In 2019, more than 36,000 people lost their lives in motor vehicle accidents in the U.S., which is about 11 deaths per 100,000 people. Despite recent advances in vehicle safety technology, these figures remain alarmingly high and raise questions about society’s current approach to roadway safety.

Conventional safety assessment methods often take a reactive approach to identifying crash hot spots, relying on individuals to determine hot zones and report the data to authorities or, alternatively, engineering studies that employ manual identification and analysis methods. But the recent prevalence of real-time big data and advances in software automation — including machine learning and artificial intelligence (AI) — enable a more automated and proactive approach to identifying crash hot spots and recommending countermeasures. These tools can be employed in large geographic areas down to individual corridors to integrate safety analysis in the transportation planning process.

Advanced data aggregation methods and powerful cloud-based analysis tools assist planning engineers in performing intricate safety analyses at speeds faster than previously possible. They enable the development of accurate and powerful predictive crash models that consider factors such as the manner of collision, driver error, severity of accident, weather, time of day and day of the week. Each analysis also factors in prior incidents, traffic volumes, the presence of signalization and more. Using AI to comb through large quantities of data and compare clusters of accidents and their underlying crash factors allows transportation planning engineers to statistically compare clusters to statewide data.

When this data is presented on digital dashboards like the one shown below, it helps transportation planning engineers identify existing areas of concern and potential future hazards, allowing departments of transportation, metropolitan planning organizations, counties and cities to stay attuned to the state of critical infrastructure and the safety of residents.

Turning these technological advancements into actionable measures allows transportation planning engineers to conduct road safety audits via Federal Highway Administration guidelines and apply crash modification factors to identify and test effective roadway design solutions, predicting future crash risks and implementing mitigation strategies.

This data-driven approach to proactively identifying crash trends is not only the future of transportation planning, it's today's solution to making sound roadway safety investments. Is this methodology the missing link between vehicle safety technology and attaining a zero-fatality roadway network? Only time will tell.


Learn more about our approach to highway safety, predictive crash algorithms, and the importance of data analyses for improving transportation safety.

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Josh Robertson is a highway department manager at Burns & McDonnell in Texas. He specializes in transportation schematic design, planning and feasibility studies, and traffic studies and analysis.