MATSim Living Labs
Virtual testbeds that model and test new transportation technologies and policies
overview
MATSim makes use of a synthetic population which is useful for modeling heterogeneous population segments. It incorporates a day-to-day adjustment process that can reflect learning from the population to achieve a social equilibrium under each technology scenario.
University campus 3D model in Blender software, UTEP
Unlike primary transportation planning tools (e.g. New York Best Practice Model used by New York Metropolitan Transportation Council), MATSim-NYC and the rest of the Network of Living Labs are developed with the intent to be a quick-response benchmarking tool for different emerging technologies or policies. If a public agency wanted to test certain transit alignments, different congestion pricing schemes, service regions for different mobility options, this tool can provide some consistent assessment of its impact on the city.Â
The key strength of this tool is the combination of dynamic traffic and heterogeneous activity scheduling behavior of the population through a multi-agent simulation. This means that the tool can capture the trade-offs between traffic congestion by time of day with the route and departure time decisions of its varied travelers at a citywide level.
MATSim-NYC
- C2SMARTER is expanding to Network of Living Labs: MATSim knowledge transfer to Abu Dhabi, Shanghai, Seattle, El Paso, and Los Angeles, among others
- Dr. Chow was nominated to the Board of Directors of the MATSim Association (only one from North America)
- I-ARPA Project “HAYSTAC” on simulating cities funded alumnus Brian He’s team at UCLA
- Argonne and Lawrence Berkeley National Lab have both sought to integrate our model calibrations into their models (MTA BEAM model, Polaris) of NYC
MATSim-NYC is used as a basis for citywide extrapolations for:
- Dollaride’s $10M CTAP project to electrify dollar vans in NYC with NYSERDA
- Off-hour delivery project evaluations with NYCDOT (see Version 2.0)
Version 2.0:
- Replica data-driven synthetic population
- Truck synthetic population
Completed Research Projects
A Multiscale Simulation Platform for Connected and Automated Transportation Systems
PI: Jeff Ban; Co-PIs: Yinhai Wang, Don MacKenzie
Development of an Open Source Multi-Agent Virtual Simulation Test Bed for Evaluating Emerging Transportation Technologies and Policies
PI: Joseph Chow; Co-PIs: Kaan Ozbay
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems
PI: Jeffrey Weidner; Co-PIs:Adeeba Abdul Raheem, Kelvin Cheu
Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Phase 2
PI: Ruimin Ke; Co-PIs:Adeeba Abdul Raheem, Jeffrey Weidner, Kelvin Cheu
Quantifying and Visualizing City Truck Route Network Efficiency Using a Virtual Testbed
PI: Joseph Chow; Co-PIs:Â Kaan Ozbay
Simulation and Analytical Evaluation of Bus Redesign Alternatives in Transit Deserts with Ride-hail Presence
PI: Joseph Chow; Co-PIs: Eric Goldwyn
One-to-Many Simulator Interface with Virtual Test Bed for Equitable Tech Transfer
PI: Joseph Chow; Co-PIs: Kaan Ozbay, Jeff Ban
Multi-agent Simulation-based Virtual Test Bed Ecosystem: MATSim-NYC
PI: Joseph Chow; Co-PIs: Kaan Ozbay
Impact of COVID on NYC Transit
C2SMARTER researchers used a MATSim-NYC-COVID model to analyze reopening scenarios, assuming there is inertia in users’ behavior, i.e. not immediately reverting back to prior behavior (they maintain the same risk aversion to shared use transportation modes). For social distancing, we see some cities have reduced vehicle capacity for public transit during the shutdown and reopening periods. For example, the MTA of Harris County, Texas, reduced seating by 50% by tagging seats as unavailable during the shutdown and the current reopening. When buses reach capacity, digital signs advise individuals to wait for the next bus. This study was conducted around May 2020, New York State was planning a four-phase reopening. We analyze two scenarios, with or without transit capacity reduction.
We use the model to analyze mobility in post-pandemic economic reopening under social distancing guidelines. The social distancing requirement, with the aim of reducing contact risk in public transit, could exacerbate traffic congestion and emissions. We evaluate the trade-offs between traffic congestion, emissions, and policies impacting travel behavior to mitigate the spread of COVID-19 including social distancing and telework.
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