A Multiview Representation Learning Framework for Large-Scale Urban Road Networks
A Multiview Representation Learning Framework for Large-Scale Urban Road Networks
复制标题
大规模城市道路网络的多视图表示学习框架
DOI：
10.3390/app12136301
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Deng Min
中科院分区：
文献类型：
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作者：
Chen Kaiqi;Chu Guowei;Lei Kaiyuan;Shi Yan;Deng Min
学习路网信息表示的方法是利用数据驱动模型解决多个交通分析任务的重要前提。现有的大多数研究都是从拓扑结构或交通属性的角度展开的，所得到的表示是有偏见的，不能完全捕捉到道路网络中人的流动性所导致的复杂的交通流模式。此外，现实世界的道路网络通常包含数以百万计的线段，这对现有方法的内存使用和计算效率提出了巨大的挑战。因此，我们提出了一种新的用于大规模城市道路网络的多视点表示学习框架，以同时保持拓扑信息和人体的流动性信息。首先，将路网建模为多重图，并提出了一种多视点随机游走方法，从拓扑感知图中获取路网的结构函数，从移动性感知图中获取车辆换乘模式。在此过程中，建立了一种大规模路网组织方法，以提高随机游走算法的效率。最后，基于多视点随机游走生成的序列，应用word2vec学习表示法。在实验中，使用了两个真实世界的数据集，通过比较分析，展示了该框架的优越性能。
Methods to learn informative representations of road networks constitute an important prerequisite to solve multiple traffic analysis tasks with data-driven models. Most existing studies are only developed from a topology structure or traffic attribute perspective, and the resulting representations are biased and cannot fully capture the complex traffic flow patterns that are attributed to human mobility in road networks. Moreover, real-world road networks usually contain millions of segments, which poses a great challenge regarding the memory usage and computational efficiency of existing methods. Consequently, we proposed a novel multiview representation learning framework for large-scale urban road networks to simultaneously preserve topological and human mobility information. First, the road network was modeled as a multigraph, and a multiview random walk method was developed to capture the structure function of the road network from a topology-aware graph and vehicle transfer pattern from a mobility-aware graph. In this process, a large-scale road network organization method was established to improve the random walk algorithm efficiency. Finally, word2vec was applied to learn representations based on sequences that were generated by the multiview random walk. In the experiment, two real-world datasets were used to demonstrate the superior performance of our framework through a comparative analysis.