Informative representation of road networks is essential to a wide variety of applications on intelligent transportation systems. In this article, we design a new learning framework, called Representation Learning for Road Networks (RLRN), which explores various intrinsic properties of road networks to learn embeddings of intersections and road segments in road networks. To implement the RLRN framework, we propose a new neural network model, namely Road Network to Vector (RN2Vec), to learn embeddings of intersections and road segments jointly by exploring geo-locality and homogeneity of them, topological structure of the road networks, and moving behaviors of road users. In addition to model design, issues involving data preparation for model training are examined. We evaluate the learned embeddings via extensive experiments on several real-world datasets using different downstream test cases, including node/edge classification and travel time estimation. Experimental results show that the proposed RN2Vec robustly outperforms existing methods, including (i) Feature-based methods : raw features and principal components analysis (PCA); (ii) Network embedding methods : DeepWalk, LINE, and Node2vec; and (iii) Features + Network structure-based methods : network embeddings and PCA, graph convolutional networks, and graph attention networks. RN2Vec significantly outperforms all of them in terms of F1-score in classifying traffic signals (11.96% to 16.86%) and crossings (11.36% to 16.67%) on intersections and in classifying avenue (10.56% to 15.43%) and street (11.54% to 16.07%) on road segments, as well as in terms of Mean Absolute Error in travel time estimation (17.01% to 23.58%).

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关于道路网络的表征学习

道路网络的信息表示对于智能交通系统的各种应用至关重要。在本文中，我们设计了一个新的学习框架，称为道路网络表示学习 (RLRN)，它探索道路网络的各种内在属性，以学习道路网络中交叉口和路段的嵌入。为了实现 RLRN 框架，我们提出了一种新的神经网络模型，即 Road Network to Vector (RN2Vec)，通过探索它们的地理局部性和同质性、道路网络的拓扑结构以及道路使用者的移动行为。除了模型设计之外，还研究了涉及模型训练数据准备的问题。我们使用不同的下游测试用例，通过对几个真实世界数据集的广泛实验来评估学习的嵌入，包括节点/边缘分类和旅行时间估计。实验结果表明，所提出的 RN2Vec 稳健地优于现有方法，包括 (i)基于特征的方法：原始特征和主成分分析（PCA）；(二)网络嵌入方法：DeepWalk、LINE 和 Node2vec；(iii)特征+基于网络结构的方法：网络嵌入和 PCA、图卷积网络和图注意网络。RN2Vec 在十字路口的交通信号分类（11.96% 到 16.86%）和十字路口（11.36% 到 16.67%）以及大道（10.56% 到 15.43%）和街道（11.54%）分类方面的 F1 分数显着优于所有这些到 16.07%），以及在旅行时间估计中的平均绝对误差（17.01% 到 23.58%）。