Joseph Chow ,

Ph.D., PE

Two pilot mobility hubs in New York's Capital District are nudging commuters out of their cars and onto buses, bikes, and car-share vehicles, according to a new analysis from NYU Tandon's C2SMART transportation research center.

But the same model suggests these demonstration hubs aren't necessarily located where they’d do the most good.

The hubs, one near UAlbany's downtown campus and the other in downtown Cohoes, work like transfer points, providing a bus stop paired with a shared bike dock that lets riders switch modes mid-trip instead of driving door to door. The Cohoes hub also offers car-share parking, a service not available at the UAlbany site.

The Tandon researchers estimate the hubs are cutting more than 75 vehicle miles driven each day and about 11 metric tons of carbon a year, roughly what two households produce annually. Riders pair two modes per trip, often combining a bus, bike-share, or walking leg with another mode, rather than driving or carpooling the whole way.

The hubs generate more than $5,000 a day in added value across both sites, a way of measuring how much better off travelers are with the extra option available, even if they never use it themselves, according to the researchers. It's not money changing hands, but a dollar figure economists use to capture things like time saved or an easier trip.

The researchers who led the study, C2SMART Deputy Director Joseph Chow, an NYU Tandon Institute Associate Professor, and Xiyuan Ren, a C2SMART postdoctoral fellow, published their findings in Transportation Research Part A.

Chow and Ren started with an existing model that predicts how New Yorkers get around, built from more than 50 million simulated trips across the state. That model can guess whether someone will drive, take transit, bike, or walk for a given trip. Mobility hubs were not reflected in the simulation because they did not exist when the underlying data was put together.

The researchers then taught the model something new: that hub trips, like driving to a bus stop then biking the rest of the way, are also an option. To make that addition realistic rather than a guess, they tuned it using real information, a 40-response survey of hub users and actual ridership numbers from the Capital District Transportation Authority (CDTA), the region's public transit agency.

They then reran that tuning process a thousand times on slightly different versions of the same small dataset, a standard statistical check to make sure their results weren't just a fluke of having so little real-world data to work with.

"You don't have enough real-world data to evaluate a pilot like this, but you also can't wait years to find out if it's working," Chow said. "Our approach borrows strength from a much larger travel model and uses the small amount of real data we have to calibrate it."

The team used the model to estimate outcomes at 1,100 locations across the region, using existing CDTA bus stops as hypothetical hub sites, not real pilots. Among all 1,100, the two actual hubs CDTA built, as part of a demonstration project that ran from April 2022 to June 2024, ranked only in the middle, roughly the 20th to 40th percentile, on measures like how many drivers they'd pull off the road and how much value riders would get.

The model suggests, in other words, that the two pilot sites weren't necessarily where a hub would have the biggest impact, likely because they were chosen for practical reasons, such as available land or existing partnerships, rather than for maximum benefit.

"It was a little surprising the two hubs CDTA built landed in the middle of the pack," Ren said. "That's not a criticism; pilots get chosen for practical reasons. But it suggests real value in running this kind of analysis before committing to a location."

The authors say the same approach, pairing a small on-site sample with a large behavioral model, could extend to other emerging services, such as autonomous shuttles or microtransit, before enough ridership data accumulates to build a model from scratch. Chow said the underlying statewide model isn't specific to mobility hubs, the team has previously used it to evaluate other mobility service programs, and since it draws on data available nationwide, the same approach could extend beyond New York.

Support for the research was provided by the New York State Energy Research and Development Authority (NYSERDA).

Xiyuan Ren, Joseph Y.J. Chow, A data fusion approach for mobility hub impact assessment and location selection: Integrating hub usage data into a large-scale mode choice model, Transportation Research Part A: Policy and Practice, Volume 211, 2026

On a sweltering August afternoon or in the teeth of a winter storm, New York City subway riders make a quiet calculation: Is the trip worth it?

A new study published in npj Sustainable Mobility and Transport takes a detailed look at how those decisions show up in ridership patterns across the system, and how they vary from station to station.

Researchers from NYU Tandon, the University of Louisville, and the University of Hong Kong analyzed hourly ridership at 10 major subway stations between 2023 and 2025. Using a statistical technique called vine copula modeling, they examined how stations’ ridership moves together under different weather conditions rather than treating each station as an isolated case.

“Think about what actually happens when a storm hits,” said Joseph Chow, one of the paper’s authors and an NYU Tandon Institute Associate Professor. “There is structure in how the riders of a system respond to the storm, almost like a unique “signature” of the system to a type of extreme weather event. Understanding these structures and how they evolve can help different cities better prepare their public transit systems to be resilient against extreme weather events.”

Heavy precipitation has the strongest effect during the evening rush hour. As detailed in the appendix, median declines during heavy rain range from nearly 29 percent at Columbus Circle to less than 8 percent at Grand Central. Outer-borough stations such as Flushing–Main Street also show large declines, approaching 26 percent.

Evening travel is more flexible than morning commutes, so heavy rain tends to shift or suppress trips rather than eliminate them entirely. Riders may leave earlier, wait out the storm, or cancel discretionary plans, leading to sharper drops during that specific peak hour even though most still get home, the researchers explained.

Extreme cold tells a different story. Even during the morning rush, when its effects are strongest, ridership declines are modest, generally between about 1 and 2.4 percent across stations. Larger effects appear off-peak, when discretionary trips are more likely to be canceled.

“Commuters maintain their routines even when temperatures plunge,” Chow said, who is also the Deputy Director of C2SMART, Tandon’s transportation research center. “It’s the discretionary traveler, the person heading to a restaurant or a friend’s apartment, who cancels the transit trip or switches to a different mode.”

The study also highlights sharp differences between nearby stations. Columbus Circle emerges as one of the most weather-sensitive locations during heavy rain, while Grand Central, less than two miles away, shows comparatively small declines.

That variation suggests borough location alone does not determine resilience. Infrastructure, station design, connectivity, and surrounding land use all appear to play a role.

“What we’re giving planners is a way to see the whole network respond to a storm or a heat wave, not just one station at a time. And this method allows them to generate other plausible ridership scenarios under extreme weather, aiding decision-making,” said Omar Wani, a NYU Tandon Assistant Professor and a paper author.

The authors emphasize important limitations. The analysis focuses on 10 high-ridership stations, and extreme weather events are relatively rare in the data. To address this, the model generates plausible ridership patterns based on observed relationships across stations.

That means the results should be interpreted as estimates of likely responses, rather than simple averages of past storms.

Even so, a clear pattern emerges. Heavy rain hits hardest during peak hours, while extreme cold has a greater effect off-peak, and the differences between stations are consistent rather than random.

The implications extend beyond operations. Because some neighborhoods rely more heavily on transit, uneven drops in ridership during extreme weather may translate into uneven burdens. As climate change increases the frequency of severe weather, understanding where and when riders stay home could help agencies plan more targeted responses.

In addition to Chow and Wani, the paper’s authors are Yan Guo and Brian Yueshuai He of the University of Louisville; and Zhiya Su of the University of Hong Kong. Funding for the research was provided by the National Science Foundation.

Guo, Y., He, B.Y., Chow, J.Y.J. et al. Assessing subway ridership resilience under extreme weather with vine copula modeling. npj. Sustain. Mobil. Transp. 3, 25 (2026). https://doi.org/10.1038/s44333-026-00094-4

Appendix

The tables below show median declines in ridership at the ten stations studied, compared with normal weather conditions. Each weather type is measured during the peak period when its effects are most pronounced, using the evening commute (4 to 5 p.m.) for heavy rain and the morning commute (8 to 9 a.m.) for extreme cold.