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.
Shared autonomous electric vehicles (SAEVs) are a promising car-sharing service expected to be implemented in the near future. However, existing studies on the optimization of SAEV systems do not consider uncertainties in the SAEV systems, which may interfere with the achievement of the desired performance or objective. From the perspective of the company, an SAEV system should be designed to minimize the total cost while securing the targeted wait time of the customer, but uncertainties in the SAEV system can cause variation in the customer wait time, which can lead to inconveniences to customers and damage to the reputation of the company. Therefore, this study considers the uncertainties in an SAEV system and applies reliability-based design optimization (RBDO) to the design of the SAEV system to minimize the total cost of system design while satisfying the target reliability of the customer wait time. A comparison of the optimization results of various wait time constraints and probabilities of failure provides observations on applying RBDO to the design of an SAEV system. Furthermore, several insights can be obtained through various parametric studies. From this study, it is verified that RBDO can be successfully applied to the design of an SAEV system and a design framework for the SAEV system that can both lower the cost and ensure the reliability of the customer wait time is proposed.
Car sharing services promise “green” transportation systems. Two vehicle technologies offer marketable, sustainable sharing: autonomous vehicles (AVs) eliminate customer requirements for car pick-up and return, and battery electric vehicles entail zero emissions. Designing an autonomous electric vehicle (AEV) fleet must account for the relationships among fleet operations, charging station (CS) operations, electric powertrain performance, and consumer demand. This paper presents a system design optimization framework integrating four subsystem problems: fleet size and assignment schedule; number and locations of charging stations; vehicle powertrain requirements; and service fees. We also compare an AEV service and autonomous vehicle (AV) service with gasoline engines. A case study for an autonomous fleet operating in Ann Arbor, MI, is used to examine AEV and AV sharing systems profitability and feasibility for a variety of market scenarios. The results provide practical insights for service system decision makers.
Despite the approaching commercialization of robo-taxis, various anxiety factors concerning the safety of autonomous vehicles are expected to form a large barrier against consumers’ use of robo-taxi services. The purpose of this study is to derive the various internal and external factors that contribute to the anxieties of robo-taxi passengers, and to propose a human-machine interface (HMI) concept to resolve such factors, by testing robo-taxi services on real, complex urban roads. In addition, a remote system for safely testing a robo-taxi in complex downtown areas was constructed, by adopting the Wizard of Oz (WOZ) methodology. From the results of our tests – conducted upon 28 subjects in the central area of Seoul – 19 major anxiety factors arising from autonomous driving were identified, and seven HMI functions to resolve such factors were designed. The functions were evaluated and their anxiety reduction effects verified. In addition, the various design insights required to increase the reliability of robo-taxis were provided through quantitative and qualitative analysis of the user experience surveys and interviews
As autonomous-vehicle technologies advance, conventional taxi and car-sharing services are being combined into a shared autonomous vehicle service, and through this, it is expected that the transition to a new paradigm of shared mobility will begin. However, before the full development of technology, it is necessary to accurately identify the needs of the service’s users and prepare customer-oriented design guidelines accordingly. This study is concerned with the following problems: (1) How should an autonomous taxi service be designed and field-tested if the self-driving technology is imperfect? (2) How can imperfect self-driving technology be supplemented by using service flexibility? This study implements an autonomous taxi service prototype through a Wizard of Oz method. Moreover, by conducting field tests with scenarios involving an actual taxi, this study examines customer pain points, and provides a user-experience-based design solutions for resolving them.