A paper by Phuriwat Worrawichaipat, Enrico H. Gerding, Ioannis Kaparias and Sarvapali Ramchurn was presented in AAMAS 2023 entitled “Multi-agent Signal-less Intersection Management with Dynamic Platoon Formation”. The paper that was presented in the Innovative Applications and is utilizing the pNEUMA dataset to validate and evaluate their work on signal-less traffic control. Abstract We propose a novel mechanism to manage…
A collaboration between researchers from EPFL LUTS and TUM TUM School of Engineering and Design resulted in the publication of a paper entitled “Treating Noise and Anomalies in Vehicle Trajectories From an Experiment With a Swarm of Drones” in IEEE Transactions on Intelligent Transportation Systems. Vishal Mahajan, Manos Barmpounakis, Md. Rakib Alam, Nikolas Geroliminis and Constantinos Antoniou utilise the pNEUMA…
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 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…
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…
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…