Venue: ICLR 2022 Conference

Reviews:

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: This paper explores a new direction of model design in traffic forecasting tasks. It proposes a neural memory module to model the spatio-temporal traffic data and designs a new traffic forecasting model based on the memory module. Experiments on a few public datasets demonstrate the effectiveness of the proposed scheme. 
main_review: Strengths
S1 The idea of using a memory network to remember traffic patterns is very interesting. It has great research value. 
S2 The experiment results are promising. 
S3 The paper is well written and easy to follow. 

Weaknesses
W1 It is interesting to know how the size of P affects the prediction accuracy. An ablation study on it (and other configurations) would potentially make this paper more interesting. 


summary_of_the_review: This is a good paper - it proposes a novel idea to solve the traffic prediction problem and conducts sufficient experiments to prove the effectiveness of the proposed idea.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper proposes a new approach for macroscopic traffic flow forecasting in which the traffic forecasting problem is reformulated as a key-value pair matching problem as opposed to the conventional way of using a neural network to predict a sequence given past input. Results have been provided on two datasets along with ablation results.



main_review: This paper is exciting. It proposes a new way of looking at a very well-studied problem in traffic management. The new approach is novel, technically sound, and well motivated. As a first step, there is much opportunity for further research into more sophisticated models for intelligent pattern extraction, filtering, and inference.

I wholeheartedly recommend acceptance not simply because the idea is novel, but because I strongly believe this paper will birth a new direction of research into traffic flow forecasting.

In order to further improve the paper, I suggest the following:
1. Currently, the results in Table 1 do not look too impressive. This may call the significance of the approach into question. Do you have any opinion on how to further improve performance?

2. Can you provide visual results for several examples where you show an input sequence, its extracted pattern, and finally the resulting prediction and comparison with ground truth.

3. The paper exposition can be improved significantly. For example, in 3.2, what do you mean by "zero-based signals", "without any duplication in range". Such cryptic phrases are scattered throughout the paper. This concern has also been raised by other reviewers, but in my opinion, assuming that the authors improve the clarity, the paper could be accepted.
summary_of_the_review: I recommend acceptance.
review_title: null

final_score: rating: 6: marginally above the acceptance threshold
review_content: summary_of_the_paper: This work studied the problem of traffic speed forecasting. In particular, the authors proposed a framework that improves forecasting performance by leveraging both the spatio-temporal dependency and extracted traffic patterns. 
main_review: Strengths:
- The idea of using both extracted traffic patterns and spatial dependencies is interesting.

Weakness:

One of the major concerns is that the paper is not well presented, and critical information on the model and problem setup is missing. 
- In the problem setup, the definition of traffic patterns is not clear. Based on Figure 1, it seems to be a sequence of traffic speeds along the timeline. 
- Section 3.2 is difficult to follow. It says that traffic patterns are average speed on each road. Intuitively, the traffic situation of each road - could be very different. Sometimes, even on the same road, the traffic situation in different directions could be very different. 
- The author's claim that the pattern set is imbalanced is unconvincing since the only evidence provided is the one arbitrary pattern in Figure 2.
- Cosine similarity is a continuous value. However, the authors treat them as discrete bins without mentioning the motivation.
- The input, output of each component is unclear. In particular, the connection between each component is not clearly described in either the overview figure or the model section.  For example, it's unknown how the p_j memory context m_j is obtained. In equation 1, M_j^l's calculation is independent with layer l, which means M_j^l, m_j^{l + 1}, ... etc., all have the same value. It doesn't make sense. If C_{i,j}^l = \alpha_{i,j}^l, then why use this new symbol C_{i,j}^l. The dimension of node embedding E_1 and E_2 is not clear. The encoder and decoder structure is unclear, either. 

Another concern is that the paper's contribution seems incremental. Just as the authors stated, GCMeme is a combination of (Madotto et al., 2018) and (Bruna et al., 2014), which takes care of memory attention and graph convolution separately. 

The last concern is that some important baselines, such as ASTGCN, STGCN, etc., are missing. Since table 1 includes the main result, it is necessary to include the error bars instead of simply providing the prediction results.
summary_of_the_review: The paper is not well presented, and critical information regarding the problem and model setup is missing, which makes the paper difficult to understand.
review_title: null

final_score: rating: 5: marginally below the acceptance threshold
review_content: summary_of_the_paper: This paper studies the traffic forecasting problem and proposes to conduct prediction by pattern matching. Authors first extract key patterns from the historical data in an offline manner and then fetch the patterns for each time series with a distance function (e.g., cosine similarity). Then, the patterns of different nodes are interacted with GCN to get node representation.
main_review: This paper studies the traffic forecasting problem and proposes to conduct prediction by pattern matching. Authors first extract key patterns from the historical data in an offline manner and then fetch the patterns for each time series with a distance function (e.g., cosine similarity). Then, the patterns of different nodes are interacted with GCN to get node representation.
While using pattern matching for traffic forecasting is reasonable and the design contains some new ideas, the paper should be carefully improved on the following points:
1. The writing is not clear. Especially, the descriptions of the extracted patterns are not enough, are they just averaged speed? Are they representative? How to sample the patterns with a time window? Eq.1 should be a sum from j=0 to k? What is $p_1$ to get the noise in Eq.4? 
2. In section 3.3, there lacks justifications about why capturing interactions among fetched patterns is necessary and also why pattern level attention is necessary?
3. The experimental results are wired. I doubt the necessity of using a different setting (e.g., 18 steps instead of 12) with existing works, which makes it hard to conduct a fair comparison. And the experimental results of some baseline methods are lower than the reported performance. I would strongly suggest authors use the same settings with existing works to demonstrate their performance and also take PeMS (e.g., PeMS-bay) dataset into consideration. 
4. Many traffic forecasting works use MLP for prediction directly, does the proposed decoder performs better than this baseline?
5. There are many grammar mistakes in the paper, such as:
	* “The goal in this work is different the previous work”
	* “Similarity distribution of original pattern” in Figure 2
	* “we further use a adjacency matrix”

summary_of_the_review: 1. The writing of the paper should be carefully improved;
2. Current experimental results can not demonstrate the effectiveness of the method;
review_title: null

Meta-review: The paper presents a neural architecture based on neural memory modules to model the spatiotemporal traffic data. The reviewers think this is an important application of deep learning and thus fits the topic of ICLR. The writing and the novelty of the proposed method need improvement.
Decision: Accept (Poster)