In this continued research on MTA’s planning under COVID, researchers at C2SMART (Profs. Chow and Ozbay, Ding Wang, and Dr. Jingqin Gao) worked with researchers from Cornell University (Prof. Gao and Dr. Tayarani) and UCLA (Dr. He) to apply the MATSim-NYC simulation to analyze trade-offs between congestion, GHG emissions, and exposure risk to COVID for different transit operating scenarios. Findings encourage some form of telework to continue, and show that the impacts of various strategies can vary for different neighborhoods around NYC, with peak hour subway lines 2, 5, and A having the highest contact exposure which overall remain low with average of 3 passengers per hundred exposed to others longer than 15 minutes.

The research was supported by C2SMART and CTECH centers. The paper can be accessed here:
https://www.sciencedirect.com/science/article/pii/S0965856421002299

Route choice behavior in Mobility-as-a-Service (MaaS) systems are governed by very different factors than conventional transportation systems; instead of the link cost being dependent on flow, it is the capacity. This study with Susan Jia Xu introduces “congestible capacities” in route choice and shows how we can model this phenomenon using flow-capacity input-output systems of equations and estimate their parameters in an offline-online estimation method. Data from Citi Bike are used to demonstrate this approach and show that online route choice models that account for congestible capacities are more accurate and can explain the observed changes in MaaS systems.

The study was funded by NSF CMMI-1634973 and CMMI-1652735.
https://ieeexplore.ieee.org/document/9520135

The team developed a nonmyopic online algorithm for rebalancing electric carshare services with explicit charging capacities (which can be readily adapted to other on-demand mobility operations) in this international collaboration with Luxembourg Institute of Socio-Economic Research and NYU Abu Dhabi. Simulation experiments using demand data shared by BMW ReachNow Brooklyn show that the proposed algorithm reduces the cost increase of switching to electric vehicles (EV) by 38% compared to myopic algorithms. This might make it more viable for fleets (both passenger and freight) to consider switching to EV, which is especially vital for environmental sustainability in this time of climate change as the U.S. moves toward more EV infrastructure investments.

The research was funded by C2SMART (USDOT award #69A3551747124, Luxembourg National Research Fund [Grant INTER/MOBILITY/17/11588252], and the NYUAD Center for Interacting Urban Networks, through the NYUAD Research Institute Award [Grant CG001] and in part by the Swiss Re Institute through the QuantumCities Initiative.

The paper can be accessed here: https://pubsonline.informs.org/doi/abs/10.1287/trsc.2021.1058

Using bike count data shared by New York City Department of Transportation, C2SMART Center’s NYC synthetic population, and the Transit Trip Planner from NYS Department of Transportation, the researchers at BUILT@NYU developed a new forecast model that incorporates statistically significant multimodal network connectivity measures. This provides a relationship between investments in transit infrastructure and bike ridership that is especially important in transit deserts and considering last mile connectivity in the post-pandemic era. Funding support from C2SMART and the NYU Undergraduate Summer Research Program.

Link to paper can be found here: https://doi.org/10.1177/03611981211021849

We developed a real-time dispatch algorithm with theoretical performance guarantees for mobility services that include shared, automated, electric vehicles (to be presented at ISTTT24 in Beijing). Data shared by then BMW ReachNow (now ShareNow) was used to test and show the algorithm outperforming state-of-the-art benchmarks. This scientific breakthrough will help fleet-based mobility services establish more sustainable and efficient operations, especially if they are looking to electrify their fleets.

The research was supported by C2SMART and NYUAD CITIES. A link to the paper is available here:
https://www.sciencedirect.com/science/article/pii/S1366554521001599

In collaboration with Kaan Ozbay and Diego Correa, we fitted Nicholas Buchholz’s macroscopic spatial-temporal equilibrium model to yellow taxi and Uber data in NYC to infer trip matches, consumer surplus, and profit comparisons between them and quantify impacts of various policies like the taxi surcharge and fare hikes. This model can be helpful to taxi and for-hire vehicle providers in understanding impacts of policies on their operations at a spatial-temporal resolution.

The research was funded by NSF CMMI-1634973 and C2SMART. The paper can be found here: https://ascelibrary.org/doi/abs/10.1061/JTEPBS.0000550

Mengyun Li’s MS thesis topic presents a “mixed ride” routing strategy for school buses to serve both general and special education students together using practical tools from ArcGIS and Google OR-Tools. The methodology was tested using data for three schools in the Brooklyn area involving a fleet of 14 buses spread over 4 types. With NYC Department of Education seeking routing tools for help (e.g. Via) this work could be of use to them. Funding support from C2SMART is acknowledged.

The publication can be accessed here: https://doi.org/10.1177/03611981211016860.