Harnessing the Power of HPC in Simulation and Optimization of Large Transportation Networks: Spatio-Temporal Traffic Management in the Greater Toronto Area

The significant growth of many urban areas comes at a cost of increasing demand for mobility and traffic congestion in large-scale urban environments. As congestion levels soar to unprecedented levels, and researchers and governments are challenged addressing the basic needs for transportation and mobility; solutions are becoming more complex and untraditional, creating a strong potential for optimization and simulation of large-scale transportation networks. In this paper, we present a generic traffic management framework for solving large-scale constraint optimization problems in advanced Intelligent Transportation systems (ITS) applications. The framework employs a distributed computing approach to enable replicating analysis of large-scale traffic simulation networks, providing a practical mechanism for solving complex optimization problems in transportation applications. We discuss employing the framework in two use cases, congestion pricing and emergency evacuation, targeting optimization of spatial and temporal traffic management. This paper focuses on the efforts exerted to develop the large-scale integrated traffic management framework, with two use cases presented briefly to show the effectiveness of the framework. However, the technical and modelling details of the congestion pricing and emergency evacuation applicationsconsidered in the use caseswere the subject of previously published papers of the authors. The findings of this paper revealed that harnessing the power of optimization, simulation, and cloud computing presents a strong case for traffic management of large-scale transportation networks.