Abstract
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.
Highlights
In recent years, automated driving technologies have been under very intense development, with several automotive companies targeting the production and delivery of highly automated vehicles (AVs) within the 5 to 10 years [1,2,3,4]
There are a number of challenges that may be addressed by the deployment of AV taxi fleets
In order to fill this gap, we extend an agent-based, graphics processing unit (GPU)-accelerated, multi-modal mobility simulator GEMSim [22], with support for large-scale fleet deployment
Summary
In recent years, automated driving technologies have been under very intense development, with several automotive companies targeting the production and delivery of highly automated vehicles (AVs) within the 5 to 10 years [1,2,3,4]. This expected widespread availability of AVs opens up new opportunities for providers of mobility services. A second example is the ever-increasing demand for parking; the substitution of private cars by AV fleets may alleviate the demand for parking and allow for the more efficient-use or alternate re-use of existing parking infrastructure [9]. A third example is increased revenues for public transit authorities, which may result when AV fleets are coordinated with public transit services [10] in order to attract more users of public transit
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