Autoregressive dynamic mechanism for urban area microscopic traffic flow models

Abstract

To study traffic congestion, city routing, intersection control, emergency cases, or other types of scenarios it is necessary to have an accurate traffic flow model. Traffic models are comprised of different mechanisms that give it its realism. In this work two basic mechanisms are studied: the dynamic movement of the vehicle and a cautious car-following behavior. The dynamic movement of the vehicle is dependent on an autoregressive acceleration algorithm, which gives the vehicle an innate fluid motion. The model also considers a cautious car-following mechanism, where the vehicle decelerates if a safe distance threshold is crossed and the lagging vehicle is traveling faster. Additionally, using the described model, we performed a study to observe the impact of the standard deviation of the velocity on the overall average velocity. This deviation is caused by human reaction times, tiredness, distractions, etc. Therefore, these results reflect the human-driving efficiency.