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3 M. Bukáček et al. / Transportation Research Procedia 2 ( 214 ) recognition mark centre of mass binary code control bit Fig. 1. Left: Experimental setting of E2, E3, and E4. The dimensions of experimental room are as follows: E4: a = 8m,b= 4.5 m, E3: a = 7.2 m, b = 3.9 m, E2: a = 7.8 m, b = 5.5 m. Black circles represent the position of cameras, technical support was situated in area X, area Y represents refreshment corner. Right: sketch of pedestrian s cap, its size corresponds to A4 sheet. Fig. 2. Snapshot from all three cameras at 2 min 21 s of the experiment E4, run #7 (α = 1.95). Fluctuating cluster of 25 pedestrians is visible. to react on the green signal is limited and the behaviour of pedestrian, who entered in a group, lacks their individual properties. Therefore, in E4 three independent devices, each at one part of the multiple entrance, have been used to support the randomness of the arrivals. To simulate the independent arrival of pedestrians to the entrance, the geometric distribution for time intervals between two green signals has been used, i.e., the probability of the time interval between two signals on one device to be Δt = k h is Pr(Δt = k h) = (α h/3)(1 α h/3) k 1, (1) where h =.6 is the shortest time interval on which the pedestrians can react relatively reliably (measured in preexperiment, the lower value the more random arrivals). Since the expected value of such distribution is 3/α seconds between two entrances of pedestrians through one entrance and considering that there are three entrances (controlled by independent signal devices), we have setted the inflow rate to α. According to the observations, real inflow of pedestrians was still little bit lower (around 9 % of α). Therefore, the comparison and data evaluation has been done with respect to the real inflow α real. 3. Inflow-Rate-Induced Phase Transition The first insight in the mechanism of the phase transition gives the Table 1. Individual runs of the experiment can be divided into three groups according to the macroscopic observation: 1. Free flow: no cluster is created in front of the exit. The interaction between pedestrians leads only to occasional delay at the exit but has no influence on the whole system

4 18 M. Bukáček et al. / Transportation Research Procedia 2 ( 214 ) Congestion: the interaction in front of the exit blocks partially the possibility of pedestrians to leave the room in the same rate as they are entering it. This causes the creation of a cluster in front of the exit, which size grows and would fill the whole room (the experiment was stopped before this happened because of the lack of volunteers). 3. Stable cluster: conflicts near the exit lead to the creation of a stable cluster. The size of the cluster fluctuates between 15 and 3 pedestrians. This is the evidence that the inflow is close to the capacity of the exit. Table 1. The macroscopic evaluation of the phase transition from experiments E2, E3, and E4. α denotes the parameter of the signalling device at the input, α real is the real inflow obtained from data analyses, J out is the measured outflow through the exit, TT the average travel time, N 15 the number of pedestrians in the room 15 s after the initiation. Experiment E2 ID α input [ped] observation # free flow # free flow # free flow # stable cluster # stable cluster # congestion # congestion # congestion # congestion Experiment E3 ID α input [ped] observation # free flow # free flow # free flow # stable cluster # stable cluster # stable cluster # transition # transition # 9-11 > congestion Experiment E4 ID α α real J out TT [s] N 15 [ped] observation # free flow # free flow # stable cluster # stable cluster # stable cluster # transition # transition # * congestion # * congestion # * congestion Four runs of experiments from Table 1 are marked with the term transition. In those cases (#7 and #8 of E3 and #7 and #11 of E4) it is not sure, whether the size of the cluster stabilized or whether there was a growing congestion. The runs have been stopped too early to decide. The reason for stopping the experiment was that there were too few pedestrians outside the room, so they were not able to react correctly to the green signal to maintain given inflow. Similar situation occurred in runs #8-1 of the experiment E4. The number of pedestrians denoted by the asterisk * is related to the maximal number of pedestrians in the room, which has been reached before 15 s from initiation; for detailed evolution of the number of pedestrians in the room see Figure 3. From the above mentioned observation we can conclude that the saturation value of the inflow is between 1.4 and 1.6 ped/s (real inflow values from E2 and E3 have not been measured, given values are parameters of signalling device). Here we note that in the congestion phase there has not been observed the equivalent to the steady state. Nevertheless, the trend of the growth indicates that after longer time the room would have became completely filled by pedestrians.

5 M. Bukáček et al. / Transportation Research Procedia 2 ( 214 ) The above mentioned conclusion is supported by the graphs in Figure 3, where the absolute occupation of the room is plotted against the time Occupancy Time [s] Fig. 3. Number of pedestrians in the room plotted against the time, data taken from experiment E4, runs are ordered with respect to the inflow as in Table 1. The phase transition can be identified by the relation between the occupancy (measured in given time) and the inflow α real, see Figure 4. From the microscopic point of view, similar dependency describes the increase of the average travel time through the room. The travel time for each pedestrian was derived from the time of the entrance and the exit of the pedestrian. 3 5 Mean travel time [s] Mean occupancy Inflow Inflow Fig. 4. The relation of mean travel time (Left) and mean ocupancy (Right) to the inflow α real, data taken from experiment E4. The multiple entrance allowed us to increase the inflow of pedestrians above the limit of the outflow through the exit. The capacity of the exit (referred to as J MAX ) during the experiment E4 was 1.38 ped/s, as visualized in Figure 5. This supports the hypothesis that realizations E4 #7 and #11 are very close to the phase transition. Therefore we conclude that the saturation point of the experimental setting is very close to α S = 1.4 or the real inflow. This trend corresponds to the dependency of the outflow on the number of pedestrians inside the room, see Figure 5. One can see that the outflow fluctuates around the value 1.38 ped/s after creation of small cluster in front of the exit.

6 11 M. Bukáček et al. / Transportation Research Procedia 2 ( 214 ) It is interesting that even in the huge cluster the conflicts do not block the movement, contrarily the outflow is little bit higher. Outflow Inflow Outflow Occupancy Fig. 5. The dependency of outflow to inflow (Left) or occupancy (Right), data taken from experiment E4. 4. Microscopical View Microscopic view is based mainly on the analysis of travel time. This quantity is affected by many factors (e.g desired velocity, willingness to overtake or to go through the dense crowd) and is significantly differers across pedestrians. In Figure 6, the travel time of each passing of each pedestrian is plotted against the average number of pedestrians during the interval spend by the pedestrian in the room. Of course, the higher the occupancy is, the longer is the travel time. Nevertheless, each pedestrian reacts on the high occupation (connected to larger crowd in front of the exit) differently. In Figure 6 the travel times of three different pedestrians are marked by circles: green pedestrian can be considered as aggressive one, because his travel time is significantly below average. Red circles belong to cautions non-conflict person, who rather waited at the border of the crowd. Violet circles mark an average person. The average travel time in dependence of the number of pedestrians in the room plotted as blue line. To compare travel time measured under different conditions, the relative time (referred to as T R ) is presented. For every number of pedestrians inside the room N, average travel time T(N) is evaluated. Then, the relative time is defined as the ratio of measured time (which corresponds to given occupancy N) to the average travel time T(N) T R = T T(N). (2) Relative travel time can be used to analyse the differences between pedestrians in detail. In Figure 7, T R is assigned to pedestrians and boxplot is provided. The records are ordered by pedestrian s median relative travel time and the gender is also visualized. It is obvious that majority of men are faster than a majority of women. The differences between pedestrians increased with increasing occupation. In free flow, the travel time is determined mainly by pedestrian s desired velocity, but is the congested state the aggression plays significant role. In Figure 8, the histograms of pedestrian median relative travel time within free flow and congested state are plotted. While in free flow the slowest and fastest pedestrian differ from the mean value only by 3 %, during the congestion median T R of slower pedestrians is several times higher. A related and important aspect of the evacuation experiment have been observed in the period from the stop of the inflow until the emptying of the room. To describe the process of melting the cluster in front of the exit, the time development of pedestrian time-headways at the door (i.e. the interval between two subsequent egress times) was evaluated. During all experiments, the loss of motivation of last pedestrians was observed. As shown in Figure 9, several last headways indicated increasing trend. In case of E1 (leave-the-room experiment), 3 pedestrians entered the room at the same time, therefore faster pedestrians with shorter reaction time reached the exit first and less motivated pedestrian left at the end. On the other hand in passing through scenarios, the arrivals of slow and fast pedestrians are

7 M. Bukáček et al. / Transportation Research Procedia 2 ( 214 ) Relative travel time all peds. fast one slow one standard one average Occupancy of the room Fig. 6. The dependency of travel time with respect to the number of pedestrians in the room. Solid line represents mean travel time related to given occupancy, three different pedestrians are marked to visualize the differences between participants Relative travel time Pedestrians Fig. 7. The boxplot of relative travel time, data were assigned to pedestrian and ordered with respect to median time. Color mark referes to gender (male: green, female: red). random, thus the increasing trend of the headways is observed later when the pedestrians waiting at the edge of the crowd got to the door. 5. Conclusions From the macroscopic and microscopic observation of the experiments E2, E3, and E4, we may conclude that:

8 112 M. Bukáček et al. / Transportation Research Procedia 2 ( 214 ) Occurence [%] Occurence [%] Relative travel time Relative travel time Fig. 8. The histograms of pedestrians median relative travel time. Left: free flow, Right: congested state. 2 2 Time headway [s] Time headway [ms] Order of pedestrian in a run Order of pedestrian in a run 25 3 Fig. 9. The process of melting the clusters: boxplot of time haedways of last 3 pedestrians from run #6 #1, E4. (Left). The boxplot of time haedways form experiment leave the room E1 (Right). 1. The phase transition from the free flow to the congestion is closely related to the inflow α and is very sensitive on the change of the inflow value. 2. The saturation of the room seems to be quite sudden, i.e., the outflow increases linearly with the increasing inflow. In the transition regime, the conflicts cause a slight decrease from the linear trend and after the critical value of the inflow α S 1.4 ped/s the outflow levels around the value 1.38 ped/s (the width of the exit is 6 cm). 3. A good identifier of the phase transition seems to be the average travel time and the average occupation of the room. The congestion is related to the dramatic increase of pedestrians in the room followed by the increase in the average travel time. 4. Even in the congested state, aggressive pedestrians can reach a relatively short travel-time by walking around the crowd or pushing through the crowd. On the other hand, the cautious pedestrians are not willing to fight in the crowd and their travel times are therefore significantly higher. Here we note that during the passing-through experiments, the volunteers have been much better motivated to leave the room and fight in the crowd then during the egress experiment E1. Therefore, such situation can better simulate the panic-like conditions. In the high density of the crowd, slight indication of the real panic has been observed, mainly caused by the fragile female students, who were trapped inside the cluster in front of the door (no one got injured during the experiment). Thanks to the unique identification of the individual pedestrians we are able to extract several properties from the evaluation of the travel time. Concerning the travel time in the free flow conditions, the travel time is closely related to the desired velocity of pedestrians. Considering contrarily the travel time in high density, while the number

9 M. Bukáček et al. / Transportation Research Procedia 2 ( 214 ) of pedestrians in the room is above 3, the travel time reflects the aggressive behaviour of the pedestrians. The identification of individual pedestrians allows even to find out the cross-correlation of those two characteristics, which can be used for the parameter distribution in pedestrian models. Such parameter analyses is the future goal of our working group. Acknowledgement This work k was supported by the grant SGS12/197/OHK4/3T/14. We would like to thank to second year students of FNSPE for assistance in mentioned experiments. References Boltes, M., Zhang, J., Seyfried, A., Steffen, B., 211. T-junction: Experiments, trajectory collection, and analysis. In: ICCV Workshops. IEEE, pp Bukáček, M., Hrabák, P., 214. Case study of phase transition in cellular models of pedestrian flow. In: Cellular Automata. Lecture Notes in Computer Science. Springer Berlin Heidelberg, to be published. Bukáček, M., Hrabák, P., Krbálek, M., 214a. Cellular model of pedestrian dynamics with adaptive time span. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (Eds.), Parallel Processing and Applied Mathematics. Vol of Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp Bukáček, M., Hrabák, P., Krbálek, M., 214b. Experimental analysis of two-dimensional pedestrian flow in front of the bottleneck. In: Traffic and Granular Flow 13. Springer Berlin Heidelberg, to be published. Davidich, M., Geiss, F., Mayer, H. G., Pfaffinger, A., Royer, C., 213. Waiting zones for realistic modelling of pedestrian dynamics: A case study using two major german railway stations as examples. Transportation Research Part C: Emerging Technologies 37 (), Ezaki, T., Yanagisawa, D., Nishinari, K., 213. Analysis on a single segment of evacuation network. Journal of Cellular Automata 8 (5-6), Hrabák, P., Bukáček, M., Krbálek, M., 213. Cellular model of room evacuation based on occupancy and movement prediction: Comparison with experimental study. Journal of Cellular Automata 8 (5-6), Kretz, T., 214. Crowd research at school: Crossing flows. In: Traffic and Granular Flow 13. ArXiv: Schadschneider, A., Chowdhury, D., Nishinari, K., 21. Stochastic Transport in Complex Systems: From Molecules to Vehicles. Elsevier Science B. V., Amsterdam. Schadschneider, A., Seyfried, A., 29. Empirical results for pedestrian dynamics and their implications for cellular automata models. In: Timmermans, H. (Ed.), Pedestrian Behavior - Models, Data Collection and Applications. Emerald Group, Bingley, pp Seyfried, A., Portz, A., Schadschneider, A., 21. Phase coexistence in congested states of pedestrian dynamics. In: Bandini, S., Manzoni, S., Umeo, H., Vizzari, G. (Eds.), Cellular Automata. Vol. 635 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp Seyfried, A., Steffen, B., Winkens, A., Rupprecht, T., Boltes, M., Klingsch, W., 29. Empirical data for pedestrian flow through bottlenecks. In: Appert-Rolland, C., Chevoir, F., Gondret, P., Lassarre, S., Lebacque, J.-P., Schreckenberg, M. (Eds.), Traffic and Granular Flow 7. Springer Berlin Heidelberg, pp Was, J., Lubaś, R., 213. Adapting social distances model for mass evacuation simulation. Journal of Cellular Automata 8 (5-6),

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