AbstractTravel time to a destination is a key piece of information that motorists desire. Traditionally, transportation agencies collect traffic state information with costly measurement sensors. In the last decade, private sector data, as an emerging data source, has been gradually adopted by many public agencies for performance measurement and travel time provision, mostly on freeways. Agencies have also used data from infrastructure sensors and floating cars to validate the quality of the private sector data on both freeways and arterials under different conditions. Although freeway data quality has been validated by many studies, nonfreeway travel time data have been considered unreliable, particularly when the segment is congested and high-density signals exist. In recent years, data vendors started to provide high-resolution travel time data, which are provided on much shorter segments than conventional traffic message channels (TMC). This study investigates the data quality of this new data source for providing travel times on nonfreeways. It evaluates the selected private sector data on nonfreeways by using floating car and Wi-Fi travel time data as the ground truth. The results show that the new private sector data on nonfreeways are generally acceptable for segments with low and moderate congestion. With higher congestion, the data quality is site-dependent. A neural network model is proposed to implicitly identify the error of a site and provide enhanced travel times. It is recommended that these new data should be validated for the deployment sites due to the site-dependent errors, and advanced data modeling techniques can be used to compensate for such errors.