Amardeep Dhingra
Pronouns: he/him
Research Mentor(s): Fred Feng
Research Mentor School/College/Department: UM-Dearborn, Department of Industrial and Manufacturing Systems Engineering / Other
Program: UROPF
Session: Session 3 (11:00am – 11:50am)
Authors: Fred Feng, Amardeep Dhingra
Abstract
In the US traffic crashes resulted in approximately 40,000 fatalities every year. There have been many studies on crash data analysis to gain insights into the crash patterns, contributing factors, etc. However, to our knowledge there has been few studies that look at the counterparties of crash fatalities for different road user types (e.g., motorists, pedestrians, bicyclists, trucks, buses). The possible different distributions of the counterparties for different road user types may provide us a unique perspective on setting transportation policies and regulations. Inspired by the “Counterparty in crashes†(https://www.roaddanger.org/statistics/counterparty) which is generated based on media coverage data, we developed a method to examine the counterparties based on a complete set of police-reported crash data in the State of Michigan. We developed a method to process traffic crash data and extract the counterparties of traffic fatalities of different road user types. The method was implemented in Python and can run automatically and generate the results and visualizations.
