Spatiotemporal periodical pattern mining in traffic data

Tanvi Jindal, Prasanna Giridhar, Lu An Tang, Jun Li, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The widespread use of road sensors has generated huge amount of traffic data, which can be mined and put to various different uses. Finding frequent trajectories from the road network of a big city helps in summarizing the way the traffic behaves in the city. It can be very useful in city planning and traffic routing mechanisms, and may be used to suggest the best routes given the region, road, time of day, day of week, season, weather, and events etc. Other than the frequent patterns, even the events that are not so frequent, such as those observed when there is heavy snowfall, other extreme weather conditions, long traffic jams, accidents, etc. might actually follow a periodic occurrence, and hence might be useful to mine. This problem of mining the frequent patterns from road traffic data has been addressed in previous works using the context knowledge of the road network of the city. In this paper, we have developed a method to mine spatiotemporal periodic patterns in the traffic data and use these periodic behaviors to summarize the huge road network. The first step is to find periodic patterns from the speed data of individual road sensor stations, and use their periods to represent the station's periodic behavior using probability distribution matrices. Then, we use density-based clustering to cluster the sensors on the road network based on the similarities between their periodic behavior as well as their geographical distance, thus combining similar nodes to form a road network with larger but fewer nodes.

Original languageEnglish (US)
Title of host publication2nd International Workshop on Urban Computing, UrbComp 2013 - Held in Conjunction with KDD 2013
DOIs
StatePublished - Sep 19 2013
Event2nd ACM SIGKDD International Workshop on Urban Computing, UrbComp 2013 - Held in Conjunction with the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 - Chicago, IL, United States
Duration: Aug 11 2013Aug 11 2013

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other2nd ACM SIGKDD International Workshop on Urban Computing, UrbComp 2013 - Held in Conjunction with the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
CountryUnited States
CityChicago, IL
Period8/11/138/11/13

Keywords

  • KL-divergence
  • density-based clustering
  • periodic patterns
  • probability distribution matrices
  • road network
  • spatiotemporal data
  • traffic data

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Jindal, T., Giridhar, P., Tang, L. A., Li, J., & Han, J. (2013). Spatiotemporal periodical pattern mining in traffic data. In 2nd International Workshop on Urban Computing, UrbComp 2013 - Held in Conjunction with KDD 2013 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2505821.2505837