Ship trajectory data shows the characteristics of low precision, sparseness and trajectory drift for the port parking area recognition. To improve the accuracy of port parking area recognition based on ship trajectory big data, a Multi-constrained and Parallel Track Stay Segment Extraction (MPTSSE) method was proposed. Firstly, the definition of stay segment based on ship trajectory data was given as a basic concept for parking area identification. Secondly, a stay segment extraction model based on multiple constraints, such as speed, time difference, dwell time and distance, was introduced. Furthermore, a parallel trajectory stay segment extraction algorithm was proposed. Finally, Hadoop framework was adopted to implement the proposed algorithm. In comparison experiments with the trajectory stay segment extraction method based on Stop/Move model based on real ship trajectory big dataset, the accuracy of MPTSSE is increased by 22% in berth recognition of three ports. The MPTSSE method can effectively avoid misdivision of track stay segment and has better execution efficiency under large-scale ship trajectory dataset.

The screening of the copy plate vehicles has timeliness, and the existing detection approaches for copy plate vehicles have slow response and low efficiency. In order to improve the real-time response ability, a new parallel detection approach, called stream computing, based on real-time Automatic Number Plate Recognition (ANPR) data stream, was proposed. To deal with the traffic information of the road on time, and plate vehicles could be timely feedback and controlled, a stream calculation model was implemented by using the threshold table of road travel time and the time sliding window, which could access real-time traffic data stream to calculate copy plate vehicles. On the platform of Storm, this system was designed and implemented. The calculation model was verified on a real-time data stream which was simulated by the real ANPR dataset of a city. The experimental results prove that a piece of license plate recognition data can be dealt with in milliseconds from the time of arrival to the calculation completion, also, the calculation accuracy is 98.7%. Finally, a display system for copy vehicles was developed based on this calculation model, which can show the copy plate vehicles from the road network on the current moment.