Advertisement

Data Signature-Based Time Series Traffic Analysis on Coarse-Grained NLEX Density Data Set

  • Reynaldo G. MaravillaJr.
  • Elise A. Tabanda
  • Jasmine A. Malinao
  • Henry N. Adorna
Part of the Communications in Computer and Information Science book series (CCIS, volume 266)

Abstract

In this study, we characterize traffic density modeled from coarse data by using data signatures to effectively and efficiently represent traffic flow behavior. Using the 2006 North Luzon Expressway Balintawak-North Bound (NLEX Blk-NB) hourly traffic volume and time mean speed data sets provided by the National Center for Transportation Studies (NCTS), we generate hourly traffic density data set. Each point in the data was represented by a 4D data signature where cluster models and 2D visualizations were formulated and varying traffic density behaviors were identified, i.e. high and low traffic congestions, outliers, etc. Best-fit curves, confidence bands and ellipses were generated in the visualizations for additional cluster information. We ascertain probable causes of the behaviors to provide insights for better traffic management in the expressway. Finally, from a finer-grained 6-minute interval NLEX Blk-NB density data set, the coarser-grained hourly density data set were validated for consistency and correctness of results.

Keywords

Data Signatures Traffic Density Analysis North Luzon Expressway Non-Metric Multidimensional Scaling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Maravilla, R., Tabanda, E., Malinao, J., Adorna, H.: Traffic Density Modeling on NLEX Time Series Data Segment. In: Proceedings of the National Conference for Information Technology Education (2011)Google Scholar
  2. 2.
    Malinao, J., Juayong, R.A., Corpuz, F.J., Yap, J.M., Adorna, H.: Data Signatures for Traffic Data Analysis. In: 7th National Conference on IT Education (2009)Google Scholar
  3. 3.
    Sigua, R.G.: Fundamentals of Traffic Engineering, 42–66 (2008)Google Scholar
  4. 4.
    Rakha, H., Wang, Z.: Estimating Traffic Stream Space-Mean Speed and Reliability from Dual and Single Loop Detectors (2005)Google Scholar
  5. 5.
    Pelleg, D., Moore, A.: X-means: Extending K-means with efficient Estimation of the Number of Clusters. In: Proceedings of the 17th International Conf. on Machine Learning (2000)Google Scholar
  6. 6.
    Wong, P., Foote, H., Leung, R., Adams, D., Thomas, J.: Data Signatures and Visualization of Scientific Data Sets. In: Pacific Northwest National Laboratory. IEEE, USA (2000)Google Scholar
  7. 7.
    Malinao, J., Juayong, R.A., Oquendo, E., Tadlas, R., Lee, J., Clemente, J., Gabucayan-Napalang, M.S., Regidor, J.R., Adorna, J.: Gabucayan-Napalang, Ma.S., Regidor, J.R., Adorna, J.: A Quantitative Analysis-based Algorithm for Optimal Data Signature Construction of Traffic Data Sets. In: Proceedings of the 1st AICS/GNU International Conference on Computers, Networks, Systems, and Industrial Engineering, CNSI 2011 (2011)Google Scholar
  8. 8.
    Malinao, J., Juayong, R.A., Becerral, J., Cabreros, K.R., Remaneses, K.M., Khaw, J., Wuysang, D., Corpuz, F.J., Hernandez, N.H., Yap, J.M., Adorna, A.: Patterns and Outlier Analysis of Traffic Flow using Data Signatures via BC Method and Vector Fusion Visualization. In: Proc. of the 3rd International Conference on Human-centric Computing, HumanCom-2010 (2010)Google Scholar
  9. 9.
    Malinao, J., Tadlas, R.M., Juayong, R.A., Oquendo, E.R., Adorna, H.: An Index for Optimal Data Signature-based Cluster Models of Coarse- and Fine-grained Time Series Traffic Data Sets. In: Proceedings of the National Conference for Information Technology Education (2011)Google Scholar
  10. 10.
    Johnson, R.: Visualization of Multidimensional Data with Vector-fusion. IEEE Trans., 298–302 (2000)Google Scholar
  11. 11.
    Cox, T., Cox, M.: Multidimensional Scaling, 42–69 (1994)Google Scholar
  12. 12.
    Oquendo, E.R., Clemente, J., Malinao, J., Adorna, H.: Characterizing Classes of Potential Outliers through Traffic Data Set Data Signature 2D nMDS Projection. Philippine Information Technology Journal 4(1) (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Reynaldo G. MaravillaJr.
    • 1
  • Elise A. Tabanda
    • 1
  • Jasmine A. Malinao
    • 1
  • Henry N. Adorna
    • 1
  1. 1.Department of Computer Science (Algorithms and Complexity Lab)University of the PhilippinesQuezon CityPhilippines

Personalised recommendations