How many probe vehicles are enough for identifying traffic congestion?-a study from a streaming data perspective

Frontiers of Earth Science

Volumn 7, Issue 1, 2013, Pages 34-42

  Wang, Handong ^a,c   Yue, Yang ^a,b   Li, Qingquan ^a,b  

^a WUHAN UNIVERSITY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^c CHANGJIANG WATER RESOURCES COMMISSION   (China)

Author keywords

floating car data; streaming data; traffic congestion; vehicle trajectory data

Indexed keywords

ACCURACY ASSESSMENT; DATA PROCESSING; TRAFFIC CONGESTION; TRANSPORT VEHICLE;

EID: 84873209690     PISSN: 20950195     EISSN: 20950209     Source Type: Journal    
DOI: 10.1007/s11707-012-0343-x     Document Type: Article

References (23)

1. Aggarwal C C (2007). An introduction to data streams. Data Streams, 31: 1-8.
2. Ankerst M, Breunig M M, Kriegel H P, Sander J (1999). OPTICS: ordering points to identify the clustering structure. In: Proceedings of ACM SIGMOD'99 International Conference on Management of Data, New York: ACM.
3. Barbará D (2002). Requirements for clustering data streams. ACM SIGKDD Explorations Newsletter, 3(2): 23-27.
4. Cheu R L, Xie C, Lee D (2002). Probe vehicles population and sample size for arterial speed estimation. Computer Aided Civil and Infrastruct Engineering, 17(1): 53-60.
5. Chi C (2007). The research about the traffic congestion evaluation system of sections in Beijing. Dissertation for Master Degree, Beijing: Beijing Jiaotong University.
6. Ester M, Kriegel H P, Sander J, Xu X (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of ACM KDD'96, Menlo Park, CA: AAAI Press.
7. Fabritiis C D, Ragona R, Valenti G (2008). Traffic estimation and prediction based on real time floating car data. In: Proceedings of International IEEE Conference on Intelligent Transportation Systems, 12-15 Oct. 2008, Beijing, China.
8. Guha S, Meyerson A, Mishra N, Motwani R, O'Callaghan L (2003). Clustering data streams: theory and practice. IEEE Transactions on Knowledge and Data Engineering, 15(3): 515-528.
9. Hartigan J, Wong M (1979). Algorithm as136: a k-means clustering algorithm. Journal of the Royal Statistical Society, Series C (Applied Statistics), 28(1): 100-108.
10. Hong J, Zhang X, Wei Z, Li L, Ren Y (2007). Spatial and temporal analysis of probe vehicle-based sampling for real-time traffic information system. In: Proceedings of International IEEE Conference on Intelligent Vehicles Symposium, 13-15 Jun. 2007, Istanbul, Turkey.
11. Kerner B S, Demir C, Herrtwich R G, Klenov S L, Rehborn H, Aleksic M, Haug A (2005). Traffic state detection with floating car data in road networks. In: Proceedings of International IEEE Conference on Intelligent Transportation Systems, 13-16 Sept. 2005, Vienna, Austria.
12. Kong Q J, Li Z P, Chen Y K, Liu Y C (2009). An approach to urban traffic state estimation by fusing multisource information. IEEE Transactions on Intelligent Transportation Systems, 10(3): 499-511.
13. Lu H P, Zhu J (2001). Analysis of the Urban Traffic. Beijing: China Water Power Press (in Chinese).
14. Nanthawichit C, Nakatsuji T, Suzuki H (2003). Application of probe vehicle data for real-time traffic state estimation and short-term travel time prediction on a freeway. In: TRB 2003 Annual Meeting CDROM, 11-15 Jan. 2003, Washington, DC, USA.
15. Pattara-atikom W, Pongpaibool P, Thajchayapong S (2006). Estimating road traffic congestion using vehicle velocity. In: Proceedings of International Conference ITS Telecommunications, 21-23 Jun. 2006, Chengdu, China.
16. Sananmongkhonchai S, Tangamchit P, Pongpaibool P (2008). Road traffic estimation from multiple GPS data using incremental weighted update. In: Proceedings of the 8th International Conference ITS Telecommunications, 24 Oct. 2008, Phuket, Thailand.
17. Shi W, Kong Q (2008). A GPS/GIS integrated system for urban traffic flow analysis. In: Proceedings of International IEEE Conference on Intelligent Transportation Systems, 12-15 Oct. 2008, Beijing, China.
18. Srinivasan K, Jovanis P (1996). Determination of the number of probe vehicles required for reliable travel time measurement in an urban network. Transportation Research Record, 1537: 15-22.
19. Wang W, Yang J, Muntz R (1997). STING: a statistical information grid approach to spatial data mining. In: Proceedings of the 23rd VLDB Conference, 25-29 Aug., 1997, Athens, Greece.
20. Yoon J, Noble B, Liu M (2007). Surface street traffic estimation. In: Proceedings of International Conference Mobile Systems, Applications and Services, 11-14 Jun. 2007, San Juan, Puerto Rico.
21. Zhang C B, Yang X, Yan X (2007a). Probe vehicles sample size for mobile traffic detection system. China Journal of Highway and Transport, 20(1): 96-101 (in Chinese).
22. Zhang C B, Yang X, Yan X (2007b). Method for floating cars sampling cycle optimization. Journal of Transportation Systems Engineering and Information, 7(3): 100-104.
23. Zou H X, Yue Y, Li Q Q, Yeh A G O (2012). An improved distance metric for the interpolation of link-based traffic data using Kriging: a case study of a large-scale urban road network. International Journal of Geographical Information Science, 26(4): 667-689.