SentiHawkes: a sentiment-aware Hawkes point process to model service quality of public transport using Twitter data

Responsive management of public transport nodes relies on constant monitoring of service quality. Social media content provides a unique opportunity to detect and monitor events impacting service quality in these nodes, as well as predicting future occurrences of such events. However, the confined geographic area of transport nodes exacerbates the sparsity of available feeds, raising two major challenges: limited observations—leading to biased models—and the asynchronous nature of observations—impeding the detection of causal patterns. Thus, this paper proposes a framework based on a multivariate Hawkes point process and sentiment analysis. The multivariate Hawkes point process allows effective modelling of events without making them discrete, hence it is less affected by data sparsity compared to time series models while enabling the prediction of how certain events can trigger future events. Besides, the extracted sentiments from social media feeds provide additional knowledge about passengers’ perception and thus, are used in the authors' approach to strengthening the model. Experiments on a real-world dataset demonstrate the effectiveness of the model in identifying causal relations over the public transport nodes. They also show the efficacy of the proposed solution in predicting events over the limited context compared to state-of-the-art approaches.

• Record URL:
  https://doi.org/10.1007/s12469-022-00310-7
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1866749X
• Supplemental Notes:
  • © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
• Authors:
  • Rahimi, Mohammad Masoud
  • 0000-0002-8847-2779
  • Naghizade, Elham
  • Stevenson, Mark
  • Winter, Stephan
• Publication Date: 2023-6

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 343-376
• Serial:
  • Public Transport
  • Volume: 15
  • Issue Number: 2
  • Publisher: Springer Publishing
  • ISSN: 1866-749X
  • EISSN: 1613-7159
  • Serial URL: http://link.springer.com/journal/12469

Subject/Index Terms

• TRT Terms: Attitudes; Network nodes; Public transit; Quality of service; Social media; Transit riders
• Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation; Safety and Human Factors;

Filing Info

• Accession Number: 01886615
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 28 2023 4:57PM