Application of Traffic Load-Balancing Algorithm—Case of Vigo

Selim Dündar (), Sina Alp, İrem Merve Ulu and Onur Dursun
Additional contact information
Selim Dündar: Faculty of Engineering and Natural Sciences, Istanbul Okan University, 34959 Istanbul, Turkey
Sina Alp: Faculty of Engineering and Natural Sciences, Istanbul Okan University, 34959 Istanbul, Turkey
İrem Merve Ulu: Faculty of Engineering and Natural Sciences, Istanbul Okan University, 34959 Istanbul, Turkey
Onur Dursun: Faculty of Engineering and Natural Sciences, Istanbul Okan University, 34959 Istanbul, Turkey

Sustainability, 2025, vol. 17, issue 19, 1-38

Abstract: 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 CO 2 , NO x , 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.

Keywords: traffic load balancing; urban traffic management; short-term traffic prediction; long short-term memory; PTV Vissim (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/19/8948/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/19/8948/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:19:p:8948-:d:1767241

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().