As mobility shifts from single operators to platforms of multiple operators, there needs to be new techniques to analyze and design such systems. In this research with Ted and Saeid, we develop a new model framework and solution method to analyze MaaS ecosystems. It accounts for the incentives of operators to compete and work together to serve multimodal trips for travelers. As such, the model can be used to analyze the impacts of one operator’s capacity increase on other operators, identification of stable cost exchanges, between operators and travelers, firm entry/exit, changes in technology (like new matching algorithms) from one operator on the ecosystem, etc. When the number of operators simplifies to one, the model becomes a conventional capacitated multicommodity flow problem. The research was funded by NSF CMMI-1634973.

The paper can be accessed here: https://doi.org/10.1016/j.trb.2020.08.002

Saeid is currently a Senior Research Engineer at Scoop Technologies while Ted is a Revenue Management Analyst at American Airlines.

Our work on incorporating AI into the process of designing transit routes is now published. This helps transit agencies that periodically update their routes to both learn from and serve their users. It also makes a case for automated transit fleets that can grow organically as they learn and adapt to their markets. The work is funded by NSF CMMI-1652735 and C2SMART.

Link: https://doi.org/10.1177/0361198120917388

The Federal Transit Administration commissioned the BUILT lab to work on a white paper reviewing and exploring the range of public transit operations from fixed route to on-demand microtransit. The completed 156-pg report is now available to access. It includes almost 300 references and links to data (B63 bus route in Brooklyn) and code for a simulation tool to evaluate different transit operations (fixed route, flex-route, door-to-door microtransit). This should be useful to transit agencies (e.g. New York City Transit) looking for an overview of state-of-the-art methods in flexible solutions.

A link to the project and the compendium can be found here. 

Gyugeun Yoon’s work looks at unlimited-ride pass pricing (specifically Citi Bike’s 1-day and 3-day passes) and finds a pricing policy that can improve both revenues for Citi Bike and user welfare. This work should be of interest to operators designing unlimited ride pass programs to determine appropriate prices to set.

The paper is available open access at the following link: https://www.sciencedirect.com/science/article/pii/S2046043020300058

In this research with Nick Caros, we studied the effects of violent crime on active transportation modes like walking and biking in NYC and found that an increase in crime has a much greater impact on bike ridership than it does on walking, but the sensitivity of the former is still several times less than to collision rate. The work was partially supported by the C2SMART UTC.

Paper available here: https://www.sciencedirect.com/science/article/pii/S2214367X18302060

As smart cities find ways to work with mobility operators, one of the questions that spring to mind is the relationship between infrastructure investments and mobility service ridership. Susan Xu used time series models to monitor the attribution of bike lane investments on Citi Bike ridership over time to provide decision support to NYC DOT. For example, each mile of bike lane investment in Manhattan in the period of study contributed to 285 more bikeshare trips. The research was funded by NSF CMMI-1634973. Link to the paper: https://www.tandfonline.com/doi/abs/10.1080/15568318.2019.1645921

As cities move toward “smart city” paradigms they will engage more and more with private mobility operators. Examples of these efforts include the Mobility Data Specification from LA DOT. Data sharing for such ecosystems require mechanisms to preserve the competitive privacy of operators’ operational policies. In our latest paper, we develop one such mechanism using concepts of constrained entropy maximization and inverse optimization constraints to ensure synthetic queries exhibit certain desirable features. This work will be presented at ISTTT23 in Lausanne and is funded by NSF CMMI-1652735. 

Link to paper: https://www.sciencedirect.com/science/article/pii/S0968090X1831622X

Recent news suggest the importance of studying the roles of public transit and mobility-on-demand services in competition and cooperation. We developed an integrated dynamic platform that can incorporate transit rides as a leg in the MOD service and showed that for certain scenarios (such as Manhattan trips from Long Island via LIRR) the MOD services will naturally converge to providing feeder access because it is more cost-effective. Joint work (NYU/LISER) with Tai-Yu Ma, Saeid Rasulkhani, and Sylvain Klein. Funding support from the Luxembourg National Research Fund (INTER/MOBILITY/17/11588252) and NSF CMMI-1634973.

https://doi.org/10.1016/j.tre.2019.07.002

A major issue in the development of reinforcement learning algorithms for autonomous vehicles is the need to make them reflect the preferences of travelers better. One of the ways to do that is to incorporate user schedule preferences under reliability-based route selection criteria into the learning mechanism. This work led by Jinkai Zhou investigates the potential of such an integration. We used data collected from queries from Google Maps to mimic airport shuttle services to train a multi-armed bandit algorithm to see how it is impacted by the consideration of on-time arrival reliability. The work was funded by NSF CMMI-1652735.

https://journals.sagepub.com/doi/full/10.1177/0361198119850457