Development of a Risk Map Analysis Tool (RiskGrid) to Support Road Safety Impact Assessment

Every time a city expands, it changes the rhythm of the road. New neighborhoods bring new families, new jobs, and new trips, all flowing onto streets that were built for a different time. For decades, the people who plan these cities have relied on a standard checklist to decide if a new development is safe. They look at traffic volume, how long it takes to get from point A to point B, and whether the roads get clogged. If the cars keep moving and the travel times stay reasonable, the project gets the green light. But this checklist has a blind spot. It often misses the hidden dangers that arise when traffic patterns shift, even if the roads don't feel any more crowded. A street can look perfectly efficient while quietly becoming a place where crashes are more likely to happen.

This gap between smooth traffic and hidden danger is what a team of researchers from universities in Rome and Naples set out to fix. They developed a new way to look at road safety, one that moves beyond counting accidents after they happen and instead tries to predict where the next one might occur before a single car is built. Their work focuses on a tool they call RiskGrid, a system designed to turn the complex dance of vehicles into a clear picture of risk. By testing this tool in a real, growing suburb of Rome, they found that the old way of checking safety was missing the mark, while their new method could spot dangerous spots with surprising speed and accuracy. The goal was simple but profound: to give planners a way to see the invisible risks in a new road network, ensuring that efficiency never comes at the cost of human life.

The researchers applied their new method to the Morena neighborhood, an area on the southeastern edge of Rome that is rapidly filling with new homes. They created a digital twin of the area, simulating the traffic that would flow through it once the new apartments were occupied. To be safe, they assumed a worst-case scenario where every single person in the new development drove a car during the busiest hour of the morning. They then ran two simulations. The first was a "do-nothing" scenario, showing how the roads would look without the new development. The second included the extra traffic from the new residents.

When they looked at the results using traditional traffic measures, the news seemed good. The new development added only a tiny amount of traffic. The average speed of cars dropped by a fraction of a percent, from 44.7 kilometers per hour to 44.6 kilometers per hour. No new traffic jams appeared. If a planner had stopped there, they would have concluded that the project was perfectly safe and had no negative impact on the road network. But the researchers knew that safety is not just about how fast cars are moving; it is about how they interact with one another.

To test this, they compared their new RiskGrid tool against the standard industry method used for years, known as the Surrogate Safety Assessment Model. This older tool works by watching the simulated cars and counting specific moments where two vehicles come dangerously close to crashing, such as when one car almost hits another from behind or when two cars nearly cross paths at an intersection. It is a precise but slow process that requires a computer to track every single near-miss event. In this study, the older tool took more than 30 minutes to analyze just one traffic scenario. More importantly, when the researchers checked its predictions against real crash data from the area between 2021 and 2023, it failed to find the danger. It correctly identified only one of the thirteen actual places where crashes had happened. It missed ten critical spots and even flagged three places as dangerous that were actually safe.

The new RiskGrid tool worked differently. Instead of counting individual near-misses, it looked at the overall flow of traffic in small squares covering the map. It asked a simple question: how many different directions are cars coming from in this specific spot? If cars are all moving in the same direction, the risk is low. But if cars are arriving from many different angles, crossing each other, merging, or turning, the risk goes up. The tool calculated a risk score for every square on the map based on this complexity. The result was a continuous map of danger, showing exactly where the network was most vulnerable.

The difference in performance was stark. RiskGrid processed the entire traffic scenario in less than one minute, a speed that makes it practical for use in everyday planning meetings. When the researchers checked its predictions against the same real crash data, it found twelve of the thirteen actual hotspots. It missed only one and flagged five extra spots as risky. In the world of safety planning, finding a few extra spots that turn out to be safe is a small price to pay for not missing a single dangerous one. Missing a dangerous spot could mean a life is lost, while checking an extra safe spot just means a planner spends a few minutes looking at a location that is fine.

When the researchers applied RiskGrid to the new development in Morena, it revealed a hidden truth that the traditional traffic check had missed. Even though the roads were not getting slower or more crowded, the new traffic patterns created a 2.1 percent increase in the likelihood of a crash. In absolute numbers, this meant the new development was expected to cause one additional crash in the local area, raising the total from 84 to 86 incidents. This finding exposed a critical flaw in how cities are currently planned: a project can look perfect on a traffic report while actually making the roads more dangerous for everyone.

The study suggests that the old way of assessing safety, which relies on counting specific near-miss events, is too rigid for complex urban environments. It tends to miss the most common and severe types of crashes, which happen when vehicles cross paths at intersections. The new approach, by focusing on the sheer complexity of where cars are coming from, captures these risks much better. It transforms the data from a list of isolated events into a living map of potential danger. This allows planners to see the structural weaknesses in a road network before they are built, rather than waiting for accidents to happen and then trying to fix them.

The researchers acknowledge that their tool is not yet perfect. It currently looks only at cars and does not include pedestrians or cyclists, who are often the most vulnerable road users. It also treats the road network as a flat map, which can sometimes confuse roads that go over or under each other. However, the core idea is sound and the results are promising. By integrating this kind of continuous risk mapping into the standard planning process, cities can move from a reactive stance, where they fix problems after crashes occur, to a proactive one, where they design safety into the network from the start.

In the end, the work of these researchers offers a new lens for looking at the roads we build. It shows that safety is not just a side note to traffic flow; it is a fundamental part of the equation. A road that moves cars quickly but hides dangerous interactions is not a success. With tools like RiskGrid, planners can finally see the whole picture, ensuring that the cities of the future are not just efficient, but truly safe for the people who use them.