Use of Data from Point Detectors and Automatic Vehicle Identification to Compare Instantaneous and Experienced Travel Times
Abbreviated Journal Title
Transp. Res. Record
PREDICTION; Engineering, Civil; Transportation; Transportation Science & Technology
Most traffic management centers use detector data to estimate instantaneous travel times. Interest is increasing in using automatic vehicle identification (AVI) readers to provide travel time measurements as well as in using predictive modeling of travel times. This study aimed to examine the differences between travel time estimation that was based on detector data versus those based on AVI data. In addition, the study compared instantaneous travel time estimates with experienced travel time estimates to determine the adequacy of disseminated instantaneous travel time information and, thus, the potential benefits of using predictive travel time modeling. The results showed that, for uncongested conditions, the difference between point detector- and AVI-based estimates and between instantaneous and experienced travel times was insignificant. During congested traffic conditions, the difference between estimates based on detector data and those based on AVI data (Bluetooth and electronic toll tag reader data) was about 6% to 17%. In addition, a difference of 10% to 20% existed between instantaneous and experienced travel times estimated from both the detector data and AVI data; this difference depended on the tested scenarios. The values of the differences between instantaneous and experienced travel times from both types of data sources are expected to be affected by the queue-forming and -dissipating speeds, route length, and the location of the congestion.
Transportation Research Record
"Use of Data from Point Detectors and Automatic Vehicle Identification to Compare Instantaneous and Experienced Travel Times" (2014). Faculty Bibliography 2010s. 6305.