Highway Accident Severity Prediction for Optimal Resource Allocation of Emergency Vehicles and Personnel

Traffic accidents could have significant impacts on people's lives. Roughly half a million highway traffic accidents occurred in 2018 in the USA. Intelligent Transportation Systems (ITSs) and Vehicular Ad Hoc Networks (VANETs) have great potential to enhance highway traffic safety and improve emergency response. This paper uses real-life traffic and accident data for a Florida highway to build prediction models to predict traffic accident severity. Accurate severity prediction is beneficial for both the responders and the drivers. First responders are in high demand, and the pandemic has made the situation worse. When an accident occurs, the emergency center dispatches a random number of emergency vehicles. Unfortunately, this number exceeds the number of vehicles needed most of the time, leaving fewer resources to respond to simultaneous accidents in different locations. Also, an increased number of emergency vehicles could introduce secondary accidents. In fact, for every ten accidents, one of them is a secondary accident. Our research gives an accurate prediction for the number of emergency vehicles needed based on the accident severity. We have used historical traffic and accident data obtained from the Florida Department of Transportation District 4 (FDOT-D4) to predict an accident and its severity. This can be used to esetimate the right number of emergency vehicles to respond to the accident. Our real-time prediction models can help reduce highway traffic accidents and congestion as well. Our prediction results demonstrate promising accuracy results and computation cost to support ITS applications.

[1]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[3]  Li-Yen Chang,et al.  Analysis of traffic injury severity: an application of non-parametric classification tree techniques. , 2006, Accident; analysis and prevention.

[4]  Timothy O. Laumann,et al.  Informatics and Data Mining Tools and Strategies for the Human Connectome Project , 2011, Front. Neuroinform..

[5]  Antonio D’Ambrosio,et al.  Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery. , 2012, Accident; analysis and prevention.

[6]  Li-Yen Chang,et al.  Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model , 2013 .

[7]  Griselda López,et al.  Extracting decision rules from police accident reports through decision trees. , 2013, Accident; analysis and prevention.

[8]  Griselda López,et al.  Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. , 2013, Accident; analysis and prevention.

[9]  Juan de Oña,et al.  Analysis of traffic accident severity using Decision Rules via Decision Trees , 2013, Expert Syst. Appl..

[10]  Xingquan Zhu,et al.  iSRD: Spam review detection with imbalanced data distributions , 2014, Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014).

[11]  Imad Mahgoub,et al.  Big vehicular traffic Data mining: Towards accident and congestion prevention , 2016, 2016 International Wireless Communications and Mobile Computing Conference (IWCMC).

[12]  Amirfarrokh Iranitalab,et al.  Comparison of four statistical and machine learning methods for crash severity prediction. , 2017, Accident; analysis and prevention.

[13]  Liping Fu,et al.  Identifying vehicle driver injury severity factors at highway-railway grade crossings using data mining algorithms , 2017, 2017 4th International Conference on Transportation Information and Safety (ICTIS).

[14]  Hassan HadiSaleh,et al.  A Survey on VANETs: Challenges and Solutions , 2018 .

[15]  Ziyuan Pu,et al.  Comparing Prediction Performance for Crash Injury Severity Among Various Machine Learning and Statistical Methods , 2018, IEEE Access.

[16]  Yinhai Wang,et al.  Identification of significant factors in fatal-injury highway crashes using genetic algorithm and neural network. , 2018, Accident; analysis and prevention.

[17]  Sangil Kwon,et al.  Model Evaluation for Forecasting Traffic Accident Severity in Rainy Seasons Using Machine Learning Algorithms: Seoul City Study , 2019, Applied Sciences.

[18]  Linjun Lu,et al.  Analysis of Accident Severity for Curved Roadways Based on Bayesian Networks , 2019, Sustainability.

[19]  Hongchao Liu,et al.  Factor Identification and Prediction for Teen Driver Crash Severity Using Machine Learning: A Case Study , 2020, Applied Sciences.

[20]  Jinchang Ren,et al.  Towards Big Data Analytics and Mining for UK Traffic Accident Analysis, Visualization & Prediction , 2020, ICMLC.

[21]  Faraneh Zarafshan,et al.  Process Modeling and Extraction of Patterns of Computer Crimes Using Data Mining , 2020, Comput. Sci. J. Moldova.

[22]  Umer Mansoor,et al.  Predicting Crash Injury Severity with Machine Learning Algorithm Synergized with Clustering Technique: A Promising Protocol , 2020, International journal of environmental research and public health.