2020
Emiliano Cristiani; Alessandro Corbetta; Caterina Balzotti; Roberto Natalini; Sara Suriano; Federico Toschi
Forecasting visitors' behaviour in crowded museums Inproceedings
In: Collective Dynamics - Pedestrian and Evacuation Dynamics 2018, pp. 499-501, 2020.
Links | BibTeX | Tags: Big data
@inproceedings{conf:GB,
title = {Forecasting visitors' behaviour in crowded museums},
author = {Emiliano Cristiani; Alessandro Corbetta; Caterina Balzotti; Roberto Natalini; Sara Suriano; Federico Toschi},
doi = {http://dx.doi.org/10.17815/CD.2020.82},
year = {2020},
date = {2020-01-01},
booktitle = {Collective Dynamics - Pedestrian and Evacuation Dynamics 2018},
journal = {Collective Dynamics},
number = {5},
pages = {499-501},
keywords = {Big data},
pubstate = {published},
tppubtype = {inproceedings}
}
2017
Alessandro Corbetta; Federico Toschi
Overhead pedestrian tracking for large scale real-life crowd dynamics analyses Incollection
In: Enrico Ronchi (Ed.): New approaches to evacuation modelling, pp. 40-51, Lund University Fire Safety Engineering Report, 2017.
Abstract | Links | BibTeX | Tags: Big data, Eindhoven Station, High statistic measurements, Pedestrian dynamics
@incollection{Corbetta_IAFSS17,
title = {Overhead pedestrian tracking for large scale real-life crowd dynamics analyses},
author = {Alessandro Corbetta and Federico Toschi},
editor = {Enrico Ronchi},
url = {http://portal.research.lu.se/portal/files/34762689/New_approaches_to_evacuation_modelling.pdf},
year = {2017},
date = {2017-09-01},
booktitle = {New approaches to evacuation modelling},
pages = {40-51},
publisher = {Lund University Fire Safety Engineering Report},
abstract = {Accurate measurements of pedestrian dynamics, in form of individual trajectories, are paramount to investigate the complex motion of walking individuals and to produce reliable crowd simulation models for ordinary and evacuation conditions. This paper reviews one pedestrian trajectory collection technique, recently employed by the same authors for acquiring crowd dynamics data in real-life conditions. Operating unsupervised, the technique has enabled unprecedented, 24/7, months-long, pedestrian measurement campaigns that provided millions of individual trajectories, allowing novel statistical insights. The tracking technique leverages overhead depth-sensors, such as Microsoft Kinects, arranged in grids, and ad hoc pedestrian localization algorithms. Here we review its relevant technological aspects in view of statistical crowd dynamics analyses. },
keywords = {Big data, Eindhoven Station, High statistic measurements, Pedestrian dynamics},
pubstate = {published},
tppubtype = {incollection}
}
Alessandro Corbetta; Chung-min Lee; Adrian Muntean; Federico Toschi
Frame vs. trajectory analyses of pedestrian dynamics asymmetries in a staircase landing Journal Article
In: Collective Dynamics, vol. 1, pp. 1-27, 2017.
Abstract | Links | BibTeX | Tags: Big data, High statistic measurements, Metaforum
@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}
}
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.
2016
Alessandro Corbetta; Chung-min Lee; Jasper Meeusen; Federico Toschi
Continuous measurements of real-life bidirectional pedestrian flows on a wide walkway Inproceedings
In: Proceedings of Pedestrian and Evacuation Dynamics 2016, pp. 18-24, 2016.
Abstract | Links | BibTeX | Tags: Big data, Eindhoven Station, High statistic measurements
@inproceedings{Corbetta_PED16,
title = {Continuous measurements of real-life bidirectional pedestrian flows on a wide walkway},
author = {Alessandro Corbetta and Chung-min Lee and Jasper Meeusen and Federico Toschi},
url = {https://arxiv.org/abs/1607.02897
https://www.researchgate.net/publication/305182380_Continuous_measurements_of_real-life_bidirectional_pedestrian_flows_on_a_wide_walkway},
year = {2016},
date = {2016-10-12},
booktitle = {Proceedings of Pedestrian and Evacuation Dynamics 2016},
pages = {18-24},
abstract = {Employing partially overlapping overhead kinectTMS sensors and automatic pedestrian tracking algorithms we recorded the crowd traffic in a rectilinear section of the main walkway of Eindhoven train station on a 24/7 basis. Beside giving access to the train platforms (it passes underneath the railways), the walkway plays an important connection role in the city. Several crowding scenarios occur during the day, including high- and low-density dynamics in uni- and bi-directional regimes. In this paper we discuss our recording technique and we illustrate preliminary data analyses. Via fundamental diagrams-like representations we report pedestrian velocities and fluxes vs. pedestrian density. Considering the density range 0 - 1.1ped/m2, we find that at densities lower than 0.8ped/m2 pedestrians in unidirectional flows walk faster than in bidirectional regimes. On the opposite, velocities and fluxes for even bidirectional flows are higher above 0.8ped/m2.},
keywords = {Big data, Eindhoven Station, High statistic measurements},
pubstate = {published},
tppubtype = {inproceedings}
}
The processing scripts for our datasets can be found on github .