The release of the National Cooperative Highway Research Program (NCHRP) Synthesis 659: Automated Traffic Signal Performance Measures: Management, Operation, and Maintenance (2026) captures a turning point in how state DOTs manage traffic signals.

The report documents national practices around both traditional high-resolution Automated Traffic Signal Performance Measures (ATSPMs) and crowdsourced Signal Performance Measures (SPM) platforms. 

“It is exciting to see signal performance measures progress from research products into everyday tools for practitioners. What was once experimental is now enabling state and local transportation agencies to generate actionable insights across entire networks.” 
— Chris Day, Iowa State University Author of NCHRP Synthesis 659 

While traditional ATSPMs remain foundational, the synthesis makes clear that crowdsourced solutions are now a meaningful part of the signal performance ecosystem. Platforms like INRIX Signal Analytics emerged early in this space, helping agencies expand performance visibility beyond intersections equipped with high-resolution hardware. 

The Challenge: Scaling Signal Performance  

Traffic signal performance is a cornerstone of modern mobility management. Agencies increasingly want quantitative measures such as delay, split failures, arrivals on green to diagnose issues and evaluate improvements. Traditional ATSPMs help deliver intersection-level insights, but they depend on: 

• Compatible controllers 
• High-resolution detection 
• Reliable communications 
• Skilled staff and data integration infrastructure

For many agencies, achieving full traditional ATSPM coverage statewide has proven difficult. The synthesis documents this challenge across the U.S., highlighting consistent roadblocks such as limited staffing, outdated hardware, and funding constraints that slow comprehensive deployment of high-resolution systems.  

Even when traditional systems exist, they tend to be concentrated on individual corridors. Agencies struggle to expand performance monitoring to corridors and networks, yet those broader views are essential for planning, investment prioritization, and before/after performance evaluation. 

The Solution: Crowdsourced Analytics to Bridge the Gap 

A growing number of agencies have turned to Signal Analytics. Signal Analytics estimates performance from probe and connected vehicle data, leveraging millions of anonymized vehicle trajectories to infer signal behavior such as approach delay and progression.  

Crowdsourced platforms such as Signal Analytics emerged as a practical response to deployment barriers. Signal Analytics requires: 

• No field hardware installation 
• No controller integration 
• No firmware upgrades 

This means agencies can deploy performance monitoring rapidly and at scale, across corridors and jurisdictions that lack high-resolution signal hardware. 

Maryland DOT in Practice  

For Maryland DOT, this simplicity was decisive. The agency migrated toward Signal Analytics because it does not require hardware or integration at intersections. However, Signal Analytics is not used in isolation. 

In practice, Maryland integrates Signal Analytics into a broader operational workflow: 

• ATMS systems are used to remotely monitor intersections and pull signal logs. 
• The CHART website is referenced to check for construction activity or incidents that may explain performance changes. 

This layered use aligns closely with the integration themes described in the synthesis: SPM tools must complement existing operations, not replace them. 

According to Ben Myrick, State Signal Systems Team Leader, at Maryland DOT State Highway Administration, a particularly impactful advancement for MDOT has been the ability to organize signalized intersections into corridors that reflect real-world system groupings. The Maryland State Highway Administration signal operations goals are generally to progress mainline traffic and clear all movements in one phase. Monthly AM, midday, PM and weekend travel times and split failures are tracked for each corridor.  

Systems with high travel times or split failures are targeted for review. By digging deeper into the Signal Analytics data, Maryland can identify specific times and locations for review.  By drilling into Signal Analytics data, engineers can pinpoint specific times and locations requiring attention. Equally important, the platform helps identify where investigation is unnecessary, allowing limited staff resources to be allocated more efficiently. 

Two corridors that have been reviewed with these methods are the US 1 Alt in Bladensburg and MD 108 in Columbia.  

• US 1 Alt in Bladensburg: Somewhat high midday travel times and significantly elevated northbound travel times and split failures during the PM peak, particularly at intersections with low side-street demand. Midday variability was traced to an upstream system operating on longer cycle lengths, which released platoons inconsistently, sometimes aligning with the green band and sometimes not. Pedestrian crossings and buses stopping also disrupted flow. Despite these findings, no timing adjustments were made for midday operations, as there was no significant congestion, and the shorter cycle length provided pedestrian benefits. In the PM, north bound queues from the adjacent system block side streets in this system (explaining the split failures). In response, engineers implemented minor timing changes to the downstream signal and recommended minor improvements.  This shows that timing can’t fix all operations and suggested low-cost improvements. 
• MD 108 in Columbia: There were poor PM travel times northbound and high travel times on the weekends. PM issues were addressed through coordination adjustments, with field observations indicating noticeable improvement. The improvement appeared significant in the field. Weekend performance problems were traced to the Lark Brown Road intersection operating in free mode, likely a temporary field adjustment made in response to heavy shopping center traffic overwhelming the existing timing plan. To address this, MDOT developed a weekend timing plan with a longer cycle which favors traffic to and from the shopping center.  

Maryland is progressively incorporating signal systems into Signal Analytics as corridor-based groupings, enabling system-level performance monitoring rather than isolated intersection analysis. Maryland has also used Signal Analytics extensively for adaptive before/after studies, leveraging its granularity to evaluate timing changes at scale. Compared to other third-party data sources, Signal Analytics has been viewed as both more granular and less expensive, making it practical for corridor-level evaluation. 

What This Means for the Industry  

The synthesis highlights an important shift: signal performance management is no longer experimental. DOTs are moving toward structured workflows that integrate: 

• High-resolution controller data (where available) 
• Probe-based systemwide analytics 
• Detection system upgrades 
• Centralized operational dashboards 

Some agencies currently have very few SPM-equipped intersections, relying primarily on crowdsourced analytics for broader visibility.  

The outcome is not a replacement of one approach by another. It is a hybrid model: 

• Traditional ATSPMs deliver deep, real-time diagnostic power at equipped intersections. 
• Crowdsourced analytics deliver scalable, corridor- and network-level insight. 
• Integrated workflows turn performance measures into operational decisions. 

Together, they form a scalable, flexible signal performance ecosystem that agencies can tailor to their resources and goals. 

The synthesis ultimately shows that the biggest transformation is not technological; it is organizational. Agencies are learning how to fund, staff, and integrate performance systems into daily practice. The challenge was scaling signal performance measurement beyond isolated pilot projects. The solution was combining hardware-based and probe-based analytics. The outcome is a more mature, hybrid, performance-driven signal management ecosystem. 

Read the full synthesis here and learn more about INRIX Signal Analytics.