Urban traffic crash analysis using deep learning techniques

• Autorzy: Sobhana Mummaneni , Vemulapalli Nihitha , Mendu Gnana Siva Sai Venkatesh , Ginjupalli Naga Deepika , Dodda Pragathi , Subramanyam Rayanoothala Bala Venkata

Identyfikatory

DOI

10.35784/iapgos.5350

Warianty tytułu

PL

Analiza kolizji w ruchu miejskim z wykorzystaniem technik głębokiego uczenia

• Języki publikacji: EN

Abstrakty

EN

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 techniquesare 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 modelsare 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.

PL

Liczba wypadków drogowych w Andhra Pradesh niepokojąco rośnie. W 2021 r. stan Andhra Pradesh odnotował 20% wzrost liczby wypadków drogowych. Niefortunna pozycja stanu, który zajmuje ósme miejsce pod względem liczby ofiar śmiertelnych, z 8946 ofiarami śmiertelnymiw 22311 wypadkach drogowych, podkreśla pilny charakter problemu. Znaczący wymiar finansowy dla ofiari ich rodziny podkreśla konieczność podjęcia skutecznych działań w celu ograniczenia liczby wypadków drogowych. W niniejszym badaniu zaproponowano system gromadzenia danych o wypadkachz regionów Patamata, Penamaluru, Mylavaram, Krishnalanka, Ibrahimpatnam i Gandhinagar w Vijayawada (India) w latach 2019–2021. Zbiór danych obejmuje ponad 12 000 rekordów danych o wypadkach. Techniki głębokiego uczenia są stosowane do klasyfikowania wagi wypadków drogowychna śmiertelne, poważne i ciężkie obrażenia. Procedura klasyfikacji wykorzystuje zaawansowane modele sieci neuronowych, w tymwielowarstwowy perceptron, pamięć długoterminową i krótkoterminową, rekurencyjną sieć neuronową i Gated Recurrent Unit. Modele te są trenowane na zebranych danych w celu dokładnego przewidywania wagi wypadków drogowych. Projekt ma wnieść istotny wkład w sugerowanie proaktywnych środków i polityk mających na celu zmniejszenie dotkliwości i częstotliwości wypadków drogowych w Andhra Pradesh.

Słowa kluczowe

EN

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

PL

klasyfikacja; pamięć długotrwała; pamięć krótkotrwała; perceptron wielowarstwowy; rekurencyjna sieć neuronowa; wypadki drogowe

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska
• Rocznik: 2023
• Tom: T. 13, nr 3
• Strony: 56--63
• Opis fizyczny: Bibliogr. 29 poz., rys., wykr.

Twórcy

autor

Sobhana Mummaneni

• Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering,Vijayawada, India

• https://orcid.org/0000-0001-5938-5740

autor

Vemulapalli Nihitha

• Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering,Vijayawada, India

• https://orcid.org/0009-0007-8626-0012

autor

Mendu Gnana Siva Sai Venkatesh

• Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering,Vijayawada, India

• https://orcid.org/0009-0004-6406-000X

autor

Ginjupalli Naga Deepika

• Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering,Vijayawada, India

• https://orcid.org/0009-0005-0436-0008

autor

Dodda Pragathi

• Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering,Vijayawada, India

• https://orcid.org/0009-0003-4879-6298

autor

Subramanyam Rayanoothala Bala Venkata

• National Institute of TechnologyWarangal, Department of CSE, Warangal, India

• https://orcid.org/0009-0005-8907-1984

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).