Traffic Improvement in Manhattan Road Networks With the Use of Parallel Hybrid Biobjective Genetic Algorithm

There are many reasons for traffic congestions such as the "stop-and-go wave effect", periodic increase in intensity of vehicle and pedestrian traffic ("rush hours"), frequent maneuvering, uncontrolled pedestrians' movement on road crossings and other factors. This paper considers the problem of biobjective optimization and rebalancing of vehicles' and pedestrians' flows with the use of Manhattan road networks (MRNs) with smart traffic lights (STLs) as the case study of intelligent transportation system (ITS). For this purpose, we have studied the possibilities of applying STLs to control of traffic in large-scale road networks providing a speed harmonization and traffic prioritization between vehicles and pedestrians. The considered multiagent system (MAS) includes agent vehicles, agent pedestrians and agent lights that interact with each other according with given rules (e.g., V2V, V2P, V2I). Such STLs use information on the traffic structure and its density to switch signals at each moment in time. In the non-stationary mode with a periodic traffic intensity to provide the analysis of traffic flows done by STLs it has been suggested to use the fuzzy clustering algorithm aggregated with the density-based spatial clustering algorithm (FCA-DBSCAN). At the uniform motion fixed durations of phases set up for STLs that computed individually with use of the suggested parallel hybrid biobjective real-coded genetic algorithm (BORCGA-BOPSO). The approach allows to improve significantly the time-efficiency of seeking optimal individualised STLs' characteristics while keeping up their quality. Moreover, the ITS based on STLs with parameters optimized with the BORCGA-BOPSO provides significant traffic improvement in MRNs in contrast to the case of uncontrolled pedestrian crossings and using usual (i.e., non-smart) traffic lights.