A Study on Public Adoption of Robo-Taxis in China

Abstract

Autonomous vehicles (AVs) have been reported to improve road safety, reduce traffic congestion, and increase urban mobility. However, the high price of AVs is currently a challenge for most consumers. Robo-taxi services, with ride-sharing services and AVs, are regarded as a good approach to solving this problem. As some companies have started testing Robo-taxis on the actual road, it has become important to investigate public adoption of Robo-taxi services before they are more widely introduced to the market. This study aims to explain and predict users’ acceptance of Robo-taxis by extending the Technology Acceptance Model by including the construct of social influence. Data were collected from an online survey in China and analyzed using linear regression models. The results indicate that perceived usefulness, perceived ease of use, and social influence have significant positive correlations with people’s behavior intentions to use Robo-taxis. Perceived ease of use further has an indirect effect on intention to use via perceived usefulness. The results of this study can serve as good references for policymakers, operators, and future transport researchers.

Optimization of On-Demand Shared Autonomous Vehicle Deployments Utilizing Reinforcement Learning

“…Recent research suggests that the deployment of shared autonomous vehicles (SAV) in the form of fleets of robo-taxis will be a natural and economically feasible way of introducing autonomous vehicles (AV) to the public that will help solve mobility problems of under-served areas and further positively impact network flow [ 1 , 2 , 3 , 4 ]. While current deployments of robo-taxis exist, much of the current literature utilizes simple models of AVs, or AV features such as adaptive cruise control (ACC) and cooperative adaptive cruise control (CACC), to extrapolate on the effects of penetration rates for AVs [ 5 ].…”

Understanding acceptance of Autonomous Mobility Services using statistical and deep learning approaches

“…For example, lower willingness to use SAMS in the form of taxi, ride-share, or ride-hailing services was observed amongst respondents from North America, Europe, and Australia compared to respondents from Asia. More specifically, more than 80% of the respondents from Singapore and China found SAMS to be a desirable service which not only they intend to use themselves (Liu et al, 2020) but are also willing to recommend to people in their circle (Liu et al, 2020).…”

“…Linear regression (LR) models are used to test the relationship between behavioral intention to use with different exogenous factors including behavioral constructs (Liu et al, 2020;Wong and Rinderer, 2020;Tussyadiah et al, 2017;Chng and Cheah, 2020;Feys et al, 2020;Madigan et al, 2017;Motak et al, 2017;Madigan et al, 2016;Zoellick et al, 2019). Other decision variables investigated using LR include perceived concerns (Wong and Rinderer, 2020), and willingness to accept (Konig and Grippenkoven, 2020).…”

“…The significance of TAM constructs (perceived usefulness, perceived ease of use, attitude) and TPB constructs (attitude, subjective norm, perceived behavioral control) has been studied as well (Herrenkind et al, 2019;Motak et al 2017). Of the different behavioral constructs, perceived usefulness or performance expectancy was consistently found to positively impact people's intentions to use SAMS (Bernhard et al, 2020;Liu et al, 2020;Nordhoff et al, 2020a). The finding emphasizes the necessity of good system performance including service reliability as well as connectivity with other available transport services.…”

“…In fact, performance expectancy affects both acceptability and acceptance (Bernhard et al, 2020). In some studies, its effect was moderated by effort expectancy (Nordhoff et al,2020a;Liu et al,2020;Herrenkind et al, 2019) and social influence (Nordhoff et al,2020a;Herrenkind et al, 2019).…”