Traffic pattern detection using topic modeling for speed cameras based on big data abstraction

Iman Gholampour, Hamid Mirzahossein, Yi Chang Chiu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The importance of traffic pattern prediction for traffic management systems has significantly increased in recent years. This paper presents a novel method to find unusual traffic patterns by using topic modeling. We have employed topic models to provide an abstraction of speed camera data from Tehran, the capital of Iran. In this methodology, topic modeling is applied to days of weeks and months in a year and extracts weekly and monthly traffic patterns. Analysis of the abstract descriptions and their adaptation to actual urban traffic patterns prove the effectiveness of the proposed method. The model training convergence is also practically verified. Based on our experiments, our method achieves an accuracy of 99% in detecting abnormal conditions, which indicates the fitness of the topic modeling abstraction. Such a powerful abstraction capability can be exploited as a method for data comparison and search procedures.

Original languageEnglish (US)
Pages (from-to)339-346
Number of pages8
JournalTransportation Letters
Issue number4
StatePublished - 2022


  • Traffic patterns
  • anomaly detection
  • big data
  • topic Modeling

ASJC Scopus subject areas

  • Transportation


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