Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32131
Dynamic Reroute Modeling for Emergency Evacuation: Case Study of Brunswick City, Germany

Authors: Yun-Pang Flötteröd, Jakob Erdmann


The human behaviors during evacuations are quite complex. One of the critical behaviors which affect the efficiency of evacuation is route choice. Therefore, the respective simulation modeling work needs to function properly. In this paper, Simulation of Urban Mobility’s (SUMO) current dynamic route modeling during evacuation, i.e. the rerouting functions, is examined with a real case study. The result consistency of the simulation and the reality is checked as well. Four influence factors (1) time to get information, (2) probability to cancel a trip, (3) probability to use navigation equipment, and (4) rerouting and information updating period are considered to analyze possible traffic impacts during the evacuation and to examine the rerouting functions in SUMO. Furthermore, some behavioral characters of the case study are analyzed with use of the corresponding detector data and applied in the simulation. The experiment results show that the dynamic route modeling in SUMO can deal with the proposed scenarios properly. Some issues and function needs related to route choice are discussed and further improvements are suggested.

Keywords: Evacuation, microscopic traffic simulation, rerouting, SUMO.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 897


[1] A. J. Pel, M. C. J. Bliemer and S. P. Hoogendoorn, “A review on travel behaviour modelling in dynamic tra c simulation models for evacuations,” Transportation, (39), 2012, pp. 97—123.
[2] Y.-P. Flötteröd and J. Erdmann, “Experiment study on the evacuation of bomb alert with SUMO” in Proc. SUMO 2016 – Traffic, Mobility, and Logistics, Berlin, 2016, pp. 39—50.
[3] D. Krajzewicz, J. Erdmann, M. Behrisch and L. Bieker, „Recent development and applications of SUMO - Simulation of Urban Mobility,” International Journal on Advances in Systems and Measurements, 5(3&4), 2012, pp-128-138.
[4] SUMO, Simulation of Urban MObility,, accessed on 14 December 2017.
[5] SUMO: SUMO: Simulation/Rerouter web site. Accessed 2017, 2017.
[6] Institute of Transportation Systems of the German Aerospace Center, “Application Platform for Intelligent Mobility” web site. Accessed 2017.
[7] Institute of Transportation Systems of the German Aerospace Center, “VABENE++ Traffic Management for Large Scale Events and Disasters” web site. Accessed 2017.
[8] S. Detzer and M. Weber, “Case study: Simulation of Transport Systems in a critical situation in Brunswick, Germany,” in Proc. 12th Int. Conf. on Information Systems for Crisis Response & Management, Kristiansand, 2015.
[9] M. Behrisch, Y.-P. Flötteröd, D. Krajzewicz and P. Wagner, “Ecological user equilibrium in traffic management?,” in Proc. 4th International Dynamic Traffic Assignment Symposium, Martha's Vineyard, Massachusetts, 2012.
[10] Y.-P. Flötteröd, P. Wagner, M. Behrisch and D. Krajzewicz, “Simulation-based Validity Analysis of Ecological User Equilibrium,” in Proc. 2012 Winter Simulation Conference, Berlin, 2012.