A distributed big data analytics model for traffic accidents classification and recognition based on SparkMlLib cores

• Autorzy: Mallahi Imad El , Riffi Jamal , Tairi Hamid , Ez-Zahout Abderrahmane , Mahraz Mohamed Adnane

Identyfikatory

DOI

10.14313/JAMRIS/4-2022/34

• Języki publikacji: EN

Abstrakty

EN

This paper focuses on the issue of big data analytics for traffic accident prediction based on SparkMllib cores; however, Spark’s Machine Learning Pipelines provide a helpful and suitable API that helps to create and tune classification and prediction models to decision-making concerning traffic accidents. Data scientists have recently focused on classification and prediction techniques for traffic accidents; data analytics techniques for feature extraction have also continued to evolve. Analysis of a huge volume of received data requires considerable processing time. Practically, the implementation of such processes in real-time systems requires a high computation speed. Processing speed plays an important role in traffic accident recognition in real-time systems. It requires the use of modern technologies and fast algorithms that increase the acceleration in extracting the feature parameters from traffic accidents. Problems with overclocking during the digital processing of traffic accidents have yet to be completely resolved. Our proposed model is based on advanced processing by the Spark MlLib core. We call on the real-time data streaming API on spark to continuously gather real-time data from multiple external data sources in the form of data streams. Secondly, the data streams are treated as unbound tables. After this, we call the random forest algorithm continuously to extract the feature parameters from a traffic accident. The use of this proposed method makes it possible to increase the speed factor on processors. Experiment results showed that the proposed method successfully extracts the accident features and achieves a seamless classification performance compared to other conventional traffic accident recognition algorithms. Finally, we share all detected accidents with details onto online applications with other users.

Słowa kluczowe

EN

big data; machine learning; traffic accident; severity prediction; convolutional neural network

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2022
• Tom: Vol. 16, No. 4
• Strony: 62--71
• Opis fizyczny: Bibliogr. 41 poz., rys.

Twórcy

autor

Mallahi Imad El

• Phd Student in Big Data analytics, traffic accidents, Artificial Intelligence, LISAC Laboratory, Department of Computer Sciences, Faculty of Science, Sidi Mohamed Ben Abdellah University of Fez, Fez, Morocco

autor

Riffi Jamal

• Sidi Mohammed ben Abdellah University, Faculty of Sciences Dhar el Mahraz, Sidi Mohammed ben Abdellah University, Faculty of Sciences Dhar el Mahraz, LISAC laboratory, Fez, Morocco

autor

Tairi Hamid

• Sidi Mohammed ben Abdellah University, Faculty of Sciences Dhar el Mahraz, Sidi Mohammed ben Abdellah University, Faculty of Sciences Dhar el Mahraz, LISAC laboratory, Fez, Morocco

autor

Ez-Zahout Abderrahmane

• Mohamed V University, Faculty of Sciences, Intelligent Processing Systems & Security Team (IPSS) Computer Science Department, Rabat, Morocco

autor

Mahraz Mohamed Adnane

• Sidi Mohammed ben Abdellah University, Faculty of Sciences Dhar el Mahraz, Sidi Mohammed ben Abdellah University, Faculty of Sciences Dhar el Mahraz, LISAC laboratory, Fez, Morocco.

Bibliografia

• [1] Road Traffic Injuries. Accessed: Jul. 18, 2018. [Online]. Available: http://www.who.int/newsroom/fact-sheets/detail/road-traffic-injuries
• [2] F. Zong, H. Xu, and H. Zhang, Prediction for traffic accident severity: Comparing the Bayesian network and regression models, Math. Problems Eng., no. 23, 2013, Art. no. 475194.
• [3] Shiran, G.; Imaninasab, R.; Khayamim, R. Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison. Sustainability 2021,13, 5670.
• [4] Abdel-Aty, M. Analysis of driver injury severity levels at multiple locations using ordered probit models. J. Saf. Res. 2003, 34, 597–603.
• [5] Sze, N.N.;Wong, S.C. Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes. Accid. Anal. Prev., vol. 39, 2007, 1267–1278.
• [6] Savolainen, P.T.; Mannering, F.; Lord, D.; Quddus, M.A. The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accid. Anal. Prev., vol. 43, 2011, 1666–1676.
• [7] Moghaddam, F.R.; Afandizadeh, S.; Ziyadi, M. Prediction of accident severity using artificial neural networks. Int. J. Civ. Eng. vol. 9, 2011, 41–48.
• [8] Taamneh, M.; Alkheder, S.; Taamneh, S. Datamining techniques for traffic accident modeling and prediction in the United Arab Emirates. J. Transp. Saf. Secur. 2017, 9, 146–166. [CrossRef]
• [9] Zheng, M.; Li, T.; Zhu, R.; Chen, J.; Ma, Z.F.; Tang, M.J.; Cui, Z.Q.;Wang, Z. Traffic Accident’s Severity Prediction: A Deep-Learning ApproachBased CNN Network. IEEE Access 2019, 7, 39897–39910.
• [10] Breiman, L. Random forests. Mach. Learn., vol. 45, 2001, 5–32.
• [11] Lu, Z.; Long, Z.; Xia, J.; An, C. A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data. Sustainability, vol. 11, 2019, 5950.
• [12] Evans, J.; Waterson, B.; Hamilton, A. Forecasting road traffic conditions using a context-based random forest algorithm. Transp. Plan. Technol., vol. 42, 2019, 554–572.
• [13] Hamad, K.; Al-Ruzouq, R.; Zeiada,W.; Abu Dabous, S.; Khalil, M.A. Predicting incident duration using random forests. Transp.A-Transp. Sci. vol. 16, 2020, 1269–1293. [CrossRef] 70
• [14] Macioszek, E. Roundabout Entry Capacity Calculation—A Case Study Based on Roundabouts in Tokyo, Japan, and Tokyo Surroundings. Sustainability, vol. 12, 2020, 1533.
• [15] Severino, A.; Pappalardo, G.; Curto, S.; Trubia, S.; Olayode, I.O. Safety Evaluation of Flower Roundabout Considering Autonomous Vehicles Operation. Sustainability, vol. 13, 2021, 10120.
• [16] Macioszek, E. The Comparison of Models for Critical Headways Estimation at Roundabouts. In Proceedings of the 13th Scientific and Technical Conference on Transport Systems. Theory and Practice (TSTP), Katowice, Poland, 19–21 September, 2016.
• [17] Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. FeiFei, Large-scale video classication with convolutional neural networks, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2014, 17251732.
• [18] S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, Face recognition: A convolutional neural-network approach, IEEE Trans. Neural Netw., vol. 8, no. 1, 1997, 98113.
• [19] M. G. Karlaftis and E. I. Vlahogianni, Statistical methods versus neural networks in transportation research: Differences, similarities and some insights, Transp. Res. C, vol. 19, no. 3, 2011, 387399.
• [20] S. Al-Ghamdi, Using logistic regression to estimate the influence of accident factors on accident severity, Accident Anal. Prevention, vol. 34, no. 6, 2002. 729741.
• [21] M. Bédard, G. H. Guyatt, M. J. Stones, and J. P. Hirdes, ``The independent contribution of driver, crash, and vehicle characteristics to driver fatalities,’’ Accid Anal. Prevention, vol. 34, no. 6, 2002, 717727.
• [22] K. M. Kockelman and Y. J. Kweon, Driver injury severity: An application of ordered probit models, Accident Anal. Prevention, vol. 34, no. 3, 2002, 313321.
• [23] Mohamed AbdElAziz, Khamis Ahmed, ElMahdy Ahmed El-Mahdy, Kholoud Osama Shata Kholoud Osama Shata, Walid Gomaa Walid Gomaa, System and Method for During Crash Accident Detection and Notification, Patent Number: 2020/771, Filing Date: 9 June 2020, Filing Place: Egypt.
• [24] Mohamed AbdElAziz Khamis, Ahmed El-Mahdy, Kholoud Osama Shata. An In-Vehicle System and Method for During Accident Detection without being Fixed to Vehicle, Patent Number: 2020/769, Filing Date: 9 June 2020, Filing Place: Egypt.
• [25] Mohamed A. Khamis, Walid Gomaa, Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework, Engineering Applications of Artificial Intelligence, Vol. 29, March, 2014.134–151 https://doi.org/10.1016/j.engappai.2014.01.007.
• [26] Mohamed AbdElAziz Khamis, Walid Gomaa, Enhanced Multiagent Multi-Objective Reinforcement Learning for Urban Traffic Light Control, Proc. of the 11th IEEE International Conference on Machine Learning and Applications (ICMLA 2012), Boca Raton, Florida, USA, 12-15 Dec. 2012, pp. 586-591.
• [27] Mohamed A. Khamis , Walid Gomaa, Hisham El-Shishiny, Multi-objective traffic light control system based on Bayesian probability interpretation, Proc. of 15th IEEE Intelligent Transportation Systems Conference (ITSC 2012), Anchorage, Alaska, USA, 16-19 Sept. 2012, pp. 995–1000.
• [28] Zhang XG. Introduction to statistical learning theory and support vector machines. Acta Automat Sinica, vol. 26, 2000, 32–41.
• [29] Yuan F and Cheu RL. Incident detection using support vector machines. Transp Res Part C Emerg Technol. vol. 11, 2003, 309–328.
• [30] Sharma B, Katiyar VK and Kumar K. Traffic accident prediction model using support vector machines with Gaussian Kernel. In: Pant M, Deep K, Bansal JC, et al. (eds) Proceedings of fifth international conference on soft computing for problem solving, vol. 437. Singapore: Springer 2016, pp.1–10.
• [31] Flores MJ, Armingol JM and de la Escalera A. Real-time warning system for driver drowsiness etection using visual information. J Intell Robot Syst., vol. 59, 2010, 103–125.
• [32] Delen D, Sharda R and Bessonov M. Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Acc Anal Prevent., vol. 38, 2006, 434–444.
• [33] Alikhani M, Nedaie A and Ahmadvand A. Presentation of clustering-classification heuristic method for improvement accuracy in classification of severity of road accidents in Iran. Safe Sci., vol. 60, 2013, 142–150.
• [34] Lee S-L. Predicting traffic accident severity using classification techniques. Adv Sci Lett., vol. 21, 2015, 3128–3131.
• [35] Ez-zahout A., A distributed big data analytics model for people re-identification based dimensionality reduction. International Journal of High Performance Systems Architecture. vol. 10, no. 2, 2021, 57–63.
• [36] Haruna Chiroma, Shafi’i M. Abdulhamid, Ibrahim A. T. Hashem, Kayode S. Adewole, Absalom E. Ezugwu, Saidu Abubakar, and Liyana Shuib. Deep Learning-Based Big Data Analytics for Internet of Vehicles: Taxonomy, Challenges, and Research Directions. Hindawi Mathematical Problems in Engineering, vol. 2021, Article ID 9022558, 20 pages, https://doi.org/10.1155/2021/9022558
• [37] Asmae Rhanizar, Zineb El Akkaoui, A Predictive Framework of Speed Camera Locations for Road Safety. Computer and Information Science., vol. 12, no. 3, 2019. URL: https://doi.org/10.5539/jmr.v12n3p92
• [38] Salma Bouaich, Mahraz, M.A., Riffi, J. et al. Vehicle Counting Based on Road Lines. Pattern Recognit. Image Anal., vol. 31, 2021, 739–748. https://doi.org/10.1134/S1054661821040076
• [39] Bouti, A., Mahraz, M.A., Riffi, J. et al. A robust system for road sign detection and classification using LeNet architecture based on convolutional neural network. Soft Comput., vol. 24, 2020, 6721. https://doi.org/10.1007/s00500-019-04307-6
• [40] Bouaich, S., Mahraz, M.A., Riffi, J., Tairi, H., Vehicle Detection using Road Lines, 3rd International Conference on Intelligent Computing in Data Sciences, ICDS, 2019, 8942305
• [41] Bouaich, S., Mahraz, M.A., Rifii, J., Tairi, H., Vehicle counting system in real-time, 2018 International Conference on Intelligent Systems and Computer Vision, ISCV 2018, May, pp. 1–4

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).