《Associating stated preferences of emerging mobility options among Gilbert City residents using Bayesian Networks》

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作者
Boniphace Kutela;Christian Mbuya;Suleiman Swai;Delphine Imanishimwe;Neema Langa
来源
CITIES,Vol.131,Issue1,Article 104064
语言
英文
关键字
作者单位
Texas A&M Transportation Institute, 1111 RELLIS Pkwy, Bryan, TX 77807, United States of America;Consor Engineers, 15310 Park Row, Houston, TX 77084, United States of America;Maryland State Highway Administration, 7491 Connelley Dr, Hanover, MD 21076, United States of America;Dept. of Civil & Environmental Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, United States of America;Department of Sociology/African American Studies, University of Houston, 3551 Cullen Boulevard, Houston, TX 77204, United States of America;Texas A&M Transportation Institute, 1111 RELLIS Pkwy, Bryan, TX 77807, United States of America;Consor Engineers, 15310 Park Row, Houston, TX 77084, United States of America;Maryland State Highway Administration, 7491 Connelley Dr, Hanover, MD 21076, United States of America;Dept. of Civil & Environmental Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, United States of America;Department of Sociology/African American Studies, University of Houston, 3551 Cullen Boulevard, Houston, TX 77204, United States of America
摘要
In the planning stage of emerging mobility options, residents' stated preferences are important to understand the acceptability, co-existence, demand estimation, and utilization. Thus, this study applied Bayesian Networks (BN) on the survey data from Gilbert City, Arizona, to explore the residents' stated preferences for Autonomous vehicles (AVs). It further explored the AVs association with four mobility options - electric vehicles (EVs), electric scooters, and docked and dockless bikes. It was revealed that about 66 % of respondents who want AVs would use them, making about 34 % overestimation when want is used to estimate demand. Furthermore, respondents interested in using EV charging stations are also more likely to want the AVs in the city as well as use them. No significant difference was observed between docked and dockless bikes on wanting the AVs but on using them. Furthermore, respondents who would not use dockless bikes have the highest predicted percentage difference between wants and use of AVs. The joint analysis of the variable revealed that the highest prediction of using AVs is attained for male respondents who would use all emerging mobility options. The practical application of this study is presented along with recommendations to operators, city engineers, and planners.