ABSTRACT
Traffic signal control is one of the most important means of managing and optimizing road transport in cities. Developing a new generation of efficient traffic signal control algorithms is challenging and it is expected that methods based on AI may bring advantages. However, explainability and interpretability of these techniques are often limited and sometimes it might be difficult even for traffic engineers to understand the decisions taken by such systems. In this chapter we focus on explainability of one of the AI-based techniques used for traffic signal-control which employs evolutionary algorithms to find heuristically optimal signal settings for given traffic conditions, and surrogate models based on graph neural networks to evaluate the quality of signal settings much faster than by traffic simulations. The considered surrogate model is a graph neural network in which the topology of connections between neurons is built based on the topology of the road network graph (neurons correspond to intersections with traffic signals, or road segments between the intersections). Therefore, it can be suspected that analyzing behaviour of these neural networks may also bring better understanding of the urban road traffic and the spatio-temporal impact of traffic conditions on various intersections. In order to investigate the effects of input features on the output of the graph neural networks, we calculated the Shapley values and applied the Zorro method. Thanks to applying these techniques, it was possible to assess importance of intersections and their impact on the times of waiting on red signals in the considered areas on points from randomly generated datasets and on datasets with $500$settings found using genetic algorithm (considered as close to local optima). It turned out that both methods produce quite consistent results. Thanks to them, it was possible to identify the most critical intersections in the road network topology which might be important from the traffic engineering perspective in tasks related to traffic signal control.
