Data-Driven Optimization for E-Scooter System Design
The objective of this project is to develop data-driven, decision-making models and computational methods for shared-mobility system design and operation. Specifically, the research team will use shared e-scooters as a representative system, with the ultimate goal of facilitating an electric shared-mobility revolution that promises a more sustainable future. In the past few years, shared e-scooter systems have gained increased popularity around the world because of their benefits to health, traffic congestion, the environment, and accessibility. As of 2018, approximately 100 U.S. cities have launched shared e-scooter programs, accounting for 38.5 million trips. However, the business model to manage e-scooter sharing remains nascent, with many challenges still poorly addressed and outstanding. As a result, the team proposes to solve several urgent questions that arise at the company and policy-maker levels for e-scooter sharing (e.g., planning, operations), by: (i) developing a data-driven robust optimization model to provide the decision makers with a robust solution that enables low cost and high service quality, and explicitly captures endogenous uncertainty in demand in the case of limited demand information; and (ii) designing computationally efficient methods with solution quality guarantees to solve the e-scooter sharing system design and operation problems. In line with the National Institute for Transportation and Communities (NITC) themes, these research results have the potential to provide e-scooter companies with new decision-making tools and methodologies to effectively design and operate shared e-scooter systems, and thus help to ensure system reliability and cost-effectiveness.
- Record URL:
Language
- English
Project
- Status: Completed
- Funding: $148761
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Contract Numbers:
NITC-1382
69A3551747112
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
TREC at Portland State University
1900 SW Fourth Ave, Suite 175
P.O. Box 751
Portland, Oregon United States 97201 -
Performing Organizations:
PO Box 210072
Tucson, AZ United States 85721 -
Principal Investigators:
Cheng, Jianqiang
Wu, Yao-Jan
- Start Date: 20200801
- Expected Completion Date: 20220131
- Actual Completion Date: 20220607
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Decision making; Electric vehicles; Optimization; Scooters; Shared mobility; System design; Travel demand; Uncertainty; Vehicle sharing
- Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; Vehicles and Equipment;
Filing Info
- Accession Number: 01746858
- Record Type: Research project
- Source Agency: National Institute for Transportation and Communities
- Contract Numbers: NITC-1382, 69A3551747112
- Files: UTC, RIP
- Created Date: Jul 28 2020 3:53PM