An integrated framework for opimizing travel time for cars in smart cities

Abstract

This thesis proposes three different approaches for optimizing the travel time of cars in large networks. Genetic Algorithm with the integration of microscopic traffic simulation is employed to search for global solution for traffic signals settings. Shortest path algorithms are utilized to regulate the congestion level of the network. Large networks are partitioned into subnetworks to enable the optimization and simulation procedure. Several case studies are analyzed in this thesis to examine the efficiency of each approach and to observe the influence of different factors in the solution quality and computation time of the optimization process.