  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.
