Mathematical Optimization Techniques for Smart Transportation Systems

Abstract

Due to the swift development of the intelligent transportation systems (ITS), the creation of sophisticated optimization strategies is required to achieve improved efficiency, safety, and sustainability of the traffic. This paper gives an in-depth analysis of the modern mathematical, heuristic, and machine learning-based optimization methods to smart transportation networks. Simulation-optimization, evolutionary algorithms, deep learning-based traffic management, and graph-theoretic models are among the different methodologies developed to deal with dynamic routing, congestion management, as well as multi-objective system planning. Findings of these strategies show that there is a major change in the traffic flow, lessening travel time, and resource distribution, and the ability to make adaptive decisions during real-time conditions. Application of the International of Vehicles, IoT-based infrastructure and data-based analytics has also improved the performance and scalability of ITS applications in urban and intercity settings. The study ends by stating that hybrid optimization models that integrate mathematical rigor and the use of computational intelligence techniques have the best potential to develop in the future ITS. The new trends emphasize the need to integrate sustainability, cost time efficiency, and predictive analytics in order to have resilient and intelligent transportation networks. On the whole, this study highlights the importance of the further development of optimization strategies as the key to effective, secure, and intelligent mobility in the current cities.