Vehicle Trace Data Can Improve Response Time and Roadway Safety

The most serious safety risks on high-speed roadways are not random or entirely unpredictable. They result from traffic conditions that can be identified and addressed as they develop. Vehicle trace data can provide a useful tool for pinpointing and managing these risks.

What Is “Individual Vehicle Trace Data”?

Individual vehicle trace data (or simply "trace data") refers to information that is passively collected via vehicle telematics and location-aware applications. Unlike traditional traffic data sources that report aggregate conditions, trace data captures the movements and operating characteristics of individual vehicles. These vehicle-level observations can then be viewed collectively to better understand systemwide traffic patterns and emerging safety conditions. In turn, this information has the potential to identify traffic incidents faster than traditional sources alone and support more timely mitigation measures.

Vehicle Trace Data Use Cases

On high-speed toll facilities, risk is often localized, forming at merge areas, ramps, work zones, and other points where traffic patterns shift abruptly. By using vehicle trace data, roadway operators could potentially gain a better understanding of where and when risk is building, how it evolves, and where intervention may be most effective. 

One of the most immediate applications is queue identification. End-of-queue conditions remain a leading operational safety concern because drivers can suddenly encounter stopped or slow-moving traffic at highway speeds. Vehicle trace data can identify the start and end of queues more precisely than traditional systems, allowing roadway operators to track how queues grow, move, and dissipate. This supports more effective deployment of queue warning systems, improves traffic management, and reduces the likelihood of rear-end and secondary crashes. It also provides insight into whether congestion is isolated, recurring, or extending into upstream segments and ramps, informing both real-time response and longer-term mitigation.

Excessive speeding is not evenly distributed and has been observed to cluster in specific locations and time periods. Rather than relying on aggregated averages, roadway operators can use vehicle trace data to gain greater insight into the spectrum of driver behavior. Having this information allows roadway operators to focus resources where the highest-risk behavior is actually occurring. This supports more targeted coordination with enforcement and better prioritization of mitigation efforts.

Hard-braking patterns have demonstrated value in identifying a meaningful share of incidents, in many cases earlier than existing systems. By supporting faster detection, vehicle trace data can help roadway operators with more timely resource deployment. Even modest gains in detection time can improve response and reduce the risk of secondary crashes.

Beyond real-time detection, repeated hard-braking patterns highlight locations that may indicate friction arising not from dynamic traffic conditions, but from the infrastructure itself. Clusters near interchanges, merge areas, tolling zones, bridges, and ramps suggest recurring friction points. These patterns provide a strong basis for screening locations for further review, supporting more proactive safety management through operational changes, signing improvements, or engineering evaluation.

In work zones and maintenance of traffic (MOT), separated traffic patterns can create speed differentials. Vehicle trace data allows operators to accurately measure travel times on individual segments and better manage traffic. Vehicle trace data can also provide improved visibility into these patterns to help roadway operators better assess how to address turbulence, driver confusion, and unsafe lane-changing behavior. This in turn may inform more efficient and safer development of MOT strategies going forward.

Integrating Trace Data with Traditional Vehicle Detection Systems

At the moment, vehicle trace data shows promise as a complement to existing systems rather than a replacement. It creates opportunity for quicker detection and allows information to reach operations centers sooner. Roadway operators can convey information through dynamic messaging, active traffic management, or connected vehicle technology.

As with any data source, there are limitations to the use of vehicle trace data. Coverage is reliant on market penetration, limited to participating vehicles, and privacy controls that may further affect availability. Additionally, effective use depends on reliable processing, careful validation, and supporting data sources. For these reasons, agencies are likely to continue relying on multiple data sources in the future. Additional experience will help determine exactly how trace data can best complement traditional sources. In the future, advances in artificial intelligence may allow roadway operators to extract insight from these trace and secondary data sources in ways that today's operations centers cannot.

In practice, trace data gives roadway operators a clearer view of where risk is forming, supports earlier detection of incidents, and helps target response more effectively, allowing for increased awareness and a more proactive approach to roadway safety.