Abrupt changes in the environment, such as increasingly frequent and intense weather events due to climate change or the extreme disruption caused by the coronavirus pandemic , have triggered massive and precipitous human mobility changes . The ability to quickly predict traffic patterns in different scenarios has become more urgent to support short-term operations and long-term transportation planning , emergency management, and resource allocation. Urban traffic exhibits a high spatial correlation in which links adjacent to a congested link are likely to become congested due to spillback effects. The spillback behavior requires modeling the entire metropolitan area to recognize all of the upstream and downstream effects from intentional or unintentional perturbations to the network. However, there is a well-known trade-off between increasing the level of detail of a model and decreasing computational performance.  This paper addresses these performance shortcomings by introducing a new platform MANTA for traffic microsimulation at the metropolitan-scale. MANTA employs a highly parallelized GPU implementation that is fast enough to run simulations on large-scale demand and networks within a few minutes. We test our platform to simulate the entire Bay Area metropolitan region over the course of the morning using half-second time steps. The runtime for the nine-county Bay Area simulation is just over four minutes, not including routing and initialization. This computational performance significantly improves  state of the art in large-scale traffic microsimulation and offers new capacity for analyzing the detailed travel patterns and travel choices of individuals for infrastructure planning and emergency management .
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 .
R
increasingly frequent and intense weather
unforeseen
D
or the extreme disruption caused by the coronavirus pandemic
R
human mobility changes
changes in human mobility
R
, emergency management, and resource allocation. Urban traffic exhibits a high spatial correlation in which links adjacent to a congested link are likely to become congested due to spillback effects. The spillback behavior requires modeling the entire metropolitan area to recognize all of
. This requires modeling entire metropolitan areas to recognize
R
from intentional or unintentional perturbations to
on
A
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.
R
these
the
R
MANTA for
, MANTA (Microsimulation Analysis for Network Traffic Assignment), for
R
fast enough to run simulations on large-scale demand and networks
capable of running metropolitan-scale simulations
R
We test our platform to simulate the entire Bay Area metropolitan region over the course of the morning
The runtime to simulate all morning trips,
R
time steps. The runtime
timesteps,
R
Bay Area simulation
San Francisco Bay Area
A
the
R
and offers new capacity for analyzing the
. MANTA expands the capacity to analyze
D
and emergency management