Microsimulation analysis for network traffic assignment (MANTA) at metropolitan-scale for agile transportation planning

Abstract

Abrupt changes in the environment have triggered massive and precipitous changes in human mobility. This requires modeling entire metropolitan areas to recognize the broader effects on the network. However, there is a trade-off between increasing the level of detail of a model and decreasing computational performance. Current implementations 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 planning. This paper introduces a new platform, MANTA (Microsimulation Analysis for Network Traffic Assignment), for traffic microsimulation at the metropolitan-scale, employing a highly efficient and parallelized GPU implementation. The runtime to simulate all morning trips, using half-second timesteps, for the nine-county San Francisco Bay Area is just over four minutes, significantly improving the state of the art in large-scale traffic microsimulation.

Keywords:

• Traffic microsimulation
• parallelization
• metropolitan scale
• agent-based modeling
• fast GPU

Acknowledgments

This report and the work described were sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The following DOE Office of Energy Efficiency and Renewable Energy (EERE) managers played important roles in establishing the project concept, advancing implementation, and providing ongoing guidance: David Anderson, Rachael Nealer, and Erin Boyd as well as Prasad Gupte.

The authors would like to give a special thanks to Kenichi Soga, Bingyu Zhao, and the cb-cities research group at the University of California, Berkeley and the University of Cambridge; Rashid Waraich, Artavazd Balayan, and the BEAM project team at Lawrence Berkeley National Laboratory; and the SUMO open-source team for remote simulation support.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was funded by the U.S. Department of Energy Vehicle Technologies Office under Lawrence Berkeley National Laboratory Contract No. DE-AC02-05CH11231.