Modeling pedestrians' interest in locations: A concept to improve simulations of pedestrian destination choice

Abstract Large environments that are designed for travel, leisure, and for everyday life – such as transport hubs, amusement parks, and shopping centers – feature different locations that are frequently visited by pedestrians. Each visit is evoked by one’s motivation to engage in some kind of activity at a certain location. By means of modeling the pedestrians’ interests in locations with the aid of computer simulations, it is possible to forecast the occupancy at locations by utilizing sophisticated pedestrian destination choice models. In the field of pedestrian dynamics research, location preference modeling is not common, but it is all the more rare to include a psychological grounding into such choice models. Here we show that our psychologically inspired and mathematically defined model to describe pedestrians’ interests in locations is able to improve the exactness of pedestrian destination choice models. The interest function model is based on the psychological concept of goal-related memory accessibility and on fundamental coherences found in pedestrian-related data that is measurable at locations. We validated the interest function model and our results provide evidence that our approach improves the simulation fidelity regarding occupancy forecasting. Because the interest concept is designed as a framework that can be coupled to existing microscopic pedestrian simulators, it can be used in most pedestrian destination choice models to describe pedestrian visiting preferences. Consequently, the reliability of the occupancy predictions of pedestrian simulations can be enhanced by integrating the interest function model into choices models.

[1]  A. Seyfried,et al.  The fundamental diagram of pedestrian movement revisited , 2005, physics/0506170.

[2]  John R. Anderson.  Cognitive Psychology and Its Implications , 1980 .

[3]  Dietmar Bauer,et al.  Estimating Pedestrian Movement Characteristics for Crowd Control at Public Transport Facilities , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[4]  Nira Liberman,et al.  Seven Principles of Goal Activation: A Systematic Approach to Distinguishing Goal Priming From Priming of Non-Goal Constructs , 2007, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[5]  Dietmar Bauer,et al.  Including Route Choice Models into Pedestrian Movement Simulation Models , 2014 .

[6]  Vikas Agrawal,et al.  An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster , 2014, Expert Syst. Appl..

[7]  Serge P. Hoogendoorn,et al.  Extension of Edie's Definitions for Pedestrian Dynamics , 2014 .

[8]  Mark H. Overmars,et al.  The corridor map method: a general framework for real‐time high‐quality path planning , 2007, Comput. Animat. Virtual Worlds.

[9]  André Borrmann,et al.  How Do Pedestrians find their Way? Results of an experimental study with students compared to simulation results , 2011 .

[10]  Christian Frey,et al.  PEDESTRIAN SIMULATION BASED ON BIM DATA , 2014 .

[11]  André Borrmann,et al.  A hybrid multi-scale approach for simulation of pedestrian dynamics , 2013 .

[12]  Luca Urbani.  Commuters Rail Sations and Pedestrians Flows: The “Hardbrücke” Station in Zurich, Switzerland , 2012 .

[13]  H. Timmermans,et al.  A Model of Pedestrian Route Choice and Demand for Retail Facilities within Inner-City Shopping Areas , 2010 .

[14]  Nalini Ambady,et al.  Predicting fluctuations in widespread interest: memory decay and goal-related memory accessibility in internet search trends. , 2014, Journal of experimental psychology. General.

[15]  Gregory L. Newmark,et al.  Shopping Trip-Chaining Behavior at Malls in a Transitional Economy , 2005 .

[16]  Dirk Helbing,et al.  Simulating dynamical features of escape panic , 2000, Nature.

[17]  Michel Bierlaire,et al.  A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures , 2014 .

[18]  Christopher E. Peters,et al.  Modeling Groups of Plausible Virtual Pedestrians , 2009, IEEE Computer Graphics and Applications.

[19]  Gerta Köster,et al.  Natural discretization of pedestrian movement in continuous space. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  Harry Timmermans.  Pedestrian Behavior:Models, Data Collection and Applications , 2009 .

[21]  André Borrmann,et al.  Towards TransiTUM: A Generic Framework for Multiscale Coupling of Pedestrian Simulation Models based on Transition Zones , 2014 .

[22]  Demetri Terzopoulos,et al.  Autonomous pedestrians , 2007, Graph. Model..

[23]  Álvaro Ramírez-Gómez,et al.  Simulation of counterflow pedestrian dynamics using spheropolygons. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  André Borrmann,et al.  Generation and use of sparse navigation graphs for microscopic pedestrian simulation models , 2012, Adv. Eng. Informatics.

[25]  Dirk Helbing,et al.  Crowd disasters as systemic failures: analysis of the Love Parade disaster , 2012, EPJ Data Science.

[26]  Kevin O'Brien,et al.  Human Behavior Models for Agents in Simulators and Games: Part I: Enabling Science with PMFserv , 2006, Presence: Teleoperators & Virtual Environments.

[27]  Serge P. Hoogendoorn,et al.  Pedestrian route-choice and activity scheduling theory and models , 2004 .

[28]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[29]  Tina Balke,et al.  How Do Agents Make Decisions? A Survey , 2014, J. Artif. Soc. Soc. Simul..

[30]  Hubert Klüpfel,et al.  The simulation of crowd dynamics at very large events — Calibration, empirical data, and validation , 2007 .

[31]  David O'Sullivan,et al.  “So Go Downtown”: Simulating Pedestrian Movement in Town Centres , 2001 .

[32]  Michael Lees,et al.  A pattern-based modeling framework for simulating human-like pedestrian steering behaviors , 2013, VRST '13.

[33]  K Karimi,et al.  Origin-destination weighting in agent modelling for pedestrian movement forecasting , 2012 .

[34]  John R. Anderson.  A Spreading Activation Theory of Memory , 1988 .

[35]  Nira Liberman,et al.  Accessibility from active and fulfilled goals , 2005 .

[36]  Stefania Bandini,et al.  Identifying Sources and Sinks and Detecting Dominant Motion Patterns in Crowds , 2014 .

[37]  Mark Hoogendoorn,et al.  An Integrated Agent Model Addressing Situation Awareness and Functional State in Decision Making , 2011, PRIMA.

[38]  T. Arentze,et al.  A need-based model of multi-day, multi-person activity generation , 2009 .

[39]  John R. Anderson,et al.  Human memory: An adaptive perspective. , 1989 .

[40]  Hyun Seung Yang,et al.  A collision avoidance behavior model for crowd simulation based on psychological findings , 2013, Comput. Animat. Virtual Worlds.

[41]  André Borrmann,et al.  Concurrent Hierarchical Finite State Machines for Modeling Pedestrian Behavioral Tendencies , 2014 .

[42]  John R. Anderson.  ACT: A simple theory of complex cognition. , 1996 .

[43]  Hjp Harry Timmermans,et al.  Modeling Planned and Unplanned Store Visits within a Framework for Pedestrian Movement Simulation , 2014 .

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

[45]  Andreas Schadschneider,et al.  An Experimental Study of Pedestrian Congestions: Influence of Bottleneck Width and Length , 2009, 0911.4350.

[46]  Hjp Harry Timmermans,et al.  Transportation systems, retail environments and pedestrian trip chaining behaviour: Modelling issues and applications , 1992 .

[47]  Elise Beck,et al.  Developing a model of evacuation after an earthquake in Lebanon , 2013, ArXiv.

[48]  Shaleeza Sohail,et al.  A Survey of Latest Approaches for Crowd Simulation and Modeling Using Hybrid Techniques , 2015 .

[49]  N. Badler,et al.  Crowd simulation incorporating agent psychological models, roles and communication , 2005 .

[50]  Ipke Wachsmuth,et al.  Affective computing with primary and secondary emotions in a virtual human , 2009, Autonomous Agents and Multi-Agent Systems.

[51]  John R. Anderson,et al.  Reflections of the Environment in Memory Form of the Memory Functions , 2022 .

[52]  Angelika Kneidl,et al.  Methoden zur Abbildung menschlichen Navigationsverhaltens bei der Modellierung von Fußgängerströmen , 2013 .

[53]  Isabella von Sivers,et al.  The Conference in Pedestrian and Evacuation Dynamics 2014 (PED2014) Humans do not always act selfishly: social identity and helping in emergency evacuation simulation , 2014 .

[54]  Peter M. Kielar,et al.  Using Raspberry Pi for scientific video observation of pedestrians during a music festival , 2015, ArXiv.

[55]  David Canca,et al.  Macroscopic attraction-based simulation of pedestrian mobility: A dynamic individual route-choice approach , 2013, Eur. J. Oper. Res..

[56]  Stefania Bandini,et al.  An Agent Model of Pedestrian and Group Dynamics: Experiments on Group Cohesion , 2011, AI*IA.

[57]  Dirk Hartmann,et al.  Adaptive pedestrian dynamics based on geodesics , 2010 .

[58]  Tapio Luttinen,et al.  Modeling Pedestrian Switching Behavior for Attractions , 2014 .

[59]  Wander Jager,et al.  CROSS: Modelling Crowd Behaviour with Social-Cognitive Agents , 2013, J. Artif. Soc. Soc. Simul..