Much progress has recently been made to enhance mesoscopic traffic simulation with the focus on advanced modeling features and traffic management support capabilities. Subsequently, the computational performance needs to be improved in order to make mesoscopic traffic simulation stay effective to real-time applications. This paper presents a framework for mesoscopic traffic simulation which offloads both the demand and supply components to GPU. The simulation algorithm divides the simulation flow into different steps and designs multiple kernels to handle the steps. A high level of data parallelism is achieved by assigning the GPU threads to the appropriate components of the traffic network. Several optimization options using an innovative data structure and improved warp execution are also deployed to harness the GPU performance while preserving simulation correctness. The performance of the framework is evaluated in a real network showing the speedup of up to nearly 5 times in the demand simulation and more than 4 times in the supply simulation compared to the sequential simulation.

Mesoscopic Traffic Simulation is an important tool in traffic analysis and traffic management support. The balance between traffic modeling details and performance has made Mesoscopic Traffic Simulation one of the key solutions for traffic controllers and policy makers. Mesoscopic traffic simulators offer acceptable speed in simulating normal traffic. However, when traffic prediction and optimization for large scale networks come into context, the performance of mesoscopic traffic simulators is unsatisfactory in optimizing a massive number of control parameters for a much longer prediction horizon. This issue again emphasizes the need to further speed up mesoscopic traffic simulation. This paper proposes a comprehensive framework to massively speed up mesoscopic traffic simulation using GPU without compromising its correctness and realistic modeling property. It also gives an in-depth analysis into the trade-off between simulation correctness and performance speedup. By combining the power of GPU with optimal design and data structures, mesoscopic traffic simulation is able to speed up to more than 6 times compared to original CPU implementation.