A Performance visualization and fine-tuning tool for arterial traffic signal systems

Abstract

Maintaining an efficient traffic signal operation is a challenging task for many traffic management agencies. Due to the intensive labor cost required, most of the traffic signals in the US are retimed once every 2-5 years. However, it has been shown in the past that traffic delay increases 3-5% per year simply because the timing plans are not kept up to date. For many resource-constrained agencies, it would be desirable to reduce the signal re-timing costs by automating all or portion of the manual process. The research makes one-step forward towards this direction. In this research, we developed a performance monitoring and visualization tool for arterial traffic signal systems, aiming at reducing the labor cost for signal retiming, and helping to identify signal parameter adjustment opportunities. Specifically, an automated data collection unit (DCU) was developed to collect high-resolution event-based data from signal controller cabinets. Using the high-resolution data, two parameter fine-tuning algorithms were proposed, one for offset and another for green splits. To fine-tune signal offsets, a practical procedure to construct the time space diagram (TS-Diagram) to visualize the progression quality on arterials was proposed. The TS-Diagram was calibrated and validated using the field data collected from the DCUs and the probe vehicle runs. Reasonable agreements between the field observations and the generated TS-Diagrams were found. A field experiment was then carried out, to illustrate how decisions of changes could be made by intuitively evaluating the TS-Diagram. For green splits, an adjusted measure of effectiveness (MOE), the utilized green time (UGT), was proposed for performance evaluation. The information was further tabulated in the form of ring-and-barrier diagram to facilitate evaluation. Field examples were also illustrated to demonstrate implementation potentials for green split evaluation and fine-tuning.