Speed and acceleration distributions at a traffic signal analyzed from microscopic real and simulated data

Modeling realistic driving behavior at signalized intersections is crucial for many applications, for instance to determine the traffic signal performance, to assess the effect of different control strategies, or to estimate traffic emissions. In these applications, often microscopic models are used to simulate the trajectory of each vehicle. Despite the possibility to model vehicles with great detail and at fractions of a second, speed, acceleration and deceleration characteristics are determined by parameters that are rarely calibrated using real data, and default parameters are often chosen. This is because collecting real vehicle trajectories near traffic signals is a challenging task. This paper presents a method to collect such dataset using image processing techniques. This methodology allows one to obtain vehicle trajectories near a signal control, and to measure individual vehicles speeds and accelerations at a microscopic level. We focus on the analysis the empirical distributions of speeds and accelerations observed with this unique dataset near and up to a few meters upstream of the stop-sign. We compared these distributions with the results of repeated simulations of two microscopic software programs, using default parameters. Some inconsistencies were found with this comparison, which suggests that the two analyzed microscopic simulation programs run with default parameters do not provide realistic results for this type of road sections.