Emmanouil Barmpounakis

Biography

I am a Postdoctoral Researcher in the Urban Transport Systems Laboratory (LUTS) of the École Polytechnique Fédérale de Lausanne (EPFL). I hold a Doctoral Degree in Transportation Engineering from the National Technical University of Athens having graduated in 2017 and a diploma in Civil Engineering (Transportation Engineering Cycle) having graduated in 2012.My primary research field is Traffic Operations, Unmanned Aerial Systems (UAS) for traffic operations and Data Science. My professional experience includes participation in projects and traffic studies in Greece.

Official source

https://people.epfl.ch/manos.barmpounakis

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2022

An automatized signal timing detection at signalized intersections with detailed vehicle trajectory data

Emmanouil Barmpounakis, Jasso Espadaler Clapés, Nikolaos Geroliminis

Signalized intersections are a fundamental part of urban networks. Their understanding is crucial to identify congestion patterns, queues, delays, and safety issues in local and network level. In this work, we analyze multimodal vehicle trajectories and propose a methodology to extract the signal timing schedule of an intersection using the pNEUMA dataset. In addition, we combine the available information from OpenStreetMap (OSM) to map match the trajectories to the underlying network and to identify more accurately the location of traffic signals. Then the methodology to extract the signal timing schedule of an intersection consists of the following steps: i) critical movements identification, ii) computation of crossing times at the traffic signals, iii) cycle length detection and iv) phase length of each critical movement. Results show that by using the OSM data, the methodology can then be applied to any intersection in the network and provide critical information in macroscopic level.

2022

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Vehicle trajectory datasets collected in urban traffic environments with drones pose unique chal- lenges in terms of denoising due to extensive visual restrictions, perspective distortions and human- induced errors. This article taps into the unexplored potential of penalties in the context of vehicle trajectory reconstruction with the example of the massive pNEUMA dataset. We contribute to the literature by shifting the focus of denoising from smoothing to anomaly detection. Specifically, we distinguish between stationary and non-stationary errors and argue that the latter accounts for the largest part of the noise. We propose a re-purposing of the Butterworth filter for the detection of anomalous events and enforce spatial autocorrelation constraints on the errors with functional data analysis. The calibration of our reconstruction makes further use of penalties and is inspired by the theory of human-machine interaction. Our method can be used for quantifying autocorrelated errors or for identifying network segments that are devoid of errors.

2022