On Representation Learning for Road Networks

Meng-Xiang Wang, Wang-Chien Lee, Tao-yang Fu, Ge Yu

Abstract:

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%).