URBAN TRAFFIC CRASH ANALYSIS USING DEEP LEARNING TECHNIQUES

Mummaneni Sobhana

sobhana@vrsiddhartha.ac.in
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
http://orcid.org/0000-0001-5938-5740

Nihitha Vemulapalli


Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
http://orcid.org/0009-0007-8626-0012

Gnana Siva Sai Venkatesh Mendu


Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
http://orcid.org/0009-0004-6406-000X

Naga Deepika Ginjupalli


Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
http://orcid.org/0009-0005-0436-0008

Pragathi Dodda


Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
http://orcid.org/0009-0003-4879-6298

Rayanoothala Bala Venkata Subramanyam


National Institute of Technology Warangal, Department of CSE (India)
http://orcid.org/0009-0005-8907-1984

Abstract

Road accidents are concerningly increasing in Andhra Pradesh. In 2021, Andhra Pradesh experienced a 20 percent upsurge in road accidents. The state's unfortunate position of being ranked eighth in terms of fatalities, with 8,946 lives lost in 22,311 traffic accidents, underscores the urgent nature of the problem. The significant financial impact on the victims and their families stresses the necessity for effective actions to reduce road accidents. This study proposes a framework that collects accident data from regions, namely Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam, and Gandhinagar in Vijayawada (India) from 2019 to 2021. The dataset comprises over 12,000 records of accident data. Deep learning techniques are applied to classify the severity of road accidents into Fatal, Grievous, and Severe Injuries. The classification procedure leverages advanced neural network models, including the Multilayer Perceptron, Long-Short Term Memory, Recurrent Neural Network, and Gated Recurrent Unit. These models are trained on the collected data to accurately predict the severity of road accidents. The project study to make important contributions for suggesting proactive measures and policies to reduce the severity and frequency of road accidents in Andhra Pradesh.


Keywords:

classification, gated recurrent unit, long-short term memory, multilayer perceptron, recurrent neural network, road accidents

Al Bataineh A., Kaur D., Jalali S. M. J.: Multi-layer perceptron training optimization using nature-inspired computing. IEEE Access 10, 2022, 36963–36977.
DOI: https://doi.org/10.1109/ACCESS.2022.3164669   Google Scholar

Alghamdi T.A., Javaid N.: A survey of preprocessing methods used for analysis of big data originated from smart grids. IEEE Access 10, 2022, 29149–29171.
DOI: https://doi.org/10.1109/ACCESS.2022.3157941   Google Scholar

Amorim B. d. S.P., et al.: A Machine Learning Approach for Classifying Road Accident Hotspots. ISPRS International Journal of Geo-Information 12(6), 2023, 227.
DOI: https://doi.org/10.3390/ijgi12060227   Google Scholar

Athiappan K., et al.: Identifying Influencing Factors of Road Accidents in Emerging Road Accident Blackspots. Advances in Civil Engineering, 2022.
DOI: https://doi.org/10.1155/2022/9474323   Google Scholar

Cai Q.: Cause analysis of traffic accidents on urban roads based on an improved association rule mining algorithm. IEEE Access 8, 2020, 75607–75615.
DOI: https://doi.org/10.1109/ACCESS.2020.2988288   Google Scholar

Chen M.-M., Chen M.-Ch.: Modeling road accident severity with comparisons of logistic regression, decision tree, and random forest. Information 11(5), 2020, 270.
DOI: https://doi.org/10.3390/info11050270   Google Scholar

Comi A., Polimeni A., Balsamo Ch.: Road accident analysis with data mining approach: evidence from Rome. Transportation research procedia 62, 2022, 798–805.
DOI: https://doi.org/10.1016/j.trpro.2022.02.099   Google Scholar

Ferreira-Vanegas C. M., Vélez J. I., García-Llinás G. A.: Analytical methods and determinants of frequency and severity of road accidents: a 20-year systematic literature review. Journal of Advanced Transportation, 2022.
DOI: https://doi.org/10.1155/2022/7239464   Google Scholar

Gatarić D., et al.: Predicting Road Traffic Accidents - Artificial Neural Network Approach. Algorithms 16(5), 2023, 257.
DOI: https://doi.org/10.3390/a16050257   Google Scholar

Gorzelanczyk P., Tylicki H.: Methodology for Optimizing Factors Affecting Road Accidents in Poland. Forecasting 5(1), 2023, 336–350.
DOI: https://doi.org/10.3390/forecast5010018   Google Scholar

Gutierrez-Osorio C., González F. A., Pedraza C. A.: Deep Learning Ensemble Model for the Prediction of Traffic Accidents Using Social Media Data. Computers 11(9), 2022, 126.
DOI: https://doi.org/10.3390/computers11090126   Google Scholar

Islam M. J., et al.: Application of min-max normalization on subject-invariant EMG pattern recognition. IEEE Transactions on Instrumentation and Measurement 71, 2022, 1–12.
DOI: https://doi.org/10.1109/TIM.2022.3220286   Google Scholar

Jia B.-B., Zhang M.-L.: Multi-dimensional classification via decomposed label encoding. IEEE Transactions on Knowledge and Data Engineering, 2021.
  Google Scholar

Kaffash Charandabi N., Gholami A., Abdollahzadeh Bina A.: Road accident risk prediction using generalized regression neural network optimized with self-organizing map. Neural Computing and Applications 34(11), 2022, 8511–8524.
DOI: https://doi.org/10.1007/s00521-021-06549-8   Google Scholar

Komol, M.M.R., et al.: Deep RNN Based Prediction of Driver’s Intended Movements at Intersection Using Cooperative Awareness Messages. IEEE Transactions on Intelligent Transportation Systems 24(7), 2023, 6902–6921.
DOI: https://doi.org/10.1109/TITS.2023.3254905   Google Scholar

Le X.-H., et al.: Application of long short-term memory (LSTM) neural network for flood forecasting. Water 11(7), 2019, 1387.
DOI: https://doi.org/10.3390/w11071387   Google Scholar

Mandal V., et al.: Artificial intelligence-enabled traffic monitoring system. Sustainability 12(21), 2020, 9177.
DOI: https://doi.org/10.3390/su12219177   Google Scholar

Novikov A., Shevtsova A., Vasilieva V.: Development of an approach to reduce the number of accidents caused by drivers. Transportation research procedia 50, 2020, 491–498.
DOI: https://doi.org/10.1016/j.trpro.2020.10.090   Google Scholar

Östh J., et al.: Driver kinematic and muscle responses in braking events with standard and reversible pre-tensioned restraints: validation data for human models. SAE Technical Paper, 2013, 2013-22-0001.
DOI: https://doi.org/10.4271/2013-22-0001   Google Scholar

Rahman M.M., et al.: Towards sustainable road safety in Saudi Arabia: Exploring traffic accident causes associated with driving behavior using a Bayesian belief network. Sustainability 14(10), 2022, 6315.
DOI: https://doi.org/10.3390/su14106315   Google Scholar

Rezk N. M., et al.: Recurrent neural networks: An embedded computing perspective. IEEE Access 8, 2020, 57967–57996.
DOI: https://doi.org/10.1109/ACCESS.2020.2982416   Google Scholar

Saravanarajan V.S., et al.: Car crash detection using ensemble deep learning. Multimedia Tools and Applications, 2023, 1–19.
DOI: https://doi.org/10.1007/s11042-023-15906-9   Google Scholar

Sobhana M., et al.: A Hybrid Machine Learning Approach for Performing Predictive Analytics on Road Accidents. 6th International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), 2022.
DOI: https://doi.org/10.1109/CSITSS57437.2022.10026404   Google Scholar

Upadhyay D., et al.: Intrusion detection in SCADA based power grids: Recursive feature elimination model with majority vote ensemble algorithm. IEEE Transactions on Network Science and Engineering 8(3), 2021, 2559–2574.
DOI: https://doi.org/10.1109/TNSE.2021.3099371   Google Scholar

Yan J., et al.: Relationship between Highway Geometric Characteristics and Accident Risk: A Multilayer Perceptron Model (MLP) Approach. Sustainability 15(3), 2023, 1893.
DOI: https://doi.org/10.3390/su15031893   Google Scholar

Yin Y., et al.: SE-GRU: Structure Embedded Gated Recurrent Unit Neural Networks for Temporal Link Prediction. IEEE Transactions on Network Science and Engineering 9(4), 2022, 2495–2509.
DOI: https://doi.org/10.1109/TNSE.2022.3164659   Google Scholar

Zarei M., Hellinga B., Izadpanah P.: CGAN-EB: A non-parametric empirical Bayes method for crash frequency modeling using conditional generative adversarial networks as safety performance functions. International Journal of Transportation Science and Technology 12(3), 2023, 753–764.
DOI: https://doi.org/10.1016/j.ijtst.2022.06.006   Google Scholar

Zheng H., et al.: A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems 22(11), 2020, 6910–6920.
DOI: https://doi.org/10.1109/TITS.2020.2997352   Google Scholar

Road Accidents in Malaysia: Top 10 Causes & Prevention. Kurnia, 21 Sept. 2022 [http://www.kurnia.com/blog/road-accidents-causes].
  Google Scholar

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Published
2023-09-30

Cited by

Sobhana, M., Vemulapalli, N., Siva Sai Venkatesh Mendu, G., Deepika Ginjupalli, N., Dodda, P., & Subramanyam, R. B. V. (2023). URBAN TRAFFIC CRASH ANALYSIS USING DEEP LEARNING TECHNIQUES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(3), 56–63. https://doi.org/10.35784/iapgos.5350

Authors

Mummaneni Sobhana 
sobhana@vrsiddhartha.ac.in
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
http://orcid.org/0000-0001-5938-5740

Authors

Nihitha Vemulapalli 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
http://orcid.org/0009-0007-8626-0012

Authors

Gnana Siva Sai Venkatesh Mendu 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
http://orcid.org/0009-0004-6406-000X

Authors

Naga Deepika Ginjupalli 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
http://orcid.org/0009-0005-0436-0008

Authors

Pragathi Dodda 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
http://orcid.org/0009-0003-4879-6298

Authors

Rayanoothala Bala Venkata Subramanyam 

National Institute of Technology Warangal, Department of CSE India
http://orcid.org/0009-0005-8907-1984

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