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 emerged new opportunities for revisiting and evaluating existing concepts but also for uncovering new ways to describe significant traffic related phenomena. This dataset is part of an open science initiative shared to the research community. The dataset 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 how such information can be utilized to extract lane-specific information from this new kind of data that is available and set the benchmark for possible future approaches. Specifically, we describe the methodological framework of two related algorithms: i) lane detection and ii) lane changing maneuver identification. The concept of azimuth (direction of movement) is the main information that is utilized in this methodological framework to overcome existing issues in the literature. When combined with clustering techniques and detailed spatial information for the lane detection algorithm, and with modern time-series analysis tools for the lane-changing identification, results show that when high-quality data is available, high accuracy predictive powered algorithms can be created without the use of complex computational efforts. The accuracy of both tools is over 95% while challenging scenarios have already been identified for future studies and further improvements.