This study was funded by the National Institute for Transportation and Communities (NITC; grant number 1382) a U.S. DOT University Transportation Center at Portland State University.
Electric scooters, Urban transportation, Local transit accessibility -- Evaluation
The objective of this project is to develop data-driven, decision-making models for shared-mobility system design and operation. Specifically, we 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, we propose to solve several urgent questions that arise at the company and policymaker levels for e-scooter sharing (e.g., planning, operations), by developing a data-driven optimization model to provide decision makers with a robust solution that enables low cost and high service quality. In line with the 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 effectivenes.
Fathabad A., Li, X., Cheng, J., Wu, Y., Data-Driven Optimization for E-Scooter System Design. NITC-RR-1382. Portland, OR: Transportation Research and Education Center (TREC), 2022.https://doi.org/10.15760/trec.274