Browse Publications Technical Papers 2020-01-5208
2020-12-30

Multinomial Logit-Based Analysis on Users’ Perceptions and Expectations to the High-Level Connected and Autonomous Vehicles 2020-01-5208

Connected and autonomous vehicles (CAVs) are regarded as the new generation vehicles. Nowadays, it is an ad-hoc worldwide research spot, especially the highly or fully CAVs. These high-level CAVs have not been put into the market yet. Since the innovation of CAV technologies keeps emerging, it is still a meaningful task to following the up-to-date public perceptions and expectations on these high-level CAVs. Using the Stated preference (SP) method, this study designed a questionnaire that included three kinds of questions, i.e., basic information, perceptions, and requirements of future potential users. The questionnaires were delivered by the social media, and finally 612 valid feedbacks were collected. Following a statistical analysis on the survey, a correlation analysis were applied to find the relationship between the individual backgrounds and their perceptions/expectations. Furthermore, this study used the Multinomial Logit (MNL) model to represent the users’ choice behavior in this survey. According to the calibrated MNL model, the influential factors with statistical significance on the users’ perception and requirement choices could be identified. In addition to some similar findings as the existing works, there were also some differential interesting findings, e.g., respondents with the experience of using driving assistant systems could accept a higher price of CAVs. The proposed analysis method and the findings could be a support for the governments to make relevant policy and regulation, for the research agents to develop the functions of CAV, and for the manufactures to design the CAV and make the pricing strategies.

SAE MOBILUS

Subscribers can view annotate, and download all of SAE's content. Learn More »

Access SAE MOBILUS »

Members save up to 18% off list price.
Login to see discount.
Special Offer: Download multiple Technical Papers each year? TechSelect is a cost-effective subscription option to select and download 12-100 full-text Technical Papers per year. Find more information here.
X