Travel time and travel time reliability are important performance measures for assessing traffic condition and extent of congestion on a roadway. This study first uses a floating car technique to assess travel time and travel time reliability on a number of Indiana highways. Then the study goes on to describe the use of Bluetooth technology to collect real travel time data on a freeway and applies it to obtain two weeks of data on Interstate 69 in Indianapolis. An autoregressive model, estimated based on the collected data, is then proposed to predict individual vehicle travel times on a freeway segment. This model includes speed, volume, time of day indicators, and previous vehicle travel times as independent variables. In addition to the autoregressive formulation, a duration model is estimated based on the same travel time data. The duration model of travel time provided insights into how one could predict the probability of a car’s duration of time on a roadway segment changed over time. Interestingly, the three duration models estimated (all hours, peak hour and night time models) showed that the point where the conditional probability of travel times becoming longer occurs roughly at the onset of level-of-service F conditions. Finally, a seemingly unrelated regression equation model to predict travel time and travel-time variability is estimated. This model predicts 15-minute interval travel times and standard deviation of travel time based on speed, volume and time of day indicators. The model has a very good statistical fit and thus can be used in the field to compute real-time travel time using data available from remote traffic microwave sensors.

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Performing Organization

Joint Transportation Research Program

Publisher Place

West Lafayette, Indiana

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