Effects of Switching Behavior for the Attraction on Pedestrian Dynamics

Walking is a fundamental activity of our daily life not only for moving to other places but also for interacting with surrounding environment. While walking on the streets, pedestrians can be aware of attractions like shopping windows. They can be influenced by the attractions and some of them might shift their attention towards the attractions, namely switching behavior. As a first step to incorporate the switching behavior, this study investigates collective effects of switching behavior for an attraction by developing a behavioral model. Numerical simulations exhibit different patterns of pedestrian behavior depending on the strength of the social influence and the average length of stay. When the social influence is strong along with a long length of stay, a saturated phase can be defined at which all the pedestrians have visited the attraction. If the social influence is not strong enough, an unsaturated phase appears where one can observe that some pedestrians head for the attraction while others walk in their desired direction. These collective patterns of pedestrian behavior are summarized in a phase diagram by comparing the number of pedestrians who visited the attraction to the number of passersby near the attraction. Measuring the marginal benefits with respect to the strength of the social influence and the average length of stay enables us to identify under what conditions enhancing these variables would be more effective. The findings from this study can be understood in the context of the pedestrian facility management, for instance, for retail stores.

[1]  Stéphane Donikian,et al.  Crowd of Virtual Humans: a New Approach for Real Time Navigation in Complex and Structured Environments , 2004, Comput. Graph. Forum.

[2]  Henry Been-Lirn Duh,et al.  A Simulation of Bonding Effects and Their Impacts on Pedestrian Dynamics , 2010, IEEE Transactions on Intelligent Transportation Systems.

[3]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[4]  Vicsek,et al.  Freezing by heating in a driven mesoscopic system , 1999, Physical review letters.

[5]  Richard G. Netemeyer,et al.  Measurement of Consumer Susceptibility to Interpersonal Influence , 1989 .

[6]  Fang Wu,et al.  Novelty and collective attention , 2007, Proceedings of the National Academy of Sciences.

[7]  A. Schadschneider,et al.  Simulation of pedestrian dynamics using a two dimensional cellular automaton , 2001 .

[8]  Dirk Helbing,et al.  Specification of the Social Force Pedestrian Model by Evolutionary Adjustment to Video Tracking Data , 2007, Adv. Complex Syst..

[9]  Tapio Luttinen,et al.  Collective dynamics of pedestrians interacting with attractions. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Terry L. Childers,et al.  The Influence of Familial and Peer-Based Reference Groups on Consumer Decisions , 1992 .

[11]  S. Dai,et al.  Centrifugal force model for pedestrian dynamics. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Dirk Helbing,et al.  How simple rules determine pedestrian behavior and crowd disasters , 2011, Proceedings of the National Academy of Sciences.

[13]  H. Bastian.  Sensation and Perception.—I , 1869, Nature.

[14]  Victor J. Blue,et al.  Cellular automata microsimulation for modeling bi-directional pedestrian walkways , 2001 .

[15]  Takayuki Kanda,et al.  Potential for the dynamics of pedestrians in a socially interacting group. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Jayashankar M. Swaminathan,et al.  Effect of Traffic on Sales and Conversion Rates of Retail Stores , 2012, Manuf. Serv. Oper. Manag..

[17]  Barton A. Weitz,et al.  When Should a Retailer Create an Exciting Store Environment , 2006 .

[18]  N. Stanietsky,et al.  The interaction of TIGIT with PVR and PVRL2 inhibits human NK cell cytotoxicity , 2009, Proceedings of the National Academy of Sciences.

[19]  Wenjian Yu,et al.  Modeling crowd turbulence by many-particle simulations. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  A. Seyfried,et al.  Basics of Modelling the Pedestrian Flow , 2005, physics/0506189.

[21]  Katarzyna Sznajd-Weron,et al.  Opinion evolution in closed community , 2000, cond-mat/0101130.

[22]  E Altshuler,et al.  Foraging at the edge of chaos: internal clock versus external forcing. , 2013, Physical review letters.

[23]  O. Bagasra,et al.  Proceedings of the National Academy of Sciences , 1914, Science.

[24]  S. Galam.  Minority opinion spreading in random geometry , 2002, cond-mat/0203553.

[25]  J. G. Hollands,et al.  Engineering Psychology and Human Performance , 1984 .

[26]  J. Herskowitz,et al.  Proceedings of the National Academy of Sciences, USA , 1996, Current Biology.

[27]  T. Kanda,et al.  Social force model with explicit collision prediction , 2011 .

[28]  Michael R. Pearce,et al.  Evaluating Promotions in Shopping Environments: Decomposing Sales Response into Attraction, Conversion, and Spending Effects , 2001 .

[29]  S. Milgram,et al.  Note on the drawing power of crowds of different size. , 1969 .

[30]  Lubos Buzna,et al.  Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions , 2005, Transp. Sci..

[31]  D. Helbing,et al.  The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics , 2010, PloS one.

[32]  Michael Batty,et al.  Crowd and environmental management during mass gatherings. , 2012, The Lancet. Infectious diseases.

[33]  Simon Garnier,et al.  Visual attention and the acquisition of information in human crowds , 2012, Proceedings of the National Academy of Sciences.