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Tao-Yang Fu

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TIST Journal 2020 Journal Article

On Representation Learning for Road Networks

Meng-Xiang Wang
Wang-Chien Lee
Tao-Yang Fu
Ge Yu

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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TIST Journal 2020 Journal Article

Trembr

Tao-Yang Fu
Wang-Chien Lee

In this article, we propose a novel representation learning framework, namely TRajectory EMBedding via Road networks (Trembr), to learn trajectory embeddings (low-dimensional feature vectors) for use in a variety of trajectory applications. The novelty of Trembr lies in (1) the design of a recurrent neural network--(RNN) based encoder--decoder model, namely Traj2Vec, that encodes spatial and temporal properties inherent in trajectories into trajectory embeddings by exploiting the underlying road networks to constrain the learning process in accordance with the matched road segments obtained using road network matching techniques (e.g., Barefoot [24, 27]), and (2) the design of a neural network--based model, namely Road2Vec, to learn road segment embeddings in road networks that captures various relationships amongst road segments in preparation for trajectory representation learning. In addition to model design, several unique technical issues raising in Trembr, including data preparation in Road2Vec, the road segment relevance-aware loss, and the network topology constraint in Traj2Vec, are examined. To validate our ideas, we learn trajectory embeddings using multiple large-scale real-world trajectory datasets and use them in three tasks, including trajectory similarity measure, travel time prediction, and destination prediction. Empirical results show that Trembr soundly outperforms the state-of-the-art trajectory representation learning models, trajectory2vec and t2vec, by at least one order of magnitude in terms of mean rank in trajectory similarity measure, 23.3% to 41.7% in terms of mean absolute error (MAE) in travel time prediction, and 39.6% to 52.4% in terms of MAE in destination prediction.

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