Idle Vehicle Relocation Strategy through Deep Learning for Shared Autonomous Electric Vehicle System Optimization

TL;DR

This paper introduces a deep learning framework for real-time idle vehicle relocation in shared autonomous electric vehicle systems, significantly reducing costs and wait times by predicting optimal strategies without continuous optimization.

Contribution

It develops a novel deep learning-based approach that predicts optimal vehicle relocation strategies instantly, improving efficiency over traditional computationally intensive methods.

Findings

Reduces operation costs substantially

Decreases customer wait times

Validates effectiveness through system optimization

Abstract

In optimization of a shared autonomous electric vehicle (SAEV) system, idle vehicle relocation strategies are important to reduce operation costs and customers' wait time. However, for an on-demand service, continuous optimization for idle vehicle relocation is computationally expensive, and thus, not effective. This study proposes a deep learning-based algorithm that can instantly predict the optimal solution to idle vehicle relocation problems under various traffic conditions. The proposed relocation process comprises three steps. First, a deep learning-based passenger demand prediction model using taxi big data is built. Second, idle vehicle relocation problems are solved based on predicted demands, and optimal solution data are collected. Finally, a deep learning model using the optimal solution data is built to estimate the optimal strategy without solving relocation. In addition, the proposed idle vehicle relocation model is validated by applying it to optimize the SAEV system. We present an optimal service system including the design of SAEV vehicles and charging stations. Further, we demonstrate that the proposed strategy can drastically reduce operation costs and wait times for on-demand services.