Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks (1705.02699v1)

Abstract: Predicting large-scale transportation network traffic has become an important and challenging topic in recent decades. Inspired by the domain knowledge of motion prediction, in which the future motion of an object can be predicted based on previous scenes, we propose a network grid representation method that can retain the fine-scale structure of a transportation network. Network-wide traffic speeds are converted into a series of static images and input into a novel deep architecture, namely, spatiotemporal recurrent convolutional networks (SRCNs), for traffic forecasting. The proposed SRCNs inherit the advantages of deep convolutional neural networks (DCNNs) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by DCNNs, and the temporal dynamics can be learned by LSTMs. An experiment on a Beijing transportation network with 278 links demonstrates that SRCNs outperform other deep learning-based algorithms in both short-term and long-term traffic prediction.

• The paper presents a hybrid CNN-LSTM framework (SRCNs) that effectively models spatiotemporal dependencies in traffic networks.
• It uses a grid-based representation to convert traffic speeds into static images, enabling CNNs to capture complex spatial relationships.
• The proposed SRCNs outperform conventional methods with lower MAPE and RMSE, offering improved accuracy for short- and long-term predictions.

Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks

This paper presents a novel approach to predicting traffic in large-scale transportation networks using Spatiotemporal Recurrent Convolutional Networks (SRCNs). The proposed methodology effectively marries convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to harness both spatial dependencies and temporal dynamics inherent in traffic flow data.

Methodological Framework

The authors introduce a grid-based network representation that transforms traffic network speeds into static images. This segmentation facilitates capturing the complex topology of networks, including interchanges and intersections, into a structured format amenable to CNN processing. The SRCN architecture capitalizes on the strengths of both deep convolutional neural networks (DCNNs) to model spatial relationships, and LSTMs to learn temporal dependencies—from short-term fluctuations to long-duration trends.

Experimental Results and Evaluation

The efficacy of SRCNs is validated using data from a Beijing traffic network encompassing 278 links. This empirical paper indicates that the SRCNs outperform existing deep learning architectures, such as standalone LSTMs, DCNNs, stacked autoencoders (SAEs), and conventional support vector machines (SVMs) on dimensions of short-term and long-term traffic prediction. Specifically, the SRCNs achieve a markedly low mean absolute percentage error (MAPE) and root mean squared error (RMSE) compared to the alternatives, demonstrating superior prediction accuracy and application feasibility.

Implications and Future Directions

The implications of this research are substantial for the domain of intelligent transportation systems. Practically, SRCNs offer an enhanced approach to traffic management and control systems, potentially optimizing routing and reducing congestion. Theoretically, this work represents a significant step forward in the integration of spatiotemporal data modeling techniques in transportation networks.

Future research may extend this work by incorporating external variables such as weather, traffic incidents, and social events to further refine predictive capabilities. Additionally, optimizing pre-training methods could ameliorate the computational burden and expediting training times. Expanding the network scale and improving representation approaches to filter out non-informative regions could further advance the application of SRCNs in real-world large-scale transportation networks.

In conclusion, the SRCNs provide a robust framework for addressing the intricate challenges of traffic prediction, paving the way for enriched data-driven decision-making in urban planning and transportation engineering.