Statistical and Genetic Algorithms Classification of Highways

Abstract

This paper reports the results of experiments comparing a conventional statistical method and an evolutionary genetic algorithms approach for classifying highway sections that is based on temporal traffic patterns. Traffic patterns are used as surrogates of two important characteristics of a highway section, namely, trip purpose and trip length distribution. Accurate classification can lead to better traffic analyses, such as estimations of annual average daily traffic volume and design hourly traffic volume, and determination of maintenance and upgrading schedules. Modern-day computers cannot solve the problem of obtaining optimal classification corresponding to minimum within-group error. The hierarchical grouping method provides a reasonable approximation of the optimal solution. However, for smaller numbers of groups, the hierarchical approach tends to move farther away from the optimal solution. The genetic algorithms based approach provides better results when the number of groups is relatively small (e.g., less than nine for the Alberta highway network). In addition to comparing the two methods, the results of additional experiments studying the characteristics of the genetic approach are included.