Intelligent Shared Mobility Systems: A Survey on Whole System Design Requirements, Challenges and Future Direction

Golpayegani, Fatemeh; Guériau, Maxime; Laharotte, Pierre-Antoine; Ghanadbashi, Saeedeh; Guo, Jiaying; Geraghty, Jack; Wang, Shen · 2022 · OpenAlex-citations

DOI: 10.1109/access.2022.3162848

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Summary

This survey paper addresses the lack of holistic perspectives in existing literature on Intelligent Shared Mobility Systems (SMS). While SMS facilitate on-demand journeys through modes like car-sharing, bike-sharing, and ride-sharing, they face interconnected challenges including facility location, resource redistribution, matching, and routing. Previous surveys typically isolate specific challenges or focus on single transportation modes, failing to account for the "whole system design" perspective. This gap is critical as SMS must integrate multiple stakeholders, support autonomous decision-making, and operate within dynamic, multi-modal urban environments. The authors aim to categorize these interconnected challenges across all transportation modes and review how current solutions address them as a unified system. The authors conducted a comprehensive review of existing literature, categorizing SMS challenges into four main areas: facility location, resource assignment, re-balancing, and routing. They analyzed how these challenges manifest across different service configurations, including human-driven services (car-sharing, bike-sharing, ride-sharing) and Shared Autonomous Vehicles (SAV). The review examines various algorithmic approaches, such as artificial intelligence, machine learning, and optimization techniques, used to solve these problems. The paper distinguishes between centralized approaches, which rely on a ubiquitous third party, and decentralized or distributed approaches, which involve independent decision-makers or regional management. It also evaluates how solutions for one challenge, such as optimized resource assignment, impact other system functions like vehicle routing. Key findings indicate that SMS challenges are inherently interconnected and shared across transportation modes. For facility location, techniques like genetic algorithms, heuristic simulations, and queuing theory are used to optimize infrastructure deployment, with recent focus on charging facilities for electric vehicles and geo-fencing for dockless bikes. Resource assignment involves dynamic matching of supply and demand, utilizing centralized or decentralized models. Re-balancing strategies employ mixed-integer linear programming, heuristic searches, and reinforcement learning to align vehicle supply with fluctuating demand, often using incentives to guide drivers. Routing problems, frequently modeled as variations of the Travelling Salesman Problem or Dial-a-Ride Problem, aim to minimize costs while satisfying constraints like user waiting times. The authors note that while many solutions are validated via simulation, there is a need for better heuristics for real-time application. The significance of this work lies in its provision of a unified framework for understanding SMS design requirements. By highlighting the interdependencies between challenges, the paper guides future research toward integrated solutions rather than isolated optimizations. It identifies key requirements for real-world deployment, including autonomous decision-making capabilities and interoperability with existing public transport. The survey underscores the need for further development in handling the complexity of multi-modal integration and the impact of emerging technologies like autonomous vehicles on system-wide performance. This holistic view is essential for developing flexible SMS that can meet growing urban mobility demands while minimizing negative environmental and traffic impacts.

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• micromobility e scooter