详情

Data-Driven Optimization for E-Scooter System Design

项目名称：	Data-Driven Optimization for E-Scooter System Design
摘要：	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.
状态：	Active
资金：	148761
资助组织：	Office of the Assistant Secretary for Research and Technology
执行机构：	University of Arizona, Tucson
开始时间：	20200801
预计完成日期：	20210731
主题领域：	Data and Information Technology；Highways；Planning and Forecasting；Vehicles and Equipment