By: Mehdi Ghatee
Published: Metaheuristics and Optimization in Computer and Electrical Engineering, Springer, Switzerland, (2021).
Intelligent Transportation Systems (ITS) refer to a range of transportation applications based on communication and information technology. These systems by the aid of modern ideas, provide comfortable, efficient and safe services for transportation users.They are located in the linkage of information technology, computer science, electrical engineering, system analysis, civil engineering, and optimization. They form a main branch of the smart cities and are fundamental for the development of countries. ITS applications, usually, use the capabilities of sensor networks, electrical devices, and computer processing units to deliver a service, however, they are not limited to the hardware devices. Instead, the modeling of transportation problems and solving them efficiently are more challenging problems. Especially, when they support good ideas for controlling the transportation systems or guiding some users. In this chapter, the application of optimization models for the transportation context are discussed. To this end, the models for data collection by a sensor network are needed. Then, for mining these data and extracting the necessary knowledge for transportation context awareness, some fundamental models such as regression analysis, frequent pattern mining, clustering or classification can be applied. These backgrounds are used to extend the appropriate models of ITS in an integrated architecture. To solve these models, the classical network and combinatorial optimization methods, simulation-optimization techniques, and metaheuristic algorithms will be explained. Then, we categorize the applications of these optimization models in the different subsystems of ITS architecture. The output of this investigation can be used to develop different ITS services for the urban and inter-cities networks.
Intelligent Transportation Systems, Traffic Assignment, Network Optimization, Mathematical Programming, Metaheuristic Algorithms, Sensor Networks, Simulation Optimization, Meta-model.