Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
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
 Summary of the reviews: Pros: • A Novel Diffusion Convolutional Recurrent Neural Network framework for traffic forecasting • Apply bidirectional random walks with nice theoretical analysis to capture the spatial dependency • Novel applications of sequence to sequence architecture and the scheduled sampling technique into modeling the temporal dependency in the traffic domain • This work is very well written and easy to follow Cons: • Needs some minor re-organization of contents between the main sections and appendix Detailed comments: D1: Some minor re-organization of contents between the main sections and the appendix will help the reader reduce cross-section references. Some examples: 1. Lemma 2.1 can be put into appendix since it is not proposed by this work while the new theoretical analysis of Appendix 5.2 (or at least a summary) can be moved to the main sections 2. Figure 9 can be moved earlier to the main section since it well supports one of the contributions of the proposed method (using the DCRNN to capture the spatial correlation) D2: Some discussions regarding the comparison of this work to some state-of-the-arts graph embedding techniques using different deep neural network architectures would be a plus
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
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
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 Summary of the reviews: Pros: • A Novel Diffusion Convolutional Recurrent Neural Network framework for traffic forecasting • Apply bidirectional random walks with nice theoretical analysis to capture the spatial dependency • Novel applications of sequence to sequence architecture and the scheduled sampling technique into modeling the temporal dependency in the traffic domain • This work is very well written and easy to follow Cons: • Needs some minor re-organization of contents between the main sections and appendix Detailed comments: D1: Some minor re-organization of contents between the main sections and the appendix will help the reader reduce cross-section references. Some examples: 1. Lemma 2.1 can be put into appendix since it is not proposed by this work while the new theoretical analysis of Appendix 5.2 (or at least a summary) can be moved to the main sections 2. Figure 9 can be moved earlier to the main section since it well supports one of the contributions of the proposed method (using the DCRNN to capture the spatial correlation) D2: Some discussions regarding the comparison of this work to some state-of-the-arts graph embedding techniques using different deep neural network architectures would be a plus
assistant
 Summary of the reviews: Pros: • A Novel Diffusion Convolutional Recurrent Neural Network framework for traffic forecasting • Apply bidirectional random walks with nice theoretical analysis to capture the spatial dependency • Novel applications of sequence to sequence architecture and the scheduled sampling technique into modeling the temporal dependency in the traffic domain • This work is very well written and easy to follow Cons: • Needs some minor re-organization of contents between the main sections and appendix Detailed comments: D1: Some minor re-organization of contents between the main sections and the appendix will help the reader reduce cross-section references. Some examples: 1. Lemma 2.1 can be put into appendix since it is not proposed by this work while the new theoretical analysis of Appendix 5.2 (or at least a summary) can be moved to the main sections 2. Figure 9 can be moved earlier to the main section since it well supports one of the contributions of the proposed method (using the DCRNN to capture the spatial correlation) D2: Some discussions regarding the comparison of this work to some state-of-the-arts graph embedding techniques using different deep neural network architectures would be a plus