URBAN TRAFFIC CRASH ANALYSIS USING DEEP LEARNING TECHNIQUES
Mummaneni Sobhana
sobhana@vrsiddhartha.ac.inVelagapudi 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 accidentsReferences
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Authors
Mummaneni Sobhanasobhana@vrsiddhartha.ac.in
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
http://orcid.org/0000-0001-5938-5740
Authors
Nihitha VemulapalliVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
http://orcid.org/0009-0007-8626-0012
Authors
Gnana Siva Sai Venkatesh MenduVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
http://orcid.org/0009-0004-6406-000X
Authors
Naga Deepika GinjupalliVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
http://orcid.org/0009-0005-0436-0008
Authors
Pragathi DoddaVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
http://orcid.org/0009-0003-4879-6298
Authors
Rayanoothala Bala Venkata SubramanyamNational Institute of Technology Warangal, Department of CSE India
http://orcid.org/0009-0005-8907-1984
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