ANALIZA KOLIZJI W RUCHU MIEJSKIM Z WYKORZYSTANIEM TECHNIK GŁĘBOKIEGO UCZENIA

Mummaneni Sobhana

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

Nihitha Vemulapalli


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

Gnana Siva Sai Venkatesh Mendu


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

Naga Deepika Ginjupalli


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

Pragathi Dodda


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

Rayanoothala Bala Venkata Subramanyam


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

Abstrakt

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 8 946 ofiarami śmiertelnymi w 22 311 wypadkach drogowych, podkreśla pilny charakter problemu. Znaczący wymiar finansowy dla ofiar i ich rodziny podkreśla konieczność podjęcia skutecznych działań w celu ograniczenia liczby wypadków drogowych. W niniejszym badaniu zaproponowano system gromadzenia danych o wypadkach z 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 drogowych na śmiertelne, poważne i ciężkie obrażenia. Procedura klasyfikacji wykorzystuje zaawansowane modele sieci neuronowych, w tym wielowarstwowy 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:

klasyfikacja, gated recurrent unit, pamięć długotrwała i krótkotrwała, perceptron wielowarstwowy, rekurencyjna sieć neuronowa, wypadki drogowe

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Opublikowane
2023-09-30

Cited By / Share

Sobhana, M., Vemulapalli, N., Siva Sai Venkatesh Mendu, G., Deepika Ginjupalli, N., Dodda, P., & Subramanyam, R. B. V. (2023). ANALIZA KOLIZJI W RUCHU MIEJSKIM Z WYKORZYSTANIEM TECHNIK GŁĘBOKIEGO UCZENIA. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(3), 56–63. https://doi.org/10.35784/iapgos.5350

Autorzy

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

Autorzy

Nihitha Vemulapalli 

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

Autorzy

Gnana Siva Sai Venkatesh Mendu 

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

Autorzy

Naga Deepika Ginjupalli 

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

Autorzy

Pragathi Dodda 

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

Autorzy

Rayanoothala Bala Venkata Subramanyam 

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

Statystyki

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