Statistical techniques were developed for extracting the most significant features (indicators) from a
transit system data base, and classifying proposed and existing transit systems according to the selected
features. The data base was constructed by using information from all previous years available by the
Mn/DOT, the Census and other sources to be used in classifying transit systems. The data base
emphasized the use of raw characteristics of the operating system and the area socioeconomics. The
feature extraction was done so that the minimum number of features were extracted that can be used for
classifying the transit systems with maximum accuracy. The classification method was designed around
the data base and is flexible so that it can use future data to update the data base at minimum cost. The
transit system patterns, resulting from the classification method, were identified according to need and
performance, and the main characteristics were specified for each pattern. These characteristics and
descriptions identifying each pattern determines whether it should be modified. A controlled experiment
was required to test the classification method. A randomly selected part of the data was classified by the
method, and then the unselected data was treated as a control group for the experiment. After the
experiment a percent of misclassifications was calculated.Minnesota Department of TransportationStephanedes, Yorgos J.. (1990). Transit System Monitoring and Design. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/157095
