Weighing Communication Overhead against Travel Time Reduction in Advanced Traffic Information Systems

Advanced traffic information systems can assist drivers in reducing their travel times by making better use of available road capacity. In assessing their practical applicability, however, it is important to assess the overhead that various advanced traffic information systems bring. This paper evaluates the communication overhead for a decentralized, delegate multi-agent systems based advanced traffic information system, and for a centralized system.We document the relationship between the communication overhead and travel time reduction for both systems. This analysis can help weighing both factors when designing a practical traffic information system in a real-world scenario.

[1]  Tom Holvoet,et al.  Exploiting the Environment for Coordinating Agent Intentions , 2006, EUMAS.

[2]  Sherburne F. Cooke The Clear Lake Example: An Ecological Approach to Pest Management , 1981 .

[3]  Danko A. Roozemond FORECASTING TRAVEL TIMES BASED ON ACTUATED AND HISTORIC DATA. , 1997 .

[4]  Danny Weyns,et al.  A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multiagent Systems , 2011, IEEE Transactions on Intelligent Transportation Systems.

[5]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[6]  Jason Noble,et al.  Distributed and Centralized Task Allocation: When and Where to Use Them , 2010, 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshop.

[7]  Stephen John Turner,et al.  Symbiotic Simulation Systems: An Extended Definition Motivated by Symbiosis in Biology , 2008, 2008 22nd Workshop on Principles of Advanced and Distributed Simulation.

[8]  Léon J. M. Rothkrantz,et al.  Travel time prediction for dynamic routing using Ant Based Control , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[9]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Robert L. Smith,et al.  Link travel time prediction for decentralized route guidance architectures , 2000, IEEE Trans. Intell. Transp. Syst..

[11]  Dongjoo Park,et al.  Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network , 1999 .

[12]  Danny Weyns,et al.  Anticipatory Vehicle Routing using Delegate Multi-Agent Systems , 2007, 2007 IEEE Intelligent Transportation Systems Conference.