URBAN TRAFFIC CRASH ANALYSIS USING DEEP LEARNING TECHNIQUES
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 na...
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| Format: | Article |
| Language: | English |
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Lublin University of Technology
2023-09-01
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| Series: | Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska |
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| Online Access: | https://ph.pollub.pl/index.php/iapgos/article/view/5350 |
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| author | Mummaneni Sobhana Nihitha Vemulapalli Gnana Siva Sai Venkatesh Mendu Naga Deepika Ginjupalli Pragathi Dodda Rayanoothala Bala Venkata Subramanyam |
| author_facet | Mummaneni Sobhana Nihitha Vemulapalli Gnana Siva Sai Venkatesh Mendu Naga Deepika Ginjupalli Pragathi Dodda Rayanoothala Bala Venkata Subramanyam |
| author_sort | Mummaneni Sobhana |
| collection | DOAJ |
| description |
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.
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| first_indexed | 2024-03-11T20:54:48Z |
| format | Article |
| id | doaj.art-615d4a89f75245068ac16aad1668c51f |
| institution | Directory Open Access Journal |
| issn | 2083-0157 2391-6761 |
| language | English |
| last_indexed | 2024-03-11T20:54:48Z |
| publishDate | 2023-09-01 |
| publisher | Lublin University of Technology |
| record_format | Article |
| series | Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska |
| spelling | doaj.art-615d4a89f75245068ac16aad1668c51f2023-09-30T18:29:57ZengLublin University of TechnologyInformatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska2083-01572391-67612023-09-0113310.35784/iapgos.5350URBAN TRAFFIC CRASH ANALYSIS USING DEEP LEARNING TECHNIQUESMummaneni Sobhana0Nihitha Vemulapalli1Gnana Siva Sai Venkatesh Mendu2Naga Deepika Ginjupalli3Pragathi Dodda4Rayanoothala Bala Venkata Subramanyam5Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and EngineeringVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and EngineeringVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and EngineeringVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and EngineeringVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and EngineeringNational Institute of Technology Warangal, Department of CSE 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. https://ph.pollub.pl/index.php/iapgos/article/view/5350classificationgated recurrent unitlong-short term memorymultilayer perceptronrecurrent neural networkroad accidents |
| spellingShingle | Mummaneni Sobhana Nihitha Vemulapalli Gnana Siva Sai Venkatesh Mendu Naga Deepika Ginjupalli Pragathi Dodda Rayanoothala Bala Venkata Subramanyam URBAN TRAFFIC CRASH ANALYSIS USING DEEP LEARNING TECHNIQUES Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska classification gated recurrent unit long-short term memory multilayer perceptron recurrent neural network road accidents |
| title | URBAN TRAFFIC CRASH ANALYSIS USING DEEP LEARNING TECHNIQUES |
| title_full | URBAN TRAFFIC CRASH ANALYSIS USING DEEP LEARNING TECHNIQUES |
| title_fullStr | URBAN TRAFFIC CRASH ANALYSIS USING DEEP LEARNING TECHNIQUES |
| title_full_unstemmed | URBAN TRAFFIC CRASH ANALYSIS USING DEEP LEARNING TECHNIQUES |
| title_short | URBAN TRAFFIC CRASH ANALYSIS USING DEEP LEARNING TECHNIQUES |
| title_sort | urban traffic crash analysis using deep learning techniques |
| topic | classification gated recurrent unit long-short term memory multilayer perceptron recurrent neural network road accidents |
| url | https://ph.pollub.pl/index.php/iapgos/article/view/5350 |
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