Efficient operation of traffic signals is greatly beneficial to drivers. Because of the intensive labor required, most traffic signals in the United States are retimed once every 3 to 5 years or more, even though signal retiming has a very high benefit–cost ratio. Such a practice may miss opportunities for operational improvements and lead to unnecessary delays. One of the major obstacles to improving the practice is the lack of data collection capability and a convenient performance monitoring tool for signalized arterials. To fill this gap, this paper proposes a performance diagnosis tool for arterial traffic signal systems. The tool aims at identifying necessary parameter changes and assisting agencies in periodically fine-tuning signal timing parameters. A flexible and low-cost data collection unit is developed to equip traffic signal cabinets with event-based data collection capability. Three major parameters of traffic signals (offset, green split, and cycle length) are evaluated in the data center by diagnosis modules that use event-based traffic data. The development of the data collection unit and the diagnosis methodologies are described in detail. The implementation of the tool is illustrated by field data analysis at intersections on Trunk Highway 13 in Burnsville, Minnesota.

Efficient operation of traffic signals is greatly beneficial to drivers. Because of the intensive labor required, most traffic signals in the United States are retimed once every 3 to 5 years or more, even though signal retiming has a very high benefit–cost ratio. Such a practice may miss opportunities for operational improvements and lead to unnecessary delays. One of the major obstacles to improving the practice is the lack of data collection capability and a convenient performance monitoring tool for signalized arterials. To fill this gap, this paper proposes a performance diagnosis tool for arterial traffic signal systems. The tool aims at identifying necessary parameter changes and assisting agencies in periodically fine-tuning signal timing parameters. A flexible and low-cost data collection unit is developed to equip traffic signal cabinets with event-based data collection capability. Three major parameters of traffic signals (offset, green split, and cycle length) are evaluated in the data center by diagnosis modules that use event-based traffic data. The development of the data collection unit and the diagnosis methodologies are described in detail. The implementation of the tool is illustrated by field data analysis at intersections on Trunk Highway 13 in Burnsville, Minnesota.

The time–space (TS) diagram is a popular visualization tool in evaluating progression quality for signalized arterials, and most signal optimization software products (such as Synchro) can generate TS diagrams as part of the optimization output. During the signal retiming process, TS diagrams generated by optimization software need to be validated by field observations, and minor changes will be made to signal control parameters if a discrepancy is observed. The validation process is time-consuming and costly. Through the use of high-resolution event-based traffic data collected from existing traffic signal controllers, a practical procedure for constructing TS diagrams for signalized arterials is proposed. The diagrams can be used as a convenient visualization tool in evaluating the performance of traffic signals and in identifying opportunities for fine-tuning in a timely manner. Reasonable agreement was found between the TS diagram and vehicle trajectory data collected from the field. A field experiment was carried out to illustrate how signal parameter changes could be made by intuitive evaluation of the TS diagram. Recommendations and limitations of the proposed approach are discussed.

Time-of-day operation (i.e., the implementation of different signal settings at different times of the day) is commonly used in traffic signal operation to accommodate time-dependent traffic patterns. In the current practice of signal retiming, traffic engineers rely largely on engineering judgments to determine the proper time-of-day transitions; this approach may lead to inefficient operations. For such a deficiency to be reduced, in this research an easy-to-use approach was proposed to fine-tune time-of-day transitions for signalized arterials. The optimal transition points were determined through the evaluation of total delays on the basis of a Highway Capacity Manual (HCM) formula. To account for the impact of the coordination and actuation of arterial signals, the researchers extended HCM formula with a simple but effective model to estimate the green duration of actuated traffic signals. The proposed approach was demonstrated and validated with a case study that used traffic data collected from the SMART SIGNAL system.

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