Understanding non-motorists’views on automated vehicle safety through Bayesian network analysis and latent dirichlet allocation  

作　　者：Subasish Das Abbas Sheykhfard Jinli Liu Md Nasim Khan 

机构地区：[1]Texas State University,601 University Drive,San Marcos,TX 78666,USA [2]Department of Civil Engineering,Babol Noshirvani University of Technology,Shariati Ave.,Babol,Mazandaran,Iran

出　　处：《International Journal of Transportation Science and Technology》2024年第2期289-304,共16页交通科学与技术(英文)

摘　　要：Automated vehicles(AVs)hold great promise for creating a safer,more efficient,more equitable,and more sustainable transportation system.However,the rapid adoption of AVs requires a thorough understanding in their coexistence with the human environment in the current roadway network,particularly with respect to interactions between AVs and non-motorists.Bike Pittsburgh(BikePGH)conducted a 2019 survey to examine nonmotorists’perceptions of AV safety.Using Bayesian network(BN)analysis,the study identified key factors such as safety perception,AV technology knowledge,and real-world interaction experiences that influence non-motorists’overall perception of AV safety using BikePGH survey data.The study also explored several counterfactual scenarios to gain insights into non-motorists’viewpoints on AV safety.Notably,the study found that the differences in the ways of AVs and human-driven vehicles interacted with non-motorists at intersections played a crucial role in shaping survey participants’opinions.By taking into account the key insights identified in this study,policymakers can develop evidence-based strategies to achieve sustainable urban mobility goals while ensuring the safety and wellbeing of all road users,particularly non-motorists.

关 键 词：Non-motorists Automated vehicle Safety BikePGH survey Bayesian network(BN)analysis 

分 类 号：TN9[电子电信—信息与通信工程]