  Abrupt changes in the environment, such as unforeseen events due to climate
change, have triggered massive and precipitous changes in human mobility. The
ability to quickly predict traffic patterns in different scenarios has become
more urgent to support short-term operations and long-term transportation
planning. This requires modeling entire metropolitan areas to recognize the
upstream and downstream effects on the network. However, there is a well-known
trade-off between increasing the level of detail of a model and decreasing
computational performance. To achieve the level of detail required for traffic
microsimulation, current implementations often compromise by simulating small
spatial scales, and those that operate at larger scales often require access to
expensive high-performance computing systems or have computation times on the
order of days or weeks that discourage productive research and real-time
planning. This paper addresses the performance shortcomings by introducing a
new platform, MANTA (Microsimulation Analysis for Network Traffic Assignment),
for traffic microsimulation at the metropolitan-scale. MANTA employs a highly
parallelized GPU implementation that is capable of running metropolitan-scale
simulations within a few minutes. The runtime to simulate all morning trips,
using half-second timesteps, for the nine-county San Francisco Bay Area is just
over four minutes, not including routing and initialization. This computational
performance significantly improves the state of the art in large-scale traffic
microsimulation. MANTA expands the capacity to analyze detailed travel patterns
and travel choices of individuals for infrastructure planning.
