Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (1802.07007v3)

Abstract: Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between the proposed traffic graph convolution and the spectral graph convolution is also discussed. An L1-norm on graph convolution weights and an L2-norm on graph convolution features are added to the model's loss function to enhance the interpretability of the proposed model. Experimental results show that the proposed model outperforms baseline methods on two real-world traffic state datasets. The visualization of the graph convolution weights indicates that the proposed framework can recognize the most influential road segments in real-world traffic networks.

• The paper introduces TGC-LSTM, a novel architecture that integrates graph convolution operators within LSTM to learn complex spatial and temporal traffic dependencies.
• It employs an innovative traffic graph convolution using both adjacency and free-flow reachable matrices to enhance interpretability and model stability.
• Empirical results demonstrate that TGC-LSTM significantly outperforms traditional models like ARIMA and standard LSTM on real-world traffic datasets.

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

The paper "Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting" introduces an advanced approach for traffic forecasting on a network-wide scale, leveraging the complexities of spatiotemporal dynamics inherent in traffic systems. This work fundamentally addresses the challenges associated with the ever-fluctuating traffic patterns and intricate spatial interactions across transportation networks.

Core Contributions

The primary innovation presented is the Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM). This model stands out by integrating graph convolutional operations within an LSTM framework to capture the nuanced interactions between roadways and predict network-wide traffic states. Key contributions include:

1. Traffic Graph Convolution Operator: A novel operator is proposed to capture spatial features effectively by integrating the adjacency matrix and a free-flow reachable matrix. This operator enhances the ability to learn features that are consistent with physical roadway characteristics.
2. Enhanced Model Interpretability: The introduction of L1-norm and L2-norm regularizations on graph convolution weights and features, respectively, offers not only enhanced stability but also the ability to interpret which road segments significantly impact traffic states.
3. Empirical Superiority: The TGC-LSTM demonstrates superior performance over several state-of-the-art baseline models, such as ARIMA, SVR, LSTM, and various CNN-based approaches, on two comprehensive real-world datasets. The results highlight its robust capability in accurately capturing both spatial and temporal dependencies within traffic networks.

Technical Overview

The TGC-LSTM leverages a combination of adjacency and free-flow reachable matrices to define its graph convolution operations. Unlike traditional convolutional approaches confining spatial understanding within Euclidean spaces, this model adapts these operations to the topological structures of traffic networks, ensuring the extraction of realistic and relevant features. By expanding the receptive field through multiple hop interactions, the model extrapolates significant spatial relationships beyond immediate neighbors without unnecessary complexity.

The integration of the described convolutions with an LSTM layer, specifically adept at managing temporal sequences, allows the TGC-LSTM to capture dynamic temporal dependencies effectively. This integration results in a sophisticated understanding of traffic patterns that accommodates both historical data influences and current network conditions.

Implications and Future Directions

The insights offered by the TGC-LSTM model extend beyond mere predictive improvements. The ability to discern influential road segments could inform targeted infrastructure improvements and adaptive traffic management strategies, thereby contributing to more efficient and resilient urban transportation systems.

Future research could explore further refinements of the convolutional operations, possibly incorporating real-time data streams or exploring variants of recurrent architectures that might offer even deeper temporal insights. Additionally, extending the framework to incorporate multimodal transportation data could prove valuable in developing holistic urban mobility solutions.

By addressing the spatial-temporal challenges specific to traffic networks and presenting a model with demonstrated empirical success, this paper contributes meaningfully to the advancement of intelligent transportation systems leveraging deep learning methodologies.