A case study developed by CEE assistant professor Eleftheria Kontou and industrial & enterprise systems engineering (ISE) associate teaching professor Chrysafis Vogiatzis gives students an opportunity to work with data and evaluate challenges of bias in automated traffic law enforcement.
Titled “Racial Bias in Automated Traffic Law Enforcement and the Price of Unjustness,” the case study is based on a 2022 ProPublica article that claims “Chicago’s race-neutral traffic cameras ticket Black and Latino drivers the most.” Since Spring 2023, it has been used as part of the curriculum in two UIUC courses: Industrial Engineering “Analysis of Data” (IE300) and Civil and Environmental Engineering “Systems Engineering and Economics” (CEE201), where students in each class perform one of the study’s two parts.
In part one, students in IE300 collect pertinent socio-demographic data and conduct hypothesis testing to assess whether ProPublica’s claim is supported. Performing this task allows students to practice applying statistical analysis methods to evaluate the social components of automated traffic law enforcement.
In part two, CEE201 students formulate a shortest path problem that a driver in Chicago, worried about accumulating tickets, would need to solve to avoid some or all intersections with automated traffic cameras. Students then solve the problem they formulated to determine how much longer the driver’s trip will take from Chicago’s south side to O’Hare International Airport.
By performing the case study, students gain experience working with data to solve real world challenges related to transportation. The case is designed for courses in data analysis and statistical modeling that emphasize hypothesis testing and also applies to courses focused on optimization and mathematical modeling.
A paper detailing the case study is published in INFORMS Transactions on Education and can be read here.