{'fraction_non_alphanumeric': 0.017928286852589643, 'fraction_numerical': 0.0, 'mean_word_length': 5.525974025974026, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Arterial Travel Time Characterization and Real-time Traffic Condition Identification Using GPS-equipped Probe Vehicles

Monitoring and predicting traffic condition on signalized urban arterials has been one of the biggest challenges in transportation engineering. In this study, a method of arterial travel time estimation based on multi-component mixture models and Markov Chain is presented. In addition, combined with travel time data collected from global positioning system (GPS) probe vehicles, a real-time traffic condition identification approach based on Bayes theorem is proposed. A preliminary calibration under two different traffic conditions of the proposed method using Next Generation Simulation (NGSIM) data is also provided. Results suggest that the proposed method can characterize the travel time of an arterial link well and provide an accurate estimation of route mean travel time. Also a single GPS probe was able to identify real-time traffic condition successfully in most cases.