Graduation Year


Document Type




Degree Granting Department

Civil Engineering

Major Professor

Steven E. Polzin, Ph.D.

Co-Major Professor

Jian Lu, Ph.D.


Service planning, Elasticities, Headway, Span of service, Routes


Public transportation, although modest in the United States carrying about 2 percent trips, still serves millions of people as the main and only means of transportation. Recently released data set by Census, the 2006 American Community Survey (ACS) shows the main mode of travel for work commute is not surprisingly the automobile with over 86 percent and public transportation with nearly 5 percent users. Transit agencies strive to provide effective, convenient, and desirable transport. Because of the constant changes in our environment, being able to predict the response of riders to different network or system changes is extremely useful. Ridership can be described as a function of the amount of service supplied such as frequency, span of service, and travel time. One of the methods for estimating ridership forecasts and evaluating ridership response is to use the new state-of-art software TBEST.

TBEST stands for Transit Boardings Estimation and Simulation Tool and is the third generation of such transit models sponsored by the Florida Department of Transportation (FDOT). Designed for comprehensive transit network and short term transit planning, it offers great benefits to its users. TBEST is a user friendly, yet very advanced transit ridership forecasting graphical software which is interfaced with ArcGIS. This paper evaluates different sensitivity tests and compares the results to known industry used elasticities. Because the current TBEST experience is modest, the results will provide users with a general idea of the model's sensitivity and help in the process of model refinements. Sensitivity tests such as service frequency, span of service, service allocation, and travel time will be carried out in a systematic order for all six time periods as defined by TBEST.

Results showed that TBEST Model is overestimating and is highly sensitive to headway changes, specifically headway decrease. The opposite effect of almost no sensitivity is shown for the in-vehicle travel times.