Trajectory Data Compression Using Hopfield Neural Network

Trajectory data can be used to predict traffic congestion and analyze crowd behavior patterns, which makes it more important in daily applications. For a long time, the volume of location data collected by various equipment will obtain an high speed increasing. Hence, it will be a enormous challenge to storage and transmit that big data. Due to the fact that traditional trajectory compression algorithms such as Douglas-Peucker algorithm are fast but not optimal algorithms. So, a kind of parallel algorithm utilizing Hopfield Neural Network is proposed in this paper. The proposed algorithm is an optimization algorithm with parallel computing capability, which satisfies fast processing to some extent. Finally, experience shows that the proposed algorithm can implement trajectory compression with fixed compression ratio and less accuracy loss compared with other existing algorithms.