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Identifying Time-of-Day Breakpoints Based on Nonintrusive Data Collection Platforms

To ensure the effective operation of traffic signal systems, different signal timings should be designed to accommodate traffic pattern variations. One of the greatest challenges is the identification of appropriate time-of-day (TOD) breakpoints, where different signal timings could be implemented during the time periods between two consecutive breakpoints. This research presents an advanced cluster analysis aimed at identifying TOD breakpoints for coordinated, semiactuated modes when it is necessary for multiple intersection operations to be considered simultaneously. Different from previous studies, this proposed methodology considers the time of traffic occurring as one dimension in clustering and uses continuous traffic data obtained through innovative, nonintrusive data collection techniques, which significantly improve this method's performance. The operability of this proposed method is demonstrated in a case study of a corridor located in Tampa, FL. The traffic simulation results reported in this article reveal that this novel procedure performs better than existing TOD signal timing plans.