City Traffic Accident Locations Can Be Predicted by Past Collisions: Study

New research indicates that historical data on auto accidents in urban areas may be crucial for predicting the locations of future crashes, which might help cities develop more effective traffic safety designs.

According to the World Health Organization, there were approximately 1.19 million traffic deaths in 2021, which is more than 3,250 per day, leading many researchers to seek information about the factors that might help reduce the number of vehicle collisions around the globe.

In a study published last month by the Multidisciplinary Digital Publishing Institute (MDPI), Chinese researchers conducted an analysis to assess the potential impact of historical accident data on crash risks in cities, and how that information can help predict future collisions. The findings suggest that access to this data could significantly improve efforts to address accident risks in certain locations.

A team of researchers from Shenyang Agricultural University in China used a comprehensive dataset, which included traffic accident data from between 2005 and 2022, as well as traffic violation data from 2020 to 2022, for vehicles in Western China.

The study was led by Jing Wang, who developed two predictive models, one based on vehicle information and the number of driver violations, and the other one merging historical accident data with accident frequency.

Wang’s team determined that the model incorporating historical accident data demonstrated significant value for predicting future accident risks.

The report also found that historical accident records significantly reduced future accident risks for drivers with one or two prior accidents. However, this effect is limited for drivers with frequent accidents.

Overall, the study confirms the importance of using historical information for traffic accident predictions, which are valuable for developing and implementing prevention strategies.

The researchers concluded that further examination is needed into the specific ways that past accidents might influence future risks, and how incorporating real-time data could improve the precision of prediction models.

Understanding Accident Risks

While Wang’s study suggests that collecting data on driver behavior and accidents could provide a better understanding of risks and help prevent crashes, other recent studies have linked crash risks to specific factors, such as distracted driving.

One recent study warned that certain roadside advertising can cause increased driver distractions, leading to the potential for more accidents.

Another study published earlier this year found that ADHD symptoms can affect driving performance. This is especially true when they are coupled with technology that might exacerbate distracted driving, such as car radios, cell phones or GPS systems.

A further study showed that handheld cell phones use is a strong predictor of risky driving behaviors.

Researchers emphasize the importance of understanding accident risks by analyzing what influences them and using that data to develop more effective risk management strategies.