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Autonomous vehicle (AV) is an innovative transport option that has the potential to disrupt all industries tied to transportation systems. The advent of AV technology will bring a novel on-demand mobility pattern such as shared autonomous vehicle (SAV). To promote AV technology, it is important to understand which factors influence travelers’ intention to use AVs and SAVs. This paper collected literature from databases such as Scopus, Web of Science and ScienceDirect, and made a systematic review. The study aims to explore the determinants that influence travelers’ behavioral intentions towards use AVs and SAVs based on an extended version of the theory of planned behavior, which incorporates knowledge and perceived risk. This study was tested empirically using a valid survey sample collected from 906 respondents in China. Structural equation model was conducted to investigate the predictors of intentions to use AVs and SAVs. Results showed that knowledge about AV technology and perceived risk are the two main potential obstacles for travelers to use AVs and SAVs. Attitude significantly affects AVs and SAV choice intentions. Subjective norm is the most critical factor affecting the travelers’ intention to use AVs. Perceived behavioral control potentially stymie the travelers’ intention to use SAVs. The findings will enhance the understanding of travelers’ choice motivation from psychological and service perspectives, and provide data support for governments and companies in improving travel management strategies and product services.
Peng Jing; Hao Huang; Bin Ran; Fengping Zhan; Yuji Shi. Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China. Sustainability 2019, 11, 1155 .
AMA StylePeng Jing, Hao Huang, Bin Ran, Fengping Zhan, Yuji Shi. Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China. Sustainability. 2019; 11 (4):1155.
Chicago/Turabian StylePeng Jing; Hao Huang; Bin Ran; Fengping Zhan; Yuji Shi. 2019. "Exploring the Factors Affecting Mode Choice Intention of Autonomous Vehicle Based on an Extended Theory of Planned Behavior—A Case Study in China." Sustainability 11, no. 4: 1155.
In the last few years, traffic congestion has become a growing concern due to increasing vehicle ownerships in urban areas. Intersections are one of the major bottlenecks that contribute to urban traffic congestion. Traditional traffic signal control systems cannot adjust the timing pattern depending on road traffic demand. This results in excessive delays for road users. Adaptive traffic signal control in a connected vehicle environment has shown a powerful ability to effectively alleviate urban traffic congestions to achieve desirable objectives (e.g., delay minimization). Connected vehicle technology, as an emerging technology, is a mobile data platform that enables the real-time data exchange among vehicles and between vehicles and infrastructure. Although several reviews about traffic signal control or connected vehicles have been written, a systemic review of adaptive traffic signal control in a connected vehicle environment has not been made. Twenty-six eligible studies searched from six databases constitute the review. A quality evaluation was established based on previous research instruments and applied to the current review. The purpose of this paper is to critically review the existing methods of adaptive traffic signal control in a connected vehicle environment and to compare the advantages or disadvantages of those methods. Further, a systematic framework on connected vehicle based adaptive traffic signal control is summarized to support the future research. Future research is needed to develop more efficient and generic adaptive traffic signal control methods in a connected vehicle environment.
Peng Jing; Hao Huang; Long Chen. An Adaptive Traffic Signal Control in a Connected Vehicle Environment: A Systematic Review. Information 2017, 8, 101 .
AMA StylePeng Jing, Hao Huang, Long Chen. An Adaptive Traffic Signal Control in a Connected Vehicle Environment: A Systematic Review. Information. 2017; 8 (3):101.
Chicago/Turabian StylePeng Jing; Hao Huang; Long Chen. 2017. "An Adaptive Traffic Signal Control in a Connected Vehicle Environment: A Systematic Review." Information 8, no. 3: 101.