| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | September 10, 2018 |
| Latest Amendment Date: | September 10, 2018 |
| Award Number: | 1839346 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Tracy Kimbrel
DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | October 1, 2018 |
| End Date: | September 30, 2022 (Estimated) |
| Total Intended Award Amount: | $425,000.00 |
| Total Awarded Amount to Date: | $425,000.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
341 PINE TREE RD ITHACA NY US 14850-2820 (607)255-5014 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
136 Hoy Road Ithaca NY US 14853-3801 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
TRIPODS Transdisciplinary Rese, Special Initiatives |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.049 |
ABSTRACT
Providing efficient, cost-effective, and sustainable transportation solutions has long been a challenge for urban planners. Traditionally, urban mobility has been satisfied via either personal vehicle ownership or mass transit systems, neither of which is an optimal outcome (the former causes congestion and pollution, while the latter suffers from a lack of "last-mile" availability). On-demand transit modes - bikesharing, ridesharing, and micro-transit - have the potential to solve many of the challenges faced by cities. They scale naturally, can alleviate last-mile problems by providing coverage in lower density areas, and reduce incentives for car ownership. However, as they grow larger and intermingle with "classical" urban transit modes, the urban planner is forced to consider both their societal consequences and their effective integration. This project focuses on the following central question: How can we design an efficient and sustainable integrated transit ecosystem, that aligns the incentives of the individual commuter, providers of both on-demand and mass transit, and society as a whole?
To address the above challenges, the investigators propose a combination of *operational* and *market-design* approaches, built around a data-driven stochastic-network model for transit system operations, and the view of a transit authority acting as a meta-platform mediating between commuters and transit providers. The first research thrust on *operations* focuses on the algorithmic challenges faced by upcoming on-demand transit platforms (micro-transit, dockless bikesharing), and also by platforms in integrating their services with the broader transit infrastructure. The second thrust focuses on *market design*, i.e., the economic challenges of an integrated ecosystem. In particular, the investigators propose a number of contractual models for how urban planners can effectively integrate on-demand transit. The investigators also plan to facilitate conversation between academics and representatives of public and private transit platforms by organizing a workshop in 2019 on the Future of Urban Transit.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The high-level aim of this project was to study the design of an efficient and sustainable integrated transit ecosystem - one that aligns the incentives of the individual commuter, providers of both on-demand and mass transit, and society as a whole. This is a critical question for determining the shape of the transport infrastructure of the future, especially with increased use of mobile apps and real-time data, and the growth of on-demand transportation services like Lyft, Citibike, Uber, Via, etc. To address these challenges, our proposal aimed to investigate a combination of operational and market-design approaches, built around new data-driven models for transit system operations, and a new economic viewpoint of a transit authority acting as a meta-platform mediating between commuters and transit providers.
The work on this grant has established new theoretical foundations for the design of on-demand transportation systems with potential applications in real-world systems, new simulation tools and platforms for testing these models and algorithms, and also practical implementation of resulting algorithms by our partners Citibike and Lyft. The project team continues to work with both industry and public sector partners in trying to incorporate theoretical advances in practice. In particular, below we highlight four representative technical results achieved as part of work done on this grant:
1. New algorithms for allocating docks and bikes in dock-based bikesharing systems. These algorithms were implemented by our partner Citibike, leading to repositioning of 100s of docks in Chicago and New York, and yielding improvements of up to 20% in comparison to their older allocation. (Allocating Capacity in Bikesharing Systems - Freund, Henderson and Shmoys)
2. New policies for online decision-making based on using prediction and simulation, which in particular provide the first constant additive-loss online policies for online matching and packing. These are fundamental primitives for a variety of online scheduling and routing problems, and the new results are highly significant in practice as the policies are much simpler, and also much better than the existing state-of-the-art. (The Bayesian Prophet: A Low-Regret Framework for Online Decision Making - Vera and Banerjee).
3. New methods for predicting the running time of ranking and selection procedures for stochastic optimization, leading to improved point-estimates of the problem configuration and the running time. These ideas have influenced work done with Lyft in using simulation optimization to better improve fleet management. (Predicting the Simulation Budget in Ranking and Selection Procedures - Ma and Henderson)
4. New algorithms for large-scale transit system redesign, incorporating demand responsive mass transit modes, and first-last mile ridesharing integration. Our results provide an efficient algorithm for this generalization of the transit line-planning problem along with an approximation guarantee. The code and algorithms have been made publicly available at https://github.com/SmartTransit-Cornell. (Real-Time Approximate Routing for Smart Transit Systems - Perivier, Hssaine, Samaranayake, Banerjee).
The work done on this project has been widely disseminated in various INFORMS and ACM venues, and resulted in several awards for the PIs and students, including the 2018 George B. Dantzig Dissertation Award (for Daniel Freund), the 2018 Daniel H. Wagner Prize for Excellence in Operations Research (for PIs Shmoys and Henderson and for Daniel Freund), NSF CAREER awards (for PIs Banerjee and Samaranayake), and the 2022 Applied Probability Society Erlang Prize (for PI Banerjee). In addition, several graduate students and postdocs involved in this project have gone on to start as tenure-track faculty (including Daniel Freund at MIT Sloan. Raga Gopalakrishnan at Queens University, Qi Luo at Clemson, Chamsi Hssaine at USC Marshall, and Sean Sinclair at Northwestern IEMS).
Last Modified: 02/28/2023
Modified by: Samitha Samaranayake
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