AI-Driven Approach for Enhancing Sustainability in Urban Public Transportation
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The functioning of modern urban environments relies heavily on the public transport system. Given spatial, economic, and sustainability criteria, public transport in larger urban areas is unrivaled. The system’s role depends on the quality of service it offers. Achieving the desired service quality requires a design that meets transport demands. This paper uses a data-driven approach to address headway deviations in public transport lines and explores ways to improve regularity during the design phase. Headway is a critical dynamic element for transport organization and passenger quality. Deviations between planned and actual headways represent disturbances. On lines with headways under 15 min, passengers typically do not consult schedules, making punctuality less crucial. Reduced headway regularity affects the average travel time, travel time uncertainty, and passenger comfort. Ideally, the public transport system operates with regular headways. However, disturbances can spread and affect subsequent departures, leading to vehicle bunching. While previous research focused on single primary disturbances, this study, with the help of AI (reinforcement learning), examines multiple primary disturbances in the cities of Belgrade, Novi Sad, and Niš. The goal is to model the cumulative impact of these disturbances on vehicle movement. By ranking parameter influences and using the automatic optimization of static line elements, this research aims to improve headway regularity and increase system resilience to disturbances. The results of this research could also be useful in developing adaptive public transport management systems that leverage AI and IoT technologies to continuously optimize headway regularity in response to real-time data, ultimately enhancing service quality and passenger satisfaction.
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One of the primary challenges is ensuring accuracy and reliability in diverse and dynamic environments.
“One of the primary challenges is ensuring accuracy and reliability in diverse and dynamic environments [88,89].”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [89]
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Passenger Counting System Using YOLO on Edge AI Devices for Smart Public Transportation
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- Authors
- Lukic Vujadinovic, V., Damnjanovic, A., Cakic, A., Petkovic, D. R., Prelevic, M., Pantovic, V., Stojanovic, M., Vidojevic, D., Vranjes, D., & Bodolo, I.
- Affiliations
- Department for Industrial Engineering, Faculty of Engineering Management and Economics, University Privredna Akademija Novi Sad, 21000 Novi Sad, Serbia
- Faculty of Business and Law, University MB, Teodora Drajzera 27, 11000 Belgrade, Serbia
- Univerzitet Privredna akademija u Novom Sadu (RS)
- Fakultet za Saobraćaj, Komunikacije i Logistiku, Žrtava Fašizma 56, 85310 Budva, Montenegro
- Faculty of Information Technology and Engineering, University “Union-Nikola Tesla”, 11070 Belgrade, Serbia
- Skupstina Autonomne Pokrajine Vojvodine, Vladike Platona 1, 21000 Novi Sad, Serbia
- Akademija Strukovnih Studija Šumadija, 34000 Kragujevac, Serbia
- Akademija Tehničko-Umetničkih Strukovnih Studija Beograd, Odsek Visoka Železnička Škola, Zdravka Čelara 16, 11000 Belgrade, Serbia
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2024-09-06
- Volume
- 16
- Issue
- 17
- Pages
- 7763
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W4402317590
- Cited by
- 63
- References
- 73
- Citations by year
- 2026: 25 · 2025: 36 · 2024: 2
- FWCI
- 19.59
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sustainability (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Transportation › Transportation Planning and Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Railway Systems and Energy Efficiency
- Keywords
- urban public transport, reinforcement learning, service quality, travel time uncertainty, passenger comfort, Internet of Things, Belgrade, Novi Sad, Ni
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