Abstract
Current travel forecasting models have had limited sensitivity to policy decisions. One of the primary challenges with travel forecasting models (both experimental and those implemented) is limitations in the data. The primary data source, the daily travel diary, is limited in both accuracy and sample size. The daily travel diary has known problems with underreporting, time inaccuracies, respondent fatigue, and other human errors. Global positioning systems (GPS) have been recently used to supplement the daily travel diary. As GPS becomes more accurate, reliable, and cost effective, could it entirely replace the daily travel diary? A number of efforts have used GPS data for route choice studies and to supplement daily travel diaries by providing more accurate time data, and determining under-reporting rates. GPS is also used in computer assisted daily travel diaries, reminding respondents of activities they may have forgotten to report. GPS devices record times and locations of each activity and the trips between those activities. To use GPS data to replace the daily travel diary one need only predict the activity types. The goal of this research is to develop and test a model to predict activity types based solely on: (1) GPS data from devices placed on the individual’s vehicle or person, (2) Land use data, such as location type, expressed as GIS data, and (3) Demographic data for the individual and the household. This thesis summarizes models developed using discriminant analysis and classification/regression trees. The models predicted in which of 26 different activity types the individual participated. Accuracy for out of home activities for the best model was 63%. When combed with the activity of being at home (which can be accurately predicted if we know the individuals home location) an accuracy of 79% was achieved (72% if you consider that GPS data may miss as much as 10% of trips). Since travel diaries have been known to underreport trips by as much as 25%, GPS data with the model developed can be very competitive. It is even more appealing considering the time inaccuracies and human error associated with travel diaries.