Seizing the Opportunity for Automation—How Traffic Density Determines Truck Drivers' Use of Cruise Control

Abstract

We analyze how traffic congestion affects the usage of cruise control in real-traffic situations. Usually, a negative relationship is assumed, but empirical evidence in naturalistic settings is rare. We make use of a large sample of truck drivers ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> = 562) in Germany. We take advantage of the volatile traffic density imposed by the first implementation of COVID-19-related measures in Germany and analyze truck drivers resulting application of cruise control using random effects models. We match official traffic density information and the share of cruise control usage as recorded by the telematics information of each truck per day. We find that on average traffic density does have the expected effect: more traffic leads to a lower usage of cruise control. However, we find great heterogeneity among drivers and a strong tendency to react nonlinear to changes in traffic density. Additionally, comparing traffic types, heavy-goods vehicle traffic density has a stronger impact on drivers' usage of cruise control than private traffic density. Again, not all drivers react similarly: we investigate eight different reaction patterns to varying types of congestion. Our results show that even in a favorable environment such as low traffic congestion, the use of cruise control cannot be taken for granted: about 25%–34% of the drivers (depending on traffic type) do not respond to changes in traffic density at all. Our results may help to inform models of traffic flow and traffic automation and may serve as a premise for more realistic assumptions about human behavior in traffic.