Spatial Optimization of Autonomous Vehicle Assignment Based on Distance-Driven Demand and Customer Patience

TL;DR

This paper presents a mathematical model for optimizing autonomous vehicle allocation by considering customer patience and vehicle pooling, aiming to improve efficiency and user satisfaction in shared mobility systems.

Contribution

It introduces a novel model that integrates customer patience levels and vehicle pooling strategies for better AV fleet management across regions.

Findings

Adding more facilities initially reduces costs

Higher customer patience enhances pooling benefits

Diminishing returns with increasing number of facilities

Abstract

Autonomous vehicles (AVs) can improve efficiency, reduce costs, and enhance road safety. They optimize traffic flow, minimize congestion, and support sustainability through shared mobility and reduced fuel consumption. A key challenge in AV deployment is allocating vehicles to parking lots across regions to meet fluctuating demand. Proper allocation reduces delays, lowers costs, and boosts user satisfaction by ensuring timely vehicle availability. This paper explores the impact of customer wait time patience on AV allocation models, allowing prioritization of ride requests while balancing fleet efficiency and user satisfaction. It also addresses the effectiveness of vehicle pooling in decentralized service areas. We propose a mathematical model integrating vehicle distribution and customer patience to maintain both efficiency and satisfaction. Results show that adding more facilities initially reduces costs, but the benefits diminish with more facilities. Increased customer patience improves inventory pooling benefits, especially when no fixed costs are tied to facility operation.