Real-time identification of probe vehicle trajectories in the mixed traffic corridor

Keywords: Semi-supervised learning technique Probe vehicle trajectory Mixed corridor Urban expressway a b s t r a c t This paper proposes three enhanced semi-supervised clustering algorithms, namely the Constrained-K-Means (CKM), the Seeded-K-Means (SKM), and the Semi-Supervised Fuzzy c-Means (SFCM), to identify probe vehicle trajectories in the mixed traffic corridor. The proposed algorithms are able to take advantage of the strengthens of topological relation judgment and the semi-supervised learning technique by optimizing the selection of pre-labeling samples and initial clustering centers of the original semi-supervised learning technique based on horizontal Global Positioning System data. The proposed algorithms were validated and evaluated based on the probe vehicle data collected at two mixed corridors on Shanghai's urban expressways. Results indicate that the enhanced SFCM algorithm could achieve the best performance in terms of clustering purity and Normalized Mutual Information, followed by the CKM algorithm and the SKM algorithm. It may reach a nearly 100% clustering purity for the uncongested conditions and a clustering purity greater than 80% for the congested conditions. Meanwhile, it could improve clustering purity averagely by 21% and 14% for the congested conditions and 6.5% and 6% for the uncon-gested conditions, as compared with the traditional K-Means algorithm and the basic SFCM. The proposed algorithms can be applied for both on-line and off-line purposes, without the need of historical data. Clustering accuracies under different traffic conditions and possible improvements with the use of historical data are also discussed. Probe vehicle systems have been widely implemented in many developed countries as a promising technology. It has also gained increasing attentions in China in recent years. So far, more than 10 mega cities in China including Beijing and Shanghai have already launched the application of probe vehicle systems since 2002, most of which are based on taxi GPS (Global Positioning System) data. Those systems usually upload taxi's GPS data with a time interval of 5–60 s. Probe vehicle systems have many applications, e.g., dynamic traffic state estimation, OD (Origin Destination) estimation, route travel time estimation. In Shanghai, the numbers of GPS-equipped buses and taxies have reached 15,390 and 48,714 respectively at the end of 2011 according to Shanghai comprehensive transportation annual report (Shanghai City Comprehensive Transportation Planning Institute, 2012). More than 40,000 GPS-equipped taxies provide GPS data to Shanghai Traffic Information Center with a time interval of 10–20 s.