This paper presents a pedestrian route choice model devised to represent the influence of the impedance generated by other pedestrians on the route choice process. This model is inspired by friction force equations, and considers that pedestrians avoid passing near other pedestrians with high relative velocity. The route choice process is based on a weighting of the impedance generated by pedestrians and the path length. A social force model was used to model pedestrian walking behavior. The model is able to reproduce emergent behavior among agents, allowing the assumption that the friction equations may provide a suitable approach to route choice behavior and can also be used as an indirect measure of pedestrian delay.
HelbingDMolnárPFarkasI. Self-organizing pedestrian movement. Environ Plann B2001: 28: 361–383.
9.
HelbingDMolnárP. Self-organization phenomena in pedestrian crowds. In: SchweitzerF (ed) Self-organization of complex structures: from individual to collective dynamics. London: Gordon and Breach, 1997, pp.569–577.
10.
IsobeMHelbingDNagataniT. Many-particle simulation of the evacuation process from a room without visibility. Phys Rev E Stat Nonlinear Soft Matter Phys2004; 69: 066132.
11.
TeknomoK. 2008. Modeling mobile traffic agents on network simulation. In: Proceedings of the 16th annual conference of the Transportation Science Society of the Philippines (TSSP), Manila, 2008.
12.
TeknomoKBauerDMatyusT. Pedestrian route choice self-organization. In: Proceedings of the 3rd international symposium of simulation in transportation, Gold Coast, Australia, 2008.
13.
HillMR. Spatial structure and decision-making of pedestrian route selection through an urban environment. PhD Thesis, University Microfilms International, 1982.
14.
SenevarantePNMorallJ. Analysis of factors affecting the choice of route of pedestrians. Transp Plann Technol1986; 10: 147–159.
15.
PapadimitriouE. Theory and models of pedestrian crossing behaviour along urban trips. Transp Res Part F Traffic Psychol Behav2012; 15: 75–94.
16.
WagoumAUKSeyfriedAHollS. Modeling the dynamic route choice of pedestrians to assess the criticality of building evacuation. Adv Complex Syst2012; 15(7). doi: 10.1142/S0219525912500294.
17.
KretzTGroßeAHengstS. Quickest paths in simulations of pedestrians. Adv Complex Syst2011; 14: 733–759.
18.
PatilSVan Den BergJCurtisSLinMCManochaD. Directing crowd simulations using navigation fields, IEEE Trans Visualization Comput Graphics2010; 17(2): 244–254.
19.
TreuilleACooperSPopovićZ. Continuum crowds. ACM Trans Graphics2006; 25(3):1160–1168.
20.
BanerjeeBAbukmailALandonK. Layered intelligence for agent-based crowd simulation. Simulation2009; 85: 621–633.
21.
KirikESYurgel’yanTBKrouglovDV. The shortest time and/or the shortest path strategies in a CA FF pedestrian dynamics model. J Siberian Fed Univ Math Phys2009; 2: 271–278.
22.
DresslerDGroßMKappmeierJ. On the use of network flow techniques for assigning evacuees to exits. Procedia Eng2010; 3: 205–215.
23.
LämmelGPlaueM. Getting out of the way: collision avoiding pedestrian models compared to the real world. In: WeidmannUKirschUSchreckenbergM (eds) Pedes-trian and evacuation dynamics 2012. Berlin: Springer, 2014.
24.
GaudNGallandSGechterF. Holonic multilevel simulation of complex systems: application to real-time pedestrians simulation in virtual urban environment. Simul Model Pract Theory2008; 16: 1659–1676.
25.
HoogendoornSBovyPDaamenW. Microscopic pedestrian wayfinding and dynamics modelling. In: SchreckenbergMSharmaS (eds) Pedestrian and evacuation dynamics. Berlin: Springer, 2002, pp.123–155.
26.
PrettoCOJacobsenACCybisHBB. A multi-layer simulation model for vehicle and pedestrian. In: Proceedings of the 90th Annual Meeting of the Transportation Research Board, Vol. 1, Washington, 2011, pp.1–15.
27.
DijkstraE. A note on two problems in connection with graphs. Numer Math1959; 1: 269–271.
