Road traffic simulation is a useful tool for studying road traffic and evaluating solutions to traffic problems. Large-scale agent-based road traffic simulation is computationally intensive, which triggers the need for conducting parallel simulation. This paper deals with the synchronization problem in parallel agent-based road traffic simulation to reduce the overall simulation execution time. We aim to reduce synchronization operations by introducing some redundant computation to the simulation. There is a trade-off between the benefit of reduced synchronization operations and the overhead of redundant computation. The challenge is to minimize the total overhead of redundant computation and synchronization. First, to determine the amount of redundant computation, we proposed a way to define extended layers of partitions in the road network. The sizes of extended layers are determined by the behavior of agents and the topology of road networks. Second, due to the dynamic nature of road traffic, a heuristic was proposed to adjust the amount of redundant computation according to traffic conditions during simulation run-time to minimize the overall simulation execution time. The efficiency of the proposed method was investigated in a parallel agent-based road traffic simulator using real-world network and trip data. Results have shown that the method can reduce synchronization overhead and improve the overall performance of the parallel simulation significantly.

Cloud-based simulation systems reduce the upfront hardware costs of running high-performance experiments and increases the ease with which simulation experiments can be repeated. The data being generated by simulations can be large. Commonly used data storage systems such as relational databases can handle large amounts of data, but the analysis is a challenging problem. Moreover, handling this amount of data in cloud services can be both expensive (bandwidth and storage costs) and time-consuming. However, a lot of the data that is generated by agent-based simulations does not contribute directly to the purpose of the experiment being conducted. We propose an extension to cloud-based simulation systems that rather than storing raw simulation output data, uses stream data processing to generate the result dataset while the simulation is running. This can then be used to store only the data required for later use, this saving both time and money.

One of the major components of Agent Based Crowd Simulation is motion planning. There have been various motion planning algorithms developed and they've become increasingly better and more efficient at calculating the most optimal path. We believe that this optimality is coming at the price of realism. Certain factors like social norms, limitations to human computation capabilities, etc. prevent humans from following their optimal path. One aspect of natural movement is related to perception and the manner in which humans process information. In this paper we propose two additions to generalmotion planning algorithms: (1) Group sensing for motion planning which results in agents avoiding clusters of other agents when choosing their collision free path. (2) Filtering of percepts based on interestingness to model limited information processing capabilities of human beings.