Determining the optimal number of seasonal adjustment factor groupings when estimating annual average daily traffic and investigating their characteristics

References

• AASHTO. 1992. Joint Task Force on Traffic Monitoring of the AASHTO Highway Subcommittee on Traffic Engineering Guidelines for Traffic Data Programs. Washington, DC: American Association of State Highway and Transportation Officials.
   Google Scholar
• AASHTO. 2009. AASHTO Guidelines for Traffic Data Programs. 2nd ed. Washington, DC: American Association of State Highway and Transportation Officials.
   Google Scholar
• Albright, D. A. 1987. “Quick Cluster Control Method: Permanent Control Station Cluster Analysis in Average Daily Traffic Calculations.” Transportation Research Record 1134: 57–64.
   Google Scholar
• Asheibi, A., D. Stirling, and D. Sutanto. 2008. “Determination of the Optimal Number of Clusters in Harmonic Data Classification.” Proceedings of the 13th International Conference on Harmonics and Quality of Power (ICHQP 2008), Wollongong, September 28–October 1, Paper 1045.
   Google Scholar
• Bock, H. H., 1974. Automatische Klassifikation (Clusteranalyse) [Automatic Classification (Cluster Analysis)]. Gottingen: Vandenhoek & Ruprecht.
   Google Scholar
• Bozdogan, H., 1993. “Choosing the Number of Component Clusters in the Mixture Model Using a New Informational Complexity Criterion of the Inverse Fisher Information Matrix.” In Information and Classification, edited by O. Opitz, B. Lausen, and R. Klar, 40–54. Berlin: Springer-Verlag.
   Google Scholar
• Cambridge Systematics and Science Application International Corporation. 1994. Use of Data from Continuous Monitoring Sites. Prepared for FHWA, Volume I and II, Documentation. Washington, DC: US Federal Highways Administration.
   Google Scholar
• Drusch, R. 1966. “Estimating Annual Average Daily Traffic from Short-term Traffic Counts.” Highway Research Record 118: 85–95.
   Google Scholar
• Engelman, L., and J. A. Hartigan. 1969. “Percentage Points of a Test for Clusters.” Journal of the American Statistical Association 64 (328): 1647–1648. doi:10.1080/01621459.1969.10501084.
   Web of Science ®Google Scholar
• Faghri, A., and J. Hua. 1995. “Roadway Seasonal Classification Using Neural Network.” Journal of Computing in Civil Engineering 9 (2): 209–215.
   Google Scholar
• Federal Highway Administration. 2001. Traffic Monitoring Guide, Office of Highway Policy 37 Information. Washington, DC: FHWA. http://www.fhwa.dot.gov/policyinformation/tmguide/.
   Google Scholar
• Flaherty, J. 1993. “Cluster Analysis of Arizona Automatic Traffic Recorder Data.” Transportation Research Record 1410: 93–99.
   Google Scholar
• Garber, N. J., and F. Bayat-Mokhtari. 1986. “A Computerized Highway Link Classification System for Traffic Volume Counts.” Transportation Research Record 1090: 1–7.
   Google Scholar
• Hallenbeck, M., M. Rice, B. Smith, C. J. Cornell-Martinez, and J. Wilkinson. 1997. Vehicle Volume Distributions by Classification. Seattle, WA: Washington State Transportation Center & Chaparral Systems Corporation.
   Google Scholar
• Hartigan, J. A., and M. A. Wong. 1979. “Algorithm AS 136: A K-Means Clustering Algorithm.” Applied Statistics 28 (1): 100–108. doi:10.2307/2346830.
   Google Scholar
• Jung Y., H. Park, D. Z. Du, and B. L. Drake. 2003. “A Decision Criterion for the Optimal Number of Clusters in Hierarchical Clustering.” Journal of Global Optimization 25 (1): 91–111.
   Web of Science ®Google Scholar
• Kaufman, L., and P. J. Rousseeuw. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. New York: Wiley.
   Google Scholar
• Lieder, N. A. 1971. Grouping Procedure. Highway Planning Technical Report, No. 22. Washington, DC: FHWA, U.S. Department of Transportation.
   Google Scholar
• Lu, Q., and J. Harvey. 2006. “Characterization of Truck Traffic in California for Mechanistic-Empirical Design.” Transportation Research Record 1945: 61–72.
   Google Scholar
• Massart, L. D., and L. Kaufman. 1989. The Interpretation of Analytical Chemical Data by the Use of Cluster Analysis. Malabar, FL: Reprint, Robert E. Krieger Publishing Company.
   Google Scholar
• Ritchie, S. G. 1986. “A Statistical Approach to Statewide Traffic Counting.” Transportation Research Record 1090: 14–21.
   Google Scholar
• Shah, S. C., and J. J. Hirschmann. 1981. Analysis of Routine Traffic Stations to Optimize Locations and Frequency. Final Report, FHWA/LA/81/150, Louisiana Department of Transportation and Development, Research and Development Section, Federal Highway Administration, 31.
   Google Scholar
• Sharma, S. C. 1983. “Improved Classification of Canadian Primary Highways According to Type of Road Use.” Canadian Journal of Civil Engineering 10 (3): 497–509. doi:10.1139/l83-077.
   Web of Science ®Google Scholar
• Sharma, S. C., and R. R. Allipuram. 1993. “Duration and Frequency of Seasonal Traffic Counts.” Journal of Transportation Engineering 119 (3): 345–359.
   Google Scholar
• Sharma, S. C., and A. Werner. 1981. “Improved Method of Grouping Provincewide Permanent Traffic Counters.” Transportation Research Record 815: 13–18.
   Google Scholar
• Shen, J., I. Shing, E. Chang, E. S. Lee, Y. Deng, and S. J. Brown. 2005. “Determination of Cluster Number in Clustering Microarray Data.” Applied Mathematics and Computation 169 (2): 1172–1185. doi:10.1016/j.amc.2004.10.076.
   Web of Science ®Google Scholar
• Tsapakis, I., W. H. Schneider, and A. P. Nichols. 2011. “Improving the Estimation of Total and Direction-based Heavy-duty Vehicle Annual Average Daily Traffic.” Transportation Planning and Technology 34 (2): 155–166. doi:10.1080/03081060.2011.554709.
   Web of Science ®Google Scholar
• Wright, T., P. S. Hu, J. Young, and A. Lu. 1997. Variability in Traffic Monitoring Data. Final Summary Report. Oak Ridge, TN: Oak Ridge National Laboratory.
   Google Scholar
• Xin, L., M. 1999. Determining the Optimal Number of Cluster by an Extended RPCL Algorithm. Hong Kong: Polytechnic University.
   Google Scholar
• Yimin, L., T. Ozyer, R. Ahajj, and K. Barker. 2005. “Cluster Validity Analysis of Alternative Results from Multi-Objective Optimization.” Proceedings of the 5th International Conference on Data Mining, Newport Beach, CA, April 21–23.
   Google Scholar
• Zhao, F., M. T. Li, and L. F. Chow. 2004. Alternatives for Estimating Seasonal Factors on Rural and Urban Roads in Florida. Final Report. Tallahassee, FL: Florida International University, Florida Department of Transportation.
   Google Scholar
• Zhao, F., S. Yang, and C. Lu. 2008. Alternatives for Estimating Seasonal Factors on Rural and Urban Roads in Florida. Phase II. Final Report. Tallahassee, FL: Lehman Center for Transportation Research, Department of Civil & Environmental Engineering, Florida International University, Florida Department of Transportation.
   Google Scholar