Traffic Prediction Benchmarks

We provide benchmark results of spatiotemporal prediction learning (STL) methods on popular traffic prediction datasets. More STL methods will be supported in the future. Issues and PRs are welcome! Currently, we only provide benchmark results, trained models and logs will be released soon (you can contact us if you require these files). You can download model files from Baidu Cloud (3t2t).

Table of Contents

• TaxiBJ Benchmarks

  • STL Benchmarks on TaxiBJ

  • MetaVP Benchmarks on TaxiBJ

Currently supported spatiotemporal prediction methods

• [x] ConvLSTM (NeurIPS’2015)

• [x] PredNet (ICLR’2017)

• [x] PredRNN (NeurIPS’2017)

• [x] PredRNN++ (ICML’2018)

• [x] E3D-LSTM (ICLR’2018)

• [x] MIM (CVPR’2019)

• [x] CrevNet (ICLR’2020)

• [x] PhyDNet (CVPR’2020)

• [x] MAU (NeurIPS’2021)

• [x] PredRNN.V2 (TPAMI’2022)

• [x] SimVP (CVPR’2022)

• [x] SimVP.V2 (ArXiv’2022)

• [x] TAU (CVPR’2023)

• [x] DMVFN (CVPR’2023)

Currently supported MetaFormer models for SimVP

• [x] ViT (ICLR’2021)

• [x] Swin-Transformer (ICCV’2021)

• [x] MLP-Mixer (NeurIPS’2021)

• [x] ConvMixer (Openreview’2021)

• [x] UniFormer (ICLR’2022)

• [x] PoolFormer (CVPR’2022)

• [x] ConvNeXt (CVPR’2022)

• [x] VAN (ArXiv’2022)

• [x] IncepU (SimVP.V1) (CVPR’2022)

• [x] gSTA (SimVP.V2) (ArXiv’2022)

• [x] HorNet (NeurIPS’2022)

• [x] MogaNet (ArXiv’2022)

TaxiBJ Benchmarks

We provide traffic benchmark results on the popular TaxiBJ dataset using \(4\rightarrow 4\) frames prediction setting. Metrics (MSE, MAE, SSIM, pSNR) of the best models are reported in three trials. Parameters (M), FLOPs (G), and V100 inference FPS (s) are also reported for all methods. All methods are trained by Adam optimizer with Cosine Annealing scheduler (5 epochs warmup and min lr is 1e-6) and single GPU.

STL Benchmarks on TaxiBJ

For a fair comparison of different methods, we report the best results when models are trained to convergence. We provide config files in configs/taxibj.

Method	Setting	Params	FLOPs	FPS	MSE	MAE	SSIM	PSNR	Download
ConvLSTM-S	50 epoch	14.98M	20.74G	815	0.3358	15.32	0.9836	39.45	model | log
E3D-LSTM*	50 epoch	50.99M	98.19G	60	0.3427	14.98	0.9842	39.64	model | log
PhyDNet	50 epoch	3.09M	5.60G	982	0.3622	15.53	0.9828	39.46	model | log
PredNet	50 epoch	12.5M	0.85G	5031	0.3516	15.91	0.9828	39.29	model | log
PredRNN	50 epoch	23.66M	42.40G	416	0.3194	15.31	0.9838	39.51	model | log
MIM	50 epoch	37.86M	64.10G	275	0.3110	14.96	0.9847	39.65	model | log
MAU	50 epoch	4.41M	6.02G	540	0.3268	15.26	0.9834	39.52	model | log
PredRNN++	50 epoch	38.40M	62.95G	301	0.3348	15.37	0.9834	39.47	model | log
PredRNN.V2	50 epoch	23.67M	42.63G	378	0.3834	15.55	0.9826	39.49	model | log
DMVFN	50 epoch	3.54M	0.057G	6347	3.3954	45.52	0.8321	31.14	model | log
SimVP+IncepU	50 epoch	13.79M	3.61G	533	0.3282	15.45	0.9835	39.45	model | log
SimVP+gSTA-S	50 epoch	9.96M	2.62G	1217	0.3246	15.03	0.9844	39.71	model | log
TAU	50 epoch	9.55M	2.49G	1268	0.3108	14.93	0.9848	39.74	model | log

Benchmark of MetaFormers on SimVP (MetaVP)

Similar to Moving MNIST Benchmarks, we benchmark popular Metaformer architectures on SimVP with training times of 50-epoch. We provide config files in configs/taxibj/simvp.

MetaFormer	Setting	Params	FLOPs	FPS	MSE	MAE	SSIM	PSNR	Download
SimVP+IncepU	50 epoch	13.79M	3.61G	533	0.3282	15.45	0.9835	39.45	model | log
SimVP+gSTA-S	50 epoch	9.96M	2.62G	1217	0.3246	15.03	0.9844	39.71	model | log
ViT	50 epoch	9.66M	2.80G	1301	0.3171	15.15	0.9841	39.64	model | log
Swin Transformer	50 epoch	9.66M	2.56G	1506	0.3128	15.07	0.9847	39.65	model | log
Uniformer	50 epoch	9.52M	2.71G	1333	0.3268	15.16	0.9844	39.64	model | log
MLP-Mixer	50 epoch	8.24M	2.18G	1974	0.3206	15.37	0.9841	39.49	model | log
ConvMixer	50 epoch	0.84M	0.23G	4793	0.3634	15.63	0.9831	39.41	model | log
Poolformer	50 epoch	7.75M	2.06G	1827	0.3273	15.39	0.9840	39.46	model | log
ConvNeXt	50 epoch	7.84M	2.08G	1918	0.3106	14.90	0.9845	39.76	model | log
VAN	50 epoch	9.48M	2.49G	1273	0.3125	14.96	0.9848	39.72	model | log
HorNet	50 epoch	9.68M	2.54G	1350	0.3186	15.01	0.9843	39.66	model | log
MogaNet	50 epoch	9.96M	2.61G	1005	0.3114	15.06	0.9847	39.70	model | log
TAU	50 epoch	9.55M	2.49G	1268	0.3108	14.93	0.9848	39.74	model | log

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