Work zone crashes remain one of the most persistent safety challenges on our nation’s highways, claiming more than 750 lives each year according to the Centers for Disease Control and Prevention. These tragedies affect construction workers and motorists alike. The root causes are well understood — excessive speed and driver distraction — but addressing them effectively has historically required waiting for incidents to occur before deploying safety resources. That approach is now being overturned by connected vehicle data (CVD), which gives transportation agencies real-time visibility into what is happening inside and around work zones. This shift from reactive to proactive safety management draws on the work of the Indiana Department of Transportation (INDOT), Purdue University, and data analytics firm Wejo. For a broader overview of systematic safety evaluation methods, see our article on Highway Safety Road Safety Audits Crash Analysis Countermeasure.
The Rising Toll of Work Zone Crashes and the Data Solution
Work zone crashes are not rare events. Every year, hundreds of fatal incidents occur on American roads near construction sites, and thousands more result in serious injuries. The financial cost runs into the billions when medical expenses, road closures, litigation, and project delays are factored in. The traditional approach to work zone safety has relied on periodic inspections, manual traffic studies, and post-incident analysis. These methods provide useful information, but they suffer from a critical time lag.
The gap between when a hazard develops and when it is identified can be weeks or even months. A pothole that causes drivers to swerve dangerously, a lane merge point that creates unexpected braking, or a queue of stopped traffic that extends well beyond the visible warning signs — all of these can go undetected until someone reports them or a crash occurs. The Indiana DOT recognized that solving this problem required a fundamentally different kind of data.
What Is Connected Vehicle Data?
Connected vehicle data comes from the thousands of sensors embedded in modern vehicles. A typical newer vehicle carries up to 1,000 sensors that track everything from speed and location to windshield wiper activation and steering angle. When anonymized and aggregated, these data streams paint a detailed picture of traffic behavior in real time.
Companies like Wejo specialize in collecting, processing, and analyzing this data at scale. The result is a continuous feed of information that reveals:
- Traffic speed and volume at any point on the road network
- Hard braking events and their precise locations
- Sudden lane changes or erratic driving patterns
- Weather-related driving behavior such as reduced speed during rain
- Queue formation behind work zones and congestion points
Real-time access to this information allows transportation agencies to spot developing hazards within hours instead of weeks, and to deploy resources before an incident occurs.
Why Traditional Methods Fall Short
Before the adoption of CVD, DOTs relied on roadside cameras, manual traffic counts, and citizen reports to understand what was happening on their roads. Each of these methods has significant limitations. Roadside cameras cover only fixed points. Manual traffic counts are expensive and provide only a snapshot. Citizen reports are always backward-looking. Connected vehicle data fills all of these gaps by providing continuous, system-wide visibility into actual driving behavior rather than proxy metrics.
How Connected Vehicle Data Transforms Work Zone Monitoring
The Indiana DOT’s partnership with Purdue University and Wejo demonstrates how CVD can be applied in practice. One of the first things the team discovered was the extent to which road conditions that seem minor to an outside observer can trigger dangerous driver responses.
A pothole that goes unreported for weeks may cause hundreds of drivers to swerve into adjacent lanes every day. A faded lane marking at a merge point can create confusion and sudden braking. These patterns are invisible to traditional monitoring but stand out clearly in connected vehicle data.
Asset Condition Monitoring at Scale
Beyond identifying immediate hazards, CVD enables DOTs to inventory their road assets systematically. The data reveals which stretches of roadway have inconsistent markings, poor signage, or deteriorated surfaces that cause drivers to behave unpredictably. Agencies can use this information to prioritize maintenance spending and plan capital improvements based on actual traffic response rather than scheduled inspections alone.
INDOT incorporated CVD into their long-term road planning to prepare for emerging technologies such as vehicle platooning and autonomous driving. This aligns closely with the principles covered in our article on Construction Safety Principles of Hazard Identification Risk Assessment, which emphasizes proactive identification before harm occurs.
Real-Time Situational Awareness
The most transformative aspect of CVD is its timeliness. Traditional data sources such as crash reports take days or weeks to compile and publish. By that time, the conditions that contributed to the crash may have changed, or the same hazard may still be present and causing further near-misses. CVD provides information on a timescale that matches operational decision-making.
The table below compares the characteristics of traditional monitoring methods with connected vehicle data for work zone safety applications.
| Monitoring Method | Data Latency | Spatial Coverage | Cost per Mile | Identifies Near-Misses |
|---|---|---|---|---|
| Roadside cameras | Seconds to minutes | Fixed points only | High (hardware) | No |
| Manual traffic counts | Days to weeks | Survey sites only | Moderate to high | No |
| Crash report analysis | Weeks to months | Incident locations only | Low (records) | No |
| Connected vehicle data | Near real-time (hours) | Full roadway network | Low to moderate | Yes |
The near real-time nature of CVD allows agencies to shift from a reactive posture to a proactive one, identifying and addressing hazards before they result in crashes.
Hard Braking Events as a Surrogate Safety Metric
One of the most important findings from the INDOT-Purdue-Wejo collaboration was the identification of hard braking events as a reliable surrogate measure of work zone safety. Rather than waiting for actual crashes to occur, the team used hard braking data to understand where and when drivers were encountering unexpected hazards.
A hard braking event occurs when a driver decelerates abruptly, typically at a rate that exceeds normal stopping behavior. These events are common near work zones when drivers encounter the back of a queue of stopped or slow-moving traffic that extends beyond the visible warning signs. The relationship between hard braking and crash risk turned out to be strong and consistent.
The Queue Problem in Work Zones
Work zones create queues of stopped traffic as lanes are reduced and speeds drop. The back of this queue — the point where fast-moving traffic first encounters the slowdown — is one of the most dangerous locations on any highway. Drivers approaching at highway speed may not see the queue in time, especially around curves, over hills, or in low-visibility conditions. The result is hard braking that can cascade into a rear-end collision.
Using CVD, the team could pinpoint exactly where hard braking events were occurring, at which mile markers, at what times of day, and how close they were to the active work zone.
From Surrogate Measure to Actionable Insight
The ability to use hard braking as a surrogate safety metric opened up new possibilities for work zone safety management. Instead of waiting for crash data to accumulate over months, agencies could assess the effectiveness of their safety measures in days.
- Faster feedback loops — Safety interventions can be evaluated within days rather than months.
- Larger data samples — A single work zone generates thousands of data points per day from passing vehicles.
- Objective measurement — Hard braking events are recorded automatically, removing reporting bias.
- Predictive capability — By establishing the relationship between hard braking and crash risk, agencies can predict which work zones are most likely to experience incidents and allocate resources accordingly. For more on technology in work zone protection, read about How Autonomous Tma Trucks Are Reshaping Construction Work Zone Safety Standards.
Implementing Data-Driven Safety: Queue Trucks and Real Results
The ultimate test of any safety innovation is whether it produces measurable reductions in crashes. INDOT and Purdue used CVD not only to understand the problem but to deploy a targeted countermeasure: advanced warning queue trucks positioned based on real-time data rather than fixed schedules.
Queue trucks are vehicles equipped with large message boards and impact attenuators deployed upstream of work zones to warn approaching drivers about the queue ahead. Traditionally, these trucks were placed at fixed locations determined by engineering judgment. CVD allowed INDOT to triage the most critical locations across the state and determine the optimal distance from the back of the queue for each deployment.
Validation through Comparative Analysis
To validate the effectiveness of this approach, the team compared hard braking events in two work zones: one where a queue truck was positioned using CVD data and one without. The results were striking. In the work zone with the data-informed queue truck placement, hard braking events dropped by more than half. Extrapolating from the established relationship between hard braking and crash risk, this suggests the potential to reduce work zone crashes by 50 percent or more.
This improvement comes from addressing the root cause of work zone rear-end collisions: the surprise factor when drivers encounter a queue that extends beyond visible warning signs. By positioning queue trucks at the precise location where hard braking data shows drivers are most at risk, agencies can warn motorists before they enter the danger zone. This principle of anticipating hazards before they cause harm is also central to Safety Precautions Structural Steel Work, where pre-task planning prevents accidents on site.
The Future of Data-Driven Work Zone Safety
As connected vehicle data becomes more widely available and the analytical tools for processing it become more sophisticated, the potential applications continue to expand. Future developments may include automated queue truck deployment using real-time data to dynamically dispatch warning vehicles as conditions change throughout the day, integration with intelligent work zone systems, predictive work zone planning using historical data to identify high-risk time windows before construction begins, and cross-agency data sharing that allows agencies to learn from each other’s experience.
The shift from reactive to proactive work zone safety, powered by connected vehicle data, represents one of the most significant advances in highway safety in recent years. As agencies like INDOT continue to demonstrate, the data needed to save lives is already being generated by the vehicles on our roads. The challenge now is to ensure that every DOT has the tools and partnerships needed to turn that data into action.