Long-Term Trend of Temporal Distribution of Roadway Traffic
Publication: Journal of Transportation Engineering
Volume 141, Issue 2
Abstract
This paper investigates the long-term trend of temporal distribution of roadway traffic using hourly traffic-count data collected in Seoul, South Korea, from 1995 to 2010. Interpolations using quadratic local polynomial regressions and a Gaussian mixture model were applied in order to identify changes in temporal distributions at a scale of less than one hour. The identified changes in distribution characteristics suggested that more attention should be given to outbound traffic, which appeared to be worsening because of the increased population and jobs outside of Seoul. Although this study did not attempt to thoroughly investigate the causal relationship between the changes of temporal distributions and the associated factors, the framework and methodology suggested are expected to play a foundational role in identifying the long-term trend of traffic flow using archived data.
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References
Allen, W. G., Jr., and Schultz, G. W. (1996). “Congestion-based peak spreading model.”, Transportation Research Board, Washington, DC, 8–15.
Hastie, T., Tibshirani, R., and Freidman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction, Springer, New York.
Joo, Y., Jang, M., and Jun, C. (2010). “Developing a time-series visualization technique using a temporal GIS database for urban growth simulation.” Int. J. Urban Sci., 14(1), 98–110.
Jun, M., Kim, J., Kwon, J., and Jeong, J. (2013). “The effects of high-density suburban development on commuter mode choices in Seoul, Korea.” Cities, 31, 230–238.
Ko, J., Hunter, M., and Guensler, R. (2008). “Measuring control delay components using second-by-second GPS speed data.” J. Transp. Eng., 338–346.
Korea Ministry of Land, Infrastructure, and Transport. (2010). “Intelligent transportation systems standard node link.” 〈http://nodelink.its.go.kr/data/data01.aspx〉 (Dec. 15, 2012).
Loudon, W. R., Ruiter, E. R., and Schlappi, M. L. (1988). “Predicting peak spreading under congested conditions.”, Transportation Research Board, Washington, DC, 1–9.
Park, B., Santra, P., Yun, I., and Lee, D. (2004). “Optimization of time-of-day breakpoints for better traffic signal control.”, Transportation Research Board, Washington, DC, 217–223.
Park, B., Zhang, Y., and Lord, D. (2010). “Bayesian mixture modeling approach to account for heterogeneity in speed data.” Transp. Res. B, 44(5), 662–673.
Smith, B., Scherer, W., Hauser, T., and Park, B. (2002). “Data-driven methodology for signal timing plan development: A computational approach.” Comput. Aided Civ. Infrastruct. Eng., 17(6), 387–395.
Stathopoulos, A., and Karlaftis, M. (2001). “Temporal and spatial variations of real-time traffic data in urban areas.”, Transportation Research Board, Washington, DC, 135–140.
Vaughan, R. (1970). “The distribution of traffic volumes.” Transp. Sci., 4(1), 97–110.
Wirasinghe, S., Kattan, L., Rahman, M., and Hubbell, J. (2013). “Bus rapid transit: A review.” Int. J. Urban Sci., 17(1), 1–31.
Zhang, J., Rufolo, A., Dueker, K., and Strathman, J. (2002). “The effects of roadway supply on peak narrowing.” J. Transp. Res. Forum, 41(3), 129–145.
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Information
Published In
Journal of Transportation Engineering
Volume 141 • Issue 2 • February 2015
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Dec 14, 2013
Accepted: Jul 1, 2014
Published online: Aug 14, 2014
Discussion open until: Jan 14, 2015
Published in print: Feb 1, 2015
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