/0361198120940989Google ScholarCross Ref
- A. Nuzzolo and A. Comi. 2016. Advanced public transport and intelligent transport systems: new modelling challenges. Transportmetrica A: Transport Science 12, 8 (2016), 674--699. https://doi.org/10.1080/23249935.2016.1166158Google ScholarCross Ref
- J. D. Gonzalez Paule, Y. Sun, and Y. Moshfeghi. 2019. On fine-grained geolocalisation of tweets and real-time traffic incident detection. Information Processing & Management 56, 3 (2019), 1119--1132. https://doi.org/10.1016/j.ipm.2018.03.011Google ScholarDigital Library
- A. Salas, P. Georgakis, and Y. Petalas. 2017. Incident detection using data from social media. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). 751--755. https://doi.org/10.1109/ITSC.2017.8317967Google ScholarDigital Library
Index Terms
- Text-Based Delay Prediction in a Public Transport Monitoring System
Recommendations
Research on Urban Public Transport Transit System in Yishan Road Station, Shanghai
ICMTMA '11: Proceedings of the 2011 Third International Conference on Measuring Technology and Mechatronics Automation - Volume 03Urban public transport transit system plays an important role in the public transportation system in Shanghai. Based on the interchange station Yishan Road station, where subway line 3, 4 and 9 interchanges, the research on public transportation transit ...
Public Transport Arrival Time Prediction Based on GTFS Data
Machine Learning, Optimization, and Data ScienceAbstractPublic transport (PT) systems are essential to human mobility. PT investments continue to grow, in order to improve PT services. Accurate PT arrival time prediction (PT-ATP) is vital for PT systems delivering an attractive service, since the ...
An Examination of the Public Transport Information Requirements of Users
This paper focuses on the provision of public transport information in Dublin, Ireland. It examines both existing and potential methods of accessing information, with particular focus on the implementation of various intelligent transport systems ...
Comments