The predictability of transportation service is of great importance to travelers. Whereas most transportation performance measures deal more directly with congestion, such as through delay measures, few quantify the level of predictability of travel. A new measure that effectively measures predictability of transportation service, the variability index, was developed and demonstrated. The variability index is a good example of the application of data mining in large transportation databases. Conceptually based on multivariate statistical quality control (MSQC), the variability index is computed by measuring the size (spatial volume) of the confidence regions defined by MSQC by using large sets of archived traffic data. In other words, experience (archived traffic data) is mined to measure the level of variability experienced by time and location. A case study application of this procedure demonstrates how this measure can clearly identify times of day and days of the week that experience relatively high degrees of variability in traffic conditions--or less predictable service for the traveler.
Abstract