Application of Traffic Load-Balancing Algorithm—Case of Vigo

Urban traffic congestion is a significant challenge faced by cities globally, resulting in delays, increased emissions, and diminished quality of life. This study introduces an innovative traffic load-balancing algorithm developed as part of the IN2CCAM Horizon 2020 project, which was specifically t...

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Vydáno v:Sustainability Ročník 17; číslo 19; s. 8948
Hlavní autoři: Dündar, Selim, Alp, Sina, Ulu, İrem Merve, Dursun, Onur
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2025
Témata:
Algorithms
Autonomous vehicles
Case studies
Efficiency
Holidays & special occasions
Infrastructure
Load
Mobility
Optimization
Simulation
Software
Technology application
Traffic assignment
Traffic congestion
Traffic control
Traffic estimation
Traffic flow
Transportation services
Spain
Italy
ISSN:2071-1050, 2071-1050
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Shrnutí:Urban traffic congestion is a significant challenge faced by cities globally, resulting in delays, increased emissions, and diminished quality of life. This study introduces an innovative traffic load-balancing algorithm developed as part of the IN2CCAM Horizon 2020 project, which was specifically tested in the city of Vigo, Spain. The proposed method incorporates short-term traffic forecasting through machine learning models—primarily Long Short-Term Memory (LSTM) networks—alongside a dynamic routing algorithm designed to equalize travel times across alternative routes. Historical speed and volume data collected from Bluetooth sensors were analyzed and modeled to predict traffic conditions 15 min ahead. The algorithm was implemented within the PTV Vissim microsimulation environment to assess its effectiveness. Results from 20 distinct traffic scenarios demonstrated significant improvements: an increase in average speed of up to 3%, an 8% reduction in delays, and a 10% decrease in total standstill time during peak weekday hours. Furthermore, average emissions of CO2, NOx, HC, and CO were reduced by 4% to 11% across the scenarios. These findings highlight the potential of integrating predictive analytics with real-time load balancing to enhance traffic efficiency and promote environmental sustainability in urban areas. The proposed approach can further support policymakers and traffic operators in designing more sustainable mobility strategies and optimizing future urban traffic management systems.
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ISSN:2071-1050
2071-1050
DOI:10.3390/su17198948