This list can be considered outdated. For a more up-to-date list, check: https://github.com/lixus7/Time-Series-Works-Conferences

Traffic Prediction

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:

• 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.

• Predicting human mobility, either indoors, or through checking-in in Point of Interest (POI), or through a transport network.

• Predicting trajectory.

• Predicting the movement of individual cars through sensors for the purpose of self-driving car.

• Traffic data imputations.

• Traffic anomaly detections.

The papers are haphazardly selected.

Summary

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

Performance

NOTES: The experimental setttings may vary. But the common setting is:

• Observation window = 12 timesteps

• Prediction horizon = 1 timesteps

• Prediction window = 12 timesteps

• Metrics = MAE, RMSE, MAPE

• Train, validation, and test splits = 7/1/2 OR 6/2/2

However, there are many caveats:

• Some use different models for different prediction horizon.

• 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.

• 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.

• Some might also add more context, such as time of day, or day of the week, or weather.

• DeepGLO in particular, since it is treating it as a multi-channel timeseries without the spatial information, use rolling validation,

• Many different treatment of missing datasets, from exclusion to imputations.

Dataset

Publically available datasets and where to find them.

• METR-LA DCRNN Google Drive; DCRNN Baidu; Sensor coordinates and adjacency matrix, also from DCRNN

• 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

  • PeMS-D7(M) PKUAI26 STGCN Github

  • PeMS-D7(L)

  • PeMS-04 ATSGCN Github; Baidu with code: "p72z"; From Davidham3 Github STSGCN

  • PeMS-08 ATSGCN github; Baidu with code: "p72z"; From Davidham3 github STSGCN

  • PeMS-03 Baidu with code: "p72z"; From Davidham3 github STSGCN

  • PeMS-07 Baidu with code: "p72z"; From Davidham3 github STSGCN

  • PeMS-SF UCI

  • PeMS-11160 GP-DCRNN github

• LOOP https://github.com/zhiyongc/Seattle-Loop-Data

• Q-Traffic https://github.com/JingqingZ/BaiduTraffic

Baidu, code: 'umqd'

• 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.

• OpenITS http://www.openits.cn/openData/index.jhtml

• FHWA https://www.fhwa.dot.gov/policyinformation/tables/tmasdata/

The following datasets are not publically available:

• INRIX https://pdfs.semanticscholar.org/4b9c/9389719caff7409d9f9cee8628aef4e38b3b.pdf

• Beijing

  • BJER4

  • BJF

  • BRF

  • BRF-L

  • W3-715

  • E5-2907

  • NE-BJ https://github.com/tsinghua-fib-lab/Traffic-Benchmark

• Xiamen https://ieeexplore.ieee.org/document/8029849

Also relevant:

• Davidham3 list

Libraries

• LibCity GitHub

• Tsinghua Fib Lab GitHub

• PyTorch Geometric

Paper

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)

model	citations	venue	published date	paper	codes
3D-TGCN	12	arXiv	3 Mar 19	3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting
AGCRN	3	arXiv	6 Jul 20	Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting	PyTorch
ARNN	0	Nat. Commun	11 Sep 20	Autoreservoir computing for multistep ahead prediction based on the spatiotemporal information transformation
ASTGCN	63	AAAI	27 Jan 19	Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting	Pytorch MXNet
CDSA	2	arXiv	23 May 19	CDSA: Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation
CLGRN	0	arXiv	4 Jan 21	Conditional Local Filters with Explainers for Spatio-Temporal Forecasting
DCRNN	427	ICLR	30 Apr 18	DIFFUSION CONVOLUTIONAL RECURRENT NEURAL NETWORK: DATA-DRIVEN TRAFFIC FORECASTING	tf PyTorch
DCRNN-RIL	2	TrustCom/BigDataSE	31 Oct 19	Diffusion Convolutional Recurrent Neural Network with Rank Influence Learning for Traffic Forecasting
DDP-GCN	1	arXiv	7 Feb 20	DDP-GCN: Multi-Graph Convolutional Network for Spatiotemporal Traffic Forecasting
DeepGLO	22	NeurIPS	8 Dec 19	Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting
DGCRN	0	ArXiv	30 Apr 21	Spatiotemporal Adaptive Gated Graph Convolution Network for Urban Traffic Flow Forecasting	PyTorch
DKFN	0	SIGSPATIAL	3 Nov 20	Graph Convolutional Networks with Kalman Filtering for Traffic Prediction	PyTorch
DSTGNN	0	arXiv	12 Mar 20	Dynamic Spatiotemporal Graph Neural Network with Tensor Network
FASTGNN	0	TII	29 Jan 21	FASTGNN: A Topological Information Protected Federated Learning Approach For Traffic Speed Forecasting
FC-GAGA	0	arXiv	30 Jul 20	FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic Forecasting
FreqST	2	ICDM	17 Nov 21	FreqST: Exploiting Frequency Information in Spatiotemporal Modeling for Traffic Prediction
GaAN	126	UAI	6 Aug 18	GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs	MXNet
GDRNN	0	NTU	16 Aug 20	Deep learning approaches for traffic prediction
GMAN	20	AAAI	7 Feb 20	GMAN: A Graph Multi-Attention Network for Traffic Prediction	tf
GRU	308	YAC	5 Jan 17	Using LSTM and GRU neural network methods for traffic flow prediction	Keras
GST-GAT	0	IEEE Access	6 Jan 21	Modeling Global Spatial–Temporal Graph Attention Network for Traffic Prediction
G-SWaN	0	IoTDI	9 May 23	Because Every Sensor Is Unique, so Is Every Pair: Handling Dynamicity in Traffic Forecasting	PyTorch
GTS	0	UCONN	3 Aug 20	End-to-End Structure-Aware Convolutional Networks on Graphs
GTS	0	ICLR	4 May 21	Discrete Graph Structure Learning for Forecasting Multiple Time Series	PyTorch
GWN	46	IJCAI	10 Aug 19	Graph WaveNet for Deep Spatial-Temporal Graph Modeling	PyTorch
GWNN-LSTM	0	J. Phys. Conf. Ser.	20 Jun 20	Graph Wavelet Long Short-Term Memory Neural Network: A Novel Spatial-Temporal Network for Traffic Prediction.
GWNV2	0	arXiv	11 Dec 19	Incrementally Improving Graph WaveNet Performance on Traffic Prediction	PyTorch
H-STGCN	0	KDD	23 Aug 20	Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data
HetGAT	0	JAIHC	23 Jan 21	HetGAT: a heterogeneous graph attention network for freeway traffic speed prediction
ISTD-GCN	0	arXiv	10 Aug 20	ISTD-GCN: Iterative Spatial-Temporal Diffusion Graph Convolutional Network for Traffic Speed Forecasting
L-VGAE	0	arXiv	18 Oct 19	Decoupling feature propagation from the design of graph auto-encoders
LSTM	39	TENCON	22 Nov 16	Traffic flow prediction with Long Short-Term Memory Networks (LSTMs)
M2	1	J. AdHoc	1 Sep 20	A performance modeling and analysis of a novel vehicular traffic flow prediction system using a hybrid machine learning-based model
MP-WaveNet	0	ArXiv	9 May 23	Message Passing Neural Networks for Traffic Forecasting
MRA-BGCN	7	AAAI	7 Feb 20	Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting
MRes-RGNN-G	20	AAAI	17 Jul 19	Gated Residual Recurrent Graph Neural Networks for Traffic Prediction
MTGNN	7	arXiv	24 May 20	Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
PSN	4	T-ITS	17 Aug 18	Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework
R-SSM	0	ICLR	13 Jan 20	Relational State-Space Model for Stochastic Multi-Object Systems
RSTAG	3	IoT-J	19 Feb 20	Reinforced Spatiotemporal Attentive Graph Neural Networks for Traffic Forecasting
SAE	1626	T-ITS	9 Sep 14	Traffic flow prediction with big data: a deep learning approach	Keras
SBU-LSTM	157	UrbComp	14 Aug 17	Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction
SCPT	0	ArXiv	9 May 23	Traffic Forecasting on New Roads Unseen in the Training Data Using Spatial Contrastive Pre-Training
Seq2Seq Hybrid	48	KDD	19 Jul 18	Deep Sequence Learning with Auxiliary Information for Traffic Prediction	tf
SGMN	1	J. TRC	20 Aug 20	Graph Markov network for traffic forecasting with missing data
SLCNN	1	AAAI	7 Feb 20	Spatio-Temporal Graph Structure Learning for Traffic Forecasting
ST-MetaNet	39	KDD	25 Jul 19	Urban traffic prediction from spatio-temporal data using deep meta learning	MXNet
ST-MetaNet+	0	TKDE	19 May 20	Spatio-Temporal Meta Learning for Urban Traffic Prediction
ST-TrafficNet	2	ELECGJ	9 Sep 20	ST-TrafficNet: A Spatial-Temporal Deep Learning Network for Traffic Forecasting
ST-UNet	11	arXiv	13 Mar 19	ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling
STDGI	3	ICLR	12 Apr 19	Spatio-Temporal Deep Graph Infomax
STGAT	0	IEEE Access	22 Jul 20	STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting
STGAT	0	IEEE Access	22 Jul 20	STGAT: Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting
STGCN	322	IJCAI	13 Jul 18	Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting	tf PyTorch MXNet
STGNN	4	WWW	20 Apr 20	Traffic Flow Prediction via Spatial Temporal Graph Neural Network
STGRAT	6	arXiv	29 Nov 19	STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting
STNN	0	T-ITS	16 Jul 20	STNN: A Spatio-Temporal Neural Network for Traffic Predictions
STSeq2Seq	0	arXiv	6 Apr 20	Forecast Network-Wide Traffic States for Multiple Steps Ahead: A Deep Learning Approach Considering Dynamic Non-Local Spatial Correlation and Non-Stationary Temporal Dependency
STGAM	1	CISP-BMEI	17 Oct 20	Spatial-Temporal Graph Attention Model on Traffic Forecasting
STSGCN	5	AAAI	7 Feb 20	Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting	MXNet
TGC-LSTM	95	T-ITS	28 Nov 19	Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
TSE-SC	0	Trans-GIS	1 Jun 20	Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting
TSSRGCN	4	ICDM	17 Nov 21	TSSRGCN: Temporal Spectral Spatial Retrieval Graph Convolutional Network for Traffic Flow Forecasting
	0	arXiv	15 Jul 20	On the Inclusion of Spatial Information for Spatio-Temporal Neural Networks	PyTorch
	96	NeuCom	27 Nov 18	LSTM-based traffic flow prediction with missing data

Things that would be in the table above if I have more time:

Other works

• Multi-Attention Temporal and Graph Convolution Network for Traffic Flow Forecasting PyTorch

• Foreseeing Congestion using LSTM on Urban Traffic Flow Clusters ICSAI 2019 Keras; dataset: CityPulse

• Using LSTM and GRU neural network methods for traffic flow prediction IEEE YAC 2016 Keras; dataset: PeMS but different from everyone else

• A Dynamic Traffic Awareness System for Urban Driving IEEE GreenCom 2019 Keras; dataset: CityPulse

• 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:

• VLUC

• STDN

• Deep Representation Learning for Trajectory Similarity Computation

• Curb-GAN SIG KDD 2020

• BusTr SIG KDD 2020

• DeepMove WWW 2018

• https://github.com/Alro10/deep-learning-time-series

• https://github.com/henriquejosefaria/CSC

• https://github.com/shakibyzn/Traffic-flow-prediction

• https://deepmind.com/blog/article/traffic-prediction-with-advanced-graph-neural-networks

Other lists:

• Davidham3 list

• https://paperswithcode.com/task/traffic-prediction

• A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges IEEE TKDE 2020

• A Comprehensive Survey on Traffic Prediction to be published in IEEE Transactions on Intelligent Transportation Systems.

• A Comprehensive Survey on Graph Neural Networks IEEE Trans. Neural Netw. Learn. Syst. 2020

• A Comprehensive Survey on Geometric Deep Learning IEEE Access 19 Feb 2020.

• Graph Neural Network for Traffic Forecasting: A Survey Expert Systems with Applications GitHub

• 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.

• Older, pre-ML approaches: On the modeling of traffic and crowds: A survey of models, speculations, and perspectives SIAM 2011

• https://github.com/Knowledge-Precipitation-Tribe/Urban-computing-papers

• https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress

• DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction CIKM 2021 GitHub PyTorch

Acknowledgement

• Willianto Aslim