The evacuation of whole cities or even regions is an important problem, as demonstrated by recent events such as evacuation of Houston in the case of Hurricane Rita or the evacuation of coastal cities in the case of Tsunamis. A robust and flexible simulation framework for such large-scale disasters helps to predict the evacuation process. Existing methods are either geared towards smaller problems (e.g. Cellular Automata techniques or methods based on differential equations) or are not microscopic (e.g. methods based on dynamic traffic assignment). This paper presents a technique that is both microscopic and capable to process large problems.BMBF, 03G0666E, Verbundprojekt FW: Last-mile Evacuation; Vorhaben: Evakuierungsanalyse und Verkehrsoptimierung, Evakuierungsplan einer Stadt - Sonderprogramm GEOTECHNOLOGIENBMBF, 03NAPAI4, Transport und Verkehr: Verbundprojekt ADVEST: Adaptive Verkehrssteuerung; Teilprojekt Verkehrsplanung und Verkehrssteuerung in Megacitie

Lämmel, Gregor

Nagel, Kai

Rieser, Marcel

DepositOnce (Techn. Univ. Berlin)

This version is available at https://doi.org/10.14279/depositonce-8268Copyright applies. A non-exclusive, non-transferable and limited right to use is granted. This document is intended solely for personal, non-commercial use.Terms of UseLämmel, G.; Rieser, M.; Nagel, K. (2009). Large Scale Microscopic Evacuation Simulation. Pedestrian and Evacuation Dynamics 2008, 547–553. https://doi.org/10.1007/978-3-642-04504-2_48      Gregor Lämmel, Marcel Rieser, Kai NagelLarge Scale Microscopic Evacuation SimulationSubmitted manuscript (Preprint)Conference paper  |Large Scale Microscopic Evacuation SimulationGregor La¨mmel1, Marcel Rieser1,2, Kai Nagel11 Transport Systems Planning and Transport Telematics – TU BerlinSalzufer 17-19 – 10587 Berlin – GermanyContact address, e-mail: laemmel@vsp.tu-berlin.de2 Institute for Transport Planning and Systems - ETH Zurich8093 Zurich – SwitzerlandSummary. The evacuation of whole cities or even regions is an important prob-lem, as demonstrated by recent events such as evacuation of Houston in the case ofHurricane Rita or the evacuation of coastal cities in the case of Tsunamis. A robustand flexible simulation framework for such large-scale disasters helps to predict theevacuation process. Existing methods are either geared towards smaller problems(e.g. Cellular Automata techniques or methods based on differential equations) orare not microscopic (e.g. methods based on dynamic traffic assignment). This paperpresents a technique that is both microscopic and capable to process large problems.1 IntroductionDisaster and evacuation planning has become an important topic in scienceand politics. In principle there are two different situations: evacuation ofbuildings, ships and airplanes or the like on the one hand, or evacuation ofwhole cities or even regions on the other hand. The former involves normallythe evacuation of pedestrians, whereas the latter is often associated withthe evacuation by car. Corresponding to the two different types of problems,there are two different basic approaches for simulating the traffic flow:(1) Methods of dynamic traffic assignment (DTA) have been applied toevacuation simulation on the city or regional scale (e.g. MITSIM [1] or DY-NASMART [2] ). The DTA approach is based on the analogy between trafficand hydrodynamic characteristics of fluids. That means DTA is a macro-scopic approach and reduces the problem of evacuation dynamics to a wellknown physical problem. However, in DTA it is not straightforward to dealwith the inhomogeneity of a population. For this, a microscopic simulationis needed, where all people are simulated as individuals.(2) Microscopic simulations are often based on Cellular Automata (CA)[3, 4]. In CA models each evacuee is designed as an individual; therefore itis possible to simulate also population structures where people have differentspeeds or ranges, or more complex behavior. The modeling of complex be-havior in evacuation simulation has become important in recent years. Peoplecould for example ignore warnings or might not choose the nearest emergencyexit, furthermore people tend to follow others (herd behavior) [5, 6]. Agent2 Gregor La¨mmel, Marcel Rieser, Kai Nageloriented research groups have modeled such behavior [7, 8]. In general it is ex-pected that complex behavior leads to longer evacuation times, consequentlya simulation that ignores such behavior patterns is probably optimistic.One possible approach to deal with large-scale scenarios (hundreds ofthousands of persons) but to retain persons as individual agents is based upon a modified queuing model [9, 10]. The queuing model simplifies streetsto edges and crossings to nodes. This graph-oriented model is defined bylengths/widths, free speed and flow capacity of the edges. This simplificationleads to a major speedup of the simulation while keeping results realistic.12 Simulation frameworkThe evacuation simulation is based on the MATSim framework(www.matsim.org), which is constructed around the notion of agents thatmake independent decisions about their actions. Each evacuee is modeled asan individual agent that optimizes its personal evacuation route. The objec-tive is a Nash equilibrium, where every agent attempts to find a route that isoptimal for the agent. In reality this might be achieved by appropriate train-ing or guidance while maintaining acceptability in the sense that no personcould gain by deviating from this solution. The results from the simulationgive an estimate of the time it could take to evacuate the endangered area.The escape routes are encoded in so-called plans. Besides the escape routea plan also contains the departure time. That means it would also be possibleto model an arbitrary variance in the evacuees’ response time. However, in thework presented here, it is assumed that all evacuees start to evacuate at thesame time. The traffic flow simulation is implemented as a queue simulation,where each street (link) is represented as a FIFO (first-in first-out) queue withthree restrictions [9]. First, each agent has to remain for a certain time on thelink, corresponding to the free speed travel time. Second, a link flow capacityis defined which limits the outflow from the link. If, in any given time step,that capacity is used up, no more agents can leave the link. Finally, a linkstorage capacity is defined which limits the number of agents on the link. Ifit is filled up, no more agents can enter this link. The difference to standardqueueing theory is that agents (particles) are not dropped but spill back,causing congestion. An illustration of the queue model is shown in figure 1.The free parameters (i.e. flow capacity, storage capacity and free speed) arechosen to approximate Weidmann’s fundamental diagram [12].2The initial escape routes are generated by applying (a time-dependent)Dijkstra’s shortest path algorithm [14] assuming free speed is possible on all1 For example, the simulation of the whole (motor) traffic of Switzerland (approx.5 million trips) takes less then 5 minutes for 24h real time [11].2 Newer studies [13] imply other fundamental diagrams then those from Weid-mann. An adaptation of these values could, in consequence, become necessary infuture.Large Scale Microscopic Evacuation Simulation 3Fig. 1. Illustration of the queue modellinks. Dijkstra’s algorithm is designed to deal with single-destination prob-lems, but in the case of an evacuation there are many different safe places pos-sible. The standard approach (e.g. [15]) is to transform this multi-destinationproblem into a single-destination problem by creating a new node as super-sink. All links which lead out of the evacuation area are connected, using vir-tual zero cost links, to that super-sink (see figure 2 a)). Doing so, Dijkstra’salgorithm will find the shortest route from any node inside the evacuationarea to this super-sink.Fig. 2. Inundation map and evacuation networkThe outcome of the traffic flow simulation (e.g. congestion) depends onthe planning decisions made by agents. It is obvious that simulating theinitial escape routes only will not provide a good or realistic solution for theevacuation problem and the results will be far away from Nash equilibrium. Tofind the Nash equilibrium, the simulation framework runs learning iterations:At the end of each iteration, every agent scores the performed plan. In this4 Gregor La¨mmel, Marcel Rieser, Kai Nagelstudy the scoring function is simply the negative of the travel time. Thisscore is then memorized for the plan. After the agents have updated theirscore, each of them decides with a certain probability to revise its plan. In there-planning procedure, the Dijkstra router is again applied to find the fastestescape route for each agent. The difference to the initial routing is that theweights for the links are no longer based on free speed travel times but on theexperienced travel times from the last iteration. For every agent that has notbeen chosen for re-planning, a probabilistic discrete-choice model selects aplan out of its memory, where the probabilities of the plans increase with theachieved score. Repeating this iteration cycle, the agent behavior will movetowards a Nash equilibrium.3 ResultsA hypothetical event of a dam-break of the Sihlsee dam was chosen as ascenarior. This would lead to an inundation of parts of the city of Zurich.According to the civil defense office there will be an advance warning time ofabout 110 minutes until the inundation will reach the city center. The civildefense office also provides an instruction sheet with an inundation map ofthe area at risk (figure 2 b)). A NAVTEQ network (www.navteq.com) forSwitzerland was chosen as evacuation network and adapted for pedestriansimulation. The affected part of the city of Zurich consists of 3037 nodesand 6120 links. The synthetic population embraces 165571 agents and wasextracted from census data and contains all people with their work placewithin the evacuation area.The simulation run was performed on a dual core CPU at 2.33 GHzwith 2 GB of RAM. The computer runs JAVA jdk1.5 012 on Linux. Theevacuation simulation has been stopped after 100 re-planning cycles. Theoverall runtime was 3 hours and 24 minutes. The simulation consumed up to1393 MB of RAM. As expected, the evacuation time decreases significantlywith the iterations and levels out at approximately 45 minutes (see figure 3a)). Using a visualizer, some bottlenecks could be detected. This is shownin figure 3 b). These bottlenecks mainly occur at bridges. The snapshot wastaken after 100 iterations of learning, consequently there seems to be nobetter solution for the individual agent than to queue up on these bridges.34 ConclusionsWe introduced a microscopic pedestrian simulation framework for large-scaleevacuations. It is implemented as a Multi Agent Simulation, where every3 For those who know the area: Since this preliminary study is based on a vehiculartraffic network, it ignores links which can be used by pedestrians only. This couldbe corrected by using different network data.Large Scale Microscopic Evacuation Simulation 5Fig. 3. Results of the simulationagent tries to optimize its individual evacuation plan in an iterative way. Thesimulation framework is demonstrated through a case study based on a hy-pothetical dam-break of the Sihlsee dam near Zurich. Despite the underlyingbehavioral model being quite simple, the simulation gives plausible resultsregarding the predicted evacuation time and bottlenecks. The runtime per-formance shows that this approach is well suited for large scale scenarios.With state of the art hardware it is no problem to simulate much larger sce-narios with over one million agents. In future work it is planned to applythis framework to an evacuation simulation in the case of a Tsunami warningfor the Indonesian city of Padang. The improvement of the behavioral model(e.g. herd behavior [5] modified for large-scale scenarios [16]) could also be atopic of future work.Acknowledgements – We thank Schutz & Rettung Zu¨rich for the permis-sion to reprint the inundation map shown in figure 2 b). This project wasfunded in part by the German Ministry for Education and Research (BMBF),under grants numbers 03G0666E (“last mile”) and 03NAPI4 (“Advest”). Asimilar but more extended paper has appeared under the title “Bottlenecksand Congestion in Evacuation Scenarios: A Microscopic Evacuation Simula-tion for Large-Scale Disasters” in [17].References1. M. Jha, K. Moore, and B. Pashaie. Emergency evacuation planning with mi-croscopic traffic simulation. Paper 04-2414, Transportation Research BoardAnnual Meeting, Washington, D.C., 2004.2. E. Kwon and S. Pitt. Evaluation of emergency evacuation strategies for down-town event traffic using a dynamic network model. Paper 05-2164, Transporta-tion Research Board Annual Meeting, Washington, D.C., 2005.3. K. Nagel and M. Schreckenberg. A cellular automaton model for freeway traffic.Journal de Physique I France, 2:2221–2229, 1992.6 Gregor La¨mmel, Marcel Rieser, Kai Nagel4. K. Nishinari, A. Kirchner, A. Nazami, and A. Schadschneider. Extended floorfield CA model for evacuation dynamics. In Special Issue on Cellular Automataof IEICE Transactions on Information and Systems, volume E84-D, January2001.5. D. Helbing, I. Farkas, and T. Vicsek. Simulating dynamical features of escapepanic. Nature, 407:487–490, 2000.6. C. Lieberman, D. Kurowski, M.R. Avila, L. Ricci, N. Thomas, C. Collyer, andB. Aguirre. Conceptual framework for simulating the pedestrian evacuation be-havior from buildings. Paper 05-2297, Transportation Research Board AnnualMeeting, Washington, D.C., 2005.7. Y. Murakami, T. Ishida, T. Kawasoe, and R. Hishiyama. Scenario descrip-tion for multi-agent simulation. In Autonomous agents and multiagent systems(AAMAS’03), Melbourne, Australia, July 2003.8. X. Pan, C.S. Han, K. Dauber, and K.H. Law. A multi-agent based frameworkfor the simulation of human and social behavior during emergency evacuations.AI and Society, 22(2):113–132, 2007.9. C. Gawron. An iterative algorithm to determine the dynamic user equilibriumin a traffic simulation model. In D.E. Wolf and M. Schreckenberg, editors,Traffic and granular flow’97, pages 469–474. Springer, Berlin, 1998.10. P. M. Simon, J. Esser, and K. Nagel. Simple queueing model applied to thecity of Portland. International Journal of Modern Physics C, 10(5):941–960,1999.11. B. Raney, N. Cetin, A. Vo¨llmy, M. Vrtic, K. Axhausen, and K. Nagel. Anagent-based microsimulation model of Swiss travel: First results. Networks andSpatial Economics, 3(1):23–41, 2003.12. U. Weidmann. Transporttechnik der Fussga¨nger, volume 90 of Schriftenreihedes IVT. Institute for Transport Planning and Systems ETH Zu¨rich, 2 edition,1993. In German.13. A. Schadschneider, W. Klingsch, H. Klu¨pfel, T. Kretz, C. Rogsch, andA. Seyfried. Evacuation dynamics: Empirical results, modelling and applica-tions. In B. Meyers, editor, Encyclopedia of Complexity and System Science.Springer, Berlin, to appear.14. E. Dijkstra. A note on two problems in connexion with graphs. NumerischeMathematik, 1:269 – 271, 1959.15. Qingsong Lu, Betsy George, and Shashi Shekhar. Capacity Constrained Rout-ing Algorithms for Evacuation Planning: A Summary of Results, volume 3633of Lecture Notes in Computer Science, pages 291–307. Springer Berlin / Hei-delberg, 2005.16. C. Gloor, P. Stucki, and K. Nagel. Hybrid techniques for pedestrian simulations.In Cellular automata, Proceedings, number 3305 in Lecture Notes in ComputerScience, pages 581–590. Springer, 2004.17. G. La¨mmel, M. Rieser, and K. Nagel. Bottlenecks and congestion in evacuationscenarios: A microscopic evacuation simulation for large-scale disasters. In Proc.of 7th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS2008), Estoril, Portugal, May 2008.

Large Scale Microscopic Evacuation Simulation

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