Community Traffic: a technology for the next generation car navigation

• Autorzy: Dembczyński K. , Gaweł P. , Jaszkiewicz A. , Kotłowski W. , Kubiak M. , Susmaga R. , Wesołek P. , Wojciechowski A. , Zielniewicz P.
• Języki publikacji: EN

Abstrakty

EN

The paper presents the NaviExpert’s Community Traffic technology, an interactive, community–based car navigation system. Using data collected from its users, Community Traffic offers services unattainable to earlier systems. On the one hand, the current traffic data are used to recommend the best routes in the navigation phase, during which many potentially unpredictable traffic-delaying and traffic-jamming events, like unexpected roadworks, road accidents, or diversions, can be taken into account and thereby successfully avoided. On the other hand, a number of istinctive features, like immediate location of various traffic dangers, are offered. Using exclusively real-life data, provided by NaviExpert, the paper presents two illustrative case studies concerned with experimental evaluation of solutions to computational problems related to the community-based services offered by the system.

Słowa kluczowe

EN

community traffic; satellite car navigation; reliability analysis; travel time prediction

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2012
• Tom: Vol. 41, no. 4
• Strony: 867--883
• Opis fizyczny: Bibliogr. 20 poz.

Twórcy

autor

Dembczyński K.

• Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland

autor

Gaweł P.

• NaviExpert Sp. z o. o., Dobrzyckiego 4, 61-692 Poznań, Poland

autor

Jaszkiewicz A.

• Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland

autor

Kotłowski W.

• Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland

autor

Kubiak M.

• NaviExpert Sp. z o. o., Dobrzyckiego 4, 61-692 Poznań, Poland

autor

Susmaga R.

• Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland

autor

Wesołek P.

• Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland

autor

Wojciechowski A.

• Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland

autor

Zielniewicz P.

• Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965 Poznań, Poland

Bibliografia

• 1. Billings, D. and Yang, J. (2006) Application of the ARIMA models to urban roadway travel time prediction — a case study. IEEE International Conference on Systems, Man and Cybernetics 3, 2529–2534.
• 2. Caschera, M.C., Ferri, F., Grifoni, P. and Guzzo, T. (2009) Multidimensional visualization system for travel social networks. Proceedings of the 6th International Conference on Information Technology: New Generations IEEE Computer Society Washington, DC, 1510–1516.
• 3. Dempster, A., Laird, N. and Rubin, D. (1997) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39, 1, 1–38.
• 4. Giaglis, G., Minis, I., Tatarakis, A. and Zeimpekis, V. (2004) Minimizing logistics risk through real-time vehicle routing and mobile technologies: Research to date and future trends. International Journal of Physical Distribution and Logistics Management 34, 9, 749–764.
• 5. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I.H. (2009) The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11, 1, 10–18.
• 6. Hastie, T., Tibshirani, R. and Friedman, J.H. (2003) Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag.
• 7. Hilligoss, B. and Rieh, S.Y. (2008) Developing a unifying framework of credibility assessment: Construct, heuristics, and interaction in context. Information Processing and Management 44, 1467–1484.
• 8. Kubiak, M. (2007) Credibility assessment in an on-line car navigation system by means of the Expectation Maximization algorithm. Foundations of Computing and Decision Sciences 32, 4, 275–294.
• 9. Liu, H., van Lint, H., van Zuylen, H. and Zhang, K. (2006) Two distinct ways of using Kalman filters to predict urban arterial travel time. Proceedings of IEEE Intelligent Transportation Systems Conference. IEEE Toronto, 845–850.
• 10. Mukai, N., Watanabe, T. and Feng, J. (2005) Proactive route planning based on expected rewards for transport systems. Proceedings of 17th IEEE International Conference on Tools with Artificial Intelligence. Hong Kong, 51–57.
• 11. Nunez, R., Wickramarathne, T., Premaratne, K., Murthi, M., Kuebler, S., Scheutz, M. and Pravia, M. (2012) Credibility assessment and inference for fusion of hard and soft information. Advances in Design for Cross-Cultural Activities, Part I. CRC Press, 96–105.
• 12. Ramos, C., Augusto, J.C. and Shapiro, D. (2008) Ambient intelligence: the next step for artificial intelligence. IEEE Intelligent Systems 23, 2, 15–18.
• 13. Rice, J. and van Zwet, E. (2004) A simple and effective method for predicting travel times on freeways. IEEE Transactions on Intelligent Transportation Systems 5, 3, 200–207.
• 14. Tseng, S. and Fogg, B.J. (1999) Credibility and computing technology. Communications of the ACM 42, 5, 39–44.
• 15. van Lint, J., Hoogendoorn, S. and van Zuylen, H. (2005) Accurate freeway travel time prediction with state-space neural networks under missing data. Transportation Research, Part C 13, 5–6, 347–369.
• 16. Wan, K. and Kornhauser, A. (2010) Turn-by-turn routing decision based on copula travel time estimation with observable floating-car data. Transportation Research Board 89th Annual Meeting Paper # 10-2723.
• 17. Wolf, J., Guensler, R. and Bachman, W. (2001) Elimination of the Ravel diary: Experiment to derive trip purpose from global positioning system travel data. Transportation Research Record: Journal of the Transportation Research Board 1768, 125–134.
• 18. Zheng, Y., Zhang, L., Xie, X. and Ma, W.Y. (2009) Mining interesting locations and travel sequences from gps trajectories. Proceedings of the 18th International Conference on World Wide Web ACM New York, NY, 791–800.
• 19. Zhu, T., Kong, X., Lv, W., Zhang, Y. and Du, B. (2010) Travel time prediction for float car system based on time series. Proceedings of the 12th International Conference on Advanced Communication Technology 2, 1503–1508.
• 20. Zografos, K.G., Androutsopoulos K.N., and Vasilakis, G.M. (2002) A real-time decision support system for roadway network incident response logistics. Transportation Research, Part C 10, 1–18.