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.