Abstract
Short-term traffic prediction consists a crucial component in intelligent transportation systems. With the explosion of automated traffic monitoring sensors and the flourishing of deep learning techniques, a growing body of deep neural network models have been employed to tackle this problem. In particular, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks have demonstrated their advantages in modeling and predicting the spatiotemporal evolution of traffic flows. In this paper, we propose a novel Convolutional LSTM neural network architecture for multi-lane short-term traffic prediction. Compared to existing methods, we highlight the importance of (1) applying multiple features to characterize traffic conditions; (2) explicitly considering the routing between neighbouring lanes and downstream/upstream traffics; and (3) predicting multiple time-step traffic in a rolling-prediction manner. Experiments on 10 months 5-minute interval observations of the US I-101 Northern freeway at California Bay Area verify the proposed model. The results show that our model has considerable advantages in predicting multi-lane short-term traffic flow.
Highlights
Accurate, in-time, and detailed traffic prediction plays a critical role in intelligent transportation systems (ITS)
We propose a novel convolutional long short-term memory (LSTM) recurrent neural network architecture for multi-lane shortterm traffic flow prediction
We design the following experiment to highlight the importance of considering neighbouring lane for traffic prediction: Experiment 2 We build a 1-D Convolutional LSTM neural network model that takes into input and predicts the traffic flow and speed at lane-average scale
Summary
In-time, and detailed traffic prediction plays a critical role in intelligent transportation systems (ITS). Many existing works have made comprehensive review of classical parametric modeling methods and the relatively novel machine and deep learning methods The latter has demonstrated their advantage in leveraging large observation data for accurate predictions. The model takes in average flow of all the lanes and treats time steps as CNN channels In their following work [28], speed data were used to learn the weights of the models for flow prediction. Compared these two studies on freeway, Yu et al [35] focused on regional network-wide traffic with hundreds of interchanges and intersections. We can iteratively apply Equation 3 to make multiple time step traffic predictions
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