Traffic signals are one of the biggest levers available to transportation agencies to manage traffic flows across their regions, but today that lever is broken. Signals across the nation often rate poorly on performance, highlighting the urgent need for improved signal management. Traditional performance measures like ATSPMs and PBSPMs have advanced the state of practice, but still fall short due to data inaccuracies and incompleteness. Enter Flow Labs' Integrated Signal Performance Measures (ISPMs) revolutionize signal management, building upon traditional methods to combine multiple datasets that provide comprehensive and accurate analyses. This white paper delves into the strengths and limitations of current measures and explores how ISPMs offer a groundbreaking solution for enhancing traffic signal performance and efficiency. Discover how this innovative approach can transform our intersections and improve mobility nationwide.

The Challenges of Signal Anaytics

Traffic signals are critical components of our transportation infrastructure, playing a pivotal role in managing traffic flow and ensuring safety at intersections. Despite their importance, signals across the country are frequently rated as performing at a suboptimal level, often receiving a grade of D. This rating reflects widespread challenges in signal management, including issues related to outdated technology, inadequate maintenance, and inefficient signal timing.

Over the years, signal performance measures have significantly advanced the state of signal practice and management. Automated Traffic Signal Performance Measures (ATSPMs) and Probe-Based Signal Performance Measures (PBSPMs) have been at the forefront of this advancement, providing traffic engineers with tools to better understand and manage signal performance. However, these traditional measures have shown limitations, particularly concerning data completeness and accuracy.

Recognizing these challenges, Flow Labs has developed Integrated Signal Performance Measures (ISPMs). ISPMs integrate multiple datasets to generate a more comprehensive view of signal performance, enabling better diagnostics, decision support, and ultimately, improved traffic signal management. This innovative approach addresses the limitations of traditional performance measures and represents a significant step forward in the quest for more efficient and effective traffic signal systems.

Automated Traffic Signal Performance Measures (ATSPMs)

‍A Brief History of ATSPMs

In the early 2000s, the push for data-driven traffic signal management began, with initial systems focusing on basic measures like vehicle counts and signal timing. A turning point came in 2012 when the Federal Highway Administration (FHWA) introduced Automated Traffic Signal Performance Measures (ATSPMs) as part of their Every Day Counts (EDC) initiative promoting innovative technologies in transportation.

Between 2012 and 2016, the Utah Department of Transportation (UDOT) became a leader in this field, developing ATSPM systems that used high-resolution data to improve signal performance in real-time. Their success demonstrated the significant benefits of ATSPMs, leading to widespread adoption by state and local agencies from 2016 onwards, supported by FHWA's guidance. This adoption highlighted ATSPMs' transformative potential in enhancing traffic flow, reducing congestion, and improving safety.

‍How do ATSPMs work?

ATSPMs combine hi-resolution (1/10th second) data logging capabilities in controllers to capture information on signal and detection status, providing deep insights into signal performance alongside charts and visualizations. 

The Benefit of ATSPMs

ATSPMs provide information on key signal performance measures that provide insights to support signal retiming. These performance measures include, but are not limited to:

• Arrivals on Green
• Split Failures
• Force-Offs
• Max-Outs
• Gap Outs
• Green Time
• Turning Movement Counts
• Split
• Cycle
• Offset
  

ATSPMs provide the most accurate (and highest resolution) source of information on signal controller behavior, which are a key component to ensure accuracy of a number of signal performance measures.

‍The Challenges and Limitations of ATSPMs

There are multiple challenges and limitations associated with ATSPMs:

• High Infrastructure Requirements: Generating ATSPMs relies on the availability of connectivity, hi-resolution logging capabilities for signal controllers, and detection at signals in order to generate most performance measures. For many agencies, a large proportion of signals do not have all of these available, limiting their usefulness for many infrastructure settings.
• Relies on Accurate and Well Configured Detection: ATSPM is dependent on detection data, and often assumes that detection data is accurate. Flow Labs’ own studies have shown that only 27.6% of detection devices operating in the field are providing accurate data. This results in 87% of major signal issues being missed, and 5% of major issues identified are false positives. 
• Configuration and Maintenance Requirements: One major challenge with ATSPM is the initial configuration and the requirements for detector layouts and signal.
• Lack of mobility-related measures: ATSPMs do not provide visibility outside of the detection zones, travel time, control delay, queue length, stops and other vital measures that can support a traffic engineer’s understanding of road user experience.

‍Considering ATSPMs

Overall, ATSPMs offer unique insights into signal controller behavior and signal performance. However, data accuracy often limits ATSPMs usefulness for proactive monitoring. While configuration and maintenance can be time consuming, agencies should consider these a critical tool or data source for traffic signal diagnostics where they have the infrastructure to support it.

‍Probe-Based Signal Performance Measures (PBSPMs)

How do PBSPMs work?

Probe-based Signal Performance Measures (PBSPMs) have gained substantial popularity in the 2020s as the availability of probe data has grown. This technology leverages probe data captured from large numbers of active vehicles on the road and communicated via cellular networks. Depending on the probe data source, they can generate data on individual vehicle locations every 1-5 seconds. This information is then used to generate vehicle trajectories, providing visibility into driver behavior and experience.

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‍The Benefits of PBSPMs

The key benefit of PBSPMs is that they are hardware-free. As they are not reliant on detection or connectivity, they require little to no ongoing maintenance, providing an effective option to generate insights into signal performance at regional scale. Because of the lack of hardware they are also a lower cost option than ATSPMs, detection, and field observation. Additionally, because they are tracking real-world drivers, they offer a simple and effective way to understand the road user experience and therefore signal performance that cannot be captured from ATSPMs.

• Control Delay
• Number of Stops
• Travel Times
• Queue Length
• Routes
• Acceleration Rate
• Deceleration Rate
• Turning Movement Percentages‍

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The Challenges and Limitations of PBSPMs

• Limited Information and Lack of Diagnostic Capabilities: While scalable, PBSPMs can often provide a limited perspective. PBSPMs are useful for proactive monitoring (and pre-empting user complaints), but offer limited diagnostic capabilities to help signal engineers identify the root cause of a signal issue.
• Variable Accuracy: The accuracy of PBSPMs is dependent on the sampling rate, or penetration rate, of probes. With lower penetration rates of 3-8%, the statistical significance and accuracy of PBSPMs can be extremely weak in low volume areas, even during specific times of day.
• Not Like for Like with ATSPMs: While PBSPMs have overlapping measures such as Arrivals on Green and Split Failures, they should not be considered a replacement for these ATSPMs, but simply an alternative. Specifically with Split Failures, PBSPMs calculate split failures as a percentage of vehicles, while ATSPMs calculate split failures as a percentage of cycles. Yes,   these are correlated, but they aren’t an apples to apples comparison.

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Considering PBSPMs

For agencies covering a large region with limited connectivity, PBSPMs provide a cost-effective, scalable solution for signal monitoring and basic signal analysis. PBSPMs are only as good as the data behind them, so it is always worth understanding the data quality, including the resilience and continuity of the data source(s), the penetration rates, and the availability of real-time data.

‍Integrated Signal Performance Measures (ISPMs)

An Introduction to ISPMs

Pioneered by Flow Labs, Integrated Signal Performance Measures (ISPMs) represent a new frontier in signal analytics, combining the strengths of both PBSPMs and ATSPMs while eliminating their respective weaknesses. ISPMs leverage data from Detection, Signals, and Probes to generate the most comprehensive and accurate performance measures available in the industry today.

‍Generating Comprehensive Measures from ISPMs

PBSPMs + ATSPMs ≠ ISPMs ISPMs are not simply the combination of ATSPMs and PBSPMs. ISPMs provide unique performance measures that are not available in either individually by combining probe data with detection data and signal data, respectively.

• Detector Health Measures: Approximately 50% of signal operational issues are caused by faulty detection systems that are rarely found by ATMS and ATSPM systems. By combining detection data with probe data, Flow Labs ISPMs uniquely provide accurate detection quality metrics, including providing an understanding of volume accuracy, presence accuracy, calibration quality, and latency.
• Turning Movement Counts: Under ISPMs,  detector data is combined with probe data and novel algorithms to provide accurate turning movement counts.
• Red Light Running (RLR) and Dilemma Zone Entry (DZE): While video-based detection is often used for Red Light Running (RLR) and Dilemma Zone Entry (DZE) detection, Flow Labs ISPMs provides a hardware-free alternative. By combining probe data with hi-resolution signal data, ISPMs provide accurate insights into RLR and DZE without the need for detection.

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‍Additional Crowdsourced Datasets The ISPM approach can combine multiple crowdsourced datasets, including:

• Location Based Services (LBS) Data to understand VRU activity at intersections.
• Fleet Telematics Data to understand Freight and Truck activity at intersections.
• Safety-Focused Probe Data to understand key safety insights (e.g. hard braking, aggressive acceleration, hard cornering) at intersections.

With additional measures and novel datasets, ISPMs provide the most comprehensive set of signal performance measures, enabling agencies to monitor: (1) signal operations (2) signal safety (3) mobility (4) asset health (5) environmental impact and provide (6) a multi-modal perspective. For a complete list of performance measures, get in touch with our team at sales@flowlabs.ai.

‍Higher Accuracy Data from ISPMs

By combining multiple datasets, ISPMs can offer higher data accuracy and reliability than either ATSPMs and PBSPMs alone. 

‍Generating Accurate Turning Movement Counts As an example, by combining probe data with detection data, the Flow Labs ISPM platform is able to compare probe data volume patterns with detection volume patterns to quickly identify inaccurate detection. By using advanced algorithms, the platform can generate accurate turning movement counts (TMCs) even where detection and ground truth counts aren’t available. 

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‍Increased Redundancy Increases Reliability No individual performance measure is perfect. By combining multiple measurement approaches, ISPMs overcome the limitations of existing calculation methodologies to generate a more robust source of truth. Take Arrivals on Green (AOG) as an example- each SPM approach calculates it differently.

• PBSPM Methodology: PBSPMs do not have information on signal phasing, so they infer if a vehicle has arrived on red or green by identifying if it has stopped or not stopped, respectively. Validation studies have shown that this methodology compares well to ATSPM generated arrivals on green despite the methodological differences.
• ATSPM methodology: ATSPMs utilize both signal event data and advance detection data, identifying when vehicles have arrived at an advanced detector and identifying the current phase (accounting for distance and expected vehicle speed). However, given that the detection location is fixed, if the queue extends beyond the advanced detection, this can often obscure progression measures and arrivals on green will not be representative of conditions. ATSPM-based arrivals on green measures often overestimate arrivals on green during highly saturated periods. Additionally, if an advanced detector is configured incorrectly or is inaccurate, these measures can also be inaccurate.
• ‍ISPM Methodology: This methodology combines probe data with signal data to create an arrival point, simulating an advanced detector. Similar to ATSPM, it identifies when probes have arrived at the arrival point and the arrival phase (again accounting for vehicle speed, and distance to the stop bar). While this methodology is similar to ATSPM, it offers the ability to dynamically adjust the arrival point based on queue length.

This ISPM methodology offers a number of advantages:

1. The ISPM approach offers higher accuracy than a PBSPM approach where signal event data is available.
2. The ISPM approach offers higher accuracy than the ATSPM approach where (1) detection is inaccurate or (2) there are high levels of queuing or saturation. 
3. Where signal event data isn’t available, the PBSPM approach can be used to generate arrivals on green, ensuring complete coverage of performance measures irrespective of the infrastructure available.

‍Opens Up Decision Support Capabilities With more accurate and comprehensive measures, ISPMs can enable exciting new applications, including Flow Labs’  software enabling engineering teams to generate optimized signal timing plans automatically and on-demand.

‍Conclusion

Despite advancements in signal performance measures, many signals across the nation are still rated poorly, reflecting widespread challenges such as outdated technology, inadequate maintenance, and inefficient signal timing. Flow Labs' Integrated Signal Performance Measures (ISPMs) offer a groundbreaking solution to these persistent issues. By combining multiple datasets—including detection, signal, and probe data—ISPMs provide a more comprehensive and accurate analysis of signal performance. This holistic approach not only addresses the limitations of traditional measures, but also enhances diagnostic capabilities, decision support, and overall traffic signal management.

Agencies that adopt ISPMs can expect several key benefits:

1. Comprehensive View: ISPMs integrate various data sources, offering a detailed and complete picture of signal performance. This integration allows for better diagnostics and more informed decision-making.
2. Higher Accuracy: By leveraging multiple datasets, ISPMs can achieve higher data accuracy and reliability, which is crucial for effective signal management.
3. Innovative Performance Measures: ISPMs provide unique performance metrics not available through ATSPMs or PBSPMs alone, including accurate turning movement counts, red light running detection, and dilemma zone entry analysis.
4. Enhanced Monitoring: ISPMs enable agencies to monitor signal operations, safety, mobility, asset health, and environmental impact more effectively, providing a multi-modal perspective that is essential for modern traffic management.
5. Scalability and Cost-Effectiveness: The ISPM approach is scalable and offers a cost-effective solution for regional signal monitoring, making it accessible for agencies of all sizes.
6. Future-Proof Infrastructure: ISPMs provide an effective solution for agencies seeking a scalable, flexible system that captures probe-based, controller-based, and other datasets. This infrastructure-agnostic approach ensures agencies are prepared for future advancements.

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Transportation agencies are invited to join the movement towards smarter and more efficient traffic signal management. Discover how ISPMs can revolutionize your traffic signal systems and enhance mobility in your region. Contact Flow Labs today at sales@flowlabs.ai to learn more and schedule a demo. Let's work together to transform our intersections and improve the quality of life for all road users.