Autonomous driving, connectivity, car sharing, electric vehicles, and the rise of renewable energy will all have powerful mutually reinforcing effects. For example, the introduction of self-driving cars in the 2020s will increase the use of EVs in high-use services such as ride-hailing because lower operating costs will offset the higher initial costs of these vehicles. The movement of people and goods is central to our society and economic activities. According to a BNEF-McKinsey & Company study, the change in how people move around cities will put the automotive and energy industries, as well as governments, under pressure. Light-duty vehicle fuel consumption could drop by up to 75% in some cities by 2030, prompting governments to look for new ways to recoup lost fuel taxes.

Corresponding authors 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 learningbased 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. Keywords: Idle vehicle relocation, deep learning, shared autonomous electric vehicle (SAEV), demand prediction, system optimization 1. Introduction Shared autonomous electric vehicles (SAEVs) that combine car sharing services, autonomous driving technology, and electric vehicles (EVs) are expected to revolutionize transportation systems in the near future [1,2]. An SAEV autonomously goes to the location requested by a customer and rides that customer to a prescribed destination, thus providing a low-stress and safe transportation service [3,4], promoting transportation accessibility [5], and reducing mobility costs [6]. In addition, EVs help reduce fuel consumption and produce less environmental pollutants and greenhouse gas emissions [7-10].

Today, that seems like a science fiction dream. In the near future, though, it will be reality. You've heard about autonomous vehicles. You've probably used a ride-sharing service like Uber or Lyft. Imagine the level of buzz when they are combined, in the form of shared autonomous electric vehicles (SAEVs).