Real time capacity assessment and incident detection on signalized arterials with heterogeneous data

Li-Kuo Rau, Purdue University


The continually worsening traffic conditions in U.S. urban areas demand better traffic management systems. This research focuses on automatic incident detection (AID) on signalized arterials with heterogeneous traffic data available. An innovative AID method using congestion patterns to detect incident status and to assess incident-affected capacities is presented in this research. The proposed method has the following advantages: (1) The method is compatible with almost all existing traffic data; (2) Multiple data sources on the same link can be used concurrently; (3) Multiple data sources on different links can be mixed intelligently; (4) No specific traffic data or surveillance devices are needed; (5) No cumbersome calibration or complicated adjustments are required; (6) The operation of traffic signals is considered and used; (7) The detection process is expected to perform in the event of incomplete information; (8) The method can detect incident clearance as well as incident occurrence; (9) The method can provide capacity estimation; and (10) The method is compatible with fuzzy engineering. The proposed AID method is not only developed step by step in this research, but also verified through a proof approach to ensure it is built on fundamental traffic principles. In addition, an experiment including 30 simulated cases is carried out to evaluate the performance of the proposed method. The evaluation shows promising results and good potential to be further improved. Finally, a discussion of using fuzzy logic to improve the proposed method is also provided as a suggestion for future studies.




Tarko, Purdue University.

Subject Area

Civil engineering

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