Dundar, SelimAlp, SinaUlu, Irem MerveDursun, Onur2025-11-152025-11-1520252071-105010.3390/su171989482-s2.0-105019108240https://doi.org/10.3390/su17198948https://hdl.handle.net/20.500.14517/8518Urban 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.eninfo:eu-repo/semantics/openAccessTraffic Load BalancingUrban Traffic ManagementShort-Term Traffic PredictionLong Short-Term MemoryPTV VissimArticleQ2Q21719WOS:001593873100001