With the rapidly increasing popularization of the automobile, challenges and greater demands have come to the fore, including traffic congestion, energy crises, traffic safety, and environmental pollution. To address these challenges and demands, enhanced data support and advanced data collection methods are crucial and highly in need. A probe-car serves as an important and effective way to obtain real-time urban road traffic status in the international Intelligent Transportation System (ITS), and probe-car technology provides the corresponding solution through advanced navigation data, offering more possibilities to address the above problems. In addition, massive spatial data-mining technologies associated with probe-car tracking data have emerged. This paper discusses the major problems of spatial data-mining technologies for probe-car tracking data, such as true path restoration and the close correlation of spatial data. To address the road-matching issue in massive probe-car tracking data caused by the strong correlation combining road topology with map matching, this paper presents a MapReduce-based technology in the second spatial data model. The experimental results demonstrate that by implementing the proposed spatial data-mining system on distributed parallel computing, the computational performance was effectively improved by five times and the hardware requirements were significantly reduced.

Keywords: Spatial Semantic Features, Probe Vehicle Path, Spatial Data Mining, Global Matching Model, Track The matching of GPS received locations to roads is challenging. Traditional matching method is based on the position of the GPS receiver, the vehicle position and vehicle behavior near the receiving time. However, for probe vehicle trajectories, the sampling interval is too sparse and there is a poor correlation between adjacent sampling points, so it cannot partition the GPS noise through the historical positions. For the data mining of probe vehicle tracks based on spatial semantics, the matching is learned from the traditional electronic navigation map matching, and it is proposed that the probe vehicle track matching algorithm is based on spatial semantic features. Experimental results show that the proposed global-path matching method gets a good matching results, and restores the true path through the probe vehicle track.