Assignment of Seasonal Factor Categories to Urban Coverage Count Stations Using a Fuzzy Decision Tree
Publication: Journal of Transportation Engineering
Volume 132, Issue 8
Abstract
Seasonal factor is an important parameter for converting coverage counts to annual average daily traffic (AADT). There have been many studies on establishing seasonal factor (SF) categories, but there is a limited understanding of how to assign SF groups to short-term count sites. In the current practice, established factor groups are typically assigned based on function class, the physical proximity of short count sites to a permanent traffic count site, and other engineering judgment. There is no sound theoretical basis or rules to guide the seasonal factor assignment process, which results in inaccurate AADT estimates for coverage count sites. This paper describes a data-driven procedure for assigning a seasonal factor category to a given portable count site by taking into consideration the similarities between the characteristics of permanent count sites in the seasonal factor group and the portable count site. A fuzzy decision tree was constructed based on the known SF groupings of permanent count sites and their land use attributes. The decision tree was then applied to determine the SF category for a given portable count site in Broward, Miami-Dade, and Palm Beach counties in Florida.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This research was sponsored by a grant from the FDOT Research Office. The opinions, findings, and conclusions expressed in this paper are those of the writers and not necessarily those of the FDOT. The writers thank Mr. Doug O’Hara, Mr. Harshad Desai, and Mr. Richard Reel of FDOT for their input during the development of this paper. Mr. Alexander Rodriguez and Mr. Brent Smith of the Planning and Environmental Management Office, FDOT District 4, provided helpful comments. Finally, the writers are grateful to the reviewers for their constructive suggestions to make this paper more readable and clear.
References
Ban, X., Liu, H. X., Hu, B., He, R. R., and Ran, B. (2004). “Traffic assignment model with fuzzy travel time perceptions.” presented at the 83rd annual meeting of the Transportation Research Board, Washington, D.C.
Bellamy, P. H. (1978). “Seasonal variations in traffic flows.” Supplementary Rep. No. 437, Prepared for the Dept. of the Environment and the Dept. of Transport, Prepared by Traffic Engineering Dept., Transport and Road Research Laboratory, Berkshire, Great Britain.
Chakroborty, P., and Kikuchi, S. (2003). “Calibrating the membership functions of the fuzzy inference system: Instantiated by car-following data.” Transp. Res., Part C: Emerg. Technol., 11, 91–119.
Davis, G. A. (1997). “Estimation theory approach to monitoring and updating average daily traffic.” Rep. Prepared for Minnesota Dept. of Transportation, St. Paul, Minn.
Davis, G. A., and Guan, Y. (1996). “Bayesian assignment of coverage count locations to factor groups and estimation of mean daily traffic.” Transportation Research Record. 1542, Transportation Research Board, Washington, D.C., 30–37.
Faghri, A., and Hua, J. (1995). “Roadway seasonal classification using neural networks.” J. Comput. Civ. Eng., 9(3), 209–215.
Faghri, A., and Demetsky, M. J. (1990). “Expert system for traffic control in work zones.” J. Transp. Eng., 116(6), 759–769.
Faghri, A., Glaubitz, M., and Parameswaran, J. (1986). “Development of integrated traffic monitoring system for Delaware.” Transportation Research Record. 1536, Transportation Research Board, Washington, D.C., 40–44.
Fang, F. C., Elefteriadou, L., Pecheux, K. K., and Pietrucha, M. T. (2003). “Using fuzzy clustering of user perception to define levels of service at signalized intersections.” J. Transp. Eng., 129(6), 657–663.
Federal Highway Administration (FHwA). (2001). Traffic monitoring guide, U.S. Dept. of Transportation, Washington, D.C.
Florida Dept. of Transportation (FDOT, (2002). Project traffic forecasting handbook, Tallahassee, Fla.
Hawas, Y. E. (2004). “Development and calibration of route choice utility models: Neurofuzzy approach.” J. Transp. Eng. 130(2), 171–182.
Janikow, C. Z. (1998). “Fuzzy decision trees: Issues and methods.” IEEE Trans. Syst., Man, Cybern., Part B: Cybern., 28(1), 1–14.
Kikuchi, S., and Rhee, J. (2003). “Adjusting trip rates in the cross-classification table by using the fuzzy optimization method.” Transportation Research Record. 1836, Transportation Research Board, Washington, D.C., 76–82.
Li, M.-T., Zhao, F., and Wu, Y. (2004). “Application of regression analysis for identifying factors affecting seasonal traffic fluctuations in Southeast Florida.” Transportation Research Record. 1870, Transportation Research Board, Washington, D.C., 153–161.
McDonald, S. M. (1999). “Prototype demonstration of a geographic information system application for the seasonal analysis of traffic data, development of seasonal factors and seasonal adjustment of roadways.” Rep. prepared for North Carolina Dept. of Transportation, Raleigh, N.C.
Naranjo, J. E., González, C., Reviejo, J., García, R., and de Pedro, T. (2003). “Adaptive fuzzy control for intervehicle gap keeping.” IEEE Trans. Intell. Transp. Syst., 4(3), 132–142.
Niittymaki, J., and Pursula, M. (2000). “Signal control using fuzzy logic.” Fuzzy Sets Syst., 116, 11–22.
Olaru, C., and Wehenkel, L. (2003). “A complete fuzzy decision tree technique.” Fuzzy Sets Syst., 138, 221–254.
Peeta, S., and Zhou, W. (2004). “Minimizing truck-car conflicts on highways.” Final Rep. FHwA/IN/JTRP 2004/16, Prepared for Indiana Dept. of Transportation, the U.S. Dept. of Transportation Federal Highway Administration, prepared by School of Civil Engineering, Purdue Univ., West Lafayette, Ind.
Peng, Y. H., and Flach, P. A. (2001). “Soft discretization to enhance the continuous decision tree induction.” Proc., European Conference on Machine Learning (ECML)/(European Conference on Principles and Practice of Knowledge Discovery in Database (PKDD)-2001 Workshop, September 3–7, 2001, Freiburg, Germany.
Quinlan, J. R. (1986). “Induction of decision trees.” Mach. Learn., 1, 81–106.
Rilett, L. R., and Park, D. (2001). “Incorporating uncertainty and multiple objectives in real-time route selection.” J. Transp. Eng., 127(6),531–539.
Sharma, S. C. (1983). “Improved classification of canadian primary highways according to type of road use.” Can. J. Civ. Eng., 10(3), 497–509.
Sharma, S. C., Lingras, P. J., Hassan, M. U., and Murthy, N. A. S. (1986). “Road classification according to driver population.” Transportation Research Record. 1090, Transportation Research Board, Washington, D.C., 61–69.
Tawfik, A. Y., Abdulhai, B., Peng, A., and Tabib, S. M. (2004). “Using decision trees to improve the accuracy of vehicle signature reidentification.” Transportation Research Record. 1886, Transportation Research Board, Washington, D.C., 24–33.
Thill, I.-C., and Wheeler, A. (2000). “Tree induction of spatial choice behavior.” Transportation Research Record. 1719, Transportation Research Board, Washington, D.C., 250–258.
Wei, C., and Tighe, S. (2004). “Development of preventive maintenance decision trees based on cost-effectiveness analysis: An ontario case study.” Transportation Research Record. 1866, Transportation Research Board, Washington, D.C., 8–19.
Wets, G., Vanhoof, K., Arentze, T., and Timmermans, H. (2000). “Identifying decision structures underlying activity patterns: An exploration of data mining algorithms.” Transportation Research Record. 1718, Transportation Research Board, Washington, D.C., 1–9.
Xie, C., Lu, J., and Parkany, E. (2003). “Work travel mode choice modeling with data mining: Decision trees and neural networks.” Transportation Research Record. 1854, Transportation Research Board, Washington, D.C., 56–61.
Xue, D., and Dong, Z. (2000). “An intelligent contraflow control method for real-time optimal traffic scheduling using artificial neural network, fuzzy pattern recognition, and optimization.” IEEE Trans. Control Syst. Technol., 8(1), 183–l91.
Yamamoto, T., Kitamura, R., and Fujii, J. (2002). “Drivers’ route choice behavior: Analysis by data mining algorithms.” Transportation Research Record. 1807, Transportation Research Board, Washington, D.C., 59–66.
Zadeh, L. A. (1965). “Fuzzy sets.” Information and Control, 8, 338–353.
Zhao, F., Li, M.-T., and Chow, L.-F. (2004). “Alternatives for estimating seasonal factors on rural and urban roads in Florida.” Rep. Prepared for Research Office, Florida Dept. of Transportation, Tallahassee, Florida. Available from ⟨http://www.dot.state.fl.us/research-center/ Completed_Proj/Summary_ PL/FDOT_BD015_03_rpt.pdf⟩.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 132 • Issue 8 • August 2006
Pages: 654 - 662
Copyright
© 2006 ASCE.
History
Received: Feb 1, 2005
Accepted: Jan 19, 2006
Published online: Aug 1, 2006
Published in print: Aug 2006
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.