CPSO for Innovative Transport Planning and Designing

Mobility and transport systems are becoming increasingly complex. In order to make them more efficient and sustainable, it is necessary to promote studies and research that can improve not only the aspects related to data acquisition but also to data processing and simulation. Several studies in the literature focus on the evolution of mathematical models and traffic simulation tools. This allows planners to use available budgets and resources as efficiently as possible when expanding or rebuilding transport systems. Simulation models help to understand the effects that different measures have on traffic volume and flow under different circumstances. Therefore, traffic simulation creates a sound basis for sound and economic decisions, making traffic and mobility safe, sustainable, equitable and resilient. Starting from the acquisition of traffic data on a case study located at the Municipality of Enna in Sicily, this study focused on the use of the aforementioned dataset for the implementation of a Continuous Particle Swam Optimization algorithm to define a Neural Network model and predict the short-term traffic flow of a smart traffic control region. This approach emphasises that the prediction aspect compared to other methods is more accurate, and the prediction results provide a basis for improved vehicle flow management and intelligent regional traffic control.