Lane-level short-term travel speed prediction for urban expressways: An attentive spatio-temporal deep learning approach

Abstract

Numerous efforts have been made to address the section-level travel speed prediction problem. However, section-level predictions can hardly be used for fine-grained applications, such as lane management and lane-level navigation. The main reason for this is that significant speed heterogeneity exists among the lanes within one section. Thus, this study proposes a three-dimensional (3D) dual attention convolution-based deep learning model for predicting the lane-level travel speed. 3D convolutions are designed to learn high-dimensional spatiotemporal traffic flow features, that is, the relationships between different sections, lanes, and periods. Dual attention modules are used to focus on the traffic flow propagation patterns and to explain the model's mechanisms. To evaluate the proposed model, an indicator is introduced to assess the spatio-temporal learning ability, based on targeting the lane-level case. Evaluation experiments are conducted based on loop detector data in Shanghai, China. The results show that high accuracy is obtained by the proposed model, with a 2.9 km/h mean absolute error, thereby outperforming several existing methods. Finally, an in-depth analysis is provided regarding the attention coefficients and interpretation of real-world lane-level traffic flow propagation patterns, so as to gain insights into the model's mechanism when capturing dynamic lane-level traffic flow.