Heuristic Approach for Estimating Arterial Signal Phases and Progression Quality from Vehicle Arrival Data

Abstract

Automated performance-monitoring systems take in intelligent transportation system sensor data in real time, archive them, and analyze them. These systems are needed to help local agencies identify problem areas, develop improvement plans, and perform before and after evaluations on the impacts of traffic management changes. Research performed in the past few years has demonstrated the utility of these systems for local transportation agencies, particularly for evaluating signal progression quality. However, acquiring the critical data items for existing arterial intelligent transportation systems—signal phase event information—is often a practical challenge because the configuration of the existing system of most arterial systems does not record or communicate signal phase events to a central location. As a solution to that problem, this paper documents an approach to estimate signal phase data with in-pavement vehicle sensors, a data source that is generally available from arterial systems. On many arterial systems, these sensors frequently communicate data from the field to a central traffic management center. The goal of this paper was to make recent arterial progression quality research implementable by developing a method to gather signal phase event data in a way that would be practical for most local transportation agencies, given their existing arterial systems. Two proposed methods were tested on a year's worth of data from a 2-mi arterial corridor in Carson, California. Results showed that sensor data from central traffic management centers could be used to develop accurate measurements of signal phase events when coupled with timing plans.