Highlights • This research used data from volume counting detectors and made Adaptive Neural Fuzzy Inference System (ANFIS) to automatically estimate real-time travel times. • To produce training data set, VISSIM was selected for simulation due to ability to produce high-resolution outputs and model any specific situation. • To facilitate usage of this process, a software package was developed, named FTTE (Freeway Travel Time Estimator). • For reliability, a different set of simulation data and for validity several actual trajectories and GPS data obtained from probe car were used. • For all three sets (training data, test data, and validation data), up to 1650 veh/15 min error is around 1.5 percent and more than 1800 veh/15 min less than 15 percent.

Abstract While volume-counting detectors are currently installed in many freeways and have the capabilities of collecting high-resolution traffic information, travel time estimations are usually only based on spot speeds collected by the detectors, which could only produce rough estimates of travel time during peak hours when roadways are congested. In addition to spot speeds, these detectors provide directional volume counts and occupancy, which are useful for a better travel time estimation. This research used data from volume counting detectors and made Adaptive Neural Fuzzy Inference System (ANFIS) to automatically estimate real-time travel times. ANFIS’s ability to learn traffic patterns allows accurate and reliable real-time travel time estimation even with missing or corrupted data. ANFIS also proves to be a powerful tool for estimating future travel times on freeways. For easier use by practitioners and researchers, the method used in this study was implemented into a software package, named FTTE (Freeway Travel Time Estimator). Finally, a case study was conducted using the new approach and the results were compared for both congested and uncongested traffic conditions.