Traffic zone division based on big data from mobile phone base stations

Transportation Research Part C: Emerging Technologies

Volumn 58, Issue , 2015, Pages 278-291

  Dong, Honghui ^a   Wu, Mingchao ^a   Ding, Xiaoqing ^a   Chu, Lianyu ^b,d   Jia, Limin ^a   Qin, Yong ^a   Zhou, Xuesong ^c  

^a BEIJING JIAOTONG UNIVERSITY   (China)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c ARIZONA STATE UNIVERSITY   (United States)

^d CLR Analytics Inc   (United States)

Author keywords

Call detail record (CDR) data; Mobile telephones; Traffic semantic analysis; Traffic zone attribute index; Traffic zone division; Travel patterns

Indexed keywords

BASE STATIONS; BIG DATA; CELLULAR TELEPHONES; CLOCK AND DATA RECOVERY CIRCUITS (CDR CIRCUITS); HOUSING; K-MEANS CLUSTERING; LAND USE; MOBILE TELECOMMUNICATION SYSTEMS; MOTOR TRANSPORTATION; ROADS AND STREETS; SEMANTICS;

CALL DETAIL RECORDS; MOBILE TELEPHONES; SEMANTIC ANALYSIS; TRAFFIC ZONES; TRAVEL PATTERNS;

DATA MINING;

CLUSTER ANALYSIS; GIS; MOBILE COMMUNICATION; REAL TIME; TRAFFIC MANAGEMENT; TRAVEL BEHAVIOR;

BEIJING [BEIJING (ADS)]; BEIJING [CHINA]; CHINA;

EID: 84940460451     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2015.06.007     Document Type: Article

References (34)

1. Ahas R., Silm S., Järv O., Saluveer E., Tiru M. Using mobile positioning data to model locations meaningful to users of mobile phones. J. Urban Technol. 2010, 17:3-27.
2. Assunção R.M., Neves M.C., Câmara G., da Costa Freitas C. Efficient regionalization techniques for socioeconomic geographical units using minimum spanning trees. Int. J. Geograph. Inform. Sci. 2006, 20. 797-81.
3. Breaban M., Luchian H. A unifying criterion for unsupervised clustering and feature selection. Pattern Recogn. 2011, 44(4):854-865.
4. Caceres N., Wideberg J.P., Benitez F.G. Deriving origin destination data from a mobile phone network. Intell. Transp. Syst., IET 2007, 1(1):15-26.
5. Calabrese F., Di Lorenzo G., Liu L., Ratti C. Estimating origin-destination flows using mobile phone location data. IEEE Pervasive Comput. 2011, 10(4):36-44.
6. Calabrese F., Diao M., Di Lorenzo G., Joseph F.J., Ratti C. Understanding individual mobility patterns from urban sensing data: a mobile phone trace example. Transp. Res. C 2013, 26:301-313.
7. De Dios Ortuzar J., Willumsen L.G. Modelling Transport [M] 1994, Wiley.
8. Deng W., Lei H., Zhou X. Traffic state estimation and uncertainty quantification based on heterogeneous data sources: a three detector approach. Transp. Res. B 2013, 57:132-157.
9. Dong, H.H., Wu, M.C., 2013. Urban residents travel analysis based on mobile communication data. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 2013, pp. 1487-1492.
10. Fort M., Sellarès J.A. Solving the k-influence region problem with the GPU. Inf. Sci. 2014, 269:255-269.
11. Gonzalez M.C., Hidalgo C.A., Barabasi A.L. Understanding individual human mobility patterns. Nature 2008, 453(7196):779-782.
12. Guo D., Wang H. Automatic region building for spatial analysis. Trans. GIS 2011, 15:29-45.
13. Han J., Kamber M., Pei J. Data mining: concepts and techniques 2006, M. Morgan Kaufmann.
14. Herrera J.C., Work D.B., Herring R., Ban X.Jeff, Jacobson Q., Bayen A.M. Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment. Transp. Res. C 2010, 18(4):568-583.
15. Hoteit S., Secci S., Sobolevsky S., Ratti C., Pujolle G. Estimating human trajectories and hotspots through mobile phone data. Comput. Netw. 2014, 64:296-307.
16. Isaacman S., Becker R., Cáceres R., Kobourov S., Martonosi M., Rowland J., Varshavsky A. Identifying important places in people's lives from cellular network data. Pervasive Computing 2011, 133-151. Springer, Berlin Heidelberg.
17. Kastrinaki V., Zervakis M., Kalaitzakis K. A survey of video processing techniques for traffic applications. Image Vis. Comput. 2003, 21(4):359-381.
18. Li X., Yang X., Chen H. Study on traffic zone division based on spatial clustering analysis. Comput. Eng. Appl. 2009, 45(5):19-22.
19. Martínez, L.M.G., 2006. TAZ Delineation and Information Loss in Transportation Planning Studies, Doctor, Instituto Superior Técnico, Universidade Técnica de Lisboa.
20. Miller H.J., Han J.W. Geographic Data Mining and Knowledge Discovery 2009, CRC Press.
21. Pan C.X., Lu J.A., Di S., Ran B. Cellular-based data-extracting method for trip distribution. Transport. Res. Board 2006, 1945:33-39.
22. Rahmani M., Koutsopoulos H.N. Path inference from sparse floating car data for urban networks. Transp. Res. C 2013, 30:41-54.
23. Ran B. Mobile data application in traffic survey and traffic planning. Urban Traffic 2013, 72-81.
24. Ratti C., Pulselli R.M., Williams S., Frenchman D. Mobile landscapes: using location data from cell phones for urban analysis. Environ. Plann. B: Plann. Des. 2006, 33(5):727-748.
25. Shao C. Traffic Planning, M 2004, China Railway Publishing House.
26. Shirabe T. A model of contiguity for spatial unit allocation. Geograph. Anal. 2005, 37(1):2-16.
27. Sohn K., Kim D. Dynamic origin-destination flow estimation using cellular communication system. IEEE Trans. Vehicular Technol. 2008, 57(5):2703-2713.
28. Song, L., 2011. Theoretical Analyses and Research on Division Method for Traffic Zone [A Dissertation Submitted for the Degree of Master]. Chang'an University.
29. Song C., Qu Z., Blumm N., Barabasi A.L. Limits of predictability in human mobility. Science 2010, 327(5968):1018-1021.
30. Sun D.H., Chen W.X. Expressway automatic incident detection method using mobile phone positioning. J. Highway Transport. Res. Dev. 2006, 2:133-136.
31. USDOT FHWA, 2006, Traffic Detector Handbook: Third Edition - Volume II, Publication No. FHWA-HRT-06-139.
32. Weichselbraun A., Gindl S., Scharl A. Enriching semantic knowledge bases for opinion mining in big data applications. Knowl.-Based Syst. 2014, 69:78-85.
33. White, J., Wells, I., 2002. Extracting origin destination information from mobile phone data. In: International Conference on Road Transportation and Control, London, UK, March 19-21, 2002, pp. 30-34.
34. Xing T., Zhou X., Taylor J. Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: an information-theoretic modeling approach. Transp. Res. B 2013, 57:66-90.