基于循环神经网络的轨迹压缩算法

Trajectory Compression Algorithm Based on Recurrent Neural Network

随着定位技术和存储技术的发展,海量的轨迹被人类记录。如何有效地压缩轨迹中最被人关注的空间路径信息并无损地将原始信息还原,引起了人们的广泛关注。轨迹压缩算法主要分为基于简化线段的压缩和基于路网的轨迹压缩两类,现有算法存在算法假设不合理、压缩能力差等缺点。文中根据路网中轨迹的分布特性以及循环神经网络对变长时序序列的建模能力,提出了基于循环神经网络的轨迹压缩算法,通过深度学习模型高效地概括轨迹分布,同时利用路网结构进一步缩小压缩空间,定量分析了不同输入对算法压缩比的影响。最后通过实验证明,基于循环神经网络的轨迹压缩算法不仅具有比现有算法更高的压缩比,还能支持未经过训练的轨迹数据的压缩;同时验证了终点信息如何对算法压缩比产生影响的假设。

With the development of positioning technology and storage technology,massive trajectories have been recorded by humans.How to effectively compress the most interesting spatial path information in the trajectory and how to restore the original information has caused extensive research.The compression algorithm for trajectories is mainly divided into line-simplified compression and road-based trajectory compression.Existing algorithms have shortcomings such as unreasonable algorithm assumptions and poor compression capability.According to the distribution characteristics of trajectories in the road network and the probabilistic modeling ability of recurrent neural networks for variable-length time series,a trajectory compression algorithm based on recurrent neural network is proposed.The trajectory distribution is efficiently summarized by our algorithm,in which the compression space is further reduced by the road network structure.Meanwhile,the influence of different input on the compression ratio of the algorithm is quantitatively analyzed.Finally,the experiment proves that the trajectory compression algorithm based on recurrent neural network not only has a higher compression ratio than existing algorithms,but also supports the compression of untrained trajectory data,and demonstrates the compression ratio of the algorithm can be improved by using the time information.