Abstract
Growing cities and the increasing number of vehicles per inhabitant lead to a higher volume of traffic in urban road networks. As space is limited and the extension of existing road infrastructure is expensive, the construction of new roads is not always an option. Therefore, it is necessary to optimise the urban road network to reduce the negative effects of traffic, for example, pollution emission and fuel consumption. Urban road networks are characterised by their large number of signalised intersections. Until now, the optimisation of these signalisations is mostly done manually through traffic engineers. As urban traffic demands tend to change constantly, it is almost impossible to foresee all runtime situations at design time. Hence, an approach is needed that is able to react adaptively at runtime to optimise signalisations of intersections according to the monitored situation. The resilient traffic management system offers a decentralised approach with communicating intersections, which are able to adapt their signalisation dynamically at runtime and establish progressive signal systems (PSS) to optimise traffic flows and the number of stops per vehicle.
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Notes
- 1.
For instance, the cycle time is 2 min maximum in England; see http://www.bbc.com/news/magazine-23869955.
- 2.
We used, for example, traffic data from a census in 2009 provided by local authorities in Hamburg, Germany.
- 3.
The results are based on simulations of an intersection as part of a broader network from Hamburg, Germany (Fig. 5) which reflects the real topology and is configured based on census data and the actual traffic signalisation.
References
Akçelik, R.: Traffic signals: capacity and timing analysis. Technical Report 123, Australian Road Research Board (1981)
Ando, Y., et al.: Pheromone model: application to traffic congestion prediction. In: Engineering Self-Organising Systems – Third International Workshop (ESOA 2005). Lecture Notes in Artificial Intelligence, vol. 3910, pp. 182–196. Springer, Berlin (2006)
Barceló, J.: GETRAM/AIMSUN: a software environment for microscopic traffic analysis. In: Proceedings of the Workshop on Next Generation Models for Traffic Analysis, Monitoring and Management, held in Tucson (USA) in Sept 2001 (2001)
Becker, C., Hähner, J., Tomforde, S.: Flexibility in organic systems - remarks on mechanisms for adapting system goals at runtime. In: Proceedings of ICINCO 2012, pp. 287–292 (2012)
Bielefeldt, C., Condie, H.: COSMOS – Congestion Management Strategies and Methods in Urban Sites. Final report, The MVA Consultancy (1999)
Burton, P., Eveleigh, H., Faber, O.: The UK demonstration of TMC – the final results and update on progress. In: Proceedings of 11th Conference on Road Transport Information and Control, pp. 9–14. IEEE, New York (2002)
Busch, F., Kruse, G.: MOTION for SITRAFFIC – a modern approach to urban traffic control. In: Proceedings of IEEE Conference on Intelligent Transportation Systems, pp. 61–64. IEEE, New York (2001)
Chrobok, R., Wahle, J., Schreckenberg, M.: Traffic forecast using simulations of large scale networks. In: Proceedings of IEEE Intelligent Transportation Systems Conference, pp. 434–439. IEEE, New York (2001)
Chrobok, R., Pottmeier, A., Marinosson, S., Schreckenberg, M.: On-line simulation and traffic forecast: applications and results. In: Internet and Multimedia Systems and Applications (IMSA 2002), pp. 113–118 (2002)
Chrobok, R., Kaumann, O., Wahle, J., Schreckenberg, M.: Different methods of traffic forecast based on real data. Eur. J. Oper. Res. 155(3), 558–568 (2004)
Dijkstra, E.W.: Cooperating sequential processes. Technical Report EWD-123, Technische Universiteit Eindhoven (1965)
Howard, D., Roberts, S.C.: The prediction of journey times on motorways using genetic programming. In: Applications of Evolutionary Computing – EvoWorkshops 2002. Lecture Notes in Computer Science, vol. 2279, pp. 141–153. Springer, Berlin (2002)
Ishak, S., Alecsandru, C.: Optimizing traffic prediction performance of neural networks under various topological, input, and traffic condition settings. J. Transp. Eng. 130(4), 452–465 (2004)
Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE Comput. 36(1), 41–50 (2003)
Mazur, F., Chrobok, R., Hafstein, S.F., Pottmeier, A., Schreckenberg, M.: Future of traffic information – online-simulation of a large scale freeway network. In: Proceedings of IADIS, pp. 665–672 (2004)
Müller-Schloer, C.: Organic Computing: on the feasibility of controlled emergence. In: CODES and ISSS 2004 Proceedings, 8–10 Sept 2004, pp. 2–5. ACM Press, New York (2004)
Parkanyi, E., Xie, C.: A complete review of incident detection algorithms and their deployment: what works and what doesn’t. Technical Report NETCR 37, New England Transp. Consortium, Storrs (2005)
Payne, H.J., Knobel, H.C.: Development and testing of incident detection algorithm. FHWA Report FHWA RD 76 21, vol. 3, Federal Highway Administration, US Department of Transportation, Washington DC (1976)
Payne, H.J., Tignor, S.C.: Freeway incident-detection algorithms based on decision trees with states. TRB Research Record 682, Transportation Research Board, Washington DC (1978)
Prothmann, H., Tomforde, S., Branke, J., Hähner, J., Müller-Schloer, C., Schmeck, H.: Organic traffic control. In: Organic Computing – A Paradigm Shift for Complex Systems, chapter 5.1, pp. 431–446. Birkhäuser, Basel (2011)
Prothmann, H., Schmeck, H., Tomforde, S., Lyda, J., Hähner, J., Müller-Schloer, C., Branke, J.: Decentralised route guidance in organic traffic control. In: Proceedings of SASO’11, pp. 219–220. IEEE, New York (2011)
Richter, U., Mnif, M., Branke, J., Müller-Schloer, C., Schmeck, H.: Towards a generic observer/controller architecture for organic computing. In: Beiträge zur Jahrestagung der Gesellschaft für Informatik 2006, pp. 112–119 (2006)
Robertson, D.I., Bretherton, R.D.: Optimizing networks of traffic signals in real time – the SCOOT method. IEEE Trans. Veh. Technol. 40(1), 11–15 (1991)
Schmeck, H., Müller-Schloer, C.: A characterization of key properties of environment-mediated multiagent systems. In: Proceedings of EEMMAS 2007, pp. 17–38. Springer, Berlin/Heidelberg (2007)
Schmeck, H., Müller-Schloer, C., Çakar, E., Mnif, M., Richter, U.: Adaptivity and self-organization in organic computing systems. ACM Trans. Auton. Adapt. Syst. 5(3), 1–32 (2010)
Sims, A.G., Dobinson, K.W.: The Sydney coordinated adaptive traffic (SCAT) system – philosophy and benefits. IEEE Trans. Veh. Technol. 29(2), 130–137 (1980)
Sommer, M., Tomforde, S., Hähner, J.: Using a neural network for forecasting in an organic traffic control management system. In: Proceedings of ICAC ’13, International Workshop on Embedded Self-Organizing Systems (2013)
Sun, D., Benekohal, R.F., Waller, S.T.: Multiobjective traffic signal timing optimization using non-dominated sorting genetic algorithm. In: Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 198–203 (2003)
Tanenbaum, A.: Computer Networks. Prentice Hall Professional Technical Reference, 4th edn. Prentice Hall, Upper Saddle River (2002)
Thancanamootoo, B., Bell, M.G.H.: Automatic detection of traffic incidents on a signal-controlled road network. Technical Report H7UNDT RR076, University of Newcastle upon Tyne, Department of Civil Engineering (1988)
Tomforde, S.: Runtime Adaptation of Technical Systems. Südwestdeutscher Verlag für Hochschulschriften (2012)
Tomforde, S., Prothmann, H., Branke, J., Hähner, J., Müller-Schloer, C., Schmeck, H.: Possibilities and limitations of decentralised traffic control systems. In: International Joint Conference on Neural Networks (IJCNN), pp. 1–9 (2010)
Webster, F.V.: Traffic signal settings. Road Research Technical Paper No. 39, Road Research Laboratory, published by HMSO (1958)
Wedde, H., Farooq, M.: Beehive: routing algorithms inspired by honey bee behavior. Künstl. Intell. 19(4), 18–24 (2005)
Wedde, H., et al.: Highly dynamic and adaptive traffic congestion avoidance in real-time inspired by honey bee behavior. In: Mobilität und Echtzeit – Fachtagung der GI-Fachgruppe Echtzeitsysteme, pp. 21–31. Springer, Berlin (2007)
Wegener, A., Hellbrück, H., Fischer, S., Hendriks, B., Schmidt, C., Fekete, S.: Designing a decentralized traffic information system – autonomos. In: Kommunikation in Verteilten Systemen (KiVS), Informatik aktuell, pp. 309–315. Springer, Berlin/Heidelberg (2009)
Wilson, S.W.: The genetic algorithm and simulated evolution. In: ALIFE, pp. 157–166 (1987)
Wilson, S.W.: Classifier fitness based on accuracy. Evol. Comput. 3(2), 149–175 (1995)
Yasdi, R.: Prediction of road traffic using a neural network approach. Neural Comput. Appl. 8(2), 135–142 (1999)
Lanzi, P.L.: Learning Classifier Systems, From Foundations to Applications. Lecture Notes in Computer Science, vol. 1813. Springer, Berlin (2000)
Hollnagel, E., Woods, D.D., Leveson, N.: Resilience Engineering: Concepts and Precepts. Ashgate Pub Co., Aldershot (2006)
Kaplan, E.: Understanding GPS: Principles and Applications. Artech House Inc., Norwood (2006)
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Sommer, M., Tomforde, S., Hähner, J. (2016). An Organic Computing Approach to Resilient Traffic Management. In: McCluskey, T., Kotsialos, A., Müller, J., Klügl, F., Rana, O., Schumann, R. (eds) Autonomic Road Transport Support Systems. Autonomic Systems. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-25808-9_7
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