This list can be considered outdated. For a more up-to-date list, check: https://github.com/lixus7/Time-Series-Works-Conferences
Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).
Things are usually better defined through exclusions, so here are similar things that I do not include:
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NYC taxi and bike (and other similar datsets, like uber), are not included, because they tend to be represented as a grid, not a graph.
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Predicting human mobility, either indoors, or through checking-in in Point of Interest (POI), or through a transport network.
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Predicting trajectory.
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Predicting the movement of individual cars through sensors for the purpose of self-driving car.
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Traffic data imputations.
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Traffic anomaly detections.
The papers are haphazardly selected.
A tabular summary of paper and publically available datasets. The paper is reverse chronologically sorted. NO GUARANTEE is made that this table is complete or accurate (please raise an issue if you spot any error).
paper | venue | published date | # other datsets | METR-LA | PeMS-BAY | PeMS-D7(M) | PeMS-D7(L) | PeMS-04 | PeMS-08 | LOOP | SZ-taxi | Los-loop | PeMS-03 | PeMS-07 | PeMS-I-405 | PeMS-04(S) | TOTAL open |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
TOTAL | 38 | 28 | 6 | 3 | 3 | 3 | 3 | 2 | 2 | 1 | 1 | 1 | 1 | 95 | |||
G-SWaN | IoTDI | 9 May 23 | 1 | 1 | 1 | 1 | 4 | ||||||||||
SCPT | ArXiv | 9 May 23 | 1 | 1 | 1 | 1 | 4 | ||||||||||
MP-WaveNet | ArXiv | 9 May 23 | 1 | 1 | 2 | ||||||||||||
GTS | ICLR | 4 May 21 | 1 | 1 | 1 | 2 | |||||||||||
FASTGNN | TII | 29 Jan 21 | 1 | 1 | |||||||||||||
HetGAT | JAIHC | 23 Jan 21 | 1 | 1 | 2 | ||||||||||||
GST-GAT | IEEE Access | 6 Jan 21 | 1 | 1 | 2 | ||||||||||||
CLGRN | arXiv | 4 Jan 21 | 3 | 1 | 1 | ||||||||||||
DKFN | SIGSPATIAL | 3 Nov 20 | 1 | 1 | 2 | ||||||||||||
STGAM | CISP-BMEI | 17 Oct 20 | 1 | 1 | 2 | ||||||||||||
ARNN | Nat. Commun | 11 Sept 20 | 1 | 1 | |||||||||||||
ST-TrafficNet | ELECGJ | 9 Sept 20 | 1 | 1 | 2 | ||||||||||||
M2 | J. AdHoc | 1 Sept 20 | 1 | 1 | 2 | ||||||||||||
H-STGCN | KDD | 23 Aug 20 | 0 | ||||||||||||||
SGMN | J. TRC | 20 Aug 20 | 1 | 1 | 2 | ||||||||||||
GDRNN | NTU | 16 Aug 20 | 1 | 1 | 2 | ||||||||||||
ISTD-GCN | arXiv | 10 Aug 20 | 1 | 1 | 2 | ||||||||||||
GTS | UCONN | 3 Aug 20 | 1 | 1 | 2 | ||||||||||||
FC-GAGA | arXiv | 30 Jul 20 | 1 | 1 | 2 | ||||||||||||
STGAT | IEEE Access | 22 Jul 20 | 1 | 1 | 2 | ||||||||||||
STNN | T-ITS | 16 Jul 20 | 0 | ||||||||||||||
AGCRN | arXiv | 6 Jul 20 | 1 | 1 | 2 | ||||||||||||
GWNN-LSTM | J. Phys. Conf. Ser. | 20 Jun 20 | 1 | 1 | |||||||||||||
A3T-GCN | arXiv | 20 Jun 20 | 1 | 1 | 2 | ||||||||||||
TSE-SC | Trans-GIS | 1 Jun 20 | 1 | 1 | 2 | ||||||||||||
MTGNN | arXiv | 24 May 20 | 1 | 1 | 2 | ||||||||||||
ST-MetaNet+ | TKDE | 19 May 20 | 1 | 1 | 2 | ||||||||||||
STGNN | WWW | 20 Apr 20 | 1 | 1 | 2 | ||||||||||||
STSeq2Seq | arXiv | 6 Apr 20 | 1 | 1 | 2 | ||||||||||||
DSTGNN | arXiv | 12 Mar 20 | 1 | 1 | |||||||||||||
RSTAG | IoT-J | 19 Feb 20 | 1 | 1 | 2 | ||||||||||||
GMAN | AAAI | 7 Feb 20 | 1 | 1 | |||||||||||||
MRA-BGCN | AAAI | 7 Feb 20 | 1 | 1 | 2 | ||||||||||||
STSGCN | AAAI | 7 Feb 20 | 1 | 1 | 1 | 1 | 4 | ||||||||||
SLCNN | AAAI | 7 Feb 20 | 1 | 1 | 1 | 3 | |||||||||||
DDP-GCN | arXiv | 7 Feb 20 | 0 | ||||||||||||||
R-SSM | ICLR | 13 Jan 20 | 1 | 1 | |||||||||||||
GWNV2 | arXiv | 11 Dec 19 | 1 | 1 | 2 | ||||||||||||
DeepGLO | NeurIPS | 8 Dec 19 | 1 | 1 | 1 | ||||||||||||
STGRAT | arXiv | 29 Nov 19 | 1 | 1 | 2 | ||||||||||||
TGC-LSTM | T-ITS | 28 Nov 19 | 1 | 1 | |||||||||||||
DCRNN-RIL | TrustCom/BigDataSE | 31 Oct 19 | 1 | 1 | 2 | ||||||||||||
L-VGAE | arXiv | 18 Oct 19 | 1 | 1 | |||||||||||||
T-GCN | T-ITS | 22 Aug 19 | 1 | 1 | 2 | ||||||||||||
GWN | IJCAI | 10 Aug 19 | 1 | 1 | 2 | ||||||||||||
ST-MetaNet | KDD | 25 Jul 19 | 1 | 1 | |||||||||||||
MRes-RGNN-G | AAAI | 17 Jul 19 | 1 | 1 | 2 | ||||||||||||
CDSA | arXiv | 23 May 19 | 1 | 1 | |||||||||||||
STDGI | ICLR | 12 Apr 19 | 1 | 1 | |||||||||||||
ST-UNet | arXiv | 13 Mar 19 | 1 | 1 | 1 | 3 | |||||||||||
3D-TGCN | arXiv | 3 Mar 19 | 1 | 1 | 1 | 3 | |||||||||||
ASTGCN | AAAI | 27 Jan 19 | 1 | 1 | 2 | ||||||||||||
PSN | T-ITS | 17 Aug 18 | 1 | 0 | |||||||||||||
GaAN | UAI | 6 Aug 18 | 2 | 1 | 1 | ||||||||||||
Seq2Seq Hybrid | KDD | 19 Jul 18 | 0 | ||||||||||||||
STGCN | IJCAI | 13 Jul 18 | 1 | 1 | 2 | ||||||||||||
DCRNN | ICLR | 30 Apr 18 | 1 | 1 | 2 | ||||||||||||
SBU-LSTM | UrbComp | 14 Aug 17 | 1 | 1 | |||||||||||||
GRU | YAC | 5 Jan 17 | 1 | 1 |
NOTES: The experimental setttings may vary. But the common setting is:
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Observation window = 12 timesteps
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Prediction horizon = 1 timesteps
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Prediction window = 12 timesteps
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Metrics = MAE, RMSE, MAPE
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Train, validation, and test splits = 7/1/2 OR 6/2/2
However, there are many caveats:
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Some use different models for different prediction horizon.
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Some use different batch size when testing previous models, as they increase the observation and prediction windows from previous studies, and have difficulties fitting it on GPU using the same batch size.
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Regarding adjacency matrix, some derive it using Gaussian RBF from the coordinates, some use the actual connectivity, some simply learn it, and some use combinations.
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Some might also add more context, such as time of day, or day of the week, or weather.
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DeepGLO in particular, since it is treating it as a multi-channel timeseries without the spatial information, use rolling validation,
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Many different treatment of missing datasets, from exclusion to imputations.
Publically available datasets and where to find them.
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METR-LA DCRNN Google Drive; DCRNN Baidu; Sensor coordinates and adjacency matrix, also from DCRNN
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California department of transportation (Caltrans) Performance Measurement System (PeMS). The website is: http://pems.dot.ca.gov/. From the website: The traffic data displayed on the map is collected in real-time from over 39,000 individual detectors. These sensors span the freeway system across all major metropolitan areas of the State of California
- PeMS-BAY DCRNN Google Drive; DCRNN Baidu
Sensor coordinates and adjacency matrix, DCRNN github
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PeMS-D7(M) PKUAI26 STGCN Github
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PeMS-D7(L)
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PeMS-04 ATSGCN Github; Baidu with code: "p72z"; From Davidham3 Github STSGCN
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PeMS-08 ATSGCN github; Baidu with code: "p72z"; From Davidham3 github STSGCN
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PeMS-03 Baidu with code: "p72z"; From Davidham3 github STSGCN
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PeMS-07 Baidu with code: "p72z"; From Davidham3 github STSGCN
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PeMS-SF UCI
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PeMS-11160 GP-DCRNN github
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RMTMC - MnDOT https://www.d.umn.edu/~tkwon/TMCdata/TMCarchive.html The data in this archive are continuously collected by the Regional Trasportation Management Center (RTMC), a division of Minesotta Deaprtment of Transport (MnDOT) USA.
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FHWA https://www.fhwa.dot.gov/policyinformation/tables/tmasdata/
The following datasets are not publically available:
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INRIX https://pdfs.semanticscholar.org/4b9c/9389719caff7409d9f9cee8628aef4e38b3b.pdf
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Beijing
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BJER4
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BJF
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BRF
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BRF-L
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W3-715
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E5-2907
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NE-BJ https://github.com/tsinghua-fib-lab/Traffic-Benchmark
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Also relevant:
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Tsinghua Fib Lab GitHub
The papers are sorted alphabetically based on model name. The citations are based on Google scholar citation.
You can find the bibtex in traffic_prediction.bib (not complete yet)
Things that would be in the table above if I have more time:
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Multi-Attention Temporal and Graph Convolution Network for Traffic Flow Forecasting PyTorch
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Foreseeing Congestion using LSTM on Urban Traffic Flow Clusters ICSAI 2019 Keras; dataset: CityPulse
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Using LSTM and GRU neural network methods for traffic flow prediction IEEE YAC 2016 Keras; dataset: PeMS but different from everyone else
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A Dynamic Traffic Awareness System for Urban Driving IEEE GreenCom 2019 Keras; dataset: CityPulse
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Inductive Graph Neural Networks for Spatiotemporal Kriging (IGNNK) AAAI 2021 PyTorch dataset: METR-LA, PeMS-BAY, LOOP, NREL, USHCN.
Other works that is not based on a static-spatial-graph of timeseries:
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Deep Representation Learning for Trajectory Similarity Computation
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Curb-GAN SIG KDD 2020
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BusTr SIG KDD 2020
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DeepMove WWW 2018
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https://deepmind.com/blog/article/traffic-prediction-with-advanced-graph-neural-networks
Other lists:
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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges IEEE TKDE 2020
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A Comprehensive Survey on Traffic Prediction to be published in IEEE Transactions on Intelligent Transportation Systems.
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A Comprehensive Survey on Graph Neural Networks IEEE Trans. Neural Netw. Learn. Syst. 2020
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A Comprehensive Survey on Geometric Deep Learning IEEE Access 19 Feb 2020.
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Graph Neural Network for Traffic Forecasting: A Survey Expert Systems with Applications GitHub
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BigsCity LibCity: Traffic Prediction Paper Collection In the paper collection, we collected traffic prediction papers published in the recent years (2016-now) on 11 top conferences and journals, namely, AAAI, IJCAI, KDD, CIKM, ICDM, WWW, NIPS, ICLR, SIGSPATIAL, IEEE TKDE and IEEE TITS. In addition, the surveys since 2016 and representative papers mentioned in the surveys are also included.
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Older, pre-ML approaches: On the modeling of traffic and crowds: A survey of models, speculations, and perspectives SIAM 2011
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https://github.com/Knowledge-Precipitation-Tribe/Urban-computing-papers
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DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction CIKM 2021 GitHub PyTorch