Applying Machine Learning Models to First Responder Collisions Beside Roads: Insights from “Two Vehicles Hit a Parked Motor Vehicle” Data

Applied Sciences

Abir Mukherjee¹  Xia Liu¹  Mohammadali Tofighi²  Ali Asgary²  Felippe Cronemberger²  Brady Podloski²  Ghassem Tofighi³ 

[1] A.U.G. Signals Ltd., Toronto, ON M5H 4E8, Canada;ADERSIM, School of Administrative Studies, Faculty of Liberal Arts & Professional Studies, York University, Toronto, ON M3J 1P3, Canada;Sheridan College Institute of Technology & Advanced Learning, Oakville, ON L6H 2L1, Canada;

关键词: emergency responders;    road safety;    road collisions;    machine learning;   

DOI  :  10.3390/app112311198

来源: DOAJ

【 摘 要 】

First responders including firefighters, paramedics, and police officers are among the first to respond to vehicle collisions on roads and highways. Police officers conduct regular roadside Please check if the country name is correct traffic controls and checks on urban and rural roads, and highways. Once first responders begin such operations, they are vulnerable to motor vehicle collisions by oncoming traffic, a circumstance that calls for a better understanding of contributing factors and the extent to which they affect tragic outcomes. In light of factors identified in the literature, this paper applies machine learning methods including decision tree and random forest to a subset of the National Collision Database (NCDB) of Canada that includes information on collisions between two vehicles (one in parked position) and the severity of these collisions as measured by having or not having injuries. Findings reveal that key measurable, predictable, and sensible factors such as time, location, and weather conditions, as well as the interconnections among them, can explain the severity of collisions that may happen between motor vehicles and first responders who are working alongside the roads. Analysis from longitudinal data is rich and the use of automated methods can be used to predict and assess the risk and vulnerability of first responders while responding to or operating on different roads and conditions.

【 授权许可】

Unknown