Mining the Situation: Spatiotemporal Traffic Prediction with Big Data

IEEE Journal on Selected Topics in Signal Processing

Volumn 9, Issue 4, 2015, Pages 702-715

  Xu, Jie ^a   Deng, Dingxiong ^b   Demiryurek, Ugur ^b   Shahabi, Cyrus ^b   Schaar, Mihaela Van Der ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b University of Southern California   (United States)

Author keywords

big data; context aware; online learning; spatiotemporal; Traffic prediction

Indexed keywords

FORECASTING; REGIONAL PLANNING; SOCIAL NETWORKING (ONLINE); TRAFFIC CONTROL; URBAN PLANNING;

CONTEXT-AWARE; CURRENT TRAFFIC SITUATIONS; IMPLEMENTATION COMPLEXITY; LONG TERM PERFORMANCE; ONLINE LEARNING; SPATIOTEMPORAL; TRAFFIC INFORMATION; TRAFFIC PREDICTION;

BIG DATA;

EID: 84926504011     PISSN: 19324553     EISSN: None     Source Type: Journal    
DOI: 10.1109/JSTSP.2015.2389196     Document Type: Article

References (28)

1. What congestion means to your town, 2007 urban area totals, Texas Transportation Inst. [Online]. Available: http://mobility.tamu.edu/ums/congestion-data/tables/national/table-2.pdf
2. Annual vehicle miles of travel, Federal Highway Administration, 2003-02-14 [Online]. Available: http://www.fhwa.dot.gov/ohim/onh00/graph1.htm
3. The highway system, Federal Highway Administration, Office of Highway Policy Information [Online]. Available: http://www.fhwa. dot.gov/ohim/onh00/onh2p5.htm
4. W. Liu, Y. Zheng, S. Chawla, J. Yuan, and X. Xing, Discovering spatio-temporal causal interactions in traffic data streams, in Proc. KDD, 2011, pp. 1010-1018.
5. M. Miller and C. Gupta, Mining traffic incidents to forecast impact, in Proc. UrbComp '12, New York, NY, USA, 2012, pp. 33-40, ACM.
6. B. Pan, U. Demiryurek, and C. Shahabi, Utilizing real-world transportation data for accurate traffic prediction, in Proc. ICDM'12, Washington, DC, USA, 2012, pp. 595-604, IEEE Computer Society.
7. B. Pan, U. Demiryurek, C. Shahabi, and C. Gupta, Forecasting spatiotemporal impact of traffic incidents on road networks, in Proc. ICDM, 2013, pp. 587-596.
8. S. Lee and D. B. Fambro, Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting, in Proc. TRR98, 1998.
9. X. Li, Z. Li, J. Han, and J.-G. Lee, Temporal outlier detection in vehicle traffic data, in Proc. IEEE 25th Int. Conf. Data Eng. (ICDE '09), 2009, pp. 1319-1322.
10. G. Giuliano, Incident characteristics, frequency, and duration on a high volume urban freeway, Transportation Res. Part A: General, vol. 23, no. 5, 1989.
11. Y. Chung and W. Recker, A methodological approach for estimating temporal and spatial extent of delays caused by freeway accidents, IEEE Trans. Intell. Transport. Syst., vol. 13, no. 3, pp. 1454-1461, Sep. 2012.
12. B. Stephen, F. David, and W. S. Travis, Naive Bayesian classifier for incident duration prediction, in Transportation Res. Board, 2007.
13. W. Kim, S. Natarajan, and G.-L. Chang, Empirical analysis and modeling of freeway incident duration, in Proc. Intell. Transport. Syst. (ITSC 08), Oct. 2008, pp. 453-457.
14. J. Kwon, M. Mauch, and P. Varaiya, The components of congestion: delay from incidents, special events, lane closures, weather, potential ramp metering gain, and demand, in Proc. 85th Annu, Meeting Transport. Res., 2006.
15. L. Breiman, Bagging predictors, Mach. Learn., vol. 24, no. 2, pp. 123-140, 1996.
16. Y. Freund, H. S. Seung, E. Shamir, and N. Tishby, Selective sampling using the query by committee algorithm, Mach. Learn., vol. 28, no. 2-3, pp. 133-168, 1997.
17. W. Fan, S. J. Stolfo, and J. Zhang, The application of Adaboost for distributed, scalable and on-line learning, in Proc. 5th ACM SIGKDD Int. Conf. Knowl. Disc. Data Mining. ACM, 1999, pp. 362-366.
18. N. Littlestone and M. K. Warmuth, The weighted majority algorithm, Inf. Comput., vol. 108, no. 2, pp. 212-261, 1994.
19. A. Blum, Empirical support for winnow and weighted-majority algorithms: Results on a calendar scheduling domain, Mach. Learn., vol. 26, no. 1, pp. 5-23, 1997.
20. M. Herbster and M. K. Warmuth, Tracking the best expert, Mach. Learn., vol. 32, no. 2, pp. 151-178, 1998.
21. A. Fern and R. Givan, Online ensemble learning: An empirical study, Mach. Learn., vol. 53, no. 1-2, pp. 71-109, 2003.
22. S. Shalev-Shwartz, Y. Singer, N. Srebro, and A. Cotter, Pegasos: Primal estimated sub-gradient solver for SVM, Math. Program., vol. 127, no. 1, pp. 3-30, 2011.
23. A. Slivkins, Contextual bandits with similarity information, ArXiv Preprint ArXiv:0907.3986, 2009.
24. C. Tekin M. V. D. Schaar, Distributed online big data classification using context information, in Proc. 51st Annu. Allerton Conf. Commun., Control, Comput., 2013.
25. C. Tekin, S. Zhang, M. V. D. Schaar, Distributed online learning in social recommender systems, IEEE J. Sel. Topics Signal Process., vol. 8, no. 4, pp. 638-652, Aug. 2014.
26. J. Xu, C. Tekin, M. V. D. Schaar, Learning optimal classifier chains for real-time big data mining, in Proc. 51st Annu. Allerton Conf. Commun., Control, Comput., 2013.
27. J. Xu, M. v. d. Schaar, J. Liu, and H. Li, Timely popularity forecasting based on social networks, in Proc. IEEE Infocom, 2015.
28. C. Tekin M. V. D. Schaar, Discover the expert: Context-adaptive expert selection for medical diagnosis, IEEE J. Emerging Topics Comput., 2015, to be published.