Discovering traffic bottlenecks in an urban network by spatiotemporal data mining on location-based services

Wei Hsun Lee, Shian Shyong Tseng, Jin Lih Shieh, Hsiao Han Chen

Research output: Contribution to journalArticlepeer-review

65 Citations (Scopus)


Discovering traffic bottlenecks and taking action to alleviate congestion to enhance the performance of a traffic network are the most important tasks for the advanced traffic management system in the intelligent transportation system. However, traffic bottlenecks are affected by several factors and vary with spatial and temporal environments, which makes them difficult to define and discover. This paper proposes a three-phase spatiotemporal traffic bottleneck mining (STBM) model, including several spatiotemporal traffic patterns and STBM algorithms that use the raw data of location-based services to discover urban network spatiotemporal traffic bottlenecks. This paper implements an STBM prototype system based on a taxi dispatching system in a Taipei, Taiwan, urban network. The experimental results show that the congestion prediction capability of the proposed heuristic methods (congestion-propagation heuristic) is up to 79.6% during workdays and 72.1% on weekends, which outperforms other methods (e.g., the congestion-converge heuristic, the congestion-drop heuristic, and congested object item), and the discovered spatiotemporal bottlenecks match the travelers' experience.

Original languageEnglish
Article number5766752
Pages (from-to)1047-1056
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number4
Publication statusPublished - 2011 Dec

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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