Srushti Rath’s last dissertation chapter on incorporating deep learning into a real options-based stochastic optimization policy for service region design: it provides a tool for decision-makers to account for uncertainty in expanding their mobility services incrementally over time. Also applicable to broader network design problems. This work was completed under Dr. Chow’s NSF CAREER project. 

Paper: https://www.sciencedirect.com/science/article/pii/S0305054823003222

With the surge in parcel deliveries especially post COVID, there is a greater need than ever to understand the trade-offs in for different delivery mechanisms. PhD student Hai (Marshall) Yang and former MS student Hector Landes (now at AECOM) developed a large scale residential parcel delivery model for NYC that approximates the vehicle miles traveled, as part of a C2SMART Center project. As NYC DOT tests out microhubs and cargo bikes and considers electric trucks, this might be able to help evaluate different scenarios. The paper was presented at the 12th International Conference on City Logistics at Bordeaux and won the Best Student Paper award. The associated data are publicly available on Zenodo: https://zenodo.org/record/7927126.

The paper can be found here: 

https://www.sciencedirect.com/science/article/pii/S2046043023000692

Building on our earlier work, we further expand the classic static dial a ride problem to model platooning between demand-responsive modular vehicles in serving customers. We invented an efficient heuristic and showed using a network of Anaheim CA how platooning can help reduce vehicle travel cost and passenger service time. Statistical tests with computational data suggest such operations and technology benefiting most from serving “enclaves” of passengers. This work should be of interest to microtransit/ridepooling or delivery operators considering more flexible vehicular technologies.

The research was funded by NSF CMMI-2022967 and USDOT #69A3551747124 via the C2SMART Center. It was also presented at the 15th International Conference on Advanced Systems in Public Transport at Tel Aviv in 2022.

Paper link: https://www.sciencedirect.com/science/article/abs/pii/S0968090X23001808

In this collaborative work with TU Delft, we invented a new insurance policy for mobility providers that allows them to borrow fleet resources from each other in the event of a major disruption. Using disruption data from transit operators in The Netherlands, we show how different sized operators carry different amounts of negotiating power in such risk pooling agreements, and how this depends on the underlying disruption risks. Should encourage more collaborative resiliency discussions between different mobility providers.

The research was funded by NSF CMMI-1634973 and CriticalMaaS no. 804469, as well as support from USDOT 69A3551747124 via the C2SMART Center.

Link to the paper: https://www.tandfonline.com/doi/abs/10.1080/23249935.2023.2210229

Whereas conventional transportation planning tends to be region-centric, we see a shift now to one that is more operator-centric, as transport providers work with different city and regional agencies. As such, understanding service impacts on different communities are more important than ever, particularly for emerging technologies. Microtransit/ridepooling is one such technology, and the limited observations (e.g. dozens) we have alone might not provide a robust statistical picture for understanding deployment needs across a landscape of different market typologies for thousands of cities. Our C2SMART Center study, in collaboration with Via, rectifies this by proposing the use of simulation-based “scenario data upscaling” (in much the same way algorithms can upscale images) to inform on metrics needed to estimate forecast models for managing deployment portfolios. Even with data from only a handful of cities, we were able to generate meaningful forecast models based on hundreds of synthesized scenarios, showing for microtransit deployment how to evaluate different service regions across a portfolio of different cities in the U.S. Should be of interest to federal policymakers, microtransit/ridepooling providers, and other emerging transportation technology providers and local agencies, particularly as we look to revamp the national tools and data we use for planning transport infrastructure.

Paper available here: https://www.sciencedirect.com/science/article/pii/S0965856423000046