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• W3104548835 abstract "Information-theoretic quantities have found wide applications in understanding interactions in complex systems primarily due to their non-parametric nature and ability to capture non-linear relationships. Increasingly popular among these tools is conditional transfer entropy, also known as causation entropy. In the present work, we leverage this tool to study the interaction among car drivers for the first time. Specifically, we investigate whether a driver responds to its immediate front and its immediate rear car to the same extent and whether we can separately quantify these responses. Using empirical data, we learn about the important features related to human driving behavior. Results demonstrate the evidence that drivers respond to both front and rear cars, and the response to their immediate front car increases in the presence of jammed traffic. Our approach provides a data-driven perspective to study interactions and is expected to aid in analyzing traffic dynamics." @default.
• W3104548835 created "2020-11-23" @default.
• W3104548835 creator A5062073055 @default.
• W3104548835 date "2020-11-01" @default.
• W3104548835 modified "2023-09-26" @default.
• W3104548835 title "Quantifying interactions among car drivers using information theory" @default.
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• W3104548835 doi "https://doi.org/10.1063/5.0023243" @default.
• W3104548835 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33261358" @default.
• W3104548835 hasPublicationYear "2020" @default.
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