Applying Machine Learning Models to First Responder Collisions Beside Roads: Insights from “Two Vehicles Hit a Parked Motor Vehicle” Data
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/23/11198 |
| _version_ | 1797508103791443968 |
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| author | Mohammadali Tofighi Ali Asgary Ghassem Tofighi Brady Podloski Felippe Cronemberger Abir Mukherjee Xia Liu |
| author_facet | Mohammadali Tofighi Ali Asgary Ghassem Tofighi Brady Podloski Felippe Cronemberger Abir Mukherjee Xia Liu |
| author_sort | Mohammadali Tofighi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T04:58:32Z |
| format | Article |
| id | doaj.art-c4df8337dd4a47e2ba5351506ae2a14a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:58:32Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c4df8337dd4a47e2ba5351506ae2a14a2023-11-23T02:04:06ZengMDPI AGApplied Sciences2076-34172021-11-0111231119810.3390/app112311198Applying Machine Learning Models to First Responder Collisions Beside Roads: Insights from “Two Vehicles Hit a Parked Motor Vehicle” DataMohammadali Tofighi0Ali Asgary1Ghassem Tofighi2Brady Podloski3Felippe Cronemberger4Abir Mukherjee5Xia Liu6ADERSIM, School of Administrative Studies, Faculty of Liberal Arts & Professional Studies, York University, Toronto, ON M3J 1P3, CanadaADERSIM, School of Administrative Studies, Faculty of Liberal Arts & Professional Studies, York University, Toronto, ON M3J 1P3, CanadaSheridan College Institute of Technology & Advanced Learning, Oakville, ON L6H 2L1, CanadaADERSIM, School of Administrative Studies, Faculty of Liberal Arts & Professional Studies, York University, Toronto, ON M3J 1P3, CanadaADERSIM, School of Administrative Studies, Faculty of Liberal Arts & Professional Studies, York University, Toronto, ON M3J 1P3, CanadaA.U.G. Signals Ltd., Toronto, ON M5H 4E8, CanadaA.U.G. Signals Ltd., Toronto, ON M5H 4E8, CanadaFirst 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.https://www.mdpi.com/2076-3417/11/23/11198emergency respondersroad safetyroad collisionsmachine learning |
| spellingShingle | Mohammadali Tofighi Ali Asgary Ghassem Tofighi Brady Podloski Felippe Cronemberger Abir Mukherjee Xia Liu Applying Machine Learning Models to First Responder Collisions Beside Roads: Insights from “Two Vehicles Hit a Parked Motor Vehicle” Data Applied Sciences emergency responders road safety road collisions machine learning |
| title | Applying Machine Learning Models to First Responder Collisions Beside Roads: Insights from “Two Vehicles Hit a Parked Motor Vehicle” Data |
| title_full | Applying Machine Learning Models to First Responder Collisions Beside Roads: Insights from “Two Vehicles Hit a Parked Motor Vehicle” Data |
| title_fullStr | Applying Machine Learning Models to First Responder Collisions Beside Roads: Insights from “Two Vehicles Hit a Parked Motor Vehicle” Data |
| title_full_unstemmed | Applying Machine Learning Models to First Responder Collisions Beside Roads: Insights from “Two Vehicles Hit a Parked Motor Vehicle” Data |
| title_short | Applying Machine Learning Models to First Responder Collisions Beside Roads: Insights from “Two Vehicles Hit a Parked Motor Vehicle” Data |
| title_sort | applying machine learning models to first responder collisions beside roads insights from two vehicles hit a parked motor vehicle data |
| topic | emergency responders road safety road collisions machine learning |
| url | https://www.mdpi.com/2076-3417/11/23/11198 |
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