Pedestrian Flow Simulation Validation and Verification Techniques

Mohamed H. Dridi

Institute of Theoretical Physics 1, University of Stuttgart, Stuttgart, Germany.

DOI: 10.4236/cus.2015.32011                

Abstract

For the verification and validation of microscopic simulation models of pedestrian flow, we have performed experiments for different kind of facilities and sites where most conflicts and congestion happens e.g. corridors, narrow passages, and crosswalks. The validity of the model should compare the experimental conditions and simulation results with video recording carried out in the same condition like in real life e.g. pedestrian flux and density distributions. The strategy in this technique is to achieve a certain amount of accuracy required in the simulation model. This method is good at detecting the critical points in the pedestrians walking areas. For the calibration of suitable models we use the results obtained from analysing the video recordings in Hajj 2009 and these results can be used to check the design sections of pedestrian facilities and exits. As practical examples, we present the simulation of pilgrim streams on the Jamarat bridge (see Figure 5). The objectives of this study are twofold: first, to show through verification and validation that simulation tools can be used to reproduce realistic scenarios, and second, gather data for accurate predictions for designers and decision makers.

Keywords

Accreditation, Data Analysis, Pedestrian Simulation, Statistical, Validation, Verification Of Simulation Tools, Optical Flow

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	AlGadhi, S. A., & Still, G. K. (2003). Jamarat Bridge; Mathematical Models, Computer Simulation and Hajjis Safety Analysis. Technical Report, Crowd Dynamics Limited. http://www.crowddynamics.com/
[2]	Barron, J. L., Fleet, D. J., & Beauchemin, S. S. (1994). Performance of Optical Flow Techniques. International Journal of Computer Vision, 12, 43-77. http://dx.doi.org/10.1007/BF01420984
[3]	Beauchemin, S. S., & Barron, J. L. (1995). The Computation of Optical Flow. ACM Computing Surveys, 27, 433-466.
[4]	Beck, M. B., Ravetz, J. R., Mulkey, L. A., & Barnwell, T. O. (1997). On the Problem of Model Validation for Predictive Exposure Assessments. Stochastic Hydrology and Hydraulics, 11, 229-254. http://dx.doi.org/10.1007/BF02427917
[5]	Blue, V. J. (2001). Cellular Automata Microsimulation for Modeling Bi-Directional Pedestrian Walkways. Transportation Research, Part B: Methodological, 35, 293-312. http://dx.doi.org/10.1016/S0191-2615(99)00052-1
[6]	Bradski, G. (2000). Opencv. Dr. Dobb’s Journal of Software Tools, © 1999-2001 Intel Corporation.
[7]	Clocksin, W. F. (2000). A New Method for Computing Optical Flow. Proceedings of the British Machine Vision Conference 2000, BMVC 2000, Bristol, 11-14 September 2000, 1-10. http://dx.doi.org/10.5244/c.14.13
[8]	Dijkum van, C., DeTombe, D., &van Kuijck, E., Eds. (1999) Validation of Simulation Models. Amsterdam: Siswo.
[9]	Dridi, M. H. (2015). Tracking Individual Targets in High Density Crowd Scenes Analysis of a Video Recording in Hajj 2009. Current Urban Studies, 3, 35-53. http://dx.doi.org/10.4236/cus.2015.31005
[10]	Fan, C. M., Namazi, N. M., & Penafiel, P. B. (1996). A New Image Motion Estimation Algorithm Based on the EM Technique. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 348-352. http://dx.doi.org/10.1109/34.485564
[11]	Fleet, D. J., & Jepson, A. D. (1990). Computation of Component Image Velocity from Local Phase Information. International Journal of Computer Vision, 5, 77-104. http://dx.doi.org/10.1007/BF00056772
[12]	Fukui, M., & Ishibashi, Y. (1999). Self-Organized Phase Transitions in CA-Models for Pedestrians. Journal of the Physical Society of Japan, 68, 2861-2863. http://dx.doi.org/10.1143/JPSJ.68.2861
[13]	Hayton, P. M., Brady, M., Smith, S. M., & Moore, N. (1999). A Non-Rigid Registration Algorithm for Dynamic Breast MR Images. Artificial Intelligence, 114, 125-156. http://dx.doi.org/10.1016/S0004-3702(99)00073-9
[14]	Helbing, D., Farkas, I. J., Molnar, P., & Vicsek, T. (2002). Simulation of Pedestrian Crowds in Normal and Evacuation Situations. In M. Schreckenberg, & S. D. E. Sharma (Eds.), Pedestrian and Evacuation Dynamics (pp. 21-58). Berlin: Springer.
[15]	Helbing, D., Farkas, I., & Vicsek, T. (2000). Simulating Dynamical Features of Escape Panic. Nature, 407, 487-490. http://dx.doi.org/10.1038/35035023
[16]	Helbing, D., Johansson, A., & Zein Al-Abideen, H. (2007). The Dynamics of Crowd Disasters: An Empirical Study. Physical Review E, 75, Article ID: 046109. http://dx.doi.org/10.1103/PhysRevE.75.046109
[17]	Horn, B. K. P., & Schunk, B. G. (1981). Determining Optical Flow. Artificial Intelligence, 17, 185-203. http://dx.doi.org/10.1016/0004-3702(81)90024-2
[18]	Johnston, D. (2004) Pedestrian Surveillance through Image Processing a Degree Project Report Submitted for the Award of MEng Software Engineering, School of Electronics and Computer Science, University of Southampton.
[19]	Kleijnen, J. P. C. (1995). Verification and Validation of Simulation Models. European Journal of Operational Research, 82, 145-162. http://dx.doi.org/10.1016/0377-2217(94)00016-6
[20]	Kleijnen, J. P. C. (1999). Validation of Models: Statistical Techniques and Data Availability. In Proceedings of the 31st Conference on Winter Simulation: Simulation—A Bridge to the Future—Volume 1 (pp. 647-654). New York: ACM Press.
[21]	Knepell, P. L., & Arangno, D. C. (1993). Simulation Validation: A Confidence Assessment Methodology. Los Alamitos, CA: Wiley-IEEE Computer Society Press.
[22]	Kovar, L., Gleicher, M., & Pighin, F. H. (2002). Motion Graphs. In Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques (pp. 473-482). New York: ACM Press. http://dx.doi.org/10.1145/566570.566605
[23]	Löhner, R. (2010). On the Modeling of Pedestrian Motion. Applied Mathematical Modelling, 34, 366-382. http://dx.doi.org/10.1016/j.apm.2009.04.017
[24]	Lucas, B., & Kanade, T. (1981). An Iterative Image Registration Technique with an Application to Stereo Vision. In Proceedings of the 7th International Joint Conference on Artificial Intelligence—Volume 2 (pp. 674-679). San Francisco, CA: Morgan Kaufmann Publishers Inc.
[25]	Masoud, O., & Papanikolopoulos, N. (2001). A Novel Method for Tracking and Counting Pedestrians in Real-Time Using a Single Camera. IEEE Transactions on Vehicular Technology, 50, 1267-1278. http://dx.doi.org/10.1109/25.950328
[26]	Muramatsu, M., & Nagatani, T. (2000). Jamming Transition in Two-Dimensional Pedestrian Traffic. Physica A: Statistical Mechanics and Its Applications, 275, 281-291.
[27]	Noonan, D. P., Mountney, P., Elson, D. S., Darzi, A., & Yang, G. (2009). A Stereoscopic Fibroscope for Camera Motion and 3d Depth Recovery during Minimally Invasive Surgery. Proceedings of the IEEE International Conference on Robotics and Automation, Kobe, 12-17 May 2009, 4463-4468. http://dx.doi.org/10.1109/robot.2009.5152698
[28]	Predtechenski, W. M., & Milinski, A. I. (1971). Personenströme in Gebäuden. Köln: Verlagsgesellschaft Rudolf Müller.
[29]	Ricquebourg, Y., & Bouthemy, P. (2000). Real-Time Tracking of Moving Persons by Exploiting Spatio-Temporal Image Slices. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 797-808. http://dx.doi.org/10.1109/34.868682
[30]	Sargent, R. G. (1996). Verifying and Validating Simulation Models. In Proceedings of the 28th Conference on Winter Simulation (pp. 55-64). New York: ACM Press. http://dx.doi.org/10.1145/256562.256572
[31]	Seki, M., Fujiwara, H., & Sumi, K. (2000). A Robust Background Subtraction Method for Changing Background. Proceedings of the Fifth IEEE Workshop on Applications of Computer Vision, Palm Springs, 4-6 December 2000, 207-213. http://dx.doi.org/10.1109/WACV.2000.895424
[32]	Shao, W., & Terzopoulos, D. (2007). Autonomous Pedestrians. Graphical Models, 69, 246-274. http://dx.doi.org/10.1016/j.gmod.2007.09.001
[33]	Still, G. K. (2003). Simulex/Myriad. Crowd Dynamics Limited. http://www.crowddynamics.com/
[34]	Sun, J. H., Yates, D. A., & Winterbone, D. E. (1996). Measurement of the Flow Field in a Diesel Engine Combustion Chamber after Combustion by Cross-Correlation of High-Speed Photographs. Experiments in Fluids, 20, 335-345. http://dx.doi.org/10.1007/BF00191015
[35]	Treuille, A., Cooper, S., & Popovic, Z. (2006). Continuum Crowds. ACM Transactions on Graphics, 25, 1160-1168. http://dx.doi.org/10.1145/1141911.1142008
[36]	Vannoorenberghe, P., Motamed, C., Blosseville, J. M., & Postaire, J. G. (1996). Monitoring Pedestrians in a Uncontrolled Urban Environment by Matching Low-Level Features. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Beijing, 14-17 October 1996, 2259-2264. http://dx.doi.org/10.1109/icsmc.1996.565511
[37]	Velastin, S. A., Yin, J. H., Davies, A. C., Vicencio-Silva, M. A., Allsop, R. E., & Penn, A. (1993). Analysis of Crowd Movements and Densities in Built-Up Environments Using Image Processing. In Proceedings of the IEE Colloquium on Image Processing for Transport Applications (pp. 8/1-8/6). London: IET.
[38]	Velastin, S. A., Yin, J. H., Davies, A. C., Vicencio-Silva, M. A., Allsop, R. E., & Penn, A. (1994). Automated Measurement of Crowd Density and Motion Using Image Processing. Proceedings of the Seventh International Conference on Road Traffic Monitoring and Control, London, 26-28 April 1994, 127-132. http://dx.doi.org/10.1049/cp:19940440
[39]	Wu, Q. X. (1995). A Correlation-Relaxation-Labeling Framework for Computing Optical Flow-Template Matching from a New Perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17, 843-853. http://dx.doi.org/10.1109/34.406650
[40]	Yonemoto, S., Nakano, H., & Taniguchi, R. (2003). Real-Time Human Figure Control Using Tracked Blobs. Proceedings of the 12th International Conference on Image Analysis and Processing, Mantova, 17-19 September 2003, 127-132. http://dx.doi.org/10.1109/iciap.2003.1234038