Accelerating agent-based demand-responsive transport simulations with GPUs

A novel GPU-accelerated simulation model of large-scale fleet deployment, which can run country-wide, multi-modal scenarios with millions of agents and fleets of tens of thousands of vehicles within a couple of minutes, is presented. Multiple scenarios of the deployment of fleets of automated vehicles in Switzerland’s largest city, Zurich, are assessed. The simulations include the whole population of Switzerland (3.5 million car owners and 1.7 million public transit users) with their detailed travel demand, the road network (1.1 million links and 0.5 million intersections), and public transit (30 000 stops and 20 000 routes). It is demonstrated that in Zurich one automated vehicle could replace 7–8 private cars with an average increase in the road travel time of 44% and with wait times in the range of 10–15 min, provided travel demand remains constant. Furthermore, for the same fleet size, this novel accelerated simulation model runs up to 9 times faster compared to existing state-of-the-art tools.ISSN:0167-739XISSN:1872-711