Utilizing recurrent neural networks in sustainable urban transport and logistics

• Autorzy: Durlik Irmina , Miller Tymoteusz , Kostecka Ewelina , Pusty Tomasz , Łobodzińska Adrianna

Identyfikatory

DOI

10.17402/609

• Języki publikacji: EN

Abstrakty

EN

Urban centers, replete with diverse amenities and opportunities, simultaneously grapple with the challenges brought on by rapid urbanization, notably in the realms of transport and logistics. A pivotal move towards energy-efficient and sustainable systems is essential to mitigate these challenges. In this landscape, machine learning (ML), and particularly recurrent neural networks (RNNs), emerge as powerful tools for effectively addressing these urban complexities. This comprehensive review zeroes in on the deployment of RNNs within sustainable urban transportation and logistics, highlighting their adeptness in processing sequential data, a critical component in various forecasting and optimization tasks. We commence with a foundational understanding of RNNs, segueing into their successful applications in urban transport and logistics. This review also critically examines the constraints of current methodologies and potential avenues for enhancement. We scrutinize the application of RNNs across several areas, encompassing the energy shift in both passenger and freight transport, logistics management, integration of low- and zero-emission vehicles, and the energy dynamics of transport and logistics. Additionally, the role of RNNs in traffic and infrastructure planning is explored, particularly in forecasting traffic flow, congestion patterns, and optimizing energy usage. The crux of this review is to amalgamate and present the existing knowledge on the instrumental role of RNNs in facilitating the transition to energy-efficient urban transportation and logistics. Our goal is to highlight effective strategies, pinpoint challenges, and map out avenues for future research in this domain.

Słowa kluczowe

EN

recurrent neural network; sustainable urban transport; urban logistics; energy transition; machine learning; traffic management; infrastructure planning; zero emission vehicle; energy efficiency; traffic flow prediction

• Wydawca: Wydawnictwo Naukowe Politechniki Morskiej w Szczecinie
• Czasopismo: Zeszyty Naukowe Politechniki Morskiej w Szczecinie
• Rocznik: 2024
• Tom: nr 78 (150)
• Strony: 131--149
• Opis fizyczny: Bibliogr. 140 poz., rys.

Twórcy

autor

Durlik Irmina

• Maritime University of Szczecin 1-2 Wały Chrobrego St., 70-500 Szczecin Poland
• Polish Society of Bioinformatics and Data Science BIODATA 4c Popiełuszki St., 71-214 Szczecin, Poland

• https://orcid.org/0000-0002-9684-072X

autor

Miller Tymoteusz

• Polish Society of Bioinformatics and Data Science BIODATA 4c Popiełuszki St., 71-214 Szczecin, Poland
• University of Szczecin, Institute of Marine and Environmental Sciences 13 Wąska St., 71-415 Szczecin, Poland

• https://orcid.org/0000-0002-5962-5334

autor

Kostecka Ewelina

• Maritime University of Szczecin 1-2 Wały Chrobrego St., 70-500 Szczecin, Poland
• Polish Society of Bioinformatics and Data Science BIODATA 4c Popiełuszki St., 71-214 Szczecin, Poland

• https://orcid.org/0009-0006-5357-6531

autor

Pusty Tomasz

• Maritime University of Szczecin 1-2 Wały Chrobrego St., 70-500 Szczecin, Poland

• https://orcid.org/0000-0003-1324-5384

autor

Łobodzińska Adrianna

• University of Szczecin, Institute of Biology 3c Felczaka St., 71-412 Szczecin, Poland
• Polish Society of Bioinformatics and Data Science BIODATA 4c Popiełuszki St., 71-214 Szczecin, Poland

• https://orcid.org/0000-0002-6669-3589

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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).