In the era of big data, new transportation-related concepts and methodologies need to be proposed for understanding how congestion propagates. pNEUMA, a unique dataset that was acquired during a first-of-its-kind experiment with a swarm of drones over a dense city center, has uncovered new opportunities for revisiting and evaluating existing concepts but also for new ways to describe significant traffic related phenomena. This dataset is part of an open science initiative shared to the research community and consists of more than half million detailed trajectories of almost every vehicle that was present in the study area. The aim of this paper is to provide a first methodological approach on howto extract information regarding shockwaves. First, we identify the critical start and stop points for every vehicle and using two different approaches(a rule-based and an unsupervised clusteringtechnique) shockwaves are identified for multilane arterials in space and time. It is seen that the rule-based approach is based on shockwaves characteristics according to traffic flow theory and can successfully identify shockwavesand spillbacks when the location of traffic signals is known, while an unsupervised clustering technique is used to identify stop-and-go conditions. Finally, the different characteristics per lane and per shockwave are calculated and compared.