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Using pNEUMA to monitor and assess the behavior of Freight Vehicles

A paper by Allister Loder, Thomas Otte and Klaus Bogenberger was published inTransportation Research Record entitled “Using Large-Scale Drone Data to Monitor and Assess the Behavior of Freight Vehicles on Urban Level”. The authors utilise the pNEUMA dataset to monitor and assess the behaviour of freight vehicles, focusing on the way their stopping behaviour affects traffic flow. Highlights Urban freight…

High Time-Resolution Queue Profile Estimation at Signalized Intersections Based on Extended Kalman Filtering

A very interesting paper has been published in IEEE Transactions on Intelligent Transportation Systems entitled “High Time-Resolution Queue Profile Estimation at Signalized Intersections Based on Extended Kalman Filtering” utilising the pNEUMA dataset.. The authors from Zhejiang University, Aalto University and University of Illinois at Urbana-Champaign, used both simulated and real-world data to evaluate the performance of their methodology. Highlights A…

Empirical investigation of the emission-macroscopic fundamental diagram

A new paper was published in Transportation Research Part D: Transport and Environment entitled “Empirical investigation of the emission-macroscopic fundamental diagram co-authored by LUTS members and Prof. Gonzales from University of Massachussets Amherts, USA. Highlights We analyse vehicle emissions in a multi-modal urban area using the pNEUMA dataset. EPA’s microscopic emission model, project level MOVES is implemented. We investigate aggregated…

TraViA: a Traffic data Visualization and Annotation tool in Python

TraViA is a new visualisation/annotation tool that supports the pNEUMA dataset. Olger Siebinga, a PhD student from Delft University of Technology, developed TraViA in Python 3 to provide solutions for common problems when working with open datasets. As he reports, “TraViA can be used to visualize and annotate data from highD, pNEUMA, and NGSIM and uses generic vehicle objects to…

Empirical observations of multi-modal network-level models: Insights from the pNEUMA experiment

After an amazing collaboration with the group of Dr. Ludovic Leclercq at Univ. Gustave Eiffel, a new paper was published in Transportation Research Part C: Emerging Technologies entitled “Empirical observations of multi-modal network-level models: Insights from the pNEUMA experiment”. Highlights Multimodal regressions are defined for mean speed with respect to accumulations or stop durations (two-fluid models). A Macroscopic traffic states analysis…

UAS4T Competition results and codes

In a previous post we announced that during the 23rd IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2020), the UAS4T Competition organized by the IEEE ITSS Technical Activities Sub-Committee “Transportation 5.0” would utilize the πNEUMA dataset. The teams had the chance to get familiar with the πNEUMA sample dataset and after almost a month of preparations 10 of…

Map-matching using pNEUMA

In July 2020, Mr. Joachim Landtmeters, MSc student from KU Leuven, successfully conducted the defence of his thesis titled “Analyzing mixed urban traffic by linking large scale trajectory dataset to underlying network”. Mr. Landtmeters had the opportunity to develop a tool to easily get traffic characteristics at any location in the network through a map-matching approach and to analyze multimodal urban…

UAS4T competition utilizes pNEUMA dataset

The UAS4T Competition, which will be held at the 23rd IEEE International Conference on Intelligent Transportation Systems and is organized by the IEEE ITSS Technical Activities Sub-Committee “Transportation 5.0”, utilizes the πNEUMA dataset. The scope of the competition is to evaluate the accuracy of statistical or CI methods in transportation-related detection problems with specific reference in queue formation in urban…

   
    
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