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The MADRAS project publications and communications are registered on this open access HAL repository.

Preprints

  1. Echeverría-Huarte, I., Roge, A., Simonin, O., & Nicolas, A. (2023) Foundations of continuous agent-based modelling frameworks for pedestrian dynamics and their implications. Preprint arXiv:2309.12798 available here.
  2. Chraibi, M., Schadschneider, A. & Tordeux, A. (2023) Social distancing and the future of pedestrian dynamics.  Preprint arXiv:2023.06065 available here.

Peer-reviewed journals

  1. Dang, H.-T., Gaudou, B., & Verstaevel, N. (2024) A literature review of dense crowd simulation. Simulation Modelling Practice and Theory, 102955, doi: 10.1016/j.simpat.2024.102955
  2. Dang, H.-T., Gaudou, B., & Verstaevel, N. (2024) HyPedSim: A Multi-Level Crowd-Simulation Framework—Methodology, Calibration, and Validation. Sensors, 24(5), 1639. doi: 10.3390/s24051639
  3. Cordes, J., Schadschneider, A., & Nicolas, A. (2024) Dimensionless numbers reveal distinct regimes in the structure and dynamics of pedestrian crowds. PNAS nexus, pgae120, doi: 10.1093/pnasnexus/pgae120
  4. Korbmacher, R. & Tordeux, A. (2024) Toward better pedestrian trajectory predictions: the role of density and time-to-collision in hybrid deep-learning algorithms. Sensors, vol. 24, no. 7, pp. 2356, doi: 10.3390/s24072356

  5. Korbmacher, R.,  Dang, H.-T. & Tordeux, A. (2024) Predicting pedestrian trajectories at different densities: A multi-criteria empirical analysis. Physica A: Statistical Mechanics and its Applications, vol. 634, pp. 129440, doi: 10.1016/j.physa.2023.129440. Preprint arXiv:2307.15442.

  6. Echeverría-Huarte, I., & Nicolas, A. (2023) Body and Mind: Decoding the dynamics of pedestrians and the effect of smartphone distraction by coupling decisional and mechanical processes. Transportation Research Part C: Emerging Technologies, 157:104365. doi: 10.1016/j.trc.2023.104365.
  7. Gnendiger, C., Chraibi M. and Tordeux, A. (2023) Come together: A unified description of the escalator capacity. PLOS ONE 18(3):e0282599. doi: 10.1371/journal.pone.0282599. 
  8. Xie, C. Z., Tang, T. Q., Zhang, B. T., & Nicolas, A. (2023) Adult-child pairs walking down stairs: Empirical analysis and optimal-step-based modeling of a complex pedestrian flow, with an exploration of flow-improvement strategies. Journal of Statistical Mechanics, 1:013404. doi: 0.1088/1742-5468/acb25f. Preprint arXiv:2210.06782
  9. Xiao, Y., Xu, J., Chraibi, M., Zhang, J., & Gou, C. (2022) A generalized trajectories-based evaluation approach for pedestrian evacuation models. Safety Science, 147:105574. doi: 10.1016/j.ssci.2021.105574. 
  10. Korbmacher, R., & Tordeux, A. (2022) Review of pedestrian trajectory prediction methods: comparing deep learning and knowledge-based approaches. IEEE Transactions on Intelligent Transportation Systems, 23(12):24126-24144. doi: 10.1109/TITS.2022.3205676. Preprint ArXiv:2204.10807.
  11. Khelfa, B., Korbmacher, R., Schadschneider, A., & Tordeux, A. (2022) Heterogeneity‑induced lane and band formation in self‑driven particle systems. Scientific Reports, 12(1):4768. doi: 10.1038/s41598-022-08649-4..

 

Peer-reviewed contributions to conference proceedings

  1. Echeverría-Huarte, I., & Nicolas, A. Revisiting the theoretical basis of agent-based models for pedestrian dynamics. In: Proceedings of the Traffic and Granular Flow 2022 (TGF22) Conference, pp. 19-26. doi: 10.1007/978-981-99-7976-9_3 
  2. Korbmacher, R., Dang, H.-T., Tordeux, A., Gaudou, B. & Verstaevel, N. Empirical comparison of different pedestrian trajectory prediction methods at high densities. In:   Proceedings of the Traffic and Granular Flow 2022 (TGF22) Conference, pp. 231-238. doi: 10.1007/978-981-99-7976-9_29

  3. Korbmacher, R. & Tordeux, A. (2024) Deep learning for predicting pedestrian trajectories in crowds. In: Intelligent Systems and Applications, Arai, Kohei, Eds. Cham: Springer Nature Switzerland, 2024, pp. 720-725. doi: 10.1007/978-3-031-47718-8_46

  4. Dang, H.-T., Korbmacher, R., Tordeux, A., Gaudou, B. & Verstaevel, N. (2023) TTC-SLSTM: Human trajectory prediction using time-to-collision interaction energy. 2023 15th International Conference on Knowledge and Systems Engineering (KSE), pp. 1-6, doi:10.1109/KSE59128.2023.10299443. Preprint hal-04251961 available here - Awarded as runner-up paper. 
  5. Dang, H.-T., Gaudou, B., & Verstaevel, N. (2023) A multi-level density-based crowd simulation architecture.  In: Mathieu, P., Dignum, F., Novais, P., De la Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. PAAMS 2023. Lecture Notes in Computer Science, vol. 13955. Springer, Cham. doi:10.1007/978-3-031-37616-0_6. Preprint hal-04104250 available here.
  6. Cordes, J., Chraibi, M., Tordeux, A., & Schadschneider, A. (2021) Time-to-collision models for single-file pedestrian motion.  Proceedings of the 10th International Conference on Pedestrian and Evacuation Dynamics (PED21), Collective Dynamics, 6:10. doi: 10.17815/CD.2021.133.
  7. Khelfa, B., Korbmacher, R., Schadschneider, A., & Tordeux, A. (2021) Initiating lane and band formation in heterogeneous pedestrian dynamics. Proceedings of the 10th International Conference on Pedestrian and Evacuation Dynamics (PED21), Collective Dynamics, 6:13. doi: 10.17815/CD.2021.129. 

 

Peer-reviewed book chapters

  1. Cordes, J., Chraibi, M., Tordeux, A. & Schadschneider, A. (2023) Single-file pedestrian dynamics: a review of agent-following models. Bellomo, N. and Gibelli, L., Eds. Crowd Dynamics (vol. 4) Cham: Springer International Publishing, 2023, pp. 143-178. doi: 10.1007/978-3-031-46359-4_3. Preprint arXiv:2308.07451.
  2. Korbmacher, R.,  Nicolas, A., Tordeux, A. & Totzeck, C. (2023) Time-continuous microscopic pedestrian models: an overview. Bellomo, Nicola and Gibelli, Livio, Eds. Crowd Dynamics (vol. 4) Cham: Springer International Publishing, 2023, pp. 55-80. doi: 10.1007/978-3-031-46359-4_6. Preprint arXiv:2308.07450.

Communications in conferences

  1. Korbmacher, R. & Tordeux, A. Lane and band formation in mixed traffic flow. 7th Annual Meeting of the Cycling Research Board, 25 - 27 October 2023, Wuppertal University, Germany. Slides.
  2. Dang, H.-T., Korbmacher, R., Tordeux, A., Gaudou, B. & Verstaevel, N. TTC-SLSTM: Human trajectory prediction using time-to-collision interaction energy. 15th IEEE International Conference on Knowledge and Systems Engineering (KSE2023), October 18 - 20, 2023, Ha Noi, Vietnam. Slides.
  3. Dang, H.-T., Gaudou, B., & Verstaevel, N. A multi-level density-based crowd simulation architecture. 21st International Conference on Practical Applications of Agents and Multi-agents Systems (PAAMS 2023), July 12 - 14, 2023, Guimarães, Portugal. Slides.

  4. Dang, H.-T., Gaudou, B., & Verstaevel, N. Empirical analysis on external factors affecting pedestrian dynamics in high-density situations. Pedestrian and Evacuation Dynamics 2023 Conference (PED23), 27 – 30 June 2023, Eindhoven Technical University, The Netherlands. Poster.

  5. Dufour, O., Rodney, D., & Nicolas, A. Single-file motion revisited: perspectives from an energy-based model. Pedestrian and Evacuation Dynamics 2023 Conference (PED23), 27 – 30 June 2023, Eindhoven Technical University, The Netherlands. Poster.

  6. Cordes, J., Nicolas, A., & Schadschneider, A. Scaling Analysis of Crowd Dynamics. Pedestrian and Evacuation Dynamics 2023 Conference (PED23), 27 – 30 June 2023, Eindhoven Technical University, The Netherlands. Slides. 

  7. Korbmacher, R., Dang, H.-T., & Tordeux, A. Using time-to-collision in the loss function of deep learning algorithm to improve pedestrian trajectory predictions. Pedestrian and Evacuation Dynamics 2023 Conference (PED23), 27 – 30 June 2023, Eindhoven Technical University, The Netherlands. Poster.

  8. Chraibi, M., Cordes, J., Dang, H.-T. Dufour, O., Gaudou, B., Korbmacher, R., Nicolas, A., Rodney, D., Tordeux, A., & Verstaevel., N. Multi-Agent modelling of Dense cRowd dynAmicS (MADRAS): Application to the Festival of Lights in Lyon. Pedestrian and Evacuation Dynamics 2023 Conference (PED23), 27 – 30 June 2023, Eindhoven Technical University, The Netherlands. Abstract; Slides.

  9. Echeverría-Huarte, I., & Nicolas, A. Revisiting the theoretical basis of agent-based models for pedestrian dynamics. Traffic and Granular Flow 2022 (TGF22), 15 – 17 October 2022, IIT Delhi, India. 
  10. Korbmacher, R., Dang, H.-T., Tordeux, A., Gaudou, B. & Verstaevel, N. Differences in pedestrian trajectory predictions for high- and low-density situations. Traffic and Granular Flow 2022 (TGF22), 15 – 17 October 2022, IIT Delhi, India. Abstract; Slides.
  11. Echeverrìa-Huarte, I., & Nicolas, A. Modélisation de l’anticipation des collisions et des contacts dans la dynamique des foules piétonnes. 18ième Journées de la Matière Condensée (JMC18), 22 – 26 August 2022, Lyon, France. Abstract.
  12. Dufour, O., Echeverrìa-Huarte, I., Rodney, D., & Nicolas, A. The role of anticipation among pedestrians in the emergence of stop and go waves. 18ième Journées de la Matière Condensée (JMC18), 22 – 26 August 2022, Lyon, France. Poster.
  13. Cordes, J., Schadschneider, A., & Tordeux, A. Noise-induced breakdown in single-file motion. 18ième Journées de la Matière Condensée (JMC18), 22 – 26 August 2022, Lyon, France. Abstract; Poster.
  14. Dang, H.-T., Gaudou, B. & Verstaevel, N. Gampy: a fast plugin for integration of Python-based deep-learning models to the GAMA platform. 2nd GAMA Days Conference,  22 – 24 June 2022, Online, France. Abstract on HAL; Slides.
  15. Dang, H.-T., Gaudou, B. & Verstaevel, N. A modular framework for multi-behavior and multi-scale simulation of pedestrians. 2nd GAMA Days Conference,  22 – 24 June 2022, Online, France. Abstract on HAL; Slides.
  16. Korbmacher, R., Dang, H.-T., Tordeux, A., Gaudou, B. & Verstaevel, N. Using synthetic data to improve performance of data-driven algorithms in high density pedestrian situations. 2nd GAMA Days Conference, 22 – 24 June 2022, Online, France. Abstract on HAL; Slides.
  17. Echeverrìa-Huarte, I., & Nicolas, A. Modélisation de l'anticipation des collisions et des contacts dans la dynamique des foules piétonnes (conversation). Regional French Conference on Complex Systems (FRCCS 2022), 20 – 22 June 2022, Paris, France. Abstract.
  18. Dufour, O., & Nicolas, A. Stop and go pedestrian wave based on a behavioural model (flash talk). Regional French Conference on Complex Systems (FRCCS 2022), 20 – 22 June 2022, Paris, France. 
  19. Korbmacher, R., & Tordeux, A. Review of Pedestrian Trajectory Prediction methods: Comparing physics-based and data-based approaches. 10th International Conference on Pedestrian and Evacuation Dynamics (PED2021). 28 – 30 November 2021, Online, Melbourne, Australia. Abstract; Slides.
  20. Cordes, J., Chraibi, M., Schadschneider, A., & Tordeux, A. Distance vs time-to-collision pedestrian models: verification and validation. 10th International Conference on Pedestrian and Evacuation Dynamics (PED2021). 28 – 30 November 2021, Online, Melbourne, Australia. Abstract; Slides.
  21. Tordeux, A., Khelfa, B. & Schadschneider, A. Initiating lane and band formation in heterogeneous pedestrian dynamics. 10th International Conference on Pedestrian and Evacuation Dynamics (PED2021). 28 – 30 November 2021, Online, Melbourne, Australia. Abstract; Slides.
  22. Korbmacher, R., & Tordeux, A. Review of Pedestrian Trajectory Prediction methods: Comparing physics-based and data-based approaches. 17ième Journées de la Matière Condensée (JMC17), 24 – 27 August 2021, Online, Rennes, France. Abstract; Slides.
  23. Tordeux, A., Khelfa, B. & Schadschneider, A.  Heterogeneity-induced lane and band formation in self-driven particle systems. 17ième Journées de la Matière Condensée (JMC17), 24 – 27 August 2021, Online, Rennes, France. Abstract; Slides.

 

Madras crowd2

      [DFG/ANR project MADRAS] Multi-agent modelling of dense crowd dynamics: Predict & Understand