Large scale multi-agent simulations for transportation applications

Michael Balmer,${}^{a,b}$ Kai Nagel,${}^{*,a,c}$ and Bryan Raney${}^a$

${}^*$ Corresponding author

${}^a$ Institute for Computational Science (ICoS),
ETH Zürich, 8092 Zürich, Switzerland

${}^b$ Institute for Transportation Planning and Systems (IVT),
ETH Zürich, 8093 Zürich, Switzerland

${}^c$ Transport Systems Planning and Transport Telematics,
TU Berlin, 10623 Berlin, Germany

Abstract:

In many transportation simulation applications including ITS, behavioral responses of individual travelers are important. This implies that simulating individual travelers directly may be useful. Such a microscopic simulation, consisting of many intelligent particles ($=$ agents), is an example of a multi-agent simulation. For ITS applications, it would be useful to simulate large metropolitan areas, with 10 million travelers or more. Indeed, when using parallel computing and efficient implementations, multi-agent simulations of transportation systems of that size are feasible, with computational speeds of up to 300 times faster than real time. It is also possible to efficiently implement the simulation of day-to-day agent-based learning, and it is possible to make this implementation modular and essentially ``plug-and-play''. Unfortunately, these techniques are not immediately applicable for within-day replanning, which would be paramount for ITS. Alternative techniques, which allow within-day replanning also for large scenarios, are discussed.