Prediction of Road Accident Using Artificial Neural Network

Prediction of Road Accident Using Artificial Neural Network
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© 2022 by IJETT Journal
Volume-70 Issue-2
Year of Publication : 2022
Authors : Mayura Yeole, Rakesh Kumar Jain, Radhika Menon
DOI :  10.14445/22315381/IJETT-V70I2P217

How to Cite?

Mayura Yeole, Rakesh Kumar Jain, Radhika Menon, "Prediction of Road Accident Using Artificial Neural Network," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 151-161, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I2P217

Abstract
A traffic accident is one of the biggest road safety concerns. Predictability models are used to explain the relationship between highway calamity and applicable parameters such as volume of traffic, conditions of roads and environmental concerns. For this work, a prediction model has been developed using ANN (Artificial Neural Network) and compared with the multiple linear regression techniques. The dataset collected and used for this study was for a mixed traffic flow of Pimpri Chinchwad Municipal Corporation (PCMC), Pune, Maharashtra, India. Weather condition, Vehicles loading condition, number of lanes and different time slots of accidents are considered as input parameters. Along with these, Traffic signals, speed breakers, the road intersects were also reflected as input parameters. For this, the accident data were collected for a period of six years ranging from 2014 to 2019. A recorded number of 887 major, as well as minor accidents, were considered for this study. For the ANN model, the available accident dataset was divided into three parts. In this study, 70% of data were used for model preparation, 15% for Training and Testing of the model and the remaining 15% for Validation of the model. Results show that the prediction model using ANN gives excellent accuracy. In this study, more emphasis has been given to the actual parameters which are responsible for accidents caused by mixed traffic flow.

Keywords
Regression, Performance, Road accident, Prediction, Artificial Neural Network.

Reference
[1] Aworemi, Joshua, R., Abdul, A., Ibraheem, A., Olabode, & Segun, O., Analytical study of the causal factors of road traffic crashes in southwestern, Educational Research ., 1(4) (2010) 118-124.
[2] Bangaram Naga Kiran et al., A Review of Road Crash Prediction Models for Developed Countries , American Journal of Traffic and Transportation Engineering; 2(2) (2017) 10-25.
[3] Emmanuel Kofi Adanu and Steven Jones ., Effects of Human-Centered Factors on Crash Injury Severities, Journal of Advanced Transportation, Article ID 1208170, (2017) 11 pages https://doi.org/10.1155/2017/1208170.
[4] F. N. Ogwueleka et al., An Artificial Neural Network Model for Road Accident Prediction: A Case Study of a Developing Country" Acta Polytechnica Hungarica., 11(5) (2014) 177 -197.
[5] Fatemeh Bagheri Khalili et al., Variable Efficiency Appraisal in Freeway Accidents Using Artificial Neural Networks -Case Study”, CICTP 2012 © ASCE., (2012).
[6] Highway traffic book
[7] https://data.gov.in/search/site?query=road+accident
[8] Report Published by accident research cell, Maharashtra. https://highway police.maharashtra.gov.in/en/
[9] https://morth.nic.in › sites › files › Road_Accidednt
[10] J. R. Treat, N. S. Tumbas, S. T. McDonald et al.., Tri-level study of the causes of traffic accidents: final report, vol. 1: causal factor tabulations and assessments, DOT HS-805 085, Indiana University: Institute for Research in Public Safety, Bloomington, Ind, USA., (1979).
[11] Jonathan J.Rolison et al., What are the factors that contribute to road accidents? An assessment of law enforcement views, ordinary drivers’ opinions, and road accident records, Elsevier Accident Analysis & Prevention, 115 (2018) 11-24.
[12] Lai Zheng & Xianghai Meng., An Approach To Predict Road Accident Frequencies: Application of Fuzzy Neural Network" Submitted to the 3rd International Conference on Road Safety and Simulation, September , Indianapolis, USA., (2011) 14-16.
[13] Li-Yen Chang., Analysis of freeway accident frequencies: Negative binomial regression versus artificial neural network" Safety Science, Elsevier 43 (2005) 541–557.
[14] M. Y. Çodur et al., An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey” Promet – Traffic & Transportation, 27(3) (2015) 217-225
[15] Mohammed Gaber et al., Traffic Accidents Prediction Model Using Fuzzy Logic: Aswan Desert Road Case Study” Journal of Engineering Sciences Assiut University Faculty of Engineering ,45(1) (2017) 28 – 44.
[16] N. R. Vatti, P. L. Vatti, R. Vatti and C. Garde., Smart Road Accident Detection and communication System, 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), (2018) 1-4 doi: 10.1109/ICCTCT.2018.8551179.
[17] Nabanita Roy et al.,Effect of Mixed Traffic on Capacity of Two-Lane Roads: Case Study on Indian Highways 10th International Scientific Conference Transbaltica 2017, Procedia Engineering 187 (2017) 53 – 58.
[18] Shan XUE and Jinxian WENG., Analysis of Uncertainty Associated with Response Time in Work Zone Traffic Accidents" CICTP: Safe, Smart, and Sustainable Multimodal Transportation Systems © ASCE, (2014) 3710-3722.
[19] Sharaf Alkheder et al., Severity Prediction of Traffic Accident Using an Artificial Neural Network, Journal of Forecasting, J. Forecast Published online in Wiley Online Library, (2016). DOI: 10.1002/for.2425.
[20] Sujata Basu and Pritam Saha., Regression Models of Highway Traffic Crashes: A Review of Recent Research and Future Research Needs , Procedia Engineering 187 (2017) 59–66, ScienceDirect, 2017
[21] Yangzexi Liu et al., Characteristic Analysis of Mixed Traffic Flow of Regular and Autonomous Vehicles Using Cellular Automata , Hindawi Journal of Advanced Transportation., (2017).
[22] Zhenke Luo, Yue Liu, Chen Guo,., Operational characteristics of mixed traffic flow under bi-directional environment using cellular automaton, Journal of Traffic and Transportation Engineering (English Edition), 1(6) (2014) 383-392, ISSN 2095-7564, https://doi.org/10.1016/S2095-7564(15)30288-9.
[23] Zhongqi Yuan et al., Characteristic Analysis and Prediction of Traffic Accidents in the Multiethnic Plateau Mountain Area, J. Transp. Eng., Part A: Systems, 146(8) (2020) ASCE.