{"id":22475,"date":"2025-01-22T09:41:57","date_gmt":"2025-01-22T15:41:57","guid":{"rendered":"https:\/\/uwm.edu\/engineering\/?p=22475"},"modified":"2026-04-15T16:36:48","modified_gmt":"2026-04-15T21:36:48","slug":"shis-work-is-transforming-traffic-data-into-road-safety-solutions","status":"publish","type":"post","link":"https:\/\/uwm.edu\/engineering\/shis-work-is-transforming-traffic-data-into-road-safety-solutions\/","title":{"rendered":"Shi\u2019s work is transforming traffic data into road safety solutions"},"content":{"rendered":"\n

Every time you drive a vehicle, one aspect of your safety comes from something you have little control over \u2013 other drivers. What if risky driving behaviors could be tracked as they are happening and immediate warnings delivered to drivers nearby?<\/p>\n\n\n\n

\u201cThe idea is that drivers may be able to take a defensive stance to avoid crashes if they could be alerted to risky driving in their vicinity,\u201d said Tom Shi, an assistant professor of civil & environmental engineering. \u201cOr police could discover where the most dangerous locations are and intervene.\u201d<\/p>\n\n\n\n

Members of Shi\u2019s lab have completed the first step in a multi-pronged data science project aimed at improving road safety by analyzing video for predictive modeling.<\/p>\n\n\n\n

Making Waves of Impact<\/strong>
A transportation engineer uses camera data and AI to predict where drivers exhibiting dangerous behaviors are.<\/div><\/div><\/div>\n\n\n\n

Since he was looking for traffic data, Shi partnered with the 51ÁÔÆæ Police Department which maintains a large network of surveillance cameras pointed at roadways all around the campus.<\/p>\n\n\n\n

Extracting answers from data<\/strong><\/p>\n\n\n\n

The lab members began by began by collecting the incidence of risky driving behaviors on certain stretches of road or at intersections at various intervals. Then, they built a digital map of what happened using a detection algorithm they developed.<\/p>\n\n\n\n

\u201cWe use deep learning methods to extract characteristics from vehicle footage, and these characteristics help us identify distracted driving behavior, such as using a phone or taking your eyes off the road,\u201d Shi said.<\/p>\n\n\n\n

A video of one of the researchers\u2019 digital maps shows cars as 3D boxes moving along the street where pedestrians are visible, marked in yellow.<\/p>\n\n\n\n

The algorithm provides the time-stamped location of the objects and their GPS coordinates, the object\u2019s speed and the size of each \u2013 whether it\u2019s a car, truck or even bike.<\/p>\n\n\n\n

\u201cGiven all those parameters, we can use data science to answer questions like, \u201cHow often does speeding occur and under what circumstances?\u201d Shi said. \u201cThe lab members assigned a score from the safest conditions to the worst scenarios. So, the algorithm is using the data to pick out the unsafe anomalies.\u201d<\/p>\n\n\n\n

An unsafe pedestrian hotspot<\/strong><\/p>\n\n\n\n

From traffic footage that spans Maryland Avenue from the Student Union to Edgewood Avenue, a long corridor that includes three intersections, the researchers have used the modeling to help them understand and analyze the interaction between the pedestrians and vehicles.<\/p>\n\n\n\n

For example, Shi\u2019s students developed a video that re-constructs traffic moving along Maryland Avenue in front of Hartford Elementary School from data collected by a camera mounted to the northeast corner of Lapham Hall.<\/em><\/p>\n\n\n\n

The group found that drivers approaching the crosswalk there often follow the car in front of them very closely in order to avoid stopping when pedestrians are present. That leaves pedestrians wondering who will stop and who won\u2019t.<\/p>\n\n\n\n

The final step<\/strong><\/p>\n\n\n\n

Shi said his work includes both the data science needed to clarify traffic problems and the development and testing of new technologies to address them.<\/p>\n\n\n\n

The final step in this project will be to create an intervention that would send out a warning of driving aberrations happening at a location in real time.<\/p>\n\n\n\n

Although the warning product has not yet been created, one idea is an audible signal on a driving app, he said. The research has the potential to lead to new products.<\/p>\n\n\n\n

Further in the future, for example, the warning product can leverage the existing cellular network. This technology is called cellular-vehicle-to-everything (C-V2X) communication.<\/p>\n\n\n\n

\u201cWe do plan to install a C-V2X roadside unit at the Hartford-Maryland intersection,\u201d Shi said. \u201cCurrently, it requires the vehicle to have a C-V2X onboard unit to receive the command we broadcast. In the future, this will be equipped on vehicles by the manufacturer.\u201d<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

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