This paper reports the results of experiments comparing a conventional stat
istical method and an evolutionary genetic algorithms approach for classify
ing highway sections that is based on temporal traffic patterns. Traffic pa
tterns are used as surrogates of two important characteristics of a highway
section, namely, trip purpose and trip length distribution. Accurate class
ification can lead to better traffic analyses, such as estimations of annua
l average daily traffic volume and design hourly traffic volume, and determ
ination of maintenance and upgrading schedules. Modern-day computers cannot
solve the problem of obtaining optimal classification corresponding to min
imum within-group error. The hierarchical grouping method provides a reason
able approximation of the optimal solution. However, for smaller numbers of
groups, the hierarchical approach tends to move farther away from the opti
mal solution. The genetic algorithms based approach provides better results
when the number of groups is relatively small (e.g., less than nine for th
e Alberta highway network). In addition to comparing the two methods, the r
esults of additional experiments studying the characteristics of the geneti
c approach are included.