Data mining tools for the support of traffic signal timing plan development in arterial networks
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Data mining tools for the support of traffic signal timing plan development in arterial networks

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English
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  • Publication/ Report Number:
    UVA-CE-ITS-02-6
  • Resource Type:
    Research Paper
  • NTL Classification:
    NTL-OPERATIONS AND TRAFFIC CONTROLS-OPERATIONS AND TRAFFIC CONTROLS
  • Abstract:
    Intelligent transportation systems (ITS) include large numbers of traffic sensors that collect enormous quantities of data. The data provided by ITS is necessary for advanced forms of control; however, basic forms of control, primarily time-of-day (TOD) which are prevalent in the United States do not directly rely on the data. Thus sensor data is typically unused and discarded in this country. The sensor data is in fact capable of providing abundant amounts of information that can aid in the development of improved TOD signal timing plans by providing historical data for automatic plan development and TOD interval identification. Data mining tools are necessary to extract the information necessary from the data to improve on timing plan development and in turn would allow the timing plan development and monitoring process to be automated rather than the time-consuming, intuition based practice currently implemented. This project describes research investigating the application of data mining tools, including statistical clustering techniques, to aid in the development of traffic signal timing plans. Specifically, a case study was conducted to illustrate that the use of hierarchical cluster analysis can be used to automatically identify temporal interval break points, based on the data, that support the design of a time-of-day (TOD) signal control system. The cluster analysis approach was able to utilize a high-resolution system state definition that takes full advantage of the extensive set of sensors deployed in a traffic signal system. Timing plans were developed based on the clustering results, providing enhanced TOD intervals and peak volumes, which were then tested through simulation and internal cluster validation, which proved that the use of data mining tools for plan development is beneficial. The results of this research indicate that advanced data mining techniques hold high potential to provide automated techniques to assist traffic engineers in signal control system design, development and operations, the entire process of plan development that is currently practiced based on hand-counted volumes and single intersection TOD intervals.
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