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Papers › PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction

PDG2Seq: Periodic Dynamic Graph to Sequence Model for Traffic Flow Prediction

5 Dec 2024Neural Networks 2024 12archive 2025-07-28

Jin Fan, Weng, Wenchao, Qikai Chen, Huifeng Wu, Jia Wu

Traffic flow prediction is the foundation of intelligent traffic management systems. Current methods prioritize the development of intricate models to capture spatio-temporal correlations, yet they often neglect the exploitation of latent features within traffic flow. Firstly, the correlation among different road nodes exhibits dynamism rather than remaining static. Secondly, traffic data exhibits evident periodicity, yet current research lacks the exploration and utilization of periodic features. Lastly, current models typically rely solely on historical data for modeling, resulting in the limitation of accurately capturing future trend changes in traffic flow. To address these findings, this paper proposes a Periodic Dynamic Graph to Sequence Model (PDG2Seq) for traffic flow prediction. PDG2Seq consists of the Periodic Feature Selection Module (PFSM) and the Periodic Dynamic Graph Convolutional Gated Recurrent Unit (PDCGRU) to further extract the spatio-temporal features of the dynamic real-time traffic. The PFSM extracts learned periodic features using time points as indices, while the PDCGRU leverages the extracted periodic features from the PFSM and dynamic features from traffic flow to generate a Periodic Dynamic Graph for extracting spatio-temporal features. In the decoding phase, PDG2Seq utilizes periodic features corresponding to the prediction target to capture future trend changes, leading to more accurate predictions. Comprehensive experiments conducted on four large-scale datasets substantiate the superiority of PDG2Seq over existing state-of-the-art baselines. Related codes are available at https://github.com/wengwenchao123/PDG2Seq.

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wengwenchao123/PDG2Seq mentioned in paperpytorch report

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Tasks

Graph-to-SequenceTraffic Predictionfeature selection

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Traffic Prediction PeMS04 PDG2Seq 12 Steps MAE 18.24 #6 of 12 Archive leaderboard report
Traffic Prediction PeMS07 PDG2Seq MAE@1h 19.28 #4 of 17 Archive leaderboard report
Traffic Prediction PeMS08 PDG2Seq MAE@1h 13.60 #9 of 13 Archive leaderboard report
Traffic Prediction PeMSD3 PDG2Seq 12 steps MAE 14.62 #2 of 6 Archive leaderboard report
Traffic Prediction PeMSD3 PDG2Seq 12 steps MAPE 14.88 #2 of 6 Archive leaderboard report
Traffic Prediction PeMSD3 PDG2Seq 12 steps RMSE 25.47 #2 of 6 Archive leaderboard report
Traffic Prediction PeMSD4 PDG2Seq 12 steps MAE 18.24 #4 of 13 Archive leaderboard report
Traffic Prediction PeMSD4 PDG2Seq 12 steps MAPE 12.09 #4 of 13 Archive leaderboard report
Traffic Prediction PeMSD4 PDG2Seq 12 steps RMSE 30.08 #4 of 13 Archive leaderboard report
Traffic Prediction PeMSD8 PDG2Seq 12 steps MAE 13.60 #8 of 13 Archive leaderboard report
Traffic Prediction PeMSD8 PDG2Seq 12 steps MAPE 8.99 #8 of 13 Archive leaderboard report
Traffic Prediction PeMSD8 PDG2Seq 12 steps RMSE 23.37 #8 of 13 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Feature Selection

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