Real-Time Data Analytics Aims to Reduce Traffic Fatalities
MetroLab Network has partnered withGovernment Technologyto bring its readers a segment called the MetroLab Innovation of the Month Series, which highlights impactful tech, data and innovation projects underway between cities and universities. If you’d like to learn more or contact the project leads, please contact MetroLab at info@metrolabnetwork.org for more information.
In our first 2019 installment of the Innovation of the Month series, we explore how the University of Central Florida, in Orlando, is using big data and analytics to predict and mitigate traffic accidents.
MetroLab’s Executive Director Ben Levine spoke with Mohamed Abdel-Aty, Pegasus Professor and Chair of the Department of Civil, Environmental and Construction Engineering at the University of Central Florida (UCF); Dean Michael Georgiopoulos, College of Engineering and Computer Science at UCF; Charles Ramdatt, director of Special Projects in the city of Orlando; and Jeremy Dilmore, Florida Department of Transportation engineer, to learn more.
Ben Levine: Dr. Abdel-Aty, you’re using data to mitigate traffic accidents. Can you tell me about your project?
Mohamed Abdel-Aty: Certainly. My team at the University of Central Florida has been utilizing data sources for real-time crash prediction for many years. This effort initially started in partnership with the Florida Department of Transportation and the Colorado Department of Transportation. The most recent research for this work grew out of the U.S. Department of Transportation’s Solving for Safety Visualization Challenge, for which our work has been selected as a Stage-I semifinalist. As part of that challenge, we stated that by integrating real-time and static data, we could develop predictive analytics to diagnose real-time traffic safety conditions.
The improvement of analytics software and emergence of a data-rich environment contribute to the data-driven analysis for traffic safety by investigating crash, traffic, weather, geometric data, etc. Traditionally, traffic safety data analyses were conducted based on static and highly aggregated data, like annual average daily traffic or annual crash frequency. These aggregated data analyses can only reveal the general trend and relationship between crash frequency and few contributing factors, which could result in unreliable findings simply because they are averages and cannot represent the real conditions at the time of a crash.
The input data are the foundation to conduct real-time data-driven analysis for road safety. In recent years, with the advancement of big data, abundant data could be used for better crash prediction. My team has utilized these data — including Microwave Vehicle Detection, Automatic Vehicle Identification, Bluetooth, and real-time weather — on both arterials and freeways, including at typical conflict areas (e.g., intersections, weaving areas, ramps, etc.), to develop accurate algorithms that can predict the increase in crash risk in real time.
Bringing all these tools and algorithms under one integrated system will enable operators to monitor safety risk in real-time and develop interventions that alleviate the potential problems and prevent crashes or at least mitigate their severity.
If readers would like to learn more, they are welcome to visit my Google Scholar homepage for more than 200 publications addressing these concepts and proctive traffic management.
Levine: I suppose it’s obvious to readers that we’d like to reduce crashes. But perhaps you could offer some more detail about the root causes of the challenges you are addressing and how government can respond.
Abdel-Aty: The advent of big data technologies enables real-time analysis for traffic safety. By integrating multiple data sources, the data could help us understand the relationship between the presence of traffic conflicts and real-time contributing factors (e.g.
