The results of a systematic quantitative validation of PEDFLOW based on the experimental data from FZJ are presented. Uni- directional flow experiments, totaling 28 different combinations with varying entry, corridor and exit widths, were considered. The condition imposed on PEDFLOW was that all the cases should be run with the same input parameters. The exit times and fundamental diagrams for the measuring region were evaluated and compared. This validation process led to modifications and enhancements of the model underlying PEDFLOW. The preliminary conclusions indicate that the results agree well for densities smaller than 3&nbsp;m-2&nbsp;and a good agreement is observed even at high densities for the corridors with&nbsp;bcor&nbsp;= 2.4&nbsp;m, and&nbsp;bcor&nbsp;= 3.0&nbsp;m. For densities between 1 and 2&nbsp;m-2&nbsp;the specific flow and velocities are underpredicted by PEDFLOW.

&nbsp

Britto, D.

Chraibi, M.

Gawenat, B.

Haug, E.

Lohner, Rainald

Zhang, J.

Scipedia

 Transportation Research Procedia  2 ( 2014 )  733 – 738 2352-1465 © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).Peer-review under responsibility of Department of Transport & Planning Faculty of Civil Engineering and Geosciences Delft University of Technologydoi: 10.1016/j.trpro.2014.09.081 Available online at www.sciencedirect.comScienceDirectThe Conference in Pedestrian and Evacuation Dynamics 2014 (PED2014)Quantitative validation of PEDFLOW for description ofunidirectional pedestrian dynamicsJ. Zhang a,∗, D. Britto b, M. Chraibi a, R. Löhner b, E. Haug b, B. Gawenat baJülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Jlich 52428, GermanybInstitute for Scientific Architecture, SL Rasch, Leinfelden-Echterdingen 70771,GermanyAbstractThe results of a systematic quantitative validation of PEDFLOW based on the experimental data from FZJ are presented. Uni-directional flow experiments, totaling 28 different combinations with varying entry, corridor and exit widths, were considered.The condition imposed on PEDFLOW was that all the cases should be run with the same input parameters. The exit times andfundamental diagrams for the measuring region were evaluated and compared. This validation process led to modifications andenhancements of the model underlying PEDFLOW. The preliminary conclusions indicate that the results agree well for densitiessmaller than 3 m−2 and a good agreement is observed even at high densities for the corridors with bcor = 2.4 m, and bcor = 3.0 m.For densities between 1 and 2 m−2 the specific flow and velocities are underpredicted by PEDFLOW.c© 2014 The Authors. Published by Elsevier B.V.Peer-review under responsibility of PED2014.Keywords: Model validation, Fundamental diagram, Unidirectional flow, Pedestrian experiment1. IntroductionDuring the last few decades, research on pedestrian and traffic flow has attracted a lot of attention (Schreckenbergand Scharma (2002); Appert-Rolland et al. (2009); Bandini et al. (2010); Klingsch et al. (2010); Schadschneider andSeyfried (2009); Schadschneider et al. (2009)). The investigation of pedestrian motion plays an important role inguaranteeing the safety of pedestrians in complex buildings or at mass events. A large number of models have beendeveloped in the past and most of them are able to reproduce phenomena of pedestrian movement qualitatively. Beforeusing a model to predict quantitative results like the total evacuation time, it needs to be calibrated thoroughly andquantitatively using empirical data. However, this is still difficult due to a lack of reliable experimental data as wellas the surprisingly large differences in available datasets (Seyfried et al. (2009)). Even for the fundamental diagramwhich states the relationship between pedestrian flow and density, obvious discrepancies can be seen in the literatureand handbooks. By comparing the density-velocity or density-specific flow relationships from different researcherslike Fruin (1971), Predtechenskii and Milinskii (1978), Weidmann (1993) and Helbing et al. (2007), it can be found∗ Corresponding author. Tel.: +49-246-161-96554 ; fax: +49-246-161-6656.E-mail address: ju.zhang@fz-juelich.de© 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).r-review under esponsibility of Department of Transport & Planning Faculty of Civil Engineering and Geosciences Delft University of Technology  on Pedestria   ti    734   J. Zhang et al. /  Transportation Research Procedia  2 ( 2014 )  733 – 738 that the density ρ0, where the velocity approaches zero due to overcrowding, ranges from 3.8 to 10 m−2, while thedensity ρc where the pedestrian flux reaches its maximum ranges from 1.75 to 7 m−2. Since these data come fromdifferent conditions and different measurement methods, it is not possible to conclude the reasons for the differences.For the calibration of models, it is difficult to set the same conditions to make comparisons. In this paper we validatethe software PEDFLOW used by SL-Rasch Special and Lightweight Structures GmbH in Germany on the basis of theempirical data from well-controlled laboratory experiments. The remainder of the paper is organized as follows: Insection 2, we describe the model implemented in the software. The validation process and results are shown in section3. Thereafter, the conclusions from our validation are discussed.2. The PEDFLOW modelThe modeling of pedestrian motion has been the focus of research and development for more than two decades.If one is only interested in average quantities (average density, velocity), continuum models (Hughes (2003)) are anoptions. For problems requiring more realism, approaches that model each individual are required (Thalmann andMusse (2007)). Among these, discrete space models (such as cellular automata (Blue and Adler (1998); Teknomoet al. (2000); Dijkstra et al. (2002); Schadschneider (2002); Kessel et al. (2002); Klüpfel (2003); Langston et al. (2006),force-based models (such as the social force model (Helbing and Molnár (1995); Helbing et al. (2002); Lakoba et al.(2005)) and agent-based techniques (Pelechano and Badler (2006); Badler et al. (2008); Guy et al. (2009, 2010);Torrens (2011); Curtis and Manocha (2012)) have been explored extensively. Together with insights from psychologyand neuroscience (e.g. Vishton and Cutting (2011); Torrens (2011)), it has become clear that any pedestrian motionalgorithm that attempts to model reality should be able to mirror the following empirically known facts and behaviors:• Newton’s laws of motion apply to humans as well: from one instant to another, we can only move within certainbounds of acceleration, velocity and space;• Contact between individuals occurs for high densities; these forces have to be taken into account;• Humans have a mental map and plan on how they desire to move globally (e.g. first go here, then there, etc.);• In even moderately crowded situations ( < 1 m−2 ), humans have a visual horizon of 2.5-5.0 m, and a perceptionrange of 120 degrees; thus, the influence of other humans beyond these thresholds is minimal;• Humans have a ‘personal comfort zone’; It is dependent on culture and varies from individual to individual, butit cannot be ignored;• Humans walk comfortably at roughly 2 paces per second (frequency: 2 Hz); they are able to change the fre-quency for short periods of time, but will return to 2 Hz whenever possible.The PEDFLOW model (Löhner (2010)) incorporates these requirements as follows: individuals move accordingto Newton’s laws of motion; they follow (via will forces) ‘global movement targets’; At the local movement level, themotion also considers the presence of other individuals or obstacles via avoidance forces (also a type of will force)and, if applicable, contact forces. PEDFLOW also incorporates a number of psychological factors that, among themany tried over the years, have emerged as important for realistic simulations. Among these, we mention:• Determination/Pushiness: it is an everyday experience that in crowds, some people exhibit a more polite behav-ior than others. This is modeled in PEDFLOW by reducing the collision avoidance forces of more determinedor ‘pushier’ individuals. Defining a determination or pushiness parameter p, the avoidance forces are reducedby (1-p).• Comfort zone: in some cultures (northern Europeans are a good example) pedestrians want to remain at someminimum distance from contacting others. This comfort zone is an input parameter in PEDFLOW, and is addedto the radii of the pedestrians when computing collisions avoidance and pre-contact forces.3. Model calibration and analysisA series of well-controlled experiments have been performed. The pedestrian trajectories were extracted fromvideo recordings semi automatically and with high precision using the software PeTrack (Boltes et al. (2010)). All735 J. Zhang et al. /  Transportation Research Procedia  2 ( 2014 )  733 – 738 Fig. 1. Snapshots from one run of the experiment.the validation work was based on trajectories from simulations and experiments. Throughout the validation process,the density-velocity and density-specific flow relationships were compared. Due to its high precision, the Voronoimethod was used for measuring these values. The same size and location of the measurement area are adopted for theexperimental and computational datasets so as to exclude their influence on the results. Moreover, in order to decreasethe scattering of data for both experimental and simulation data, a time period was defined in such a way that theeffects of the starting and ending conditions of the observed run are minimal and fluctuations are low. This was doneby inspecting the time series of density and velocity. Details for the measurement method can be found in Steffen andSeyfried (2010) and Zhang et al. (2011).3.1. Validation scenariosIn this study, the PEDFLOW simulation software tool was calibrated with data from the experiment of unidirec-tional flow in straight corridors. Fig. 1 shows two snapshots from one experimental run. A total of 28 runs in corridorswith widths of bcor = 1.8 m, 2.4 m and 3.0 m were performed. To regulate the pedestrian density in the corridors thewidths of the entrance bentrance and the exit bexit were changed in each run. In this way the in- and outflow of thecorridors are controlled by the entrance and exit. The details of exit and entrance width in the experiment settingcan be found in Zhang et al. (2011). At the beginning of each run, the participants were held within a waiting area.When the experiment starts, they passed through a 4 m passage into the corridor. The passage was used as a buffer tominimize the effect of the entrance. Therefore, the pedestrian flow in the corridor was nearly homogeneous over itsentire width. The focus of the study was on the motion dynamics in the 8 m long corridor. An average free velocityv0 = 1.55 ± 0.18 m/s was obtained by measuring the free movement of 42 participants.3.2. Simulation configurationIn the simulation, identical geometrical set-ups as in the experiment, including corridor widths and exit widthswere implemented. The sketch of the setup and a snapshot from the simulation can be seen in Fig. 2. The differencefrom the simulation and experimental setup is that the inflow rate into the corridor is not controlled by the entrancewidth in simulations. At the beginning of the corridor pedestrians are generated based on the timely varying flux datafrom the experiment. In this way it is possible to dispense with the density-triggering by the waiting area, and a furthersource of errors is eliminated. The parameters used in the simulation are shown in Table 1.3.3. Validation resultsThis validation process led to modifications and enhancements of the model underlying PEDFLOW. After severalenhancements of the model, the simulation results show a good agreement with the experiment results. Fig. 3 showsthe comparison of the fundamental diagrams from experiments and PEDFLOW for 1.8 m, 2.4 m and 3.0 m widecorridors. It found that the results agree well for densities smaller than 3 m−2 and a good agreement is observed even736   J. Zhang et al. /  Transportation Research Procedia  2 ( 2014 )  733 – 738 Fig. 2. The sketch of the simulation scenario setup and a snapshot of the simulation result.Table 1. Parameters set up used in the simulation.Parameters Value Parameters Valueitype 1 radi 0.24mtype 0 vrad 0.1otype 1 ellmi/mx 0.50/1.00col 1 pshmi/mx 0.00/0.00velo 1.60 ± 0.01 m/s czone 0.15trlx 0.50 ± 0.05at high densities for the corridors with bcor = 2.4 m, and bcor = 3.0 m. For densities between 1 and 2 m−2 the specificflow and velocities are under-predicted by PEDFLOW. The simulation has shown that a user of PEDFLOW is able toreproduce a decrease of the velocity with the density with one set of parameters. The simulation results agree wellwith the experimental ones for densities smaller than 3 m−2.4. SummaryIn this paper, the model underlying the Software PEDFLOW that is used by SL-Rasch Special and LightweightStructures GmbH in Germany was validated. The validation process was based on the well-controlled laboratoryexperiments in straight corridors. Unidirectional pedestrian flows in a straight corridor with different widths wereselected as the calibration scenarios. Since one is able to obtain a high precision for the trajectories in both theexperiments and the simulations, the validations were mainly made using the trajectories. This validation-process ledto modifications and enhancements of the model underlying PEDFLOW and the simulation has shown that a user ofPEDFLOW is able to reproduce a decrease of the velocity with the density with one set of parameters. The simulationresults agree well with the experimental ones for densities smaller than 3 m−2.ReferencesAppert-Rolland, C., Chevoir, F., Gondret, P., Lassarre, S., Lebacque, J.-P., and Schreckenberg, M., editors (2009). Traffic and Granular Flow ’07.Springer, Berlin Heidelberg. nur Inhaltsverzeichnis verfgbar.Badler, N., Allbeck, J., and Pelechano, N. (2008). Virtual Crowds: Methods, Simulation, and Control (Synthesis Lectures on Computer Graphicsand Animation). Morgan and Claypool Publishers.Bandini, S., Manzoni, S., Umeo, H., and Vizzari, G., editors (2010). 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Quantitative Validation of PEDFLOW for Description of Unidirectional Pedestrian Dynamics

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Quantitative Validation of PEDFLOW for Description of Unidirectional Pedestrian Dynamics

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