Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (1707.01926v3)

Abstract: Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (3) inherent difficulty of long-term forecasting. To address these challenges, we propose to model the traffic flow as a diffusion process on a directed graph and introduce Diffusion Convolutional Recurrent Neural Network (DCRNN), a deep learning framework for traffic forecasting that incorporates both spatial and temporal dependency in the traffic flow. Specifically, DCRNN captures the spatial dependency using bidirectional random walks on the graph, and the temporal dependency using the encoder-decoder architecture with scheduled sampling. We evaluate the framework on two real-world large scale road network traffic datasets and observe consistent improvement of 12% - 15% over state-of-the-art baselines.

• The paper introduces GCRNN, a novel architecture that combines diffusion-based graph convolution with GRU to capture both spatial and temporal traffic dynamics.
• It achieves 12%-15% improvement over traditional models in predicting traffic speeds across varied forecasting horizons.
• The integration of sequence-to-sequence learning and scheduled sampling minimizes error propagation in multi-step forecasting.

Graph Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

The paper presents a novel approach towards spatiotemporal forecasting, focusing specifically on traffic prediction, by introducing the Graph Convolutional Recurrent Neural Network (GCRNN). This method addresses the complex nature of traffic flow dynamics by explicitly capturing spatial and temporal dependencies essential for accurate long-term forecasting.

Background and Challenges

Traffic forecasting plays a pivotal role in smart transportation systems and has significant implications for sustainability, energy management, and operational efficiency. The complexity arises from factors such as non-linear temporal dynamics influenced by fluctuating road conditions and spatial dependencies dictated by road network topology. Notably, the inherent difficulty in long-term time series forecasting further complicates predictive efforts.

Traditional methods, including both knowledge-driven (queuing theory) and data-driven approaches (ARIMA and Kalman Filtering models), often face limitations due to assumptions of data stationarity, which are frequently violated in real-world traffic scenarios. Furthermore, deep learning models to date have inadequately considered spatial structures specific to road networks.

Proposed Solution

The proposed GCRNN seeks to overcome these challenges. The technique models spatial correlations between traffic sensors via a directed graph and captures temporal dynamics using diffusion processes. This leverages the current state of graph representation techniques, particularly convolution operations adapted to work on non-Euclidean data found in transport networks.

Key elements of the architecture involve:

1. Diffusion Convolution Operation: This models pairwise spatial correlations using the diffusion process, which considers bidirectional graph random walks to reflect traffic flow's impact from both upstream and downstream directions.
2. Diffusion Convolutional Layer: Implementing diffusion convolution across neural network layers ensures efficient computation and captures localized spatial dependencies.
3. Temporal Dynamics via GRU: The model integrates Gated Recurrent Units enhanced with diffusion convolution, addressing temporal dependencies effectively.
4. Sequence to Sequence Learning and Scheduled Sampling: To mitigate error propagation in multi-step forecasting, the incorporation of sequence to sequence frameworks alongside scheduled sampling establishes robust prediction mechanisms.

Performance and Implications

Evaluated on real-world datasets (METR-LA and PEMS-BAY), GCRNN provides substantial improvements over existing baselines, achieving 12%-15% better accuracy in predicting traffic speeds across varied forecasting horizons. This demonstrates the effectiveness of integrating spatial dependency modeling with recurrent neural architectures tailored to the non-linear and complex nature of traffic data.

The implications of this research are manifold. In practical terms, deploying such a model can enhance traffic management systems, reduce congestion, and improve the allocation of resources for urban planning. Theoretically, the approach reinforces the utility of graph-based neural networks in fields that demand contextual and relational understanding of data.

Future Directions

The paper suggests that future research could extend the applicability of the GCRNN framework to other domains requiring spatiotemporal forecasting, such as climate modeling or disease prediction. Additionally, an intriguing direction lies in adapting the model to accommodate dynamic graph structures that evolve with moving objects, a step that could further refine accuracy and adaptiveness in real-time situations.

The introduction of Graph Convolutional Recurrent Neural Network advances our ability to engage with complex prediction landscapes, setting a precedent for the intersection of graph theory with deep learning in practical applications. This work not only provides a sound methodology but also opens pathways for expansive exploration into more generalized spatiotemporal problems.