Predicting traffic congestion in presence of planned special events

Proceedings: DMS 2014 - 20th International Conference on Distributed Multimedia Systems

Volumn , Issue , 2014, Pages 357-364

  Kwoczek, Simon ^a   Di Martino, Sergio ^a   Nejdl, Wolfgang ^b  

^a VOLKSWAGEN AG   (Germany)

^b L3S RESEARCH CENTER   (Germany)

Author keywords

Event analysis; Planned special event; Traffic prediction

Indexed keywords

ADVANCED TRAVELER INFORMATION SYSTEMS; FORECASTING; MULTIMEDIA SYSTEMS; NEAREST NEIGHBOR SEARCH; PATTERN RECOGNITION; ROADS AND STREETS;

EVENT ANALYSIS; K NEAREST NEIGHBOR ALGORITHM; PLANNED SPECIAL EVENT; RESEARCH ACTIVITIES; SPATIAL AND TEMPORAL RESOLUTIONS; TRAFFIC PREDICTION; TRANSPORTATION NETWORK; URBAN ENVIRONMENTS;

TRAFFIC CONGESTION;

EID: 84923885010     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. N. S. Altman. An introduction to kernel and nearestneighbor nonparametric regression. The American Statistician, 46(3):175–185, 1992.
2. R. Chrobok, O. Kaumann, J. Wahle, and M. Schreckenberg. Different methods of traffic forecast based on real data. European Journal of Operational Research, 155(3):558–568, 2004.
3. S. Clark. Traffic prediction using multivariate nonparametric regression. Journal of transportation engineering, 129(2):161–168, 2003.
4. P. Compieta, S. Di Martino, M. Bertolotto, F. Ferrucci, and T. Kechadi. Exploratory spatio-temporal data mining and visualization. Journal of Visual Languages and Computing, 18(3):255–279, 2007. Visual Languages and Techniques for Human-GIS Interaction.
5. A. Corazza, S. Di Martino, F. Ferrucci, C. Gravino, F. Sarro, and E. Mendes. Using tabu search to configure support vector regression for effort estimation. Empirical Software Engineering, 18(3):506–546, 2013.
6. W. M. Dunn and S. P. Latoski. Managing travel for planned special events. In Institute of Transportation Engineers (ITE) 2003 Technical Conference and Exhibit, number CD-020, 2003.
7. E. Horvitz, J. Apacible, R. Sarin, and L. Liao. Prediction, expectation, and surprise: Methods, designs, and study of a deployed traffic forecasting service. In In Twenty-First Conference on Uncertainty in Artificial Intelligence, 2005.
8. S. Ishak and H. Al-Deek. Performance evaluation of short-term time-series traffic prediction model. Journal of Transportation Engineering, 128(6):490–498, 2002.
9. J. Kwon, M. Mauch, and P. Varaiya. Components of congestion: Delay from incidents, special events, lane closures, weather, potential ramp metering gain, and excess demand. Transportation Research Record: Journal of the Transportation Research Board, 1959(1):84–91, 2006.
10. D. Lei-lei, G. Jin-gang, S. Zheng-liang, and Q. Hongtong. Study on traffic organization and management strategies for large special events. In System Science and Engineering (ICSSE), 2012 International Conference on, pages 432–436, June 2012.
11. J. W. C. Van Lint. Online learning solutions for freeway travel time prediction. Intelligent Transportation Systems, IEEE Transactions on, 9(1):38–47, March 2008.
12. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, and A. H. Byers. Big data: The next frontier for innovation, competition, and productivity. Technical report, McKinsey Global Institute, 2011.
13. S. Di Martino, S. Bimonte, M. Bertolotto, and F. Ferrucci. Integrating google earth within olap tools for multidimensional exploration and analysis of spatial data. In Enterprise Information Systems, volume 24 of Lecture Notes in Business Information Processing, pages 940–951. Springer Berlin Heidelberg, 2009.
14. M. Miller and C. Gupta. Mining traffic incidents to forecast impact. In Proceedings of the ACM SIGKDD International Workshop on Urban Computing, pages 33–40. ACM, 2012.
15. B. Pan, U. Demiryurek, and C. Shahabi. Utilizing realworld transportation data for accurate traffic prediction. In Data Mining (ICDM), 2012 IEEE 12th International Conference on, pages 595–604. IEEE, 2012.
16. B. Pan, U. Demiryurek, C. Shahabi, and C. Gupta. Forecasting spatiotemporal impact of traffic incidents on road networks. In Data Mining (ICDM), 2013 IEEE 13th International Conference on, 2013.
17. D. Park, L. Rilett, and G. Han. Spectral basis neural networks for real-time travel time forecasting. Journal of Transportation Engineering, 125(6):515–523, 1999.
18. TomTom International. White paper - how tomtoms hd traffic and iq routes data provides the very best routing. Technical report, TomTom International, 2009.
19. L. Vanajakshi and L.R. Rilett. A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed. In Intelligent Vehicles Symposium, 2004 IEEE, pages 194–199, June 2004.
20. C. Wu, J. Ho, and D.T. Lee. Travel-time prediction with support vector regression. Intelligent Transportation Systems, IEEE Transactions on, 5(4):276–281, Dec 2004.
21. X. Zhang and J. A. Rice. Short-term travel time prediction. Transportation Research Part C: Emerging Technologies, 11(34):187–210, 2003. Traffic Detection and Estimation.