XXLTraffic: Expanding and Extremely Long Traffic forecasting beyond test adaptation

## Abstract

Abstract Traffic forecasting is crucial for smart cities and intelligent transportation initiatives, where deep learning has made significant progress in modeling complex spatio-temporal patterns in recent years. However, current public datasets have limitations in reflecting the ultra-dynamic nature of real-world scenarios, characterized by continuously evolving infrastructures, varying temporal distributions, and temporal gaps due to sensor downtimes or changes in traffic patterns. These limitations inevitably restrict the practical applicability of existing traffic forecasting datasets. To bridge this gap, we present XXLTraffic, the largest available public traffic dataset with the longest timespan and increasing number of sensor nodes over the multiple years observed in the data , curated to support research in ultra-dynamic forecasting. Our benchmark includes both typical time-series forecasting settings with hourly and daily aggregated data and novel configurations that introduce gaps and down-sample the training size to better simulate practical constraints. We anticipate the new XXLTraffic will provide a fresh perspective for the time-series and traffic forecasting communities. It would also offer a robust platform for developing and evaluating models designed to tackle ultra-dynamic and extremely long forecasting problems. Our dataset supplements existing spatio-temporal data resources and leads to new research directions in this domain.

## 1 Introduction

Traffic prediction plays a vital role in intelligent transportation systems by offering accurate and dependable forecasts of future traffic conditions that serve as critical references for route design, urban construction, and road management. The long-term spatio-temporal relationship is essential and irreplaceable for traffic forecasting, i.e., the long-term relationship can not only provide context that helps understand anomalies or irregularities in short-term data. but also help identify patterns and trends over different timescales, such as daily, weekly, monthly, and yearly cycles. In recent years, significant work has focused on short-term and long-term traffic flow prediction. Various deep learning techniques, such as GNNs, emphasize extracting spatial relationships in traffic networks [], while Transformer variants and upgraded architectures and methods focus on capturing short-term and long-term temporal relationships []. Existing work has achieved promising performance in both short-term and long-term predictions, but this is limited to relatively simple experimental setups.

The inevitable tendency in intelligent transportation systems is to design models that incorporate more realistic circumstances rather than just simple experimental conditions. This requires the use of data sets that accurately reflect real-world scenarios. This serves as a strong incentive for us to create and develop XXLTraffic, i.e., why does XXLTraffic focus on ultra-dynamic forecasting? Why is it necessary to include extremely long-term data? We will introduce the commonalities and developments of existing datasets, as well as the difficulties in establishing XXLTraffic more specifically.

Figure: Figure 1: Our dataset are evolving and longer than existing datasets. Existing datasets are either limited by short temporal spans or insufficient spatial nodes. In contrast, our dataset features an evolving growth of spatial nodes and spans over 20 years.
Refer to caption: x1.png

Recent Advances in Expanding Traffic Datasets. Real-world traffic scenarios necessitate more complex prediction settings, involving extended temporal horizons or broader spatial coverage in experiments. In the temporal domain, new settings are typically proposed based on previously published work rather than introducing new datasets:  [] and [] expanded input and output lengths up to four times on existing datasets.From a spatial perspective, [] published a dataset with nodes growing annually and provided an evolving network to support new node predictions. [] proposed a continual learning framework with pattern expansion mechanisms based on []. Additionally, [] and [] offered larger-scale spatial node datasets to support subsequent researchers. Recent work has explored longer temporal step experimental settings and released traffic datasets spanning up to five years and thousands of nodes. However, in specific scenarios, such as future traffic prediction for highway planning, these data and experimental settings fall short. As shown in Figure 1, most existing datasets have limitations in temporal span, which inspired us to develop a dataset for expanding and extremely long traffic forecasting. Highway construction typically spans several years, requiring predictions several years in advance. These extremely long prediction scenarios necessitate traffic datasets with even longer temporal spans for support. As the temporal span extends, urban infrastructure development and road construction can lead to shifts in traffic patterns, resulting in an evolving domain shift. This observation motivated us to provide an expanding and extremely long traffic dataset. Additionally, the combination of these factors enables the extraction of more temporal patterns from extremely long sequences, allowing for the possibility of longer input sequences.

Challenges. The ultra-dynamic challenge encompasses three key aspects: (1) Continuously evolving states of the underlying spatio-temporal infrastructures, characterized by an expanding number of nodes over the years. This continuous growth introduces complexity as the infrastructure adapts and expands. (2) Evolving temporal distributions over an extremely long observation period, which is crucial for extremely long forecasting. This requires models to adapt to changes in patterns and trends over extensive temporal spans. (3) Ultra spatio-temporal dynamicity with temporal gaps. These gaps can occur due to various reasons such as sensor data becoming unavailable during road construction, maintenance activities, or shifts in data distributions. This dynamic nature necessitates robust modeling techniques to handle inconsistencies and interruptions in data.

Contribution. We have constructed a traffic dataset with an exceptionally long temporal span and broader regional coverage, providing aggregated data and benchmarking, as well as a benchmarking setup considering extremely long prediction scenarios for future exploration:

- •
We propose XXLTraffic, a dataset that spans up to 23 years and exhibits evolutionary growth. It includes data from 9 regions, with detailed data collection and processing procedures for expansion and transformation. This dataset supports both temporally scalable and spatially scalable challenges in traffic prediction.
- •
We present an experimental setup with temporal gaps for extremely long prediction with gaps scenarios and provide a benchmark of aggregated versions of hourly and daily datasets.
- •
We provide the exploration of input features through evolving temporal distributions over an extremely long observation period. Additionally, our datasets support zero-shot forecasting for new sensors.

## 2 Preliminaries

In this section, we will define traffic data and traffic prediction tasks.

#### Definition 1. Traffic datasets:

Traffic data primarily consists of vehicle flow detection data collected by sensors distributed across various locations in the traffic network. It is generally represented by
$X_{i}$ $\in$ $\mathbb{R}^{N\times T\times C}$, where $T$ denotes the time steps, $N$ denotes the number of sensors, and $C$ denotes the number of features.

#### Definition 2. Short-term traffic prediction:

Short-term traffic prediction primarily focuses on forecasting traffic speed or flow within the next hour. As shown in Equation 1, the input length $\alpha$ and output length $\beta$ are generally set to 12 steps.

$$ $[X_{t-(\alpha-1)},...,X_{t-1},X_{t}]\rightarrow[X_{t+1},X_{t+2},...,X_{t+\beta }],$ (1) $$

#### Definition 3. Long-term multivariate prediction:

This task mainly focuses on long-sequence time series prediction, which includes the traffic dataset. As shown Table 1, the sequence length can reach up to 2880 steps.

#### Definition 4. Extremely Long Prediction with Gaps:

Based on Equation 1, the observation and prediction are not adjacent but are instead separated by a gap period $g$, as shown in the following formula.

$$ $[X_{t-(\alpha-1)},...,X_{t-1},X_{t}]\rightarrow[X_{t+g+1},X_{t+g+2},...,X_{t+g +\beta}],$ (2) $$

## 3 Gaps and Comparison with Existing Traffic Datasets

Figure: Figure 2: Our expanding and extremely long prediction addresses the existing limitations in both short-term and long-term predictions.
Refer to caption: x2.png

As shown in Table 1, existing traffic prediction work can easily be divided into short-term and long-term settings. The short-term setting originated from the STGCN[] work, while the long-term setting was first introduced by LSTNet [] and subsequently established as a widely adopted experimental framework by Informer []. In recent years, short-term prediction typically has a maximum step length of 12 steps, while long-term prediction reaches up to 720 steps. However, works such as Witran [] and DAN [] recognized the need for even longer step predictions in practical applications, extending the length to a maximum of four times the typical length. Despite the differences in step lengths, their observed and predicted values are concatenated tightly together, as shown in Equation 1. To accommodate complex real-world scenarios, such as highway route planning predictions, it is necessary to introduce a gap of several years between observation and prediction. Typically, existing datasets lack the temporal coverage required to support gaps exceeding one year.At the same time, predicting several years in advance also implies the need to forecast traffic for sensors at new locations, taking into account the evolving nature of the road network. Even when such coverage is available, works like [] and [] utilize evolving datasets but do not provide sufficient data to train models for extended durations. To overcome these gaps, our Expanding and Extremely Long Traffic Dataset robustly supports these complex scenarios.

**Table 1: Summary of recent short-term traffic forecasting and long-term multivariate forecasting**
| Datasets | Model | Series Length |
| --- | --- | --- |
| Short-term | STGCN [] | {3,6,9,12} |
| DCRNN [], GWN [], BTF [], DMSTGCN [], | {3,6,12} |  |
| GTS [], STGODE [], PM-MemNet [], STAEFormer [] |  |  |
| AGCRN [], STSGCN [], ,DSTAGNN [] D2STGNN [], | {12} |  |
| DyHSL [],PDFormer [], MultiSPANS [], GMSDR [] |  |  |
| Long-term | MTGNN [],LSTNet [] | {3,6,12,24} |
| ARU [] | {12,24,48,168,336} |  |
| LogSparse_Trans [] | {24,48,72,96,120,144,168,192} |  |
| AST [] | {8,24,168,336} |  |
| SSDNet [] | {20,24,30,138} |  |
| Informer [], Autoformer [], FEDformer [], | {24,48,96,192,336,720} |  |
| Linear [], Triformer [], Pyraformer [] |  |  |
| DSformer [],DeepTime [],DLinear [] |  |  |
| Witran [] | {168, 336, 720, 1440, 2880} |  |
| DAN [] | {288, 672, 1440} |  |

## 4 The XXLTraffic Datasets

### 4.1 Data Collection

We obtained the expanding and extremely long traffic sensor data from the California Department of Transportation (CalTrans) Performance Measurement System(^1^11[https://pems.dot.ca.gov/](https://pems.dot.ca.gov/)) (PeMS) []. PeMS is an online platform that collects traffic data from 19,561 sensors distributed across California state highways. These sensor locations are divided into nine districts. We downloaded all the raw data for these nine districts from the initial data release up to March 20, 2024. The system automatically generates a daily data file for each district, containing data from all sensors within each district. We have stored the complete raw data files in an open-source repository for quick access, which will be released after the publication.

**Table 2: Comparison between our XXLTraffic dataset and the existing traffic datasets.**
| Reference | Dataset | Samples | Nodes | Time Interval | Time Span | Time Period |
| --- | --- | --- | --- | --- | --- | --- |
| DCRNN [] | METR-LA | 34,272 | 207 | 5 mins | 4 months | 03/2012 - 06/2012 |
| PEMS-BAY | 52,116 | 325 | 5 mins | 6 months | 01/2017 - 05/2017 |  |
| LSTNet [] | Traffic | 17,544 | 862 | 1 hour | 2 years | 01/2015 - 12/2016 |
| STGCN [] | PEMSD7(M) | 12,672 | 228 | 5 mins | 2 months | 05/2012 - 06/2012 |
| PEMSD7(L) | 12,672 | 1026 | 5 mins | 2 months | 05/2012 - 06/2012 |  |
| ASTGCN [] | PEMSD4-I | 17,002 | 228 | 5 mins | 2 months | 01/2018 - 02/2018 |
| PEMSD8-I | 17,856 | 1,979 | 5 mins | 2 months | 07/2016 - 08/2016 |  |
| STSGCN [] | PEMS03 | 26,208 | 358 | 5 mins | 11 months | 01/2018 - 11/2018 |
| PEMS04 | 16,992 | 307 | 5 mins | 2 months | 01/2018 - 02/2018 |  |
| PEMS07 | 28,224 | 883 | 5 mins | 2 months | 05/2017 - 06/2017 |  |
| PEMS08 | 17,856 | 170 | 5 mins | 2 months | 07/2016 - 08/2016 |  |
| Large-ST [] | CA | 525,888 | 8,600 | 5 mins | 5 years | 01/2017 - 12/2021 |
| GLA | 525,888 | 3,834 | 5 mins | 5 years | 01/2017 - 12/2021 |  |
| GBA | 525,888 | 2,352 | 5 mins | 5 years | 01/2017 - 12/2021 |  |
| SD | 525,888 | 716 | 5 mins | 5 years | 01/2017 - 12/2021 |  |
| Ours | Full_PEMS03 | 2,419,488 | 1809 | 5 mins | 23.00 years | 03/2001 - 03/2024 |
| Full_PEMS04 | 2,287,872 | 4,089 | 5 mins | 21.75 years | 06/2002 - 03/2024 |  |
| Full_PEMS05 | 1,998,720 | 573 | 5 mins | 19.00 years | 03/2005 - 03/2024 |  |
| Full_PEMS06 | 1,945,728 | 705 | 5 mins | 18.50 years | 09/2005 - 03/2024 |  |
| Full_PEMS07 | 2,287,872 | 4,888 | 5 mins | 21.75 years | 06/2002 - 03/2024 |  |
| Full_PEMS08 | 2,419,488 | 2,059 | 5 mins | 23.00 years | 03/2001 - 03/2024 |  |
| Full_PEMS10 | 1,998,720 | 1,378 | 5 mins | 19.00 years | 03/2005 - 03/2024 |  |
| Full_PEMS11 | 2,261,376 | 1,440 | 5 mins | 21.50 years | 09/2002 - 03/2024 |  |
| Full_PEMS12 | 2,331,360 | 2,587 | 5 mins | 22.16 years | 01/2002 - 03/2024 |  |

As illustrated in Table 2, our collected dataset significantly exceeds existing datasets in terms of both temporal coverage and the number of spatial nodes. The dataset sample will be available on: [https://github.com/cruiseresearchgroup/XXLTraffic](https://github.com/cruiseresearchgroup/XXLTraffic), which includes the raw data, sensor metadata (containing sensor IDs, geographical coordinates, associated road information, etc.), the data processing pipeline code, and the processed datasets.

### 4.2 Data Preprocessing

Based on the 23 years of raw data we collected, we conducted rigorous data filtering and aggregation. The PeMS system has continuously evolved, expanding from a few sensors in 2001 to over 4,000 sensors in some districts today. To support our setting of extremely long forecasting with gaps, we selected a subset of sensors that were installed in the early stages and have consistently collected new data up to the present(named gap dataset), which is shown in the Appendix. This extensive gap dataset effectively underpins the extremely long forecasting with gaps demonstrated in Figure 2. Utilizing the gap dataset, we performed both hourly and daily aggregations, which will be employed for gap-free long-term forecasting benchmarking. We will provide standard long-term forecasting benchmarks for both the hourly and daily datasets.

### 4.3 Data Overview

The XXLTraffic dataset is distributed across highways in the state of California, as illustrated in the Figure 3(a). The nine colors represent nine districts. From Figures 3(b),  3(c), and 3(d), we can clearly observe the evolutionary growth of the sensors. The sensors are extensively distributed across both urban and suburban areas, offering diverse modalities. Additionally, the sensors are densely interconnected, enabling the formation of a high-quality traffic graph dataset.

Figure: (a)
Refer to caption: x3.png

It is evident that sensors at the same location may collect completely different distributions over the course of urban evolution. As shown in Figure 4, some sensors have maintained the same distribution from 2005 to 2024, while others have experienced significant changes in distribution. The temporal changes causing domain shifts present a significant challenge for our extremely long forecasting.

Figure: Figure 4: Sensor traffic status distribution of District 8 from 2005 to 2024. While some sensors exhibit minimal changes, others show significant distribution differences, regardless of whether they are in low-traffic or high-traffic areas. This presents substantial challenges for extremely long forecasting with long gaps.

### 4.4 XXLTraffic Licence

The XXLTraffic dataset is licensed under CC BY-NC 4.0 International: [https://creativecommons.org/licenses/by-nc/4.0](https://creativecommons.org/licenses/by-nc/4.0). Our code is available under the MIT License: [https://opensource.org/licenses/MIT](https://opensource.org/licenses/MIT). Please check the official repositories for the licenses of any specific baseline methods used in our codebase.

## 5 Experiments

We conducted experiments for both extremely long forecasting with gaps using gap dataset and conventional long-term forecasting using hourly dataset and daily dataset. Additionally, referring to the definition in Figure 2, we set the gap parameter $g$ as 1 year, 1.5 years, and 2 years for the gap dataset, as illustrated by Figure 5.

Figure: Figure 5: Problem definition. The yellow boxes represent typical predictions, the gray boxes denote gap periods between observation and prediction, and the blue boxes indicate extended predictions.
Refer to caption: x8.png

### 5.1 Datasets

We conducted experiments on all proposed sub-datasets. To maintain consistency with previous state-of-the-art benchmarks, we selected districts 03, 04, 07, and 08 (widely recognized as PEMS03/04/07/08) for our experiments, using the gap, hourly, and daily datasets from these districts. Results for other datasets are presented in Appendix. All sub-datasets were divided into training, validation, and test sets using a 6:2:2 ratio. For the gap dataset, due to the extensive span of up to 20 years resulting in a large sample size, we fixed a seed during data preprocessing to select 10% of the dataset for training and testing to quickly demonstrate our results. In our experiments, due to GPU memory constraints, we pruned PEMS07_ours and PEMS12_ours by removing nodes with a missing data ratio greater than 10%, reducing the node number to 1,613 and 867. The details of the datasets used in our benchmarking is in Appendix A.1.

**Table 3: Comparison in gap dataset. The bold text indicates the best.**
| Methods | Mamba | iTransformer | DLinear | Autoformer |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Metrics | MSE | MAE | MSE | MAE | MSE | MAE | MSE | MAE |  |  |
| PEMS03_gap | 1-year gap | 96 | 1.457 | 0.913 | 1.636 | 0.989 | 1.500 | 0.933 | 1.301 | 0.906 |
| 192 | 1.472 | 0.922 | 1.597 | 0.970 | 1.542 | 0.945 | 1.266 | 0.867 |  |  |
| 336 | 1.434 | 0.913 | 1.512 | 0.935 | 1.531 | 0.935 | 1.137 | 0.807 |  |  |
| 1.5-year gap | 96 | 1.485 | 0.942 | 1.879 | 1.078 | 1.653 | 0.976 | 1.467 | 0.945 |  |
| 192 | 1.442 | 0.934 | 1.753 | 1.017 | 1.642 | 0.968 | 1.460 | 0.923 |  |  |
| 336 | 1.446 | 0.938 | 1.662 | 0.987 | 1.632 | 0.968 | 1.345 | 0.882 |  |  |
| 2-year gap | 96 | 1.359 | 0.833 | 1.844 | 1.048 | 1.568 | 0.954 | 1.328 | 0.894 |  |
| 192 | 1.216 | 0.772 | 1.729 | 1.008 | 1.473 | 0.896 | 1.235 | 0.837 |  |  |
| 336 | 1.294 | 0.801 | 1.614 | 0.960 | 1.407 | 0.870 | 1.220 | 0.838 |  |  |
| PEMS04_gap | 1-year gap | 96 | 1.325 | 0.914 | 1.644 | 1.037 | 1.396 | 0.955 | 0.819 | 0.717 |
| 192 | 1.438 | 0.642 | 1.587 | 1.018 | 1.440 | 0.965 | 0.941 | 0.767 |  |  |
| 336 | 1.424 | 0.935 | 1.447 | 0.960 | 1.421 | 0.954 | 0.853 | 0.730 |  |  |
| 1.5-year gap | 96 | 1.204 | 0.890 | 2.014 | 1.193 | 1.488 | 0.995 | 0.981 | 0.779 |  |
| 192 | 0.982 | 0.787 | 1.649 | 1.038 | 1.301 | 0.913 | 0.867 | 0.735 |  |  |
| 336 | 0.961 | 0.792 | 1.352 | 0.929 | 1.298 | 0.908 | 0.762 | 0.679 |  |  |
| 2-year gap | 96 | 1.220 | 0.893 | 1.652 | 1.074 | 1.446 | 0.977 | 0.909 | 0.755 |  |
| 192 | 1.004 | 0.807 | 1.189 | 0.870 | 1.268 | 0.893 | 0.909 | 0.747 |  |  |
| 336 | 1.198 | 0.864 | 1.584 | 1.016 | 1.269 | 0.898 | 0.898 | 0.736 |  |  |
| PEMS07_gap | 1-year gap | 96 | 1.719 | 1.006 | 1.756 | 1.024 | 1.680 | 1.024 | 1.215 | 0.867 |
| 192 | 1.637 | 0.996 | 1.816 | 1.061 | 1.776 | 1.043 | 1.202 | 0.852 |  |  |
| 336 | 1.637 | 0.991 | 1.784 | 1.028 | 1.774 | 1.034 | 1.098 | 0.774 |  |  |
| 1.5-year gap | 96 | 1.571 | 0.972 | 1.705 | 1.016 | 1.585 | 0.987 | 1.184 | 0.844 |  |
| 192 | 1.578 | 0.982 | 1.657 | 1.004 | 1.624 | 0.996 | 1.088 | 0.800 |  |  |
| 336 | 1.818 | 1.127 | 1.712 | 1.028 | 1.668 | 1.036 | 0.991 | 0.729 |  |  |
| 2-year gap | 96 | 5.411 | 1.319 | 2.055 | 1.149 | 1.746 | 1.048 | 1.099 | 0.802 |  |
| 192 | 12.620 | 1.370 | 2.006 | 1.128 | 1.705 | 1.002 | 1.284 | 0.875 |  |  |
| 336 | 9.614 | 1.378 | 1.812 | 1.057 | 1.605 | 0.968 | 0.972 | 0.734 |  |  |
| PEMS08_gap | 1-year gap | 96 | 5.411 | 1.319 | 1.514 | 0.950 | 1.771 | 1.058 | 1.153 | 0.843 |
| 192 | 12.620 | 1.370 | 1.499 | 0.900 | 1.762 | 1.059 | 1.202 | 0.845 |  |  |
| 336 | 9.614 | 1.378 | 1.654 | 0.984 | 1.961 | 1.131 | 1.184 | 0.849 |  |  |
| 1.5-year gap | 96 | 4.453 | 1.061 | 2.286 | 1.196 | 1.978 | 1.072 | 1.428 | 0.901 |  |
| 192 | 9.413 | 1.046 | 1.713 | 0.971 | 1.606 | 0.928 | 1.314 | 0.833 |  |  |
| 336 | 10.457 | 1.063 | 1.890 | 1.488 | 1.736 | 0.985 | 1.320 | 0.836 |  |  |
| 2-year gap | 96 | 5.117 | 0.953 | 2.400 | 1.232 | 1.969 | 1.069 | 1.474 | 0.895 |  |
| 192 | 12.769 | 0.933 | 1.974 | 1.070 | 1.670 | 0.942 | 1.505 | 0.883 |  |  |
| 336 | 13.382 | 0.946 | 1.936 | 1.056 | 1.628 | 0.926 | 1.464 | 0.874 |  |  |

### 5.2 Baselines

In our comparison experiments, we adopted four popular baselines, including MLP, Transformer, and Mamba architectures. Autoformer [], an earlier state-of-the-art model, leverages a decomposition architecture and auto-correlation mechanism to enhance efficiency and accuracy in long-term time series forecasting, outperforming traditional Transformer models. iTransformer [] is the latest and most effective Transformer-based model, utilizing attention and feed-forward networks on inverted dimensions, embedding time points into variate tokens. DLinear [] challenges the effectiveness of Transformer models by proposing a simple one-layer linear model that captures temporal relations in an ordered set of continuous points. It employs positional encoding and uses tokens to embed sub-series, preserving some ordering information in Transformers. Lastly, Mamba [], a well-known sequential model from last year, uses a bidirectional Mamba block to extract inter-variate correlations and temporal dependencies.

### 5.3 Implementation Details

We adopted the default settings of the Time-Series-Library [] to conduct a comprehensive comparison of baselines. We use the results of five random seeds as the average. We used 96 time steps as input and 336 time steps as ground truth. The code was implemented in PyTorch and executed on a V100 GPU with 32GB memory and 384GB RAM, provided by NCI Australia, an NCRIS-enabled capability supported by the Australian Government.

### 5.4 Results of Extremely Long Forecasting with gaps

We use Mean Squared Error (MSE) and Mean Absolute Error (MAE) metrics to evaluate performance, averaging results across different seeds. It is observed that nearly all results are poor, highlighting the significant challenge posed by domain shifts over time for extremely long forecasting with gaps. These baseline results also indicate that traditional SOTA rankings and methodologies are no longer effective. Notably, Autoformer, which performs the worst under conventional data and settings, shows the best performance in our setting. This is understandable because Autoformer’s core technology focuses on exploring the correlation within the data itself. This insight suggests that future efforts to tackle this problem should place greater emphasis on leveraging the intrinsic potential of the data.

Considering that our dataset provides an extensive temporal range for training, we can theoretically extend the input step length considerably. We extended the 96 steps input from Table 3 to a maximum of 1440 steps for testing. As shown in Table 4, the performance improves significantly with the increase in input step length, further demonstrating the substantial potential of our dataset for deep exploration.

**Table 4: Results of ablation study with 4 different input step lengths**
| DLinear (Input Length) | 96 | 192 | 336 | 720 |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Metrics | MSE | MAE | MSE | MAE | MSE | MAE | MSE | MAE |  |  |
| PEMS03_gap | 1-year gap | 96 | 1.500 | 0.933 | 1.455 | 0.912 | 1.462 | 0.906 | 1.392 | 0.887 |
| 192 | 1.542 | 0.945 | 1.147 | 0.910 | 1.457 | 0.906 | 1.445 | 0.906 |  |  |
| 336 | 1.531 | 0.935 | 1.447 | 0.907 | 1.462 | 0.906 | 1.439 | 0.904 |  |  |

### 5.5 Results of Hourly and Daily Forecasting

We believe that both the hourly and daily datasets are equally significant. Multi-scale, diverse datasets can provide the community with valuable references. We observe that the performance degrades progressively from the hourly to the daily to the gap datasets. Smaller time scales help reduce complexity and uncertainty, thereby improving prediction accuracy. Research has shown that clustering at different scales can enhance model performance []. Therefore, our aggregated version of the data will contribute new external features to the community.

**Table 5: Comparison in hourly and daily datasets**
| Methods | Mamba | iTransformer | DLinear | Autoformer |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Metrics | MSE | MAE | MSE | MAE | MSE | MAE | MSE | MAE |  |  |
| PEMS03_agg | Hourly | 96 | 0.144 | 0.222 | 0.530 | 0.535 | 0.159 | 0.222 | 0.241 | 0.346 |
| 192 | 0.173 | 0.237 | 0.215 | 0.289 | 0.153 | 0.208 | 0.235 | 0.340 |  |  |
| 336 | 0.158 | 0.220 | 0.519 | 0.527 | 0.167 | 0.216 | 0.260 | 0.362 |  |  |
| Daily | 96 | 0.754 | 0.503 | 0.606 | 0.419 | 0.602 | 0.426 | 0.771 | 0.537 |  |
| 192 | 0.968 | 0.604 | 0.781 | 0.500 | 0.794 | 0.509 | 0.897 | 0.577 |  |  |
| 336 | 1.210 | 0.706 | 0.967 | 0.579 | 0.984 | 0.584 | 1.058 | 0.630 |  |  |
| PEMS04_agg | Hourly | 96 | 0.137 | 0.244 | 0.240 | 0.339 | 0.161 | 0.245 | 0.178 | 0.295 |
| 192 | 0.132 | 0.239 | 0.260 | 0.361 | 0.142 | 0.223 | 0.175 | 0.288 |  |  |
| 336 | 0.121 | 0.216 | 0.226 | 0.413 | 0.145 | 0.226 | 0.197 | 0.316 |  |  |
| Daily | 96 | 0.672 | 0.499 | 0.534 | 0.442 | 0.507 | 0.415 | 0.644 | 0.506 |  |
| 192 | 0.720 | 0.549 | 0.634 | 0.508 | 0.610 | 0.483 | 0.749 | 0.580 |  |  |
| 336 | 0.795 | 0.602 | 0.706 | 0.555 | 0.663 | 0.522 | 0.728 | 0.569 |  |  |
| PEMS07_agg | Hourly | 96 | 0.212 | 0.302 | 0.375 | 0.425 | 0.203 | 0.259 | 0.307 | 0.390 |
| 192 | 0.201 | 0.288 | 0.297 | 0.368 | 0.182 | 0.231 | 0.313 | 0.391 |  |  |
| 336 | 0.191 | 0.245 | 0.126 | 0.156 | 0.190 | 0.241 | 0.291 | 0.364 |  |  |
| Daily | 96 | 1.719 | 0.736 | 1.426 | 0.613 | 1.414 | 0.606 | 1.703 | 0.762 |  |
| 192 | 2.005 | 0.842 | 1.772 | 0.730 | 1.756 | 0.720 | 1.903 | 0.804 |  |  |
| 336 | 2.290 | 0.949 | 2.078 | 0.819 | 2.051 | 0.813 | 2.171 | 0.884 |  |  |
| PEMS08_agg | Hourly | 96 | 0.245 | 0.287 | 0.363 | 0.379 | 0.253 | 0.272 | 0.305 | 0.377 |
| 192 | 0.269 | 0.292 | 0.341 | 0.354 | 0.254 | 0.259 | 0.340 | 0.401 |  |  |
| 336 | 0.283 | 0.298 | 0.369 | 0.369 | 0.281 | 0.272 | 0.452 | 0.468 |  |  |
| Daily | 96 | 0.870 | 0.558 | 0.766 | 0.486 | 0.746 | 0.478 | 0.913 | 0.604 |  |
| 192 | 1.023 | 0.635 | 0.911 | 0.554 | 0.906 | 0.548 | 1.026 | 0.648 |  |  |
| 336 | 1.161 | 0.697 | 1.024 | 0.617 | 1.022 | 0.602 | 1.127 | 0.689 |  |  |

## 6 Prospects and Constraints

Prospects. Our dataset spans the longest time period among existing datasets, and it is not only the largest spatially, but also evolving in growth. It can continue to update in line with the updates from the PeMS system in the future. It is specifically designed for various complex scenarios, such as those already mentioned, including extremely long forecasting with long gaps, and hourly and daily predictions. Additionally, zero-shot forecasting designed for evolving growth scenarios will also be included in Appendix.

Constraints. The limitations of our dataset are also quite evident. Due to the sheer size of our dataset, it requires more computational resources. However, with the maturation of large language models and foundational models, we believe its large volume will become an advantage, contributing more diverse data to the spatio-temporal large model community.

## Acknowledgments and Disclosure of Funding

We would like to acknowledge the support of Cisco’s National Industry Innovation Network (NIIN) Research Chair Program and the ARC Centre of Excellence for Automated Decision-Making and Society (CE200100005). We acknowledge the resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government.

## Checklist

- 1.
For all authors…
(a)
Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope?
[Yes] See Section 1
(b)
Did you describe the limitations of your work?
[Yes] See Section 6
(c)
Did you discuss any potential negative societal impacts of your work?
[Yes] We have discussed and identified no negative societal impact.
(d)
Have you read the ethics review guidelines and ensured that your paper conforms to them?
[Yes]
- 2.
If you are including theoretical results…
(a)
Did you state the full set of assumptions of all theoretical results?
[N/A]
(b)
Did you include complete proofs of all theoretical results?
[N/A]
- 3.
If you ran experiments (e.g. for benchmarks)…
(a)
Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?
[Yes] See Section 4.1
(b)
Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)?
[Yes] See Section 5.1 and Section 5.3
(c)
Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?
[Yes] As discussed in Section 5.3, our experiments area averaged over five random seeds.
(d)
Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)?
[Yes] See Section 5.3
- 4.
If you are using existing assets (e.g., code, data, models) or curating/releasing new assets…
(a)
If your work uses existing assets, did you cite the creators?
[Yes] We have cited the creators in Section 4.1
(b)
Did you mention the license of the assets?
[Yes] See Section 4.4. Our dataset complies with the CC BY-NC 4.0 license, and the code adheres to the MIT license.
(c)
Did you include any new assets either in the supplemental material or as a URL?
[No]
(d)
Did you discuss whether and how consent was obtained from people whose data you’re using/curating?
[Yes] Please refer to Section 4.1 for the detailed discussion.
(e)
Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content?
[No] This dataset is distributed across the highway, collecting traffic speed, traffic flow, and traffic occupancy. It does not contain personally identifiable information or offensive content.
- 5.
If you used crowdsourcing or conducted research with human subjects…
(a)
Did you include the full text of instructions given to participants and screenshots, if applicable?
[N/A]
(b)
Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable?
[N/A]
(c)
Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation?
[N/A]

## Appendix A Appendix

### A.1 Dataset

To support our setting of extremely long forecasting with gaps, we selected a subset installed in the early stages and have consistently collected new data up to the present, which is shown as follows:

**Table 6: Overview of Gap, Hourly, and Daily Aggregated Data Based on Processed Raw Data**
| Datasets(Gap/Hour/Day) | Time Period | Nodes |
| --- | --- | --- |
| PEMS03_gap&agg | 03/2001 - 03/2024 | 151 |
| PEMS04_gap&agg | 06/2002 - 03/2024 | 822 |
| PEMS05_gap&agg | 03/2012 - 03/2024 | 103 |
| PEMS06_gap&agg | 12/2009 - 03/2024 | 130 |
| PEMS07_gap&agg | 06/2002 - 03/2024 | 3062 |
| PEMS08_gap&agg | 03/2001 - 03/2024 | 212 |
| PEMS10_gap&agg | 06/2007 - 03/2024 | 107 |
| PEMS11_gap&agg | 09/2002 - 03/2024 | 521 |
| PEMS12_gap&agg | 01/2002 - 03/2024 | 1543 |

### A.2 Results

Here we provided the results of District 5,6,10,11,12, as shown in Table 7 and Table 8:

**Table 7: Comparison in hourly and daily datasets in District 05,06,10,11,12. The symbol $-$ indicates that the result is an outlier.**
| Methods | Mamba | iTransformer | DLinear | Autoformer |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Metrics | MSE | MAE | MSE | MAE | MSE | MAE | MSE | MAE |  |  |
| PEMS05_agg | Hourly | 96 | 0.121 | 0.205 | 0.226 | 0.324 | 0.148 | 0.236 | 0.155 | 0.272 |
| 192 | 0.118 | 0.197 | 0.214 | 0.312 | 0.132 | 0.216 | 0.159 | 0.268 |  |  |
| 336 | 0.115 | 0.194 | 0.220 | 0.318 | 0.134 | 0.217 | 0.167 | 0.274 |  |  |
| Daily | 96 | 0.655 | 0.511 | 0.654 | 0.510 | 0.607 | 0.477 | 0.650 | 0.522 |  |
| 192 | 0.798 | 0.598 | 0.775 | 0.576 | 0.698 | 0.535 | 0.643 | 0.512 |  |  |
| 336 | 0.907 | 0.614 | 0.787 | 0.582 | 0.745 | 0.556 | 0.764 | 0.578 |  |  |
| PEMS06_agg | Hourly | 96 | 0.142 | 0.234 | 0.269 | 0.336 | 0.188 | 0.254 | 0.164 | 0.271 |
| 192 | 0.138 | 0.218 | 0.256 | 0.326 | 0.166 | 0.227 | 0.188 | 0.291 |  |  |
| 336 | 0.137 | 0.207 | 0.226 | 0.413 | 0.171 | 0.227 | 0.197 | 0.316 |  |  |
| Daily | 96 | 0.516 | 0.419 | 0.414 | 0.389 | 0.405 | 0.340 | 0.518 | 0.437 |  |
| 192 | 0.642 | 0.485 | 0.543 | 0.468 | 0.510 | 0.395 | 0.580 | 0.460 |  |  |
| 336 | 0.734 | 0.519 | 0.675 | 0.492 | 0.596 | 0.437 | 0.658 | 0.481 |  |  |
| PEMS10_agg | Hourly | 96 | 0.213 | 0.256 | 0.391 | 0.413 | 0.272 | 0.309 | 0.260 | 0.346 |
| 192 | 0.205 | 0.250 | 0.387 | 0.412 | 0.246 | 0.277 | 0.380 | 0.417 |  |  |
| 336 | 0.211 | 0.255 | 0.394 | 0.413 | 0.258 | 0.281 | 0.329 | 0.386 |  |  |
| Daily | 96 | 1.161 | 0.671 | 0.926 | 0.513 | 0.951 | 0.567 | 1.079 | 0.647 |  |
| 192 | 1.459 | 0.784 | 1.414 | 0.730 | 1.228 | 0.681 | 1.429 | 0.786 |  |  |
| 336 | 1.715 | 0.855 | 1.478 | 0.768 | 1.451 | 0.751 | 1.552 | 0.817 |  |  |
| PEMS11_agg | Hourly | 96 | - | 0.472 | - | 0.538 | - | 0.306 | - | 0.800 |
| 192 | - | 0.470 | - | 0.443 | - | 0.291 | - | 0.742 |  |  |
| 336 | - | 0.487 | - | 0.435 | - | 0.300 | - | 0.745 |  |  |
| Daily | 96 | - | - | - | - | - | - | - | - |  |
| 192 | - | - | - | - | - | - | - | - |  |  |
| 336 | - | - | - | - | - | - | - | - |  |  |
| PEMS12_agg | Hourly | 96 | 0.145 | 0.237 | 0.083 | 0.157 | 0.174 | 0.242 | 0.188 | 0.287 |
| 192 | 0.142 | 0.226 | 0.091 | 0.159 | 0.154 | 0.216 | 0.196 | 0.289 |  |  |
| 336 | 0.146 | 0.217 | 0.104 | 0.170 | 0.162 | 0.219 | 0.209 | 0.298 |  |  |
| Daily | 96 | 1.373 | 0.621 | 1.456 | 0.622 | 1.052 | 0.510 | 1.348 | 0.649 |  |
| 192 | 1.722 | 0.726 | 1.675 | 0.678 | 1.403 | 0.611 | 1.548 | 0.686 |  |  |
| 336 | 2.066 | 0.823 | 1.984 | 0.641 | 1.654 | 0.675 | 1.801 | 0.746 |  |  |

**Table 8: Comparison in gap dataset in District 05,06,10,11,12. The bold text indicates the best.**
| Methods | Mamba | iTransformer | DLinear | Autoformer |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Metrics | MSE | MAE | MSE | MAE | MSE | MAE | MSE | MAE |  |  |
| PEMS05_gap | 1-year gap | 96 | 2.079 | 1.209 | 1.945 | 1.164 | 1.291 | 0.916 | 1.065 | 0.796 |
| 192 | 2.132 | 1.256 | 1.984 | 1.185 | 1.750 | 1.099 | 1.063 | 0.809 |  |  |
| 336 | 2.377 | 1.340 | 2.067 | 1.234 | 1.894 | 1.144 | 1.135 | 0.827 |  |  |
| 1.5-year gap | 96 | 1.852 | 1.122 | 1.879 | 1.078 | 1.683 | 1.054 | 1.060 | 0.785 |  |
| 192 | 1.929 | 1.182 | 1.593 | 1.032 | 1.633 | 1.045 | 0.912 | 0.712 |  |  |
| 336 | 2.370 | 1.313 | 2.214 | 1.071 | 0.794 | 1.184 | 1.345 | 0.882 |  |  |
| 2-year gap | 96 | 1.868 | 1.106 | 1.580 | 0.969 | 1.602 | 1.018 | 0.828 | 0.672 |  |
| 192 | 2.219 | 1.274 | 1.481 | 0.958 | 1.589 | 1.027 | 1.018 | 0.772 |  |  |
| 336 | 2.695 | 1.212 | 2.207 | 1.201 | 1.922 | 1.139 | 1.186 | 0.839 |  |  |
| PEMS06_gap | 1-year gap | 96 | 1.806 | 1.066 | 0.875 | 0.692 | 1.173 | 0.837 | 1.216 | 0.859 |
| 192 | 1.928 | 1.112 | 1.227 | 0.848 | 1.410 | 0.942 | 0.961 | 0.751 |  |  |
| 336 | 2.181 | 1.212 | 1.594 | 1.003 | 1.501 | 0.976 | 0.992 | 0.769 |  |  |
| 1.5-year gap | 96 | 1.549 | 0.997 | 1.331 | 0.891 | 1.484 | 0.963 | 0.885 | 0.710 |  |
| 192 | 1.746 | 1.054 | 1.077 | 0.778 | 1.353 | 0.920 | 1.010 | 0.768 |  |  |
| 336 | 1.605 | 1.018 | 1.500 | 0.961 | 1.587 | 1.011 | 0.955 | 0.739 |  |  |
| 2-year gap | 96 | 1.226 | 0.851 | 1.864 | 1.106 | 1.691 | 1.033 | 1.013 | 0.768 |  |
| 192 | 0.949 | 0.720 | 1.343 | 0.879 | 1.259 | 0.858 | 0.853 | 0.691 |  |  |
| 336 | 0.945 | 0.710 | 1.550 | 0.970 | 1.415 | 0.934 | 0.955 | 0.739 |  |  |
| PEMS10_gap | 1-year gap | 96 | 2.310 | 1.233 | 0.882 | 0.698 | 1.466 | 0.963 | 1.208 | 0.865 |
| 192 | 2.878 | 1.396 | 1.606 | 0.993 | 1.834 | 1.106 | 1.152 | 0.860 |  |  |
| 336 | 2.584 | 1.327 | 1.413 | 0.825 | 1.887 | 1.121 | 1.130 | 0.830 |  |  |
| 1.5-year gap | 96 | 2.181 | 1.206 | 1.812 | 1.052 | 1.765 | 1.057 | 1.368 | 0.913 |  |
| 192 | 2.525 | 1.318 | 1.645 | 0.986 | 1.791 | 1.069 | 1.151 | 0.828 |  |  |
| 336 | 2.488 | 1.310 | 2.051 | 1.134 | 1.976 | 1.139 | 1.111 | 0.809 |  |  |
| 2-year gap | 96 | 1.165 | 0.822 | 2.772 | 1.383 | 1.977 | 1.119 | 1.188 | 0.845 |  |
| 192 | 1.082 | 0.792 | 1.974 | 1.084 | 1.490 | 0.932 | 0.971 | 0.751 |  |  |
| 336 | 1.021 | 0.764 | 2.157 | 1.168 | 1.619 | 0.989 | 1.096 | 0.809 |  |  |
| PEMS11_gap | 1-year gap | 96 | 5.199 | 0.891 | 5.417 | 0.997 | 5.276 | 0.936 | 4.930 | 0.854 |
| 192 | 5.300 | 0.920 | 5.504 | 1.029 | 5.393 | 0.974 | 5.114 | 0.867 |  |  |
| 336 | 5.251 | 0.901 | 5.410 | 0.985 | 5.364 | 0.955 | 4.830 | 0.849 |  |  |
| 1.5-year gap | 96 | 5.871 | 0.968 | 6.045 | 1.296 | 5.914 | 1.224 | 5.691 | 0.878 |  |
| 192 | 5.968 | 1.012 | 6.121 | 1.314 | 5.993 | 1.229 | 5.792 | 0.884 |  |  |
| 336 | 6.167 | 1.074 | 6.214 | 1.326 | 6.025 | 1.299 | 5.947 | 0.967 |  |  |
| 2-year gap | 96 | 5.914 | 0.996 | 6.136 | 1.318 | 5.945 | 1.243 | 5.761 | 0.978 |  |
| 192 | 6.541 | 1.043 | 6.213 | 1.327 | 6.014 | 1.289 | 6.245 | 1.001 |  |  |
| 336 | 6.541 | 1.086 | 6.221 | 1.332 | 6.024 | 1.300 | 6.268 | 1.024 |  |  |
| PEMS12_gap | 1-year gap | 96 | 1.751 | 1.025 | 1.624 | 1.002 | 1.611 | 1.005 | 1.060 | 0.789 |
| 192 | 1.726 | 1.024 | 1.424 | 0.929 | 1.537 | 0.972 | 1.150 | 0.840 |  |  |
| 336 | 1.751 | 1.029 | 1.672 | 1.015 | 1.683 | 1.017 | 0.889 | 0.719 |  |  |
| 1.5-year gap | 96 | 1.554 | 0.967 | 1.479 | 0.921 | 1.468 | 0.910 | 0.954 | 0.875 |  |
| 192 | 1.401 | 0.898 | 1.314 | 0.867 | 1.301 | 0.862 | 0.943 | 0.869 |  |  |
| 336 | 1.417 | 0.906 | 1.322 | 0.869 | 1.298 | 0.859 | 0.921 | 0.846 |  |  |
| 2-year gap | 96 | 0.956 | 0.704 | 0.876 | 0.671 | 0.872 | 0.664 | 0.846 | 0.659 |  |
| 192 | 0.846 | 0.656 | 0.813 | 0.653 | 0.806 | 0.649 | 0.785 | 0.628 |  |  |
| 336 | 0.814 | 0.628 | 0.789 | 0.631 | 0.776 | 0.624 | 0.754 | 0.617 |  |  |

Additionally, we provided the results of naive zero-shot forecasting, as shown in Table 9. The poor performance of this method indicates significant potential for improvement.

**Table 9: Results of zero shot forecasting in PEMS03. In this study, we utilized the inputs from 151 existing nodes in PEMS03 to predict 52 new nodes, resulting in a test set comprising a total of 203 nodes.**
| Datasets | 96 | 192 | 336 |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Metrics | MSE | MAE | MSE | MAE | MSE | MAE |  |
| PEMS03_gap | 1-year gap | 4.658 | 3.465 | 4.945 | 3.648 | 5.198 | 3.892 |
| 1.5-year gap | 4.891 | 3.657 | 5.124 | 3.842 | 5.263 | 3.996 |  |
| 2-year gap | 4.547 | 3.410 | 4.895 | 3.539 | 5.103 | 3.758 |  |

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 5
presentation: 2
questions: See Weaknesses.
soundness: 3
strengths: 1. A brand-new dataset is proposed, which is the largest available public traffic dataset in extremely long traffic forecasting.

2. Numerous comparative experiments validate the differences in performance of different models on this dataset, providing a good guide for peers to understand the latest technological developments.
summary: The authors present XXLTraffic, the longest available public traffic dataset with the longest timespan collected from Los Angeles, USA, and New South Wales, Australia, curated to support research in extremely long forecasting beyond test adaptation. The benchmark includes both typical time-series forecasting settings with hourly and daily aggregated data and novel configurations that introduce gaps and down-sample the training size to better simulate practical constraints.
weaknesses: 1. The maximum number of nodes in the proposed dataset is less than the Large-ST, which seems to need improvement, after all, the real road network is very large.

2. I think it is necessary for the authors to explain in more depth the motivation for constructing such a long-term dataset, because as transportation infrastructure advances and human travel modes change, traffic data that is too ancient is not always helpful enough to understand future transportation patterns.

3.  Due to the sheer size of the proposed dataset, it requires more computational resources. Therefore, whether this dataset can be popularized and whether peers can easily adopt it in their own research is also a concern.

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 3
questions: Please refer to the Weaknesses.
soundness: 1
strengths: (1) A new traffic dataset is proposed, comprising data from numerous regions across California and New South Wales over an extended time period.

(2) The temporal distribution evolution of selected data was visualized, revealing the evolutionary characteristics of the data.

(3) Tests were conducted on relevant prediction tasks, including scenarios with time gaps and varying input lengths.
summary: This paper presents a traffic dataset containing extensive data records from multiple regions over a long-term period, aimed at supporting research in ultra-long-term traffic prediction.
weaknesses: (1) Compared to existing works like LargeST, this work's contribution is insufficient as it merely expands the temporal and spatial scope of data collection. These data can be obtained through the open-source PEMS system in the same way.

(2) Although the number of regions and time span are substantial, the vast majority of data is limited to California. Including data from more cities and countries might have been a better approach.

(3) Most baselines in the experiments are specifically designed for time series prediction, lacking models specifically designed for traffic prediction/spatiotemporal prediction, such as PDFormer[1], ASTGCN[2], STGCN[3], AGCRN[4], GWN[5], etc. It is important to explore the performance of these types of baselines on traffic datasets.

[1] PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction. AAAI 2023.

[2] Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. AAAI 2019.

[3] Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. IJCAI 2018.

[4] Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. NeurIPS 2020.

[5] Graph WaveNet for Deep Spatial-Temporal Graph Modeling. IJCAI 2019.

---

confidence: 5
contribution: 2
initial_rating: 5
presentation: 3
questions: The authors can address the above concerns.
soundness: 2
strengths: 1.	The proposed XXLTraffic is the largest publicly available traffic dataset so far. The PEMS part spans up to 23 years and the NSW part also has around 11 years’ data.

2.	The authors evaluated the performance of existing baselines on the proposed datasets with various settings. The overall results verify the importance of introducing large-scale datasets with extremely long time spans.
summary: This paper proposed large-scale datasets, named XXLTrafic, to address limitations in existing traffic forecasting datasets. It provides extensive, real-world datasets with long timespans to support research in extremely long forecasting and adaptive model development for evolving traffic patterns.
weaknesses: 1.	The most significant weakness is that this paper has no novelty. Although this conference has a dataset and benchmarks area, I do not think, for a top-tier conference, it is enough to propose a large-scale dataset without any model contribution. Given the fact that there are existing large-scale datasets available with just the number of nodes or time spans fewer than XXTraffic, my concern becomes more intense.

2.	Some motivations remain unclear. In the introduction section, the authors mentioned ‘beyond test adaptation’. However, it is not easy to understand how this relates to the proposed datasets. Is the concept of ‘beyond test adaptation’ better or more realistic than test time adaptation?

3.	The setting of Extremely Long Prediction with Gaps is a bit strange to me. Here, the authors used an example of highway route planning prediction to justify the necessity of the gap. It is possible that we need to stimulate the traffic flows of uninstalled sensors to facilitate road planning. Thus, the optimal setting should be using existing sensors to predict traffic flows of uninstalled sensors, plus the gap. However, it seems the authors still used historical signals of uninstalled sensors in this setting. If we already know their historical readings, we do not have to plan anything.

---

confidence: 4
contribution: 4
initial_rating: 8
presentation: 3
questions: How is the gap dataset collected, given that "gap" typically refers to missing data? Can this dataset be directly used to address traditional missing data scenarios?
soundness: 4
strengths: 1. This paper introduces an XXLTraffic dataset that spans up to 23 years from different regions, which means the dataset include sufficient information for the traffic feature.
2. This paper presents many potential conditions that the dataset could be applied to such as short-term traffic prediction, long-term multivariate traffic prediction and extremely long term prediction with gaps. All of these domains are worthwhile problem to focus.
summary: This paper present an XXLTraffic traffic dataset spanning up to 23 years in different countries, which can be useful for the other researchers. They have also implemented the datasets with many advanced approaches to confirm the range and different experiments directions.
weaknesses: 1. The dataset is valuable, but performing extremely long-term predictions is challenging, as traffic patterns can change significantly over the years, and even the road infrastructure itself may be reshaped over a 23-year span. Such factors could potentially reduce the impact of the dataset's contribution, because many open datasets are enough for normal short and long term prediction.
2. Some typos, e.g., ‘beyongd’  should be ‘beyond’ around line 130;

Meta-review: This paper presents XXLTraffic with the longest timespan collected from Los Angeles, USA, and New South Wales, Australia, curated to support research in extremely long forecasting beyond test adaptation. The reviewers and I believe that providing this benchmark is a very meaningful contribution to the research community.  However, as some reviewers pointed out, the benchmark itself should have enough novelty to open or support a new research problem.   The reviewers feel that increasing the temporal range of the benchmark dataset may not be sufficient for that perspective.  I also agree with the reviewers' opinions.  When I submitted a benchmark paper to NeurIPS Datasets & Benchmarks track, I have often received similar comments.  Overall, considering the reviewers' opinions, I am sorry to recommend a reject.

Decision: Reject