A harmony search algorithm for the reproduction of experimental data in the social force model. (English) Zbl 1442.90039

Summary: Crowd dynamics is a discipline dealing with the management and flow of crowds in congested places and circumstances. Pedestrian congestion is a pressing issue where crowd dynamics models can be applied. The reproduction of experimental data (velocity-density relation and specific flow rate) is a major component for the validation and calibration of such models. In the social force model, researchers have proposed various techniques to adjust essential parameters governing the repulsive social force, which is an effort at reproducing such experimental data. Despite that and various other efforts, the optimal reproduction of the real life data is unachievable. In this paper, a harmony search-based technique called HS-SFM is proposed to overcome the difficulties of the calibration process for SFM, where the fundamental diagram of velocity-density relation and the specific flow rate are reproduced in conformance with the related empirical data. The improvisation process of HS is modified by incorporating the global best particle concept from particle swarm optimization (PSO) to increase the convergence rate and overcome the high computational demands of HS-SFM. Simulation results have shown HS-FSM’s ability to produce near optimal SFM parameter values, which makes it possible for SFM to almost reproduce the related empirical data.


90B20 Traffic problems in operations research
90C59 Approximation methods and heuristics in mathematical programming
Full Text: DOI


[1] Teknomo, K., Microscopic pedestrian flow characteristics: development of an image processing data collection and simulation model [Ph.D. thesis] (2002), Tokyo, Japan: Tohoku University, Tokyo, Japan
[2] May, A. D., Traffic Flow Fundamental (1990), New Jersey, NJ, USA: Prentice Hall, New Jersey, NJ, USA
[3] Haight, F. A., Mathematical Theories of Traffic Flow (1963), New York, NY, USA: Academic Press, New York, NY, USA
[4] Wolfram, S., Theory and Applications of Cellular Automata, 1 (1986), Singapore: World Scientific, Singapore
[5] Blue, V. J.; Adler, J. L., Cellular automata microsimulation of bidirectional pedestrian flows, Journal of the Transportation Research Board, 1678, 135-141 (2000)
[6] Blue, V.; Adler, J.; Schreckenberg, M.; Sharma, S., Flow capacities from cellular automata modeling of proportional splits of pedestrians by direction, Pedestrian and Evacuation Dynamics, 115-121 (2001), Berlin, Germany: Springer, Berlin, Germany
[7] Burstedde, C.; Klauck, K.; Schadschneider, A.; Zittartz, J., Simulation of pedestrian dynamics using a two-dimensional cellular automaton, Physica A: Statistical Mechanics and its Applications, 295, 3-4, 507-525 (2001) · Zbl 0978.90018
[8] Klüpfel, H. L., A cellular automaton model for crowd movement and egress simulation [Ph.D. thesis] (2003), Universität Duisburg-Essen
[9] Helbing, D., A mathematical model for the behavior of pedestrians, Behavioral Science, 36, 298-310 (1991)
[10] Helbing, D.; Molnár, P., Social force model for pedestrian dynamics, Physical Review E, 51, 5, 4282-4286 (1995)
[11] Helbing, D., Traffic Dynamics: New Physical Modeling Concepts (1997), Berlin, Germany: Springer, Berlin, Germany
[12] Helbing, D.; Molnár, P.; Schweitzer, F., Self-organization phenomena in pedestrian crowds, Self-Organization of Complex Structures: From Individual to Collective Dynamics, 569-577 (1997), London, UK: Gordon and Breach, London, UK
[13] Helbing, D.; Farkas, I. J.; Vicsek, T., Freezing by heating in a driven mesoscopic system, Physical Review Letters, 84, 6, 1240-1243 (2000)
[14] Helbing, D.; Farkas, I.; Vicsek, T., Simulating dynamical features of escape panic, Nature, 407, 6803, 487-490 (2000)
[15] Helbing, D.; Farkas, I.; Molnár, I. J.; Vicsek, T.; Schreckenberg, M.; Deo Sarma, S., Simulation of pedestrian crowds in normal and evacuation situations, Pedestrian and Evacuation Dynamics, 21-58 (2002), Berlin, Germany: Springer, Berlin, Germany
[16] Helbing, D.; Buzna, L.; Johansson, A.; Werner, T., Self-organized pedestrian crowd dynamics: experiments, simulations, and design solutions, Transportation Science, 39, 1, 1-24 (2005)
[17] Helbing, D.; Johansson, A.; Al-Abideen, H. Z., Dynamics of crowd disasters: an empirical study, Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, 75, 4 (2007)
[18] Lakoba, T. I.; Kaup, D. J.; Finkelstein, N. M., Modifications of the Helbing-Molnar-Farkas-Vicsek SFM for pedestrian evolution, Simulation, 81, 5, 339-352 (2005)
[19] Yu, W.; Johansson, A., Modeling crowd turbulence by many-particle simulations, Physical Review E, 76, 4 (2007)
[20] Fruin, J. J., Pedestrian Planning and Design (1971), New York, NY, USA: Metropolitan association of Urban Designers and Environmental Planners, New York, NY, USA
[21] Sarkar, A. K.; Janardhan, K. S. V. S., A study on pedestrian flow characteristics, CD-ROM With Proceedings (1997), Washington, DC, USA: Transportation Research Board, Washington, DC, USA
[22] Older, S. J., Movement of pedestrians on footways in shopping streets, Traffic Engineering and Control, 10, 160-163 (1968)
[23] Weidmann, U., Transporttechnik der Fussgänger, Transporttechnische Eigenschaften des Fussgängerverkehrs. Transporttechnik der Fussgänger, Transporttechnische Eigenschaften des Fussgängerverkehrs, Schriftenreihe des IVT, 90 (1993), Zürich, Switzerland: Instituts für Verkehrsplanung und Transportsysteme, Zürich, Switzerland
[24] Parisi, D. R.; Gilman, M.; Moldovan, H., A modification of the SFM can reproduce experimental data of pedestrian flows in normal conditions, Physica A: Statistical Mechanics and its Applications, 388, 17, 3600-3608 (2009)
[25] Johansson, A.; Helbing, D.; Shukla, P. K., Specification of the social force pedestrian model by evolutionary adjustment to video tracking data, Advances in Complex Systems, 10, 271-288 (2007) · Zbl 1151.91737
[26] Zainuddin, Z.; Shuaib, M., Incorporating decision making capability into the social force model in unidirectional flow, Research Journal of Applied Sciences, 5, 6, 388-393 (2010)
[27] Seyfried, A.; Steffen, B.; Lippert, T., Basics of modelling the pedestrian flow, Physica A: Statistical Mechanics and its Applications, 368, 1, 232-238 (2006)
[28] Geem, Z. W.; Kim, J. H.; Loganathan, G. V., A new heuristic optimization algorithm: harmony search, Simulation, 76, 2, 60-68 (2001)
[29] Alia, O. M.; Mandava, R., The variants of the harmony search algorithm: an overview, Artificial Intelligence Review, 36, 1, 49-68 (2011)
[30] Eberhart, R.; Kennedy, J., A New optimizer using particle swarm theory, Proceedings of the 6th International Symposium on Micro Machine and Human Science
[31] Al-Betar, M. A.; Khader, A. T.; Blazewicz, J.; Drozdowski, M.; Kendall, G.; McCollum, B., A hybrid harmony search for university course timetabling, Proceedings of the 4nd Multidisciplinary Conference on Scheduling: Theory and Applications (MISTA ’09)
[32] Shuaib, M. M.; Alia, O. M.; Zainuddin, Z., Incorporating prediction factor into the investigation capability in the social force model: application on avoiding grouped pedestrians, Applied Mathematics & Information Sciences, 7, 1, 323-331 (2013)
[33] Zhang, J.; Klingsch, W.; Schadschneider, A.; Seyfried, A., Ordering in bidirectional pedestrian flows and its influence on the fundamental diagram, Journal of Statistical Mechanics: Theory and Experiment, 2012, 2 (2012)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.