Irregular Traffic Time Series Forecasting Based on Asynchronous
Spatio-Temporal Graph Convolutional Network
- URL: http://arxiv.org/abs/2308.16818v2
- Date: Fri, 1 Sep 2023 07:27:52 GMT
- Title: Irregular Traffic Time Series Forecasting Based on Asynchronous
Spatio-Temporal Graph Convolutional Network
- Authors: Weijia Zhang, Le Zhang, Jindong Han, Hao Liu, Jingbo Zhou, Yu Mei, Hui
Xiong
- Abstract summary: We propose an Asynchronous Spatio-tEmporal graph convolutional nEtwoRk (ASeer) to predict the traffic states of the lanes entering intelligent intersections in a future time window.
To capture the temporal dependency within irregular traffic state sequence, a learnable personalized time encoding is devised to embed the continuous time for each lane.
Extensive experiments on two real-world datasets demonstrate the effectiveness of ASeer in six metrics.
- Score: 35.79257816518065
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurate traffic forecasting at intersections governed by intelligent traffic
signals is critical for the advancement of an effective intelligent traffic
signal control system. However, due to the irregular traffic time series
produced by intelligent intersections, the traffic forecasting task becomes
much more intractable and imposes three major new challenges: 1) asynchronous
spatial dependency, 2) irregular temporal dependency among traffic data, and 3)
variable-length sequence to be predicted, which severely impede the performance
of current traffic forecasting methods. To this end, we propose an Asynchronous
Spatio-tEmporal graph convolutional nEtwoRk (ASeer) to predict the traffic
states of the lanes entering intelligent intersections in a future time window.
Specifically, by linking lanes via a traffic diffusion graph, we first propose
an Asynchronous Graph Diffusion Network to model the asynchronous spatial
dependency between the time-misaligned traffic state measurements of lanes.
After that, to capture the temporal dependency within irregular traffic state
sequence, a learnable personalized time encoding is devised to embed the
continuous time for each lane. Then we propose a Transformable Time-aware
Convolution Network that learns meta-filters to derive time-aware convolution
filters with transformable filter sizes for efficient temporal convolution on
the irregular sequence. Furthermore, a Semi-Autoregressive Prediction Network
consisting of a state evolution unit and a semiautoregressive predictor is
designed to effectively and efficiently predict variable-length traffic state
sequences. Extensive experiments on two real-world datasets demonstrate the
effectiveness of ASeer in six metrics.
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