% % This file was created by the TYPO3 extension % publications % --- Timezone: CEST % Creation date: 2024-03-29 % Creation time: 07:12:54 % --- Number of references % 8 % @Article { KORBMACHER2024129440, author = {Korbmacher, Raphael and Dang, Huu-Tu and Tordeux, Antoine}, title = {Predicting pedestrian trajectories at different densities: A multi-criteria empirical analysis}, year = {2024}, reviewed = {1}, issn = {0378-4371}, DOI = {10.1016/j.physa.2023.129440}, journal = {Physica A: Statistical Mechanics and its Applications}, volume = {634}, pages = {129440}, keywords = {ANRDFG2020 VZU_DL} } @Inproceedings { 10.1007/978-3-031-47718-8_46, author = {Korbmacher, Raphael and Tordeux, Antoine}, title = {Deep Learning for Predicting Pedestrian Trajectories in Crowds}, year = {2024}, reviewed = {1}, DOI = {10.1007/978-3-031-47718-8_46}, booktitle = {Intelligent Systems and Applications}, publisher = {Springer Nature Switzerland}, address = {Cham}, editor = {Arai, Kohei}, pages = {720--725}, keywords = {VZU_DL ANRDFG2020} } @Inproceedings { 10299443, author = {Dang, Huu-Tu and Korbmacher, Raphael and Tordeux, Antoine and Gaudou, Benoit and Verstaevel, Nicolas}, title = {TTC-SLSTM: Human Trajectory Prediction Using Time-to-Collision Interaction Energy}, year = {2023}, reviewed = {1}, DOI = {10.1109/KSE59128.2023.10299443}, booktitle = {2023 15th IEEE International Conference on Knowledge and Systems Engineering (KSE)}, pages = {1-6}, keywords = {ANRDFG2020 VZU_DL} } @Article { subaih2022questioning, author = {Subaih, Rudina and Maree, Mohammed and Tordeux, Antoine and Chraibi, Mohcine}, title = {Questioning the anisotropy of pedestrian dynamics: An empirical analysis with artificial neural networks}, year = {2022}, DOI = {10.3390/app12157563}, journal = {Applied Sciences}, volume = {12}, publisher = {MDPI}, pages = {7563}, number = {15}, keywords = {VZU_gaense VZU_ped-model VZU_DL VZU_predict} } @Article { korbmacher2022review, author = {Korbmacher, Raphael and Tordeux, Antoine}, title = {Review of pedestrian trajectory prediction methods: Comparing deep learning and knowledge-based approaches}, year = {2022}, reviewed = {1}, DOI = {10.1109/TITS.2022.3205676}, journal = {IEEE Transactions on Intelligent Transportation Systems}, volume = {23(12)}, publisher = {IEEE}, pages = {24126-24144}, keywords = {ANRDFG2020 VZU_DL VZU_predict} } @Article { tordeux2020prediction, author = {Tordeux, Antoine and Chraibi, Mohcine and Seyfried, Armin and Schadschneider, Andreas}, title = {Prediction of pedestrian dynamics in complex architectures with artificial neural networks}, year = {2019}, reviewed = {1}, DOI = {10.1080/15472450.2019.1621756}, journal = {Journal of Intelligent Transportation Systems}, volume = {24}, publisher = {Taylor {\\&} Francis}, pages = {556-568}, number = {6}, keywords = {VZU_DL VZU_predict} } @Inproceedings { tordeux2019artificial, author = {Tordeux, Antoine and Chraibi, Mohcine and Seyfried, Armin and Schadschneider, Andreas}, title = {Artificial neural networks predicting pedestrian dynamics in complex buildings}, year = {2019}, reviewed = {1}, DOI = {10.1007/978-3-030-28665-1_27}, organization = {Springer}, booktitle = {Workshop on Stochastic Models, Statistics and their Application}, pages = {363-372}, keywords = {VZU_DL VZU_predict} } @Inproceedings { tordeux2017prediction, author = {Tordeux, Antoine and Chraibi, Mohcine and Seyfried, Armin and Schadschneider, Andreas}, title = {Prediction of pedestrian speed with artificial neural networks}, year = {2019}, reviewed = {1}, DOI = {10.1007/978-3-030-11440-4_36}, organization = {Springer}, booktitle = {International Conference on Traffic and Granular Flow}, pages = {327-335}, keywords = {VZU_predict VZU_DL} }