Intelligent Model for Avoiding Road Accidents Using Artificial Neural Network
DOI:
https://doi.org/10.15837/ijccc.2023.5.5317Keywords:
Machine Learning, Internet of Things, Intelligent System, Artificial Neural Network, Linear Discriminant AnalysisAbstract
Accidents typically occurred on roads, resulting in significant societal losses. Road accidents are a worldwide issue that result in the loss of precious human lives and property. The purpose of this paper is to create an intelligent system-based on Machine Learning model for avoiding road accidents, as well as a system that effectively reduces road accidents severities. The Artificial Neural Network (ANN) algorithm, along with others such as Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Nave Bayes (NB), Stochastic Gradient Descent (SGD), Random Forest (RF), Gradient Boosting (GB), and AdaBoost, is used to create an intelligence system. Many driving collaborator procedures, installed in a few vehicles, assist drivers in avoiding vehicle crashes by providing early cautioning messages. The intelligence road crash avoidance system model is built on dataset of 29 columns and 1048575 rows. Preprocessing, feature selection, and feature extraction performed with the help of heat map and correlation matrix are used to select features. Linear Discriminant Analysis (LDA) is used for feature extraction. The testing dataset revealed that the proposed ANN method outperforms other algorithms with an accuracy of 0.856. Intelligent systems aid in the prevention of traffic accidents, which aids police officers and researchers in developing new policies.References
Xie, Guo, et al. "Unmanned System Safety Decision-Making Support: Analysis and Assessment of Road Traffic Accidents." IEEE/ASME Transactions on Mechatronics 26.2 (2020): 633-644.
https://doi.org/10.1109/TMECH.2020.3043471
Fu, Yuchuan, et al. "A survey of driving safety with sensing, vehicular communications, and artificial intelligence-based collision avoidance." IEEE Transactions on Intelligent Transportation Systems (2021).
https://doi.org/10.1109/TITS.2021.3083927
Chang, Yohan, et al. "Exploring Contributing Factors of Hazardous Events in Construction Zones Using Naturalistic Driving Study Data." IEEE Transactions on Intelligent Vehicles 5.3 (2020): 519-527.
https://doi.org/10.1109/TIV.2020.2980741
Hadjidimitriou, Natalia Selini, et al. "Machine learning for severity classification of accidents involving powered two wheelers." IEEE Transactions on Intelligent Transportation Systems 21.10 (2019): 4308-4317.
https://doi.org/10.1109/TITS.2019.2939624
Mansoor, Umer, et al. "Crash severity prediction using two-layer ensemble machine learning model for proactive emergency management." IEEE Access 8 (2020): 210750-210762.
https://doi.org/10.1109/ACCESS.2020.3040165
Nascimento, Alexandre Moreira, et al. "A systematic literature review about the impact of artificial intelligence on autonomous vehicle safety." IEEE Transactions on Intelligent Transportation Systems 21.12 (2019): 4928-4946.
https://doi.org/10.1109/TITS.2019.2949915
Shen, Xiaoyan, and ShanshanWei. "Application of XGBoost for hazardous material road transport accident severity analysis." IEEE Access 8 (2020): 206806-206819.
https://doi.org/10.1109/ACCESS.2020.3037922
Zhou, Bei, et al. "Comparing factors affecting injury severity of passenger car and truck drivers." IEEE Access 8 (2020): 153849-153861.
https://doi.org/10.1109/ACCESS.2020.3018183
Lindow, Friedrich, et al. "Ai-based driving data analysis for behavior recognition in vehicle cabin." 2020 27th Conference of Open Innovations Association (FRUCT). IEEE, 2020.
https://doi.org/10.23919/FRUCT49677.2020.9211020
Singhal, Vivek, et al. "Artificial intelligence enabled road vehicle-train collision risk assessment framework for unmanned railway level crossings." IEEE Access 8 (2020): 113790-113806.
https://doi.org/10.1109/ACCESS.2020.3002416
Manzoor, Mubariz, et al. "RFCNN: traffic accident severity prediction based on decision level fusion of machine and deep learning model." IEEE Access 9 (2021): 128359-128371.
https://doi.org/10.1109/ACCESS.2021.3112546
Kim, J., et al. "Collision Risk Assessment Algorithm via Lane-Based Probabilistic Motion Prediction of Surrounding Vehicles......" IEEE Trans. Intell. Transp. Syst 19.9 (2018): 1-10.
https://doi.org/10.1109/TITS.2017.2768318
George, Yannis, Theofilatos Athanasios, and Pispiringos George. "Investigation of road accident severity per vehicle type." Transportation research procedia 25 (2017): 2076-2083.
https://doi.org/10.1016/j.trpro.2017.05.401
Halim, Zahid, et al. "Artificial intelligence techniques for driving safety and vehicle crash prediction." Artificial Intelligence Review 46.3 (2016): 351-387.
https://doi.org/10.1007/s10462-016-9467-9
Nozaki, Daichi, et al. "AI management system to prevent accidents in construction zones using 4K cameras based on 5G network." 2018 21st International Symposium on Wireless Personal Multimedia Communications (WPMC). IEEE, 2018.
https://doi.org/10.1109/WPMC.2018.8712896
Tonni, Surraiya Islam, et al. "Artificial intelligence based driver vigilance system for accident prevention." 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD). IEEE, 2021.
https://doi.org/10.1109/ICICT4SD50815.2021.9396916
S. C. Venkateswarlu, N. U. Kumar and A. Karthik, "Speech Enhancement Using Recursive Least Square Based on Real-time adaptive filtering algorithm," 2021 6th International Conference for Convergence in Technology (I2CT), Maharashtra, India, 2021, pp. 1-4, doi: 10.1109/I2CT51068.2021.9417929.
https://doi.org/10.1109/I2CT51068.2021.9417929
Lacatan, Luisito L., et al. "Brake-Vision: A Machine Vision-Based Inference Approach of Vehicle Braking Detection for Collision Warning Oriented System." 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). IEEE, 2021.
https://doi.org/10.1109/ICCIKE51210.2021.9410750
Lin, Da-Jie, et al. "Intelligent traffic accident prediction model for Internet of Vehicles with deep learning approach." IEEE Transactions on Intelligent Transportation Systems 23.3 (2021): 2340-2349.
https://doi.org/10.1109/TITS.2021.3074987
Emu, Mahzabeen, et al. "Fatality Prediction for Motor Vehicle Collisions: Mining Big Data Using Deep Learning and Ensemble Methods." IEEE Open Journal of Intelligent Transportation Systems 3 (2022): 199-209.
https://doi.org/10.1109/OJITS.2022.3160404
Thakare, Kamalakar Vijay, et al. "Object Interaction-Based Localization and Description of Road Accident Events Using Deep Learning." IEEE Transactions on Intelligent Transportation Systems (2022).
https://doi.org/10.1109/TITS.2022.3170648
D. Narsaiah, R. S. Reddy, A. Kokkula, P. A. Kumar and A. Karthik, "A Novel Full Reference-Image Quality Assessment (FR-IQA) for Adaptive Visual Perception Improvement," 2021 6th International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India, 2021, pp. 726-730, doi: 10.1109/ICICT50816.2021.9358610.
https://doi.org/10.1109/ICICT50816.2021.9358610
Lin, Sung-Chiang, Chih-Jou Chen, and Tsung-Ju Lee. "A multi-label classification with hybrid label-based meta-learning method in internet of things." IEEE Access 8 (2020): 42261-42269.
https://doi.org/10.1109/ACCESS.2020.2976851
Karthik, A., MazherIqbal, J.L. Efficient Speech Enhancement Using Recurrent Convolution Encoder and Decoder. Wireless Pers Commun 119, 1959-1973 (2021). https://doi.org/10.1007/s11277-021-08313-6
https://doi.org/10.1007/s11277-021-08313-6
Onesimu, J. Andrew, et al. "Internet of things based intelligent accident avoidance system for adverse weather and road conditions." Journal of Reliable Intelligent Environments 7.4 (2021): 299-313.
https://doi.org/10.1007/s40860-021-00132-7
Song, Xianmin, Yaqian Sun, and Pengfei Tao. "A Dynamic Bayesian Network Model for Real-Time Risk Propagation of Secondary Rear-End Collision Accident Using Driving Risk Field." IEEE Access 10 (2022): 72429-72443.
https://doi.org/10.1109/ACCESS.2022.3188281
Severity Classification in Smart Transportation System." Arabian Journal for Science and Engineering (2022): 1-15.
Abirami, M. S., et al. "A classification model to predict onset of smoking and drinking habits based on socio-economic and sociocultural factors." Journal of Ambient Intelligence and Humanized Computing 12.3 (2021): 4171-4179.
https://doi.org/10.1007/s12652-020-01796-4
World Health Organization, "https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries", 19 February 2018.
Ji, Ang, and David Levinson. "Injury severity prediction from two-vehicle crash mechanisms with machine learning and ensemble models." IEEE Open Journal of Intelligent Transportation Systems 1 (2020): 217- 226.
https://doi.org/10.1109/OJITS.2020.3033523
Samsudeen, S., and G. Senthil Kumar. "A Data-Driven Predictive Model for Speed Control in Automotive Safety Applications." IEEE Sensors Journal 22.23 (2022): 23258-23266.
https://doi.org/10.1109/JSEN.2022.3208305
Umer, Muhammad, et al. "Comparison analysis of tree based and ensembled regression algorithms for traffic accident severity prediction." arXiv preprint arXiv:2010.14921 (2020).
J. L. M. Iqbal and A. Karthik, "Speech Enhancement Techniques in the Digital, Analog, and Hybrid Domains," 2022 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2022, pp. 1-3, doi: 10.1109/ICCCI54379.2022.9740883.
https://doi.org/10.1109/ICCCI54379.2022.9740883
Lakshme Gowda, Monisha. Traffic Accidents Prediction Using Ensemble Machine Learning Approach. Diss. Dublin, National College of Ireland, 2020.
Aldhari, Ibrahim, et al. "Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques." Applied Sciences 13.1 (2023): 233.
https://doi.org/10.3390/app13010233
Department for Transport, https://data.gov.uk, Licence: Open Government Licence, Upadated: 12 October 2018.
Poloju, n., & rajaram, a. (2022). Data mining techniques for patients healthcare analysis during covid-19 pandemic conditions. Journal of environmental protection and ecology, 23(5), 2105-2112.
Chiranjeevi, P., & Rajaram, A. (2022). Twitter Sentiment Analysis for Environmental Weather Conditions in Recommendation of Tourism. JOURNAL OF ENVIRONMENTAL PROTECTION AND ECOLOGY, 23(5), 2113-2123.
Chiranjeevi, P., & Rajaram, A. (2023). A lightweight deep learning model based recommender system by sentiment analysis. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-14.
Additional Files
Published
Issue
Section
License
Copyright (c) 2023 Manoj Kushwaha, M.S. Abirami
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
