Optimal Vehicle Dimensioning for Multi-Class Autonomous Electric Mobility On-Demand Systems

TL;DR

This paper develops an optimal vehicle dimensioning model for multi-class autonomous electric mobility on-demand systems, ensuring bounded response times and efficient resource management through a queuing-based approach.

Contribution

It introduces a queuing model for multi-class AEMoD systems to determine optimal vehicle proportions and charging strategies, enhancing response time guarantees and system efficiency.

Findings

Model guarantees bounded response times under certain stability conditions.

Optimized vehicle class proportions reduce in-flow and waiting times.

Proposed scheme outperforms existing methods in typical and critical scenarios.

Abstract

Autonomous electric mobility on demand (AEMoD) has recently emerged as a cyber-physical system aiming to bring automation, electrification, and on-demand services for the future private transportation market. The expected massive demand for such services and its resulting insufficient charging time/resources prohibit the use of centralized management and full vehicle charging. A fog-based multi-class solution for these challenges was recently suggested, by enabling per-zone management and partial charging for different classes of AEMoD vehicles. This paper focuses on finding the optimal vehicle dimensioning for each zone of these systems in order to guarantee a bounded response time of its vehicles. Using a queuing model representing the multi-class charging and dispatching processes, we first derive the stability conditions and the number of system classes to guarantee the response time bound. Decisions on the proportions of each class vehicles to partially/fully charge, or directly serve customers are then optimized so as to minimize the vehicles in-flow to any given zone. Excess waiting times of customers in rare critical events, such as limited charging resources and/or limited vehicles availabilities, are also investigated. Results show the merits of our proposed model compared to other schemes and in usual and critical scenarios.