This paper presents a pedestrian route choice model and its calibration with real data. The model explicitly represents interaction between pedestrians as an impedance force influences their route choice. This model approach was inspired by friction force equations and considered efforts by pedestrians to avoid passing near other pedestrians with high relative velocity. The route choice process was a function of impedance force and route length. A social force model was used to model pedestrian walking behavior. The calibration had its basis in data acquired from a real experiment developed in a simplified network. Data collection had its basis in video analysis. The paper presents and discusses results from the calibration processes. The model presented in this paper differs from other pedestrian route choice models because it seamlessly incorporates a pedestrian social force model into the route choice decision process.
Get full access to this article
View all access options for this article.
References
1.
HelbingD., BuznaL., JohanssonA., and WernerT.Self-Organized Pedestrian Crowd Dynamics. Transportation Science, Vol. 39, No. 1, 2005, pp. 1–24.
2.
HelbingD., FarkasI., and VicsekT.Simulating Dynamical Features of Escape Panic. Nature, Vol. 407, 2000, pp. 487–490.
3.
SumpterD.The Principles of Collective Animal Behaviour. Philosophical Transactions B of the Royal Society, Vol. 361, No. 1465, 2006, pp. 5–22.
4.
HelbingD.A Mathematical Model for the Behavior of Pedestrians. Behavioral Science, Vol. 36, No. 4, 1991, pp. 298–310.
HelbingD., MolnárP., FarkasI., and BolayK.Self-Organizing Pedestrian Movement. Environmental Planning B, Vol. 28, No. 3, 2001, pp. 361–383.
7.
HelbingD., and MolnárP.Self-Organization Phenomena in Pedestrian Crowds. In Self-Organization of Complex Structures: From Individual to Collective Dynamics (SchweitzerF., ed.), Gordon and Breach, London, 1997, pp. 569–577.
8.
IsobeM., HelbingD., and NagataniT.Experiment, Theory, and Simulation of the Evacuation of a Room Without Visibility. Physical Review E, Vol. 69, No. 6, 2004.
9.
TeknomoK.Modeling Mobile Traffic Agents on Network Simulation.Proc., 16th Annual Conference of Transportation Science Society of the Philippines, Manila, 2008.
10.
HillM. R.Spatial Structure and Decision-Making of Pedestrian Route Selection Through an Urban Environment. PhD dissertation. University of Nebraska–Lincoln, 1982.
11.
SeneviratneP. N., and MorrallJ.Analysis of Factors Affecting the Choice of Route of Pedestrians. Transportation Planning and Technology, Vol. 10, No. 2, 1986, pp. 147–159.
12.
KirikE. S., Yurgel'yanT. B., and KrouglovD. V.The Shortest Time and/or the Shortest Route Strategies in a CA FF Pedestrian Dynamics Model. Journal of Siberian Federal University. Mathematics and Physics, Vol. 2, No. 3, 2009, pp. 271–278.
13.
DresslerD., GrossM., KappmeierJ., KelterT., KulbatzkiJ., PlümpeD., SchlechterG., SchmidtM., SkutellaM., and TemmeS.On the Use of Network Flow Techniques for Assigning Evacuees to Exits. Procedia Engineering, 3, 2010, pp. 205–215.
14.
LämmelG., and PlaueM.Getting Out of the Way: Collision Avoiding Pedestrian Models Compared to the Real World. In Pedestrian and Evacuation Dynamics (WeidmannU., KirschU., and SchreckenbergM., eds.), Springer International Publishing, Switzerland, 2012, pp. 1275–1289.
15.
PapadimitriouE.Theory and Models of Pedestrian Crossing Behaviour Along Urban Trips. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 15, No. 1, 2012, pp. 75–94.
16.
KretzT., GrosseA., HengstS., KautzschL., PohlmannA., and VortischP.Quickest Routes in Simulations of Pedestrians. Advances in Complex Systems, Vol. 14, No. 5, 2011, pp. 733–759.
17.
PatilS., BergJ., CurtisS., LinM., and ManochaD.2010. Directing Crowd Simulations Using Navigation Fields. IEEE Transactions on Visualization and Computer Graphics, 2010, pp. 244–254.
18.
TreuilleA., CooperS., and PopovićZ.Continuum Crowds. ACM Transactions on Graphics, Vol. 25, No. 3, 2006.
19.
TeknomoK., BauerD., and MatyusT.Pedestrian Route Choice Self-Organization.Proc., 3rd International Symposium of Simulation in Transportation, Gold Coast, Australia, 2008.
20.
CharreyronS., JacksonS., and Miranda-MorenoL. F.Toward a Flexible System for Pedestrian Data Collection with a Microsoft Kinect Motion-Sensing Device. In Transportation Research Record: Journal of the Transportation Research Board, No. 2339, Transportation Research Board of the National Academies, Washington, D.C., 2013, pp. 80–89.
21.
MoussaïdM., HelbingD., GarnierS., JohanssonA., CombeM., and TheraulazG.Experimental Study of the Behavioural Mechanisms Underlying Self-Organization in Human Crowds. Proceedings of the Royal Society B: Biological Sciences, Vol. 276, No. 1668, 2009, pp. 2755–2762.
22.
HoogendoornS. P., DaamenW., and BovyP. H.Extracting Microscopic Pedestrian Characteristics from Video Data. Presented at 82nd Annual Meeting of the Transportation Research Board, Washington, D.C., 2003.
23.
MingT., and HongfeiJ.An Approach for Calibration and Validation of the Social Force Pedestrian Model.Proc., 2011 International IEEE Conference on Transportation, Mechanical, and Electrical Engineering, Changun, China, 2011.
24.
WangT., WuJ., ZhengP., and McDonaldM.Study of Pedestrians' Gap Acceptance Behavior When They Jaywalk Outside Crossing Facilities.Proc., 13th International IEEE Conference on Intelligent Transportation Systems, Funchal, Portugal, 2010.
25.
SchönauerR., StubenschrottM., Schrom-FeiertagH., and MenšikK.Social and Spatial Behaviour in Shared Spaces. Proceedings REAL CORP, 2012, pp. 759–767.
26.
WerberichB., PrettoC., and CybisH.Pedestrian Route Choice Model Based on Friction Forces. Simulation, Vol. 90, No. 10, 2014, pp. 1177–1187.
27.
HoogendoornS., BovyP., and DaamenW.Microscopic Pedestrian Wayfinding and Dynamics Modelling. In Pedestrian and Evacuation Dynamics (SchreckenbergM., and SharmaS., eds.), Springer-Verlag, Berlin2002, pp. 123–155.
28.
GaudN., GallandS., GechterF., HilaireV., and KoukamA.Holonic Multilevel Simulation of Complex Systems: Application to Real-Time Pedestrians Simulation in Virtual Urban Environment. Simulation Modelling Practice and Theory, Vol. 16, No. 10, 2008, pp. 1659–1676.
29.
PrettoC. O., JacobsenA. C., and CybisH. B. B.A Multilayer Simulation Model for Vehicle and Pedestrian Interaction. Presented at 90th Annual Meeting of the Transportation Research Board, Washington, D.C., 2011.
30.
DijkstraE. W.A Note on Two Problems in Connexion with Graphs. Numerische Mathematik, Vol. 1, 1959, pp. 269–271.
31.
WerberichB. R., PrettoC. O., and CybisH.Pedestrians' Route Choice Based on Friction Forces Assuming Partial and Full Environment Knowledge. Presented at 93rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2014.
JohanssonA., HelbingD., and ShuklaP.Specification of the Social Force Pedestrian Model by Evolutionary Adjustment to Video Tracking Data. Advances in Complex Systems, Vol. 10, No. 2, 2007, pp. 271–288.
