Simulation and Neural Models for Traffic Light Importance Analysis in Urban Networks

We investigate the use of traffic simulation tools combined with surrogate models based on neural networks and interpretable machine learning techniques, to analyze and explain complex dynamics at play in urban traffic contexts. We focus in particular on signalized junctions, as one of the main elements affecting the performance of an urban traffic network. Evaluating the impact of traffic light programs and traffic flows can provide very useful information that can be exploited for monitoring and optimization purposes. Yet, a detailed and systematic evaluation is difficult when the area of interest is not trivial, especially when time-consuming micro-simulation tools are employed. In the paper we show how interpretable machine learning techniques, in combination with traffic micro-simulation tools, can provide a computationally efficient way to evaluate the impact of a network of signalized junctions in a given zone. Simulation tests are presented to showcase the methodology in a blueprint scenario, related to the interactions between traffic dynamics and traffic lights behavior.