Traffic speed prediction for urban transportation network: A path based deep learning approach. (March 2019)

• Record Type: Journal Article
• Authors: Wang, Jiawei; Chen, Ruixiang; He, Zhaocheng
• Is Part Of: Transportation research. Volume 100(2019)
• Page Start: 372
• Page End: 385
• Publication Date: 2019-03
• Subjects: Traffic speed prediction -- Urban network -- Deep learning -- Bidirectional long short-term memory neural network -- Model interpretability; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2019.02.002
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: We propose path-based deep learning framework for network-wise traffic prediction. Bidirectional long short-term memory neural network is firstly used to model path in the road network. The proposed model outperforms other benchmark models. We demonstrate that the model can be interpreted with transportation domain knowledge. Abstract: Traffic prediction, as an important part of intelligent transportation systems, plays a critical role in traffic state monitoring. While many studies accomplished traffic forecasting task with deep learning models, there is still an open issue of exploiting spatial-temporal traffic state features for better prediction performance, and the model interpretability has not been taken serious. In this study, we propose a path based deep learning framework which can produce better traffic speed prediction at a city wide scale, furthermore, the model is both rational and interpretable in the context of urban transportation. Specifically, we divide the road network into critical paths, which is helpful to mine the traffic flow mechanism. Then, each critical path is modeled through the bidirectional long short-term memory neural network (Bi-LSTM NN), and multiple Bi-LSTM layers are stacked to incorporate temporal information. At the stage of traffic prediction, the spatial-temporal features captured from these processes are fed into a fully-connected layer. Finally, results for each path are ensembled for network-wise traffic speed prediction. In the empirical studies, we compare the proposed model with multiple benchmark methods. Under a series of prediction scenarios (i.e., different input and prediction horizons), the superior performance of the proposed framework is validated. Moreover, by analyzing feature from hidden-layer output, the study explains the physical meaning of the hidden feature and illustrate model's interpretability.