No abstract

No abstract

This study has done an empirical analysis of long-haul truck drivers’ route choice decision making as they navigate the U.S. highway network. The most important factor that has been analyzed is how long-haul truck drivers trade off between distance and time when faced with multiple routes. From information gathered from a revealed preference data set consisting of about 250,000 trucks over a 13-day period, a logistic model was constructed to describe route choice behavior when truck drivers are faced with alternate routes. The logistic model predicted the percentage of trucks that used the bypass route as a function of the perceived speed on the downtown route. The results of this study show that time is a significant factor in the decision-making process.

No abstract

The purpose of this study was to investigate different methods of estimating speeds on links in United States' nationwide transportation network from different sources, such as probe vehicles and loop detectors. The Trip Speed Assignment Technique was developed and applied to the probe vehicle data to create speed distributions by time of day for many of the major roadway segments of the U.S. highway system. The technique can generate average speeds for a large network using a relatively small sample size. The average speeds follow general traffic patterns. Thus it is possible to use the probe vehicle data with the Trip Speed Assignment Technique to provide appropriate travel speed information for a large area. Exhaustive spot loop detector data have been used to compare the accuracy of the speeds derived from the Trip Speed Assignment Technique. Discrepancies are discussed and recommendations are made to improve the Trip Speed Assignment Technique.