Microscopic traffic modeling on parallel high performance computers

Abstract

A simple, rule-based approach to traffic flow can yield astonishingly realistic results and is therefore a candidate for very fast large scale microscopic traffic simulations. In the present article, we evaluate two conceptually different codings of the same dynamics on parallel supercomputers. We use a Parsytec GCel-3 (1024 nodes), an Intel iPSC/860 (32 nodes), and a Connection Machine CM-5 (32 nodes). For comparison purposes, we use as well a NEC-SX3/11 traditional single node vector computer, and a net of coupled workstations. Compared to published computing speeds of microscopic traffic models, our model proves to be up to about 1000 times faster. We find our highest computing speed by employing a single-bit coding scheme used in, e.g., Ising-model programming. As traffic flow is a one-dimensional problem, a complication is that geometric parallelization has to be done in the same direction as single-bit coding. Nevertheless, we reach efficiencies near 100 percent for large systems. We use these computational resources in order to obtain high quality data of the model's average behavior (fundamental diagrams). In addition, we present results from modeling a road network, whose composition out of basic objects leads in a natural way to some temporal ‘slackness’ which helps balancing load asymmetries.