Kui Yang
Tongji University
13 Papers
24 Citations
Kui Yang is an academic researcher from Tongji University. The author has contributed to research in topics: Crash & Computer science. The author has an hindex of 5, co-authored 9 publications. Previous affiliations of Kui Yang include Technische Universität München & Chinese Ministry of Education.
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Papers
Impact of data aggregation approaches on the relationships between operating speed and traffic safety.
TL;DR: It has been concluded that the scenario-based approach shared similar findings with those of the disaggregated crash risk analysis approach in which a U-shaped relationship between operating speed and crash occurrence was identified, however, the commonly adopted segment-based aggregation approach revealed a monotonous negative relationship between speed andCrash frequency.
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A Bayesian dynamic updating approach for urban expressway real-time crash risk evaluation
TL;DR: The Bayesian dynamic logistic regression (Bayesian Dynamic LR) model was introduced to develop the real-time crash risk evaluation model and its prediction performance was proved by a full set in real-world, and the implementation challenges inreal-world was discussed.
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Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach.
TL;DR: For the purpose of unveiling crash occurrence mechanisms and further developing Active Traffic Management (ATM) control strategies to improve traffic safety, disaggregate crash risk analysis models with loop detector traffic data and historical crash data are developed.
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Impacts of variable message signs on en-route route choice behavior
TL;DR: The findings from this study can assist traffic authorities in designing the most appropriate VMS for different traffic congestions based on driver characteristic distribution and the road capacity for improving the practicability of VMS information and further provide the theoretical evidence for the design of in-vehicle personalized information service systems.
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The extended theory of planned behavior considering heterogeneity under a connected vehicle environment: A case of uncontrolled non-signalized intersections.
TL;DR: In this article, the authors developed a model which considers the heterogeneity between drivers with the aid of the extended theory of planned behavior, and examined the relationships between subjective norms, attitudes, risk perceptions, perceived behavioral control and driving intentions, and studies how such driving intentions are simultaneously related to driver characteristics and experiences in the connected vehicle environment.
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