Browse > Article
http://dx.doi.org/10.5351/KJAS.2016.29.5.949

Cluster analysis for highway speed according to patterns and effects  

Kim, Byungsoo (Department of Statistics, Inje University)
An, Soyoung (Department of Statistics, Inje University)
Son, Jungmin (Department of Statistics, Inje University)
Park, Hyemi (Department of Statistics, Inje University)
Publication Information
The Korean Journal of Applied Statistics / v.29, no.5, 2016 , pp. 949-960 More about this Journal
Abstract
This paper uses all sections of highway data (VDS) for two years (Jan. 2014-Dec. 2015), with 15 minute units. The first purpose of this study is to find clusters with similar patterns that appear repeatedly with time variables of month, week and hour. The cluster analysis results indicate a variety of patterns of average traffic speeds by time variables depending on the clusters; subsequently, these can be utilized to model for the forecast of the speed at a specific time. The second purpose is to do cluster analysis for grouping sections by effect nets that are closely related to each other. For the similarity measure we use cross-correlation functions calculated after pre-whitening the speed of each section. The cluster analysis gets 19 clusters, and sections within a cluster are geographically close. These results are expected to help to forecast a real-time speed.
Keywords
cluster analysis; cross-correlation function; pattern; sections of highway; speed;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 AASHTO (1994). A Policy on Geometric Design of Highway and Streets, Washington, D.C.
2 Cho, J., Kim, B., Kim, S., and Kang, W. (2011). Development of a daily pattern clustering algorithm using historical profiles, Journal of the Korea Institute of ITS, 10, 11-23.
3 Choi, B., Kang, H., Lee, S., and Han, S. (2009). A study for traffic forecasting using traffic statistic information, The Korean Journal of Applied Statistics, 22, 1177-1190.   DOI
4 Flaherty, J. (1993). Cluster analysis of Arizona automatic traffic record data, Transportation Research Record, 1410, 93-99.
5 Kim, S. and Cho, J. (2008). A comparative study on a hierarchical clustering method for road classification by traffic characteristics, Journal of Engineering & Technology, 17, 49-58.
6 Korea Expressway Corporation (2015). 2014 highway traffic statistics.
7 Lee, J., Do, M., Kim, S., and Ryu, S. (2003). Real-time adjustment of traffic volume-based on the national highway route 3, The Korean Journal of Applied Statistics, 16, 203-215.   DOI
8 Lee, M., Lee, S., Namkoong, S., and Choi, K. (2014). Study on the classification methodology for DSRC travel speed patterns using decision trees, Journal of the Korea Institute of ITS, 13, 1-11.
9 Wei, W. W. (2006). Time Series Analysis (2nd Ed.), Addison-Wesley, Redwood City, California.