Srushti Rath’s MS thesis work on air taxis planning suggests access to airports as an initial market that can help drive investment. We developed a hub location model that is sensitive to mode choice between ground taxis and the air taxis. Findings suggest that 9 skyports would be adequate to serve such demand to accommodate variations in transfer times from ground taxi to the skyports. The research was partially supported by the C2SMART Center under USDOT grant #69A3551747124.

The paper can be found here: https://www.sciencedirect.com/science/article/abs/pii/S0969699722001132

Given the importance of data sharing between mobility providers, it is a highly understudied topic area. Qi Liu studies the properties of data sharing between competitive mobility (transit) providers as a coopetitive game with coalition structure formation for partitioning data sharing and Bayesian game to compute the value of noncooperative equilibria under such a setting. We highlight that having everyone share with each other is not a foregone conclusion, even under complementary services (e.g. multimodal trips). The insights from this work can help guide policy toward more incentive-aware data sharing structures, quantifying subsidies or revenue allocations that can encourage cooperation, and for mobility aggregators (e.g. Whim, Transit App, Cubic, and the like) to consider data sharing requirements between different members.

The work was funded by NSF CMMI-1652735 and USDOT #69A3551747124.

Link to paper: https://www.sciencedirect.com/science/article/pii/S0191261522001126

For mobility companies interested in global city classification data to help planning deployments, this work by Srushti Rath crowdsourced original data from Wikipedia using a new natural language processing algorithm. We expanded city classifications developed by Jimi Oke for ~300 cities up to a set of ~2000+ global cities. Links to data and code are included in the paper. Developed as part of a collaborative C2SMART project with Via.

Paper link: https://www.sciencedirect.com/science/article/pii/S0968090X22001048?dgcid=author

Qi Liu developed an algorithm to estimate transit passenger flows at a network level (the first of its kind) using stop count data (e.g. Transit Wireless, smartcard data, etc.). Testing on Shanghai bus data in Qingpu District with 4 bus lines and 120 segments, we show the algorithm leads to average of the segment flows that are only 2.6% off from the average of the observed flows. Code is available on Github and should be of interest to transit operators (e.g. NYC Transit Authority). Funding support from C2SMART and U.S. National Science Foundation (NSF) CMMI-1652735.

Paper link: https://www.tandfonline.com/doi/abs/10.1080/21680566.2022.2060370

In this collaborative work with UNSW, we present a modified dial-a-ride problem that incorporates user demand response, and several heuristic algorithms to solve it. Such a model can evaluate different pricing structures (tested zone-based, distance-based, and flat fee structures using data from NYC, and found that a zonal scheme can provide the best revenue and ridership optimization). This can allow planners to design service zones with different pricing structures (including integrated fare bundles with transit in a MaaS environment), integrate pricing with route and destination recommendation, and equity considerations with customized pricing for underserved population segments.

The paper can be found here:
https://www.sciencedirect.com/science/article/pii/S1366554521003562?dgcid=coauthor

From an NYU Vertically Integrated Projects (VIP) collaboration with NYC DCAS, our lab developed a tool to analyze a population’s access to different charging station location configurations and station mixes considering queue delays (which we used to analyze Revel’s charging hub last year in a Linkedin article). In this publication, we present the underlying algorithm and evaluate DCAS investment policies of adding DCFC chargers to existing stations. A policy of adding DCFC to stations based on those with highest utilization ratio was more effective than choosing those with highest queue delays.

The paper can be found here:
https://www.tandfonline.com/doi/full/10.1080/15568318.2022.2029633

Operating costs in microtransit can be alleviated with coordinated transfers. We (Zhexi Jesse Fu) developed a new model and algorithm to route passengers with transfers that can be generated at any location in the network. For grids up to 200×200 nodes with 100 vehicles and 300 requests, we show that over 50% of vehicle routes can be further improved by synchronized en-route transfers with vehicle travel distances reduced by up to 20%. This improvement potential reduces under less dense settings. The algorithm would also enable modular automated vehicles to determine en-route transfer points.

This work was funded by C2SMART (US DOT #69A3551747124) and NSF CMMI-2022967. The paper can be accessed here:
https://www.sciencedirect.com/science/article/pii/S1366554521003203

Bus network redesigns should impact travelers’ mode, route, and departure time choices. We design a network frequency setting model that incorporates these behavioral factors with a simulation-based optimization in which MATSim-NYC is used as the simulator. We propose a frequency setting for Eric Goldwyn’s bus network redesign for Brooklyn that is projected to improve farebox recovery ratio from ~0.22 under existing to 0.35 and show that 2.5% of new trips would be substituting ride-hail while 74% would replace driving. Further work can be done to identify individuals from our NYC synthetic population using the service for #equity analysis. The open-source tool can be used to evaluate other MATSim-embedded transit network design efforts, especially at low cost in NYC given the existing code (i.e. post-pandemic network designs, electric bus fleets, last mile microtransit, redesigns addressing equity, etc.).

The work was supported by C2SMART Center (USDOT #69A3551747124) and the FHWA Dwight David Eisenhower Fellowship program. The paper can be found here: https://journals.sagepub.com/eprint/IFD92ZR8JQFSRPI8HGFG/full