Abstract
The amount of vehicular traffic has reached the capacity of road networks in many densely populated regions world-wide. Especially in North Rhine-Westphalia, situated in the western part of Germany, growing traffic demand gives rise to more and more congestion on the autobahn network. Therefore, the need for intelligent information systems has become increasingly important. Here we present a combination of real time traffic data analysis and microscopic traffic simulations as the basis of an online-tool that provides full information of the traffic state on the complete autobahn network. Using a java applet the results are made topical minute by minute in the internet (www.autobahn.nrw.de).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ahmed, Samir A. (1983). Stochastic processes in freeway traffic, Traffic Engineering and Control 24, pp306–310.
BarcelĂ³, J. and Casas, J. (1999). The use of neural networks for short-term prediction of traffic demand, In Proceedings of the 14 th International Symposium on Transportation and Traffic Theory, Ceder, A. (Ed.), Pergamon, Amsterdam, pp419–443.
Barlovic, R., Santen, L., Schadschneider, A., and Schreckenberg, M. (1998). Metastable states in cellular automata for traffic flow, Eur. Phys. J. B 5, pp793–800.
Barrett, C., Wolinsky, M., and Olesen, M.W. (2000). Emergent local control properties in particle hopping traffic simulations, In Traffic and Granular Flow, Wolf, D.E., Schreckenberg, M., and Bachem, A. (Eds.), World Scientific, Singapore, pp 169–173.
Chowdhury, D., Santen, L., and Schadschneider, A. (2000). Statistical Physics of Vehicular Traffic and Some Related Systems, Phys. Rep. 329, pp 199–329.
Chrobok, R., Grunewald, T., Pottmeier, A., and Schreckenberg, M. (2002). Analysis and validation of variable message signs using cellular automaton traffic simulations, In Proceedings of the 9th Meeting of the EURO Working Group on Transportation, DellOrco, M. and Ottomanelli, M. (Eds.), Bari, pp469–472.
Chrobok, R., Kaumann, O., Wahle, J., and Schreckenberg, M. (2000). Three categories of traffic data: historical, current, and predictive. In Proceedings of the 9th IFAC Symposium Control in Transportation Systems 2000, Schnieder, E. and Becker, U. (Eds.), IFAC, Braunschweig, pp 250–255.
Dia, H. (2001). An object-oriented neural network approach to short-term traffic forecasting, Euro. J. Op. Res. 131, pp253–261.
Edie, L.C., and Foot, R.S. (1958). Traffic flow in tunnels, Proc. HRB, 37, pp334–344.
Esser, J. and Schreckenberg, M. (1997). Microscopic simulation of urban traffic based on cellular automata, Int. J. of Mod. Phys. C 8, pp1025–1036.
George, H. (1961). Measurement and evaluation of traffic congestion, Bureau of Highway Traffic, Yale University, pp43–68.
Helbing, D., Herrmann, H., Schreckenberg, M., and Wolf, D.E. (Eds.) (2000). Traffic and Granular Flow’ 99, Springer, Heidelberg.
Helbing, D. (1997). Empirical traffic data and their implications for traffic modeling, Phys. Rev. E 55, R25–R28.
Helbing, D., Henneke, A., and Treiber, M. (1999). Phase diagram of traffic states in the presence of inhomogenities, Phys. Rev. Lett. 8, pp4360–4363.
Highway Capacity Manual (1965). HRB Spec. Rep. 87. U.S. Department of Commerce, Bureau of Public Road, Washington, D.C.
Kaumann, O., Froese, K., Chrobok, R., Wahle, J., Neubert, L., and Schreckenberg, M. (2000). On-line simulation of the freeway network of North Rhine-Westphalia, In Traffic and Granular Flow’ 99 (Helbing, D., Herrmann, H., Schreckenberg, M., and Wolf D.E.), Springer, Heidelberg, pp 351–356.
Kerner, B.S. and Rehborn, H. (1996). Experimental features and characteristics of traffic jams, Phys. Rev. E. 53, pp R1297–1300.
Kerner, B.S. and Rehborn, H. (1997). Experimental properties of phase transitions in traffic flow, Phys. Rev. Lett. 79, pp4030–4033.
Kerner, B.S., Rehborn, H., and Aleksic, M. (2000). Forecasting of traffic congestion, In Traffic and Granular Flow’ 99 (Helbing, D., Herrmann, H.J., Schreckenberg, M., and Wolf, D.E.), Springer, Heidelberg, pp339–344.
Kerner, B.S. (2001). Complexity of synchronized flow and related problems for basic assumptions of traffic flow theories, Network and Spatial Economics 1, pp35–76.
Knospe, W. (2002). Synchronized traffic: Microscopic modeling and empirical observations, Ph.D. Thesis, Gerhard-Mercator-University Duisburg, Germany (http://www.ub.uni-duisburg.de/ETD-db/theses/available/duett-08212002-212839/unrestricted/index.html).
Knospe, W., Santen, L., Schadschneider, A., and Schreckenberg, M. (2000). Towards a realistic microscopic description of highway traffic, J. Phys. A 33, L1–L6.
Knospe, W., Santen, L., Schadschneider, A., and Schreckenberg, M. (2001). Human behavior as origin of traffic phases, Phys. Rev. E 65, pp015101-1–4.
Miller A. (1961). A queuing model for road traffic flow, J. of the Royal Stat. Soc. B 23, pp64–75.
Nagel, K., Esser, J., and Rickert, M. (2000). Large-scale traffic simulations for transport planning, In Ann. Rev. of Comp. Phys. VII, Stauffer, D. (Ed.), World Scientific, Singapore, pp 151–202.
Nagel, K. and Schreckenberg, M. (1992). A cellular automaton model for freeway traffic, J. Physique I 2, pp2221–2229.
Nagel, K., Wolf, D.E., Wagner, P., and Simon, P. (1998). Two-lane traffic rules for cellular automata: A systematic approach, Phys. Rev. E 58, pp1425–1437.
Nair, A. S., Liu J.-C, Rilett, L, and Gupta, S. (2001). Non-linear analysis of traffic flow, In Proc. of the 4th International IEEE Conference on Intelligent Transportation Systems, Stone (B., Conroy, P., and Broggi, A.), IEEE, Oakland, pp683–687.
Pfefer, R.C. (1976). New safety and service guides for sight distances, Transportation Engineering Journal of American Society of Civil Engineers 102, pp683–697.
Rickert M. and Wagner P. (1996). Parallel real-time implementation of large-scale, route-plan-driven traffic simulation, Int. J. of Mod. Phys. C 7, pp133–153.
Schreckenberg, M., Neubert, L., and Wahle, J. (2001). Simulation of traffic in large road networks, Future Generation Computer Systems 17, pp649–657.
Schreckenberg, M. and Wolf, D.E. (Eds.) (1998). Traffic and Granular Flow’ 97, Springer, Singapore.
Smith, B. L., Williams, B. M., and Oswald, R. K. (2002). Comparison of parametric and nonparametric models for traffic flow forecasting, Transp. Res. C 10,7–8, pp 1133–1152.
Sun H., Liu H.X., and Ran B. (2003). Short term traffic forecasting using the local linear regression model, Accepted for Proceedings of the 82nd Transportation Research Board Annual Meeting and for Publication in the Transportation Research Record.
SURVIVE http://www.traffic.uni-duisburg.de/survive/.
Treiterer, J. (1975). Investigation of traffic dynamics by areal photogrammatic techniques. Tech. report, Ohio State University Tech. Rep. PB 246, Columbus, USA.
Van der Voort, M., Dougherty, M., and Watson, S. (1996). Combining Kohonen maps with ARIMA time series models to forecast traffic flow, Transp. Res. C 4,5, pp307–318.
Van Iseghem, S., and Danech-Pajouh, M. (1999). Forecasting traffic one or two days in advance-an intermodal approach, Recherche Transports Securite, 65, pp79–97.
Wahle, J., Annen, O., Schuster, C., Neubert, L., and Schreckenberg, M. (2001). A dynamic route guidance system based on real traffic data, Euro. J. Op. Res. 131, pp302–308.
Wild, D. (1997). Short-term forecasting based on a transformation and classification of traffic volume time series, Int. J. of Forecasting, 13, pp63–72.
Williams, B. M. (2001). Multivariate vehicular traffic flow prediction: an evaluation of ARIMAX modelling. In Proceedings of the 80th Annual Meeting of the Transportation Research Board, Mira Digital Publishing, Washington D.C..
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer Science+Business Media, Inc.
About this chapter
Cite this chapter
Schreckenberg, M., Pottmeier, A., Hafstein, S.F., Chrobok, R., Wahle, J. (2005). Simulation of the Autobahn Traffic in North Rhine-Westphalia. In: Kitamura, R., Kuwahara, M. (eds) Simulation Approaches in Transportation Analysis. Operations Research/Computer Science Interfaces Series, vol 31. Springer, Boston, MA. https://doi.org/10.1007/0-387-24109-4_8
Download citation
DOI: https://doi.org/10.1007/0-387-24109-4_8
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-24108-1
Online ISBN: 978-0-387-24109-8
eBook Packages: Business and EconomicsEconomics and Finance (R0)