In this work with Hai (Marshall) Yang, and Purdue University colleagues Tho Le, and Yuchen Du, we developed a highly scalable algorithm for designing fleet and battery sizing for sidewalk delivery robots like those of Starship Technologies and Kiwibot. Test results make use of synthesized data based on the Purdue campus setting, and are available to share with other researchers. Funding from National Science Foundation (NSF). Department of Civil & Urban Engineering at NYU TandonC2SMARTER Center

Paper link:  https://www.sciencedirect.com/science/article/abs/pii/S1366554525002613?dgcid=author 

In this work with Xiyuan Ren and Prateek Bansal, we examine mixed logit market share models with market-specific parameters and develop a method to estimate them. We leverage data shared by Replica to show that roughly 40% of the NYC population value driving into lower Manhattan at $9 or more, whereas 90% of travelers from outside NYC into lower Manhattan value driving in the same way. The method was used to produce the NYS statewide mode choice model parameters which are freely accessible on Zenodo (https://lnkd.in/duqn6p9w).

Paper link here.

In this collab with KAIST (Yichan An, Jinwoo Lee) and Konkuk University researchers (Soomin Woo), we looked at the large-scale spacing and sizing design of electric charging stations in a region with spatial variations in demand and travel time, taking into account the delays from waiting for charger availability. An analysis of the five boroughs of NYC illustrates its applicability and improvement over a naive assumption without such variations. This provides a new tool for policymakers to plan resources for charging infrastructure at a much larger scale than before.

Paper available here.

Two of the challenges for public agencies evaluating privately-operated Mobility-on-Demand (MOD) systems: capacities are dynamic, but the operational policies are private (unobservable). We (Bingqing Liu, David Watling) invented a new method to evaluate the steady state equilibrium performance of these dynamics that can be fitted to data using inverse optimization, without needing to assume knowledge of the company’s operational policy. This breakthrough allows agencies to evaluate impacts of changes in demand, supply, or operating policies, to different multimodal MOD systems. Builds off earlier work developed by JIA XU. Pre-proof in EJOR in link below.

https://www.sciencedirect.com/science/article/pii/S0377221724009809

What are the large-scale fleet sizing ramifications for sidewalk delivery robots? Hai (Marshall) Yang and Prof. Chow collaborated with Tho Le and Yuchen Du (Purdue University) to investigate this problem. We developed a new analytical model of pickup and delivery problems with bounded makespans to study such fleets. Using neighborhoods in NYC as case study, we observed: a diminishing effect of economies of scale when demand increases; a high dependency on dwell/service time at each stop on the operating cost; and the major impact of sidewalk level of service. Paper link:

https://www.sciencedirect.com/science/article/pii/S0968090X24004996

As transit agencies test out emerging technologies for providing service, they encounter a challenge of having limited data compared to conventional transit operations with well-designed survey processes and an existing rider base. In such circumstances, the selection of new routes need to play a double role, not just to provide access to travelers, but also to act as “sensors” to learn how the demand responds. We developed a framework for incrementally growing out such a transit network that uses these optimal learning techniques to decide where next to expand. His tests identify the circumstances when “knowledge gradient” policies with correlated beliefs can best be fashioned into such sequential transit network design processes — a next step toward AI-driven transit network evolution that can help expedite the adoption of new transit technologies and policies.

Paper can be found here: https://www.sciencedirect.com/science/article/pii/S1366554524002989

We developed this model framework to allow policymakers and mobility market regulators to evaluate different designs and scenarios: different mixes of mobility operators and their coverage areas, pricing policies, service capacities, subsidy provision, etc., considering both fixed route and on-demand service providers. As a generalized tool, it can be used to examine special cases: mobility hub design, electric MaaS markets, first/last mile design for transit providers, cooperation and competition among mobility providers, among others. The code to the model solution algorithm can be found on Github (https://github.com/BUILTNYU/MaaS-Platform-Equilibrium).

Paper can be found here: https://www.sciencedirect.com/science/article/abs/pii/S019126152400184X

Public data for transportation planning often undersamples underserved population groups. For example, the margin of error (MOE) for population counts at census tract level for people with disabilities can make up 36% of the estimate. If we are to move towards a more equity-aware profession, the data we use need to account for this. In this work with Bingqing Liu and Farnoosh Namdarpour, we develop an algorithm to aggregate zones together to ensure sufficient data reliability for different underserved groups. We apply this algorithm for NYC to produce a set of 574 zones that result in lower MOEs compared to similarly aggregated zonal structures. We also show that this reduction in error translates to reduced variance in downstream transportation applications, such as population synthesis. The data and algorithm are publicly available and can work well with data standards like MDS.