TY - JOUR
TI - An efficient algorithm for traffic flow evaluation on smart cities based on deep learning
AU - Bahaddad Adel A
AU - Almarhabi Khalid Ali
AU - Alshahrani Mohammed
AU - Mnzool Mohammed
AU - Elhassan Adil A.m
AU - Alzughaibi Arwa
AU - Alghamdi Ahmed Mohammed
JN - Thermal Science
PY - 2025
VL - 29
IS - 2
SP - 1597
EP - 1606
PT - Article
AB - Following the emergence of the knowledge-based economy, the digital economy,  and the intelligent economy, smart cities are poised to represent the next  phase in urban development. These cities aim not only to leverage both  physical and digital infrastructures for urban advancement but also to  harness intellectual and social capital as essential elements of  urbanization. Smart cities are recognized as pivotal catalysts for  transforming various sectors by integrating multiple municipal systems such  as transportation, healthcare, and operational frameworks. The concept of a  smart society evolves from smart cities, characterized by a digitally  interconnected, knowledge-driven community that actively pursues social,  environmental, and economic sustainability. Recently, deep learning has  gained traction due to its ability to effectively tackle complex problems  across diverse applications using both supervised and unsupervised learning  methods. This approach relies on advanced techniques for managing large  datasets and multilayer neural networks, which often outperform traditional  ANN in processing historical data. This paper introduces a novel algorithm  based on deep learning designed to accurately predict traffic flow  behavior. The algorithm learns from multivariate sequence data by analyzing  spatio-temporal dependencies and non-linear correlations. Simulation  results demonstrate that the proposed method surpasses existing algorithms  in performance.