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

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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.
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)
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