Continuous measurements of real-life bidirectional pedestrian flows on a wide walkway

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 $0$ - $1.1\,$ped/m$^2$, we find that at densities lower than $0.8\,$ped/m$^2$ pedestrians in unidirectional flows walk faster than in bidirectional regimes. On the opposite, velocities and fluxes for even bidirectional flows are higher above $0.8\,$ped/m$^2$.Comment: 9 pages, 7 figure

Physics-based modeling and data representation of pedestrian pairwise interactions

The possibility to understand and to quantitatively model the physics of the interactions between pedestrians walking in crowds has compelling relevant applications, e.g. related to the design and safety of civil infrastructures. In this work we study pedestrian-pedestrian interactions from observational experimental data in diluted crowds. While in motion, pedestrians adapt their walking paths trying to preserve mutual comfort distances and to avoid collisions. In mathematical models this behavior is typically modeled via "social" interaction forces. Leveraging on a high-quality, high-statistics dataset - composed of few millions of real-life trajectories acquired from state-of-the-art observational experiments - we develop a quantitative model capable of addressing interactions in the case of binary collision avoidance. We model interactions in terms of both long- and short-range forces, which we superimpose to our Langevin model for non-interacting pedestrian motion [Corbetta et al. Phys.Rev.E 95, 032316, 2017]. The new model that we propose here features a Langevin dynamics with "fast" random velocity fluctuations that are superimposed to the "slow" dynamics of a hidden model variable: the "intended" walking path. The model is capable of reproducing relevant statistics of the collision avoidance motion, such as the statistics of the side displacement and of the passing speed. Rare occurrences of bumping events are also recovered. Furthermore, comparing with large datasets of real-life tracks involves an additional challenge so far neglected: identifying, within a database containing very heterogeneous conditions, only the relevant events corresponding to binary avoidance interactions. To tackle this challenge, we propose a novel approach based on a graph representation of pedestrian trajectories, which allows us to operate complexity reduction for efficient data selection.Comment: 17 figures, 18 page

The Crowdsourced Replication Initiative: Investigating Immigration and Social Policy Preferences. Executive Report.

In an era of mass migration, social scientists, populist parties and social movements raise concerns over the future of immigration-destination societies. What impacts does this have on policy and social solidarity? Comparative cross-national research, relying mostly on secondary data, has findings in different directions. There is a threat of selective model reporting and lack of replicability. The heterogeneity of countries obscures attempts to clearly define data-generating models. P-hacking and HARKing lurk among standard research practices in this area.This project employs crowdsourcing to address these issues. It draws on replication, deliberation, meta-analysis and harnessing the power of many minds at once. The Crowdsourced Replication Initiative carries two main goals, (a) to better investigate the linkage between immigration and social policy preferences across countries, and (b) to develop crowdsourcing as a social science method. The Executive Report provides short reviews of the area of social policy preferences and immigration, and the methods and impetus behind crowdsourcing plus a description of the entire project. Three main areas of findings will appear in three papers, that are registered as PAPs or in process

Numerical Study of Liquid Metal Turbulent Heat Transfer in Cross-Flow Tube Banks

Heavy liquid metals (HLM) are attractive coolants for nuclear fission and fusion applications due to their excellent thermal properties. In these reactors, a high coolant flow rate must be processed in compact heat exchangers, and as such, it may be convenient to have the HLM flowing on the shell side of a helical coil steam generator. Technical knowledge about HLM turbulent heat transfer in cross-flow tube bundles is rather limited, and this paper aims to investigate the suitability of Reynolds Average Navier–Stokes (RANS) models for the simulation of this problem. Staggered and in-line finite tube bundles are considered for compact (a=1.25), medium (a=1.45), and wide (a=1.65) pitch ratios. The lead bismuth eutectic alloy with Pr=2.21×10−2 is considered as the working fluid. A 2D computational domain is used relying on the k−ω Shear Stress Transport (SST) for the turbulent momentum flux and the Prt concept for the turbulent heat flux prediction. The effect of uniform and spatially varying Prt assumptions has been investigated. For the in-line bundle, unsteady k−ω SST/Prt=0.85 has been found to significantly underpredict the integral heat transfer with regard to theory, featuring a good to acceptable agreement for wide bundles and Pe≥1150. For the staggered tube bank, steady k−ω SST and a spatially varying Prt has been the best modeling strategy featuring a good to excellent agreement for medium and wide bundles. A poor agreement for compact bundles has been observed for all the models considered

Liquid metal turbulent heat transfer in cross-flow bundles for advanced nuclear reactors

Heavy liquid metals (HLM) are attractive coolants for innovative heat exchangers in both nuclear fission and fusion applications due to their excellent thermal properties. In this paper, the ANSYS Fluent CFD code is used to characterize the fluid dynamics and heat transfer for the case of HLM (Pr=0.021) turbulent cross-flow in square and triangular rod bundles, for both loose (S=1.25) and tight (S=1.45) pitch lattice arrangements. Extensive code validation is performed for water and LM cross-flow cases, finally selecting the k-ω SST model for the purpose of the study. Steady-state simulations are performed for a test geometry with at least 10 rod ranks for uniform wall temperature and heat flux boundary conditions, in the range Pe=767÷1150. Numerical results are compared with simplified theoretical models based on experimental data, observing large underprediction (40%÷54%) and slight overprediction (12%÷31%) for the average Nusselt number in square and triangular bundles, respectively

Physics-based modeling and data representation of pairwise interactions among pedestrians

\u3cp\u3eIn this work we study pedestrian-pedestrian interactions from observational experimental data in diluted pedestrian crowds. While in motion, pedestrians continuously adapt their walking paths trying to preserve mutual comfort distances and to avoid collisions. Leveraging on a high-quality, high-statistics data set, composed of several few millions real-life trajectories acquired from state-of-the-art observational experiments (about 6 months of high-resolution pedestrian tracks acquired in a train station), we develop a quantitative model capable of addressing interactions in the case of binary collision avoidance. We model interactions in terms of both long-range (sight based) and short-range (hard-contact avoidance) forces, which we superimpose on our Langevin model for noninteracting pedestrian motion [Corbetta, Phys. Rev. E 95, 032316 (2017)2470-004510.1103/PhysRevE.95.032316] (here further tested and extended). The model that we propose here features a Langevin dynamics with fast random velocity fluctuations that are superimposed on the slow dynamics of a hidden model variable: the intended walking path. In the case of interactions, social forces may act both on the intended path and on the actual walked path. The model is capable of reproducing quantitatively relevant statistics of the collision avoidance motion, such as the statistics of the side displacement and of the passing speed. Rare occurrences of actual bumping events are also recovered. Furthermore, comparing with large data sets of real-life tracks involves an additional computational challenge so far neglected: identifying automatically, within a database containing very heterogeneous conditions, only the relevant events corresponding to binary avoidance interactions. In order to tackle this challenge, we propose a general approach based on a graph representation of pedestrian trajectories, which allows us to effectively operate complexity reduction for efficient data classification and selection.\u3c/p\u3

Brain and exercise: A first approach using electrotomography

Purpose: The impact of exercise on brain function has gained broad interest. As hemodynamic and imaging studies are difficult to perform during and after exercise, electroencephalography (EEG) is often the method of choice. Within this study we aimed (1) to extend prior work examining changes in scalp-recorded brain electrical activity associated with exercise and (2) use a distributed source localization algorithm (sLORETA) to model the probable neural sources of changes in EEG activity after exercise. Methods: Electro cortical activity of twenty-two recreational runners (21-45y) was recorded before and after exhaustive treadmill ergometry. Data were analyzed using standardised low resolution brain electromagnetic tomography (sLORETA). Results: There was an increase in alpha-1 activity (7.5-10Hz) immediately after exercise, which was localized to the left frontal gyrus (Brodmann area 8). This finding is consistent with alterations in emotional processing. Fifteen minutes post exercise a decrease in alpha-2 (10-12.5Hz), beta-1 (12.5-18Hz) and gamma activity (35-48Hz) were observed in Brodmann areas 18 and 20-22, which are well known to be involved in language processing. Conclusion: This study demonstrates that sLORETA is a robust method that allows brain activity maps to be generated from standardized EEG recordings following exercise