Work

Uncertainties in shared mobility optimization problems: Survey and perspective

Year: 2025

Type: article

Abstract: Shared mobility systems encompass a wide range of decisions, from real-time operations to strategic long-term planning, all influenced by inherent uncertainties. This review categorizes these uncertainties into four primary types: demand, supply, behavioral, and operational environment, and examines their unique impacts on strategic, tactical, and operational decision-making processes. We review traditional and emerging optimization methodologies, including stochastic programming, robust optimization, data-driven approaches, and reinforcement learning. By linking these methodologies to specific uncertainty types and decision-making levels, this review provides insights into their applications, strengths, and limitations. Finally, we identify research gaps and highlights opportunities for advancing the optimization of shared mobility systems under uncertainty.

Source: Transportation Research Part E Logistics and Transportation Review

DOI: 10.1016/j.tre.2025.104350

Authors: Jie Gao, Yongqiu Zhu, Oded Cats

Institution: Delft University of Technology

Language: English

Topic: Transportation and Mobility Innovations

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $4,240