Applications of Big Data in Safety Analysis
As Big Data becomes increasingly popular and available, it can be difficult to distinguish which specific datasets are best for evaluating safety performance. Collaborating with local agencies, data vendors, and nonprofits, Fehr & Peers has helped create a guide for transportation professionals to determine the most effective data sources to apply when scoping, implementing, and evaluating safety projects. The brief includes important vendor terminology for understanding how datasets are packaged and processed as well as case studies that offer real-world examples using Big Data to evaluate safety performance.
Big Data allows experts to look beyond traditional crash data. Traditional methods are useful in identifying high crash frequency locations; however, the downside is that they are reactive meaning a crash must occur for the data to be collected. Instead, Big Data is allowing practitioners to study a larger number of potential traffic incidents and trouble spots by evaluating specific contributing factors (i.e., driver behavior) to find out what might cause accidents before they happen. As technology improves and the industry shifts towards the use of even more advanced tools like real-time sensors and automated data collection systems, (i.e., Smart Cities), agencies will be able to proactively explore options for using Big Data as they upgrade and expand their existing systems.
To learn more about applying the right data sets to your safety projects, access the brief here. Be sure to tune in to the upcoming ITE webinar featuring ITE Brief co-author, Diwu Zhou, on May 23, 2023.
Senior Transportation Engineer PE, RSP1
Bay Area Principal in Charge