The impact of error-tolerance using customized DIJKSTRA on large scale parallel crowd simulations / Luuk Sterke - Luuk , Sterke (2022)
This Dissertation aims at exploring and quantifying the errors that occur in parallel crowd simulation when not implementing measures to prevent these errors. Consequently, the simulation should run faster, as less code needs to be executed. A fully functional crowd simulator has been developed and various features for measuring accuracy and performance have been implemented. Part of the research was identifying the right metrics for measuring these quantities. It turns out that a heat map in combination with waiting times, walking times and local flow measuring entities together create a bijection between this data and the simulation producing it. The impact of allowing errors on the simulator’s accuracy can be measured quite well by comparing these statistics. The performance is then measured using different internal stopwatches keeping track of the time needed to simulate a fixed amount of agents. From many simulation environments and settings, it becomes clear that significant speedups can be achieved using the proposed techniques. These speedups of up to 15% are achieved at the expense of the simulator’s accuracy, where flow and waiting times are off by single-digit percentages. Heat maps deviate more percentage-wise because they are more sensitive to small changes. In one case, error tolerance was not faster and less accurate since error tolerance was not applied on the scale of all agents but on reducing internal overhead time. Allowing errors to examine many simulations quickly and then perform precise simulations on just the promising ones are recommended. Whether the errors observed are large or small is not conclusive as that depends on the simulated event and because such conclusions are outside of this dissertation’s scope.