Abstract
Travel time characteristics of transit vehicles such as mean and standard deviation (SD) are of critical importance in both transit planning and operations. Predicting these measures not only helps transit agencies schedule and allocate resources more accurately but also facilitates the development of more robust mode choice and departure time models. Data from automatic vehicle locations and automatic passenger counting, as well as outputs from a travel forecasting model, were used in presenting a methodology to predict the mean and the SD of travel times for proposed transit routes. Models were generated in two ways. First, mean and SD of travel time were estimated by regressing observed values against roadway and operational characteristics. The SD was estimated between origins and destinations by considering the SDs of individual segments and the correlation between segments. Advantages and disadvantages of these two methods were evaluated. The models were calibrated and validated with automatic vehicle location data from the bus system serving the Regional Municipality of Waterloo in Ontario, Canada.
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