Publications

@article{nokey,

title = {Trends in crowd accidents based on an analysis of press reports},

author = {C. Feliciani; A. Corbetta; M. Haghani; K. Nishinari},

doi = {https://doi.org/10.1016/j.ssci.2023.106174},

year  = {2023},

date = {2023-08-01},

urldate = {2023-08-01},

journal = {Safety Science},

volume = {164},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Stochastic fluctuations of diluted pedestrian dynamics along curved paths},

author = {G. vd Vleuten; F. Toschi; W. Schilders; A. Corbetta},

url = {https://arxiv.org/abs/2306.12090},

year  = {2023},

date = {2023-06-21},

urldate = {2023-06-21},

journal = {Submitted},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{nokey,

title = {A psychological approach to understading microscopic and macroscopic structures during train boarding},

author = {R. Kodapanakkal; C. Pouw; G. Bombaerts; A. Corbetta; A. Dameski; A. Haans; J. Ham; A. Spahn; F. Toschi},

year  = {2023},

date = {2023-06-01},

urldate = {2023-06-01},

journal = {Submitted},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{nokey,

title = {Public-space sonification for pedestrian trajectory nudging},

author = {A. Corbetta; T. Senan; L. Woestemeier; B. Hengeveld},

year  = {2023},

date = {2023-06-01},

urldate = {2023-06-01},

journal = {Submitted},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{nokey,

title = {Physics of Human Crowds},

author = {A. Corbetta; F. Toschi},

url = {https://doi.org/10.1146/annurev-conmatphys-031620-100450},

year  = {2022},

date = {2022-11-30},

urldate = {2022-11-30},

journal = {Annual Review of Condesed Matter Physics},

volume = {14},

pages = {311-333},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Fluctuations in pedestrian dynamics routing choices},

author = {A. Gabbana; F. Toschi; P. Ross; A. Haans; A. Corbetta},

doi = {https://doi.org/10.1093/pnasnexus/pgac169},

year  = {2022},

date = {2022-09-01},

urldate = {2022-09-01},

journal = {PNAS Nexus},

volume = {1},

issue = {4},

pages = {169},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{pouw:benchmark,

title = {Benchmarking high-fidelity pedestrian tracking systems for research, real-time monitoring and crowd control},

author = {Cas Pouw; Joris Willems; Frank van Schadewijk; Jasmine Thurau; Federico Toschi; Alessandro Corbetta},

doi = {https://doi.org/10.17815/CD.2021.134 },

year  = {2022},

date = {2022-01-26},

urldate = {2022-01-26},

journal = {Collective Dynamics},

volume = {6},

keywords = {High statistic measurements},

pubstate = {published},

tppubtype = {article}

}

@article{onofri:rssi,

title = {RSSi-based visitor tracking in museums via cascaded AI classifiers and coloured graph representations},

author = {Elia Onofri; Alessandro Corbetta},

doi = {https://doi.org/10.17815/CD.2021.131 },

year  = {2021},

date = {2021-08-01},

urldate = {2021-08-01},

journal = {Collective Dynamics},

volume = {6},

number = {1-17},

keywords = {museum dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Managing Crowded Museums: Visitors Flow Measurement, Analysis, Modeling, and Optimization},

author = {P. Centorrino, A. Corbetta, E. Cristiani, E. Onofri},

doi = {https://doi.org/10.1016/j.jocs.2021.101357},

year  = {2021},

date = {2021-07-01},

journal = {Journal of Computational Science},

volume = {53},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{museum:jcompsci,

title = {Managing Crowded Museums: Visitors Flow Measurement, Analysis, Modeling, and Optimization},

author = {Pietro Centorrino; Alessandro Corbetta; Emiliano Cristiani; Elia Onofri},

doi = {https://doi.org/10.1016/j.jocs.2021.101357},

year  = {2021},

date = {2021-04-01},

journal = {J. Comput. Sci.},

pages = {101357},

keywords = {museum dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{Pouw2020,

title = {Monitoring physical distancing for crowd management: Real-time trajectory and group analysis},

author = {Caspar Pouw and Federico Toschi and Frank van Schadewijk and Alessandro Corbetta},

doi = {https://doi.org/10.1371/journal.pone.0240963},

year  = {2020},

date = {2020-10-29},

journal = {PLOS ONE},

volume = {15},

number = {10},

keywords = {High statistic measurements},

pubstate = {published},

tppubtype = {article}

}

@article{Willems2020,

title = {Pedestrian orientation dynamics from high-fidelity measurements},

author = {Joris Willems and Alessandro Corbetta and Vlado Menkovski and Federico Toschi},

doi = {https://doi.org/10.1038/s41598-020-68287-6},

year  = {2020},

date = {2020-07-15},

journal = {Scientific Reports},

volume = {10},

number = {11653},

keywords = {High statistic measurements},

pubstate = {published},

tppubtype = {article}

}

@article{conf:glow,

title = {A large-scale real-life crowd steering experiment via arrow-like stimuli},

author = {Alessandro Corbetta; Werner Kroneman; Maurice Donners; Antal Haans; Philip Ross; Marius Trouwborst; Sander vd Wijdeven; Martijn Hultermans; Dragan Sekulowski; Fedsja vd Heijden; Sjoerd Mentink; Federico Toschi},

doi = {http://dx.doi.org/10.17815/CD.2020.34},

year  = {2020},

date = {2020-01-01},

booktitle = {Pedestrian and Evacuation Dynamics 2018 (to appear)},

journal = {Collective Dynamics},

number = {5},

pages = {61-68},

keywords = {High statistic measurements},

pubstate = {published},

tppubtype = {article}

}

@article{hog,

title = {Accurate pedestrian localization via height-augmented HOG},

author = {Werner Kroneman; Alessandro Corbetta; Federico Toschi},

doi = {http://dx.doi.org/10.17815/CD.2020.30},

year  = {2020},

date = {2020-01-01},

booktitle = {Pedestrian and Evacuation Dynamics 2018 (to appear)},

journal = {Collective Dynamics},

number = {5},

pages = {33-40},

keywords = {Deep Learning, High statistic measurements},

pubstate = {published},

tppubtype = {article}

}

@article{haghani1,

title = {Panic, irrationality, herding: Three ambiguous terms in evacuation dynamics research},

author = {Milad Haghani; Nikolai Bode; Maik Boltes; Emiliano Cristiani; Alessandro Corbetta},

doi = {https://doi.org/10.1155/2019/9267643},

year  = {2019},

date = {2019-12-01},

journal = {J. Adv. Transport.},

pages = {9267643},

keywords = {review},

pubstate = {published},

tppubtype = {article}

}

@article{fireEnrico,

title = {New approaches to evacuation modelling for fire safety engineering applications},

author = {Enrico Ronchi, Alessandro Corbetta, Edwin Galea, Max Kinateder, Erika Kuligowski, Denise McGrath, Adam Pel, Youssef Shiban, Peter Thompson, Federico Toschi},

year  = {2019},

date = {2019-05-10},

journal = {Fire Safety Journal},

number = {106},

pages = {197-209},

keywords = {High statistic measurements},

pubstate = {published},

tppubtype = {article}

}

@article{glossary,

title = {A Glossary for Research on Human Crowd Dynamics},

author = {Juliane Adrian, Nikolai Bode, Martyn Amos, Mitra Baratchi, Mira Beermann, Maik Boltes, Alessandro Corbetta, Guillaume Dezecache, John Drury , Zhijian Fu, Roland Geraerts, Steve Gwynne, Gesine Hofinger, Aoife Hunt, Tinus Kanters, Angelika Kneidl, Krisztina Konya, Gerta Köster, Mira Küpper, Georgios Michalareas, Fergus Neville, Evangelos Ntontis, Stephen Reicher, Enrico Ronchi, Andreas Schadschneider, Armin Seyfried, Alastair Shipman, Anna Sieben, Michael Spearpoint, Gavin Brent Sullivan, Anne Templeton, Federico Toschi, Zeynep Yücel, Francesco Zanlungo, Iker Zuriguel, Natalie van der Wal , Frank van Schadewijk, Cornelia von Krüchten, Nanda Wijermans},

doi = {http://dx.doi.org/10.17815/CD.2019.19	},

year  = {2019},

date = {2019-03-01},

journal = {Collective Dynamics},

volume = {4},

pages = {1-13},

keywords = {crowd dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{pre-interactions,

title = {Physics-based modeling and data representation of pairwise interactions among pedestrians},

author = {Alessandro Corbetta, Jasper Meeusen, Chung-min Lee, Roberto Benzi, Federico Toschi},

url = {https://journals.aps.org/pre/abstract/10.1103/PhysRevE.98.062310},

doi = {https://doi.org/10.1103/PhysRevE.98.062310},

year  = {2018},

date = {2018-12-14},

journal = {Physical Review E},

volume = {98},

pages = {062310},

keywords = {Deep Learning, Eindhoven Station, High statistic measurements},

pubstate = {published},

tppubtype = {article}

}

@article{Corbetta_PRE_2017,

title = {Fluctuations around mean walking behaviours in diluted pedestrian flows},

author = {Alessandro Corbetta and Chung-min Lee and Roberto Benzi and Adrian Muntean and Federico Toschi},

url = {https://journals.aps.org/pre/abstract/10.1103/PhysRevE.95.032316},

year  = {2017},

date = {2017-03-15},

journal = {Physical Review E},

volume = {95},

pages = {032316},

abstract = {Understanding and modeling the dynamics of pedestrian crowds can help with designing and increasing the safety of civil facilities. A key feature of a crowd is its intrinsic stochasticity, appearing even under very diluted conditions, due to the variability in individual behaviors. Individual stochasticity becomes even more important under densely crowded conditions, since it can be nonlinearly magnified and may lead to potentially dangerous collective behaviors. To understand quantitatively crowd stochasticity, we study the real-life dynamics of a large ensemble of pedestrians walking undisturbed, and we perform a statistical analysis of the fully resolved pedestrian trajectories obtained by a yearlong high-resolution measurement campaign. Our measurements have been carried out in a corridor of the Eindhoven University of Technology via a combination of Microsoft Kinect 3D range sensor and automatic head-tracking algorithms. The temporal homogeneity of our large database of trajectories allows us to robustly define and separate average walking behaviors from fluctuations parallel and orthogonal with respect to the average walking path. Fluctuations include rare events when individuals suddenly change their minds and invert their walking directions. Such tendency to invert direction has been poorly studied so far, even if it may have important implications on the functioning and safety of facilities. We propose a model for the dynamics of undisturbed pedestrians, based on stochastic differential equations, that provides a good agreement with our field observations, including the occurrence of rare events.},

keywords = {High statistic measurements, ILIAD15_STA, Langevin Equations, Metaforum, Pedestrian dynamics},

pubstate = {published},

tppubtype = {article}

}

@article{Corbetta_CD17,

title = {Frame vs. trajectory analyses of pedestrian dynamics asymmetries in a staircase landing},

author = {Alessandro Corbetta and Chung-min Lee and Adrian Muntean and Federico Toschi},

url = {https://collective-dynamics.eu/index.php/cod/article/view/A10},

year  = {2017},

date = {2017-02-03},

journal = {Collective Dynamics},

volume = {1},

pages = {1-27},

abstract = {Real-life, out-of-laboratory, measurements of pedestrian walking dynamics allow extensive and fully-resolved statistical analyses. However, data acquisition in real-life is subjected to the randomness and heterogeneity that characterizes crowd flows over time. In a typical real-life location, disparate flow conditions follow one another in random order: for instance, a low density pedestrian co-flow dynamics may suddenly turn into a high density counter-flow scenario and then back again. Isolating occurrences of similar flow conditions within the acquired data is a paramount first step in the analyses in order to avoid spurious statistics and to enable qualitative comparisons.



In this paper we extend our previous investigation on the asymmetric pedestrian dynamics on a staircase landing, where we collected a large statistical database of measurements from ad hoc continuous recordings. This contribution has a two-fold aim: first, method-wise, we discuss an analysis workflow to consider large-scale experimental measurements, suggesting two querying approaches to automatically extract occurrences of similar flow scenarios out of datasets. These pursue aggregation of similar scenarios on either a frame or a trajectory basis. Second, we employ these two different perspectives to further explore asymmetries in the pedestrian dynamics in our measurement site. We report cross-comparisons of statistics of pedestrian positions, velocities and accelerations vs. flow conditions as well as vs. querying approach.},

keywords = {Big data, High statistic measurements, Metaforum},

pubstate = {published},

tppubtype = {article}

}

@article{Corbetta_MBE15,

title = { Parameter estimation of social forces in pedestrian dynamics models via a probabilistic method},

author = {Alessandro Corbetta and Adrian Muntean and Kiamars Vafayi},

url = {https://www.aimsciences.org/journals/displayArticlesnew.jsp?paperID=10700},

doi = {10.3934/mbe.2015.12.337},

year  = {2015},

date = {2015-04-01},

journal = {Mathematical Biosciences and Engineering},

volume = {12},

number = {2},

pages = {337 - 356},

abstract = {Focusing on a specific crowd dynamics situation, including real life experiments and measurements, our paper targets a twofold aim: (1) we present a Bayesian probabilistic method to estimate the value and the uncertainty (in the form of a probability density function) of parameters in crowd dynamic models from the experimental data; and (2) we introduce a fitness measure for the models to classify a couple of model structures (forces) according to their fitness to the experimental data, preparing the stage for a more general model-selection and validation strategy inspired by probabilistic data analysis. Finally, we review the essential aspects of our experimental setup and measurement technique.},

keywords = {High statistic measurements, Metaforum},

pubstate = {published},

tppubtype = {article}

}

@article{Corbetta_TRP14,

title = {High statistics measurements of pedestrian dynamics},

author = {Alessandro Corbetta and Luca Bruno and Adrian Muntean and Federico Toschi},

url = {http://www.sciencedirect.com/science/article/pii/S2352146514000490},

doi = {10.1016/j.trpro.2014.09.013},

year  = {2014},

date = {2014-10-24},

journal = {Transportation Research Procedia},

volume = {2},

pages = {96-104},

abstract = {Aiming at a quantitative understanding of basic aspects of pedestrian dynamics, extensive and high-accuracy measurements of real-life pedestrian trajectories have been performed. A measurement strategy based on Microsoft KinectTM has been used. Specifically, more than 100.000 pedestrians have been tracked while walking along a trafficked corridor at the Eindhoven University of Technology, The Netherlands. The obtained trajectories have been analyzed as ensemble data.

The main result consists of a statistical descriptions of pedestrian characteristic kinematic quantities such as positions and fundamental diagrams, possibly conditioned to the local crowd flow (e.g. co-flow or counter-flow).



},

keywords = {High statistic measurements, ILIAD15_STA, Metaforum, Pedestrian dynamics},

pubstate = {published},

tppubtype = {article}

}