Towards large-scale what-if traffic simulation with exact-differential simulation
To analyze and predict a behavior of large-scale traffics with what-if simulation, it needs to repeat many times with various patterns of what-if scenarios. In this paper, we propose new techniques to efficiently repeat what-if simulation tasks with exact-differential simulation. The paper consists of two main efforts: what-if scenario filtering and exact-differential cloning. The what-if scenario filtering enables to pick up meaningful what-if scenarios and reduces the number of what-if scenarios, which directly decreases total execution time of repeating. The exact-differential cloning enables to execute exact-differential simulation tasks in parallel to improve its total execution time. In our preliminary evaluation in Tokyo bay area's traffic simulation, we show potential of our proposals by estimating how the what-if scenarios filtering reduces the number of meaningless scenarios and also by estimating a performance improvement from our previous works with the exact-differential cloning.