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arxiv: 2605.15798 · v1 · pith:CJ5O3NMRnew · submitted 2026-05-15 · ⚛️ physics.soc-ph · cond-mat.dis-nn

Event-based spatiotemporal networks for modelling emergent phenomena in complex systems

Matthijs Romeijnders , Michiel van Boven , Francesco Corman , Carl D. Modes , Phillip Staniczencko , Debabrata Panja This is my paper

Pith reviewed 2026-05-19 19:51 UTC · model grok-4.3

classification ⚛️ physics.soc-ph cond-mat.dis-nn
keywords spatiotemporal networksevent-based modelingcomplex systemsemergent phenomenadisease transmissiontransportation systemsnetwork framework
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The pith

Event-based spatiotemporal networks generate emergent behaviors in complex systems by encoding processes as discrete space-time events.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper develops event-based spatiotemporal networks as a way to model how fine-grained processes in complex systems produce large-scale patterns. The framework represents system activities as discrete events that are located in both space and time. A sympathetic reader would care because it offers a practical way to connect detailed local data to overall dynamics in areas like epidemiology and transportation without needing to average or simplify too early. The method is illustrated with real examples of tracking a pathogen outbreak and delay spread in trains. It suggests this event focus can help in other fields where emergence matters.

Core claim

Event-based spatiotemporal networks encode system processes as discrete events anchored in space and time. They provide a unified, flexible and efficient approach to generate emergent behaviour in complex systems across space and time from these events. This is demonstrated through applications to tracking transmission routes during a local outbreak of a novel respiratory pathogen in the Netherlands and modeling the propagation of delays in the S-bahn public transportation system around Zürich, Switzerland.

What carries the argument

Event-based spatiotemporal networks that encode system processes as discrete events anchored in space and time.

If this is right

  • Enable fine-grained tracking of transmission routes and infection patterns through space and time in disease outbreaks.
  • Model propagation of delays in public transportation systems.
  • Improve data analysis, simulation, and collection strategies in developmental biology and community ecology by focusing on events rather than static states.
  • Handle large, high-resolution datasets to derive macroscopic dynamics from micro-level data in complex systems.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Researchers in other domains could define custom event types to apply the same structure to phenomena like urban traffic or species interactions.
  • Data collection efforts might shift toward logging discrete occurrences with precise locations and timings instead of continuous measurements.
  • Comparing results from this event-based method to traditional continuous models could reveal when discretization preserves or alters key emergent features.

Load-bearing premise

Real-world system processes can be adequately represented as discrete events anchored in space and time without critical loss of information or introduction of artifacts that distort emergent dynamics.

What would settle it

Running the networks on the Netherlands pathogen outbreak data and finding that predicted infection patterns do not match the actual observed spread would indicate the representation loses critical information.

Figures

Figures reproduced from arXiv: 2605.15798 by Carl D. Modes, Debabrata Panja, Francesco Corman, Matthijs Romeijnders, Michiel van Boven, Phillip Staniczencko.

Figure 1
Figure 1. Figure 1: From real-world data to network representations. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Comparison between EBSTN and the current best, TN, for the infectious disease epidemiology example. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Simulations of the early stages of an outbreak of a novel pathogen that is introduced in working adults in selected Dutch municipalities. The model includes ∼ 170,000 actors, who are categorised into 11 demographic groups and assigned to 355 municipalities of residence. Actors’ movements are tracked on an hourly resolution, obeying day-night and weekday-weekend mobility rhythms, and they mixed within munic… view at source ↗
Figure 4
Figure 4. Figure 4: Modelling of public transport system, specifically railway operations. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Space-time diagram showing delay propagation on infrastructure (to avoid cluttering the figure, event start and end times are marked only for blocking section B1). (a) The schedule for two different services – preceding (gray) and following (blue) – travelling through three sequential blocking sections of different lengths B1 → B2 → B3 on an infrastructure link. The event that the preceding (resp. followin… view at source ↗
read the original abstract

Complex systems display emergent phenomena that vary significantly across spatial and temporal scales. These variations originate from fine-grained system processes, yet arriving at macroscopic dynamics from micro-level data -- particularly when large, high-resolution datasets are available -- remains a persistent challenge. Here we develop event-based spatiotemporal networks, a computational modelling framework that encodes system processes as discrete events anchored in space and time. Event-based spatiotemporal networks offer a unified, flexible and efficient approach to generate emergent behaviour in complex systems across space and time from these events. We demonstrate the effectiveness of event-based spatiotemporal networks through two illustrative real-world applications. First, following a local outbreak of a novel respiratory pathogen in the Netherlands, spatiotemporal networks enable fine-grained tracking of transmission routes and infection patterns through space and time. Second, we use spatiotemporal networks to model propagation of delays in a public transportation system (S-bahn) around Z\"urich, Switzerland. We also discuss broader uses of event-based spatiotemporal networks in fields like developmental biology and community ecology, where focusing on events rather than static system states can improve data analysis, simulation, and collection strategies.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces event-based spatiotemporal networks as a modeling framework that represents complex system processes as discrete events anchored in space and time, aiming to generate emergent macroscopic behaviors from micro-level data. It demonstrates the approach through two real-world applications: fine-grained tracking of a novel respiratory pathogen transmission in the Netherlands and modeling delay propagation in the Zurich S-bahn public transportation system. Broader potential uses are discussed in fields such as developmental biology and community ecology.

Significance. If the framework receives a rigorous mathematical formulation and empirical validation, it could provide a flexible, unified method for bridging high-resolution event data to emergent dynamics across spatial and temporal scales in complex systems. This has potential value for simulation, analysis, and data collection strategies in physics of social systems and related disciplines, particularly where static state-based models fall short.

major comments (2)
  1. [Framework description] Framework section: The central claim that event-based spatiotemporal networks constitute a 'unified, flexible and efficient approach' to generate emergent behaviour lacks any equations, formal definitions, or algorithmic specifications for event encoding, network construction, or emergence generation. This is load-bearing for the manuscript's contribution, as the abstract and description provide no basis to evaluate reproducibility or advantages over existing spatiotemporal models.
  2. [Applications] Applications section: The two illustrative demonstrations (pathogen outbreak tracking and delay propagation) are presented without quantitative metrics, error analysis, baseline comparisons, or falsifiable predictions. This undermines the assertion of 'effectiveness' and leaves the support for the framework's practical utility unassessable.
minor comments (1)
  1. [Abstract] Abstract: The LaTeX-style notation 'Zürich' should be rendered consistently in the final typeset version.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which have identified key opportunities to strengthen the formal presentation and empirical grounding of our work. We address each major comment point by point below, indicating the revisions we will undertake.

read point-by-point responses

  1. Referee: Framework section: The central claim that event-based spatiotemporal networks constitute a 'unified, flexible and efficient approach' to generate emergent behaviour lacks any equations, formal definitions, or algorithmic specifications for event encoding, network construction, or emergence generation. This is load-bearing for the manuscript's contribution, as the abstract and description provide no basis to evaluate reproducibility or advantages over existing spatiotemporal models.

    Authors: We agree that the current version would be strengthened by explicit mathematical formalization. In the revised manuscript we will insert a new subsection in the Framework section that defines events as structured tuples (location, timestamp, event type, attributes), specifies the network construction procedure (spatiotemporal proximity rules for edge formation with explicit distance and time thresholds), and details the emergence generation mechanism (iterative propagation and aggregation operators that map micro-events to macro observables). We will also provide pseudocode and a brief comparison to related approaches such as event-driven simulation and spatiotemporal graph neural networks to clarify advantages in flexibility and efficiency for high-resolution data. revision: yes

  2. Referee: Applications section: The two illustrative demonstrations (pathogen outbreak tracking and delay propagation) are presented without quantitative metrics, error analysis, baseline comparisons, or falsifiable predictions. This undermines the assertion of 'effectiveness' and leaves the support for the framework's practical utility unassessable.

    Authors: The applications were conceived as illustrative demonstrations of the framework's workflow rather than exhaustive validation studies. We nevertheless accept that quantitative support is necessary to substantiate claims of effectiveness. In revision we will add, for the pathogen case, metrics such as route reconstruction precision against contact-tracing ground truth and comparison to a standard compartmental model; for the S-bahn case, we will report delay prediction RMSE and compare against a baseline queueing model. We will also state explicit falsifiable predictions (e.g., expected infection wavefront speed under different event densities) and note any data limitations that constrain full statistical testing. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper introduces event-based spatiotemporal networks as a conceptual computational modeling framework for generating emergent behavior from discrete events in space and time. No mathematical equations, derivations, fitted parameters, or self-citations are presented in the abstract or described content that would reduce any claimed prediction or result to its own inputs by construction. The two applications are framed explicitly as illustrative demonstrations rather than quantitative validations or forced outcomes. The central claim rests on the adequacy of event discretization for complex systems, which is an independent modeling choice without internal reduction to prior results or definitions within the provided text. This qualifies as a self-contained conceptual contribution with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no equations, methods section, or explicit assumptions; therefore no free parameters, axioms, or invented entities can be identified from the given text.

pith-pipeline@v0.9.0 · 5744 in / 1074 out tokens · 25170 ms · 2026-05-19T19:51:46.707966+00:00 · methodology

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Works this paper leans on

67 extracted references · 67 canonical work pages

  1. [1]

    Ambühl, M

    L. Ambühl, M. Menendez, M. C. González, Understanding congestion propagation by combining percolation theory with the macroscopic fundamental diagram,Commun. Phys.6, 26 (2023)

    work page 2023

  2. [2]

    M. U. G. Kraemeret al, Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence, Science373, 889 (2021)

    work page 2021

  3. [3]

    Viscek, A

    T. Viscek, A. Zafeiris, Collective motion,Phys. Rep.517, 71 (2012)

    work page 2012

  4. [4]

    M. G. Saunders, G. A. Voth, Coarse-graining methods for computational biology,Annual Rev. Biophys.42, 73 (2013)

    work page 2013

  5. [5]

    H. R. Wilson, J. D. Cowan, Excitatory and inhibitory interactions in localized populations of model neurons,Biophys. J.12, 1 (1972). 13

    work page 1972

  6. [6]

    Hanski, M

    I. Hanski, M. E. E. Gilpin,Metapopulation biology: Ecology, genetics and evolution(Academic Press, San Diego, CA, USA, 1997)

    work page 1997

  7. [7]

    Diekmann, H

    O. Diekmann, H. Heesterbeek, T. Britton,Mathematical Tools for Understanding Infectious Disease Dynamics(Princeton University Press, Princeton, NJ, USA, 2013)

    work page 2013

  8. [8]

    J. A. Ward, A. Tapper, P. L. Simon, R. P. Mann, Micro-scale foundation with error quantification for the approximation of dynamics on networks,Commun. Phys.5, 71 (2022)

    work page 2022

  9. [9]

    Barrat, M

    A. Barrat, M. Barthelémy, A. Vespignani,Dynamical Processes on Complex Networks(Cambridge University Press, Cambridge, UK, 2012)

    work page 2012

  10. [10]

    M. M. Dekker, R. D. Schram, J. Ou, D. Panja, Hidden dependence of spreading vulnerability on topological complexity,Phys. Rev. E105, 054301 (2022)

    work page 2022

  11. [11]

    M. M. Dekker,et al., Quantifying agent impacts on contact sequences in social interactions,Sci. Rep. 12, 3483 (2022)

    work page 2022

  12. [12]

    Holme, J

    P. Holme, J. Saramäki, Temporal networks,Phys. Rep.519, 97 (2012)

    work page 2012

  13. [13]

    Last accessed June 18, 2025

    sociopatterns.org, Sociopatterns,http://www.sociopatterns.org. Last accessed June 18, 2025

    work page 2025

  14. [14]

    M. C. Romeijnders, M. van Boven, D. Panja, Risk mapping novel respiratory pathogens with large-scale dynamic contact networks,Commun. Med.6, 229 (2026)

    work page 2026

  15. [15]

    George, S

    B. George, S. Kim,Spatio-temporal networks: modeling and algorithms(Springer, 2013)

    work page 2013

  16. [16]

    M. J. Williams, M. Musolesi, Spatio-temporal networks: reachability, centrality and robustness,Roy. Soc. Open Sci.3, 160196 (2016)

    work page 2016

  17. [17]

    Zhang, Y

    J. Zhang, Y. Zheng, J. Sun, D. Qi, Flow prediction in spatio-temporal networks based on multitask deep learning,IEEE Trans. Knowl. Data Eng.32, 468 (2020)

    work page 2020

  18. [18]

    Barthelemy, Spatial networks,Phys

    M. Barthelemy, Spatial networks,Phys. Rep.499, 1 (2011)

    work page 2011

  19. [19]

    W. O. Kermack, A. G. McKendrick, A contribution to the mathematical theory of epidemics,Proc. Roy. Soc. A (London)115, 700 (1927)

    work page 1927

  20. [20]

    Pastor-Satorras, A

    R. Pastor-Satorras, A. Vespignani, Epidemic spreading in scale-free networks,Physical Review Letters 86, 3200 (2001)

    work page 2001

  21. [21]

    A. L. Lloyd, R. M. May, How viruses spread among computers and people,Science292, 1316 (2001)

    work page 2001

  22. [22]

    M. E. Newman, Spread of epidemic disease on networks,Phys. Rev. E66, 016128 (2002)

    work page 2002

  23. [23]

    M. J. Keeling, K. T. Eames, Networks and epidemic models,J. Roy. Soc. Interface2, 295 (2005)

    work page 2005

  24. [24]

    Gösgens,et al., Trade-offs between mobility restrictions and transmission of sars-cov-2,J

    M. Gösgens,et al., Trade-offs between mobility restrictions and transmission of sars-cov-2,J. Roy. Soc. Interface18, 20200936 (2021)

    work page 2021

  25. [25]

    J. W. Glasser,et al., Analysis of metapopulation models of the transmission of sars-cov-2 in the united states,J. Math. Biol.87, 1234 (2023)

    work page 2023

  26. [26]

    Mossong,et al., Social contacts and mixing patterns relevant to the spread of infectious diseases, PLOS Medicine5, 1 (2008)

    J. Mossong,et al., Social contacts and mixing patterns relevant to the spread of infectious diseases, PLOS Medicine5, 1 (2008)

    work page 2008

  27. [27]

    W. W. Thompson,et al., Mortality associated with influenza and respiratory syncytial virus in the united states,J. Am. Med. Assoc.289, 179 (2003)

    work page 2003

  28. [28]

    Diekmann, Thresholds and travelling waves for the geographical spread of infection,J

    O. Diekmann, Thresholds and travelling waves for the geographical spread of infection,J. Math. Biol. 6, 109 (1978)

    work page 1978

  29. [29]

    van den Bosch, J

    F. van den Bosch, J. A. J. Metz, O. Diekmann, The velocity of spatial population expansion,J. Math. Biol.28, 529 (1990)

    work page 1990

  30. [30]

    van Boven,et al., Estimating transmission parameters for respiratory syncytial virus and predicting the impact of maternal and pediatric vaccination,J

    M. van Boven,et al., Estimating transmission parameters for respiratory syncytial virus and predicting the impact of maternal and pediatric vaccination,J. Infect. Dis.222, S688 (2020). 14

    work page 2020

  31. [31]

    Viana,et al., Controlling the pandemic during the sars-cov-2 vaccination rollout,Nat

    J. Viana,et al., Controlling the pandemic during the sars-cov-2 vaccination rollout,Nat. Commun. 12, 3674 (2021)

    work page 2021

  32. [32]

    Mintiens,et al., Risk analysis of the spread of classical swine fever virus through ’neighbourhood infections’ for different regions in belgium,Prev

    K. Mintiens,et al., Risk analysis of the spread of classical swine fever virus through ’neighbourhood infections’ for different regions in belgium,Prev. Vet. Med.60, 27 (2003)

    work page 2003

  33. [33]

    G. J. Boender,et al., Risk maps for the spread of highly pathogenic avian influenza in poultry,PLoS Comput. Biol.3, e71 (2007)

    work page 2007

  34. [34]

    G. J. Boender, H. J. van Roermund, M. C. de Jong, T. J. Hagenaars, Transmission risks and control of foot-and-mouth disease in the netherlands: spatial patterns,Epidemics2, 36 (2010)

    work page 2010

  35. [35]

    G. J. Boender, R. van den Hengel, H. J. van Roermund, T. J. Hagenaars, The influence of between- farm distance and farm size on the spread of classical swine fever during the 1997–1998 epidemic in the netherlands,PLoS One9, e95278 (2014)

    work page 1997

  36. [36]

    Brockmann, D

    D. Brockmann, D. Helbing, The hidden geometry of complex, network-driven contagion phenomena, Science342, 1337 (2013)

    work page 2013

  37. [37]

    Masuda, P

    N. Masuda, P. Holme, eds.,Temporal Network Epidemiology(Springer, 2017)

    work page 2017

  38. [38]

    Liu,et al., Model-based evaluation of alternative reactive class closure strategies against COVID-19,Nat

    Q.-H. Liu,et al., Model-based evaluation of alternative reactive class closure strategies against COVID-19,Nat. Commun.13, 322 (2022)

    work page 2022

  39. [39]

    Belik, T

    V. Belik, T. Geisel, D. Brockmann, Natural Human Mobility Patterns and Spatial Spread of Infectious Diseases,Phys. Rev. X1, 011001 (2011)

    work page 2011

  40. [40]

    M. M. Dekker, L. E. Coffeng, F. P. Pijpers, D. Panja, S. J. de Vlas, Reducing societal impacts of sars-cov-2 interventions through subnational implementation,eLife12, e80819 (2023)

    work page 2023

  41. [41]

    Simini, G

    F. Simini, G. Barlacchi, M. Luca, et al., A deep gravity model for mobility flows generation,Nat. Commun.12, 6576 (2021)

    work page 2021

  42. [42]

    D. G. Malcolm, J. H. Roseboom, C. E. Clark, W. Fazar, Application of a technique for research and development program evaluation,Oper. Res.7, 646 (1959)

    work page 1959

  43. [43]

    Blazewicz, D

    J. Blazewicz, D. Kobler, Review of properties of different precedence graphs for scheduling problems, Eur. J. Oper. Res.142, 435 (2002)

    work page 2002

  44. [44]

    Büker, B

    T. Büker, B. Seybold, Stochastic modelling of delay propagation in large networks,J. Rail Transp. Plan. Manag.2, 34 (2012)

    work page 2012

  45. [45]

    R. M. Goverde, Railway timetable stability analysis using max-plus system theory,Transp. Res. B: Methodol.41, 179 (2007)

    work page 2007

  46. [46]

    Mascis, D

    A. Mascis, D. Pacciarelli, Job-shop scheduling with blocking and no-wait constraints,Eur. J. Oper. Res.143, 498 (2002)

    work page 2002

  47. [47]

    Schöbel, An eigenmodel for iterative line planning, timetabling and vehicle scheduling in public transportation,Transp

    A. Schöbel, An eigenmodel for iterative line planning, timetabling and vehicle scheduling in public transportation,Transp. Res. C Emerg. Technol.74, 348 (2017)

    work page 2017

  48. [48]

    Büchel, T

    B. Büchel, T. Spanninger, F. Corman, Empirical dynamics of railway delay propagation identified during the large-scale rastatt disruption,Sci. Rep.10, 18584 (2020)

    work page 2020

  49. [49]

    E. J. Pearl, J. Li, J. B. A. Green, Cellular systems for epithelial invagination,Phil. Trans. R. Soc. B: Biol. Sci.372, 20150526 (2017)

    work page 2017

  50. [50]

    Harmansa, A

    S. Harmansa, A. Erlich, C. Eloy, G. Zurlo, T. Lecuit, Growth anisotropy of the extracellular matrix shapes a developing organ,Nat. Commun.14, 1220 (2023)

    work page 2023

  51. [51]

    C. M. Nelson, On Buckling Morphogenesis,J. Biomech. Eng.138, 021005 (2016)

    work page 2016

  52. [52]

    B. C. Vellutini,et al., Patterned embryonic invagination evolved in response to mechanical instability, bioRxiv: 2023.03.30.534554(2025)

    work page 2023

  53. [53]

    J. F. Fuhrmann,et al., Active shape programming drives Drosophila wing disc eversion,Sci. Adv.10, eadp0860 (2024). 15

    work page 2024

  54. [54]

    Modes, M

    C. Modes, M. Warner, Shape-programmable materials,Phys. Today69, 32 (2016)

    work page 2016

  55. [55]

    D. Bi, X. Yang, M. C. Marchetti, M. L. Manning, Motility-Driven Glass and Jamming Transitions in Biological Tissues,Phys. Rev. X6, 021011 (2016)

    work page 2016

  56. [56]

    A. G. Fletcher, M. Osterfield, R. E. Baker, S. Y. Shvartsman, Vertex Models of Epithelial Morpho- genesis,Biophys. J.106, 2291 (2014)

    work page 2014

  57. [57]

    Scheffer, S

    M. Scheffer, S. Carpenter, J. A. Foley, C. Folke, B. Walker, Catastrophic shifts in ecosystems,Nature 413, 591 (2001)

    work page 2001

  58. [58]

    A. B. Olin,et al., Predation and spatial connectivity interact to shape ecosystem resilience to an ongoing regime shift,Nat. Commun.15, 1304 (2024)

    work page 2024

  59. [59]

    Berkström, L

    C. Berkström, L. Wennerström, U. Bergström, Ecological connectivity of the marine protected area network in the baltic sea, kattegat and skagerrak: current knowledge and management needs,Ambio 51, 1485 (2021)

    work page 2021

  60. [60]

    C. B. voor de Statistiek (CBS), Bevolking; geslacht, leeftijd, regio,https://opendata.cbs.nl/ statline/#/CBS/nl/dataset/03759ned/table(2024). Open data set, last accessed 2026-05-15

    work page 2024

  61. [61]

    Last accessed: 2026-05-15

    Kences, Landelijke monitor studentenhuisvesting 2020, https://www.kences.nl/publicaties/ landelijke-monitor-studentenhuisvesting-2020/(2020). Last accessed: 2026-05-15

    work page 2020

  62. [62]

    Last accessed: 2026-05-15

    Centraal Bureau voor de Statistiek, Bijna 4 op de 10 werkt en woont in dezelfde gemeente,https: //zenodo.org/records/18152707(2017). Last accessed: 2026-05-15

    work page arXiv 2017

  63. [63]

    K. Prem, A. R. Cook, M. Jit, Projecting social contact matrices in 152 countries using contact surveys and demographic data,PLOS Comput. Biol.13, 1 (2017)

    work page 2017

  64. [64]

    Hansen, J

    I. Hansen, J. Pachl,Railway timetabling and operations: Analysis, modelling, optimisation, simulation, performance, evaluation(Eurailpress, 2014)

    work page 2014

  65. [65]

    Romeijnders, Event-based spatiotemporal networks for modelling emergent phenomena in complex systems, ebstn-data, DOI:10.5281/zenodo.20178763 (2026)

    M. Romeijnders, Event-based spatiotemporal networks for modelling emergent phenomena in complex systems, ebstn-data, DOI:10.5281/zenodo.20178763 (2026)

    work page doi:10.5281/zenodo.20178763 2026

  66. [66]

    Romeijnders, Event-based spatiotemporal networks for modelling emergent phenomena in complex systems, ebstn-trainsim, DOI:10.5281/zenodo.20178769 (2026)

    M. Romeijnders, Event-based spatiotemporal networks for modelling emergent phenomena in complex systems, ebstn-trainsim, DOI:10.5281/zenodo.20178769 (2026)

    work page doi:10.5281/zenodo.20178769 2026

  67. [67]

    Romeijnders, Event-based spatiotemporal networks for modelling emergent phenomena in complex systems, ebstn-episim, DOI:10.5281/zenodo.20178775 (2026)

    M. Romeijnders, Event-based spatiotemporal networks for modelling emergent phenomena in complex systems, ebstn-episim, DOI:10.5281/zenodo.20178775 (2026). Acknowledgements We thank Jan Lordieck for providing the schematic figure 4(b), and many helpful discussions on delay propagation in public transport. Author contributions DP conceived the idea for EBST...

    work page doi:10.5281/zenodo.20178775 2026