@article{ref1,
title="Quantifying interactions among car drivers using information theory",
journal="Chaos, solitons and fractals",
year="2020",
author="Roy, Subhradeep",
volume="30",
number="11",
pages="e113125-e113125",
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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="0960-0779",
doi="10.1063/5.0023243",
url="http://dx.doi.org/10.1063/5.0023243"
}