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Using pNEUMA to calibrate car-following models

A paper by Yifan Zhang, Xinhong Chen, Jianping Wang, Zuduo Zheng, Kui Wu was published inTransportation Research Part C: Emerging Technologies entitled “A generative car-following model conditioned on driving styles”. The authors utilise the pNEUMA dataset to calibrate an intelligent Intelligent Driver Model (IDM) with time-varying parameters and train the neural process (NP)-based model. Highlights Abstract Car-following (CF) modeling, an…

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…

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…

   
    
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