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Influence of Participation Rates and Service Level Differentiation on Community Driven Predictions

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Advances in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection (PAAMS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8473))

Abstract

Anticipatory Vehicle Routing based on Intention Propagation (AVRIP) can help reduce drivers travel times and avoid forming congestion. The route guidance system uses information shared by participating drivers to predict future link traversal times, the time it will take a vehicle to traverse a road at a certain time in the future. Both participating and non-participating drivers benefit from these link travel time predictions. Participating drivers will receive the predictions and will adapt their route to avoid any congestion. Non-participating drivers experience less congestion because of these diversions.

The percentage of drivers participating in the AVRIP guidance is an important factor. This participation rate influences the efficiency of the system in two ways: it affects the accuracy of the predictions and it changes the number of drivers influenced by the predictions.

This paper provides a first study on the influence of the participation rate on the efficiency of AVRIP by varying the participation rate while keeping all other parameters constant in a simulated traffic network.

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References

  1. Claes, R., Holvoet, T., Weyns, D.: A decentralized approach for anticipatory vehicle routing using delegate multi-agent systems. IEEE Transactions on Intelligent Transportation Systems 12(2), 364–373 (2011)

    Article  Google Scholar 

  2. Wunderlich, K.E., Kaufman, D.E., Smith, R.L.: Link travel time prediction for decentralized route guidancearchitectures. IEEE Transactions on Intelligent Transportation Systems 1(1), 4–14 (2000)

    Article  Google Scholar 

  3. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 26(1), 29–41 (1996)

    Article  Google Scholar 

  4. Claes, R., Holvoet, T.: Ant colony optimization applied to route planning using link travel time predictions. In: 2011 IEEE International Symposium on Parallel & Distributed Processing Workshops, pp. 358–365 (2011)

    Google Scholar 

  5. Claes, R., Holvoet, T.: Ad hoc link traversal time predictions. In: Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems, pp. 1803–1808 (2011)

    Google Scholar 

  6. Wahle, J., Schreckenberg, M.: A multi-agent system for on-line simulations based on real-world traffic data. In: Proceedings of the 34th Annual Hawaii International Conference on System Sciences, p. 9 (2001)

    Google Scholar 

  7. Kai, C., Mo, Z.: Design of real-time traffic information prediction and simulation system based on aosvr and on-line learning. In: IEEE International Conference on Vehicular Electronics and Safety, ICVES 2006, pp. 189–193 (2006)

    Google Scholar 

  8. Adler, J.: Investigating the learning effects of route guidance and traffic advisories on route choice behavior. Transportation Research Part C: Emerging Technologies 9, 1–14 (2001)

    Article  Google Scholar 

  9. Peeta, S., Mahmassani, H.S.: Multiple user classes real-time traffic assignment for online operations: A rolling horizon solution framework. Transportation Research Part C: Emerging Technologies 3, 83–98 (1995)

    Article  Google Scholar 

  10. Claes, R., Holvoet, T.: Gridlock: A microscopic traffic simulation platform. In: International Conference on Models and Technologies for Intelligent Transportation Systems (2011)

    Google Scholar 

  11. Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Phys. Rev. E 62, 1805–1824 (2000)

    Article  Google Scholar 

  12. Kesting, A., Treiber, M., Helbing, D.: General lane-changing model mobil for car-following models. Transportation Research Record: Journal of the Transportation Research Board 1999, 86–94 (2007)

    Article  Google Scholar 

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© 2014 Springer International Publishing Switzerland

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Claes, R., Van den Berghe, K., Holvoet, T. (2014). Influence of Participation Rates and Service Level Differentiation on Community Driven Predictions. In: Demazeau, Y., Zambonelli, F., Corchado, J.M., Bajo, J. (eds) Advances in Practical Applications of Heterogeneous Multi-Agent Systems. The PAAMS Collection. PAAMS 2014. Lecture Notes in Computer Science(), vol 8473. Springer, Cham. https://doi.org/10.1007/978-3-319-07551-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-07551-8_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07550-1

  • Online ISBN: 978-3-319-07551-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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