Study Found Predictive Tool Improved Signal Maintenance Effort and Increased Arrival-on-Green Rate from 79 to 87 Percent.
US Nationwide Study Explored the Usage of Performance-Based Management of Traffic Signals.
Nationwide, United States
Performance-Based Management of Traffic Signals
Summary Information
Modernizing traffic signal management has become increasingly important as agencies face growing congestion, aging infrastructure, and constrained budgets. This study provided information to help agencies invest in signal performance measures to provide better-informed, data driven decisions. Performance-based management of traffic signals aims to leverage automated data collection systems, allowing transportation agencies to track signal operations cycle by cycle, monitor equipment health, and receive automated alerts. This study specifically focuses on automated traffic signal performance measures (ATSPMs) developed using high-resolution controller data.
METHODOLOGY
The study employed a deployment-based observational methodology to assess and improve traffic signal operations using ATSPMs. The research team interviewed agencies using ATSPMs to identify success factors and approaches, developed use cases, reviewed data sources, and created a guide tailored to agencies with varying capabilities. These ATSPMs collected data through various techniques, including high-resolution signal controller logs, vehicle detectors (such as stop bar, advance, and speed detectors), and Bluetooth-based probe vehicle data. Manual video recordings and visual traffic counts were also used to validate and calibrate sensor accuracy. Evaluation methods involved quantitative comparisons between automated detections and manual observations, a signal coordination performance visualizer, and statistical analyses of deviations from historical averages. These tools monitored system performance metrics such as phase terminations, arrivals on green, pedestrian activity, and equipment status. Automated alerts and dashboards were configured to flag anomalies in real time. The data types analyzed included controller logs, detector activations, pedestrian pushbutton events, speed and travel time data, and communication system logs.
FINDINGS
Performance-based traffic signal management supports faster problem resolution, proactive management through automated alerts, reduced modeling needs, better identification of high-priority areas, progress tracking toward agency goals, and the creation of shareable reports that highlight impacts. Examples of these benefits are shown below:
- ATSPMs enhanced preventative maintenance by providing continuous data that helps technicians identify issues in advance, capturing errors that might be missed during limited field visits.
- ATSPMs helped agencies track equipment performance over time, enabling data-driven prioritization of short-term maintenance and informing smarter replacement cycles. This can reduce reliance on capital-based replacements and support more sustainable, long-term maintenance strategies.
- ATSPMs enable Utah Department of Transportation (UDOT) Traffic Operations Center (TOC) staff to proactively identify and resolve signal issues through daily check, reducing public complaints and allowing quicker, more informed responses when service requests are received.
- In Utah, a predictive analysis tool that identifies recommended offset adjustments and calculates optimized signal timing between intersections helped increase the percentage of vehicles hitting green lights (i.e., arrival-on-green rate) along a corridor from79 percent to 87 percent.
- In Oregon, after the implementation of an adaptive advanced traffic signal system that adjusted cycle lengths, splits, and offsets, most vehicles arrived on green instead of red during the peak AM hours.
