Our paper on "Spatio-Temporal Meta-Graph Learning for Traffic Forecasting" has been
accepted to the top international conference on artificial intelligence, AAAI2023.
― Realizing high-accuracy, high-efficiency, and high-interpretability traffic prediction
by cutting-edge meta-graph learning ―InfoTech
Connected Advanced Development Div.
TOYOTA MOTOR CORPORATION
Jiawei Yong(Project Leader)
Yasumasa Kobayashi
Shintaro Fukushima(Group Manager)
Toyota Motor Corporation announces that a research paper on traffic prediction, as a joint
research achievement with Assistant Professor Renhe Jiang, the University of Tokyo, hasbeen accepted to the top international conference on artificial intelligence, AAAI2023.
Presentation Highlights:In order to develop a traffic prediction method that achieves high accuracy, highefficiency, and high interpretability, we designed a Meta-graph Convolutional
Recurrent Network (MegaCRN) by combining Meta-Graph Learner and EncoderDecoder Graph Convolutional Recurrent Network.

On benchmark datasets METR-LA, PEMS-BAY, and Toyota's original dataset
EXPY-TKY, our proposed method achieved higher prediction accuracy than existing
methods in most accuracy evaluation metrics for road speed prediction. Additionally,our proposed method has relatively fewer model parameters compared to existing
methods, resulting in significant reduction of learning time.

Various applications enhanced by this method, including implementation in real –world services, are expected in the future.

Presentation ContentsResearch Background

Although considerable effort has been devoted to developing traffic prediction methods,
it is still necessary to address the challenges associated with the unique characteristics oftraffic data to achieve high accuracy, efficiency, and interpretability.
Characteristics (1)Traffic conditions exhibit spatio-temporal heterogeneity, meaning that their naturevaries significantly depending on the road and time (resulting in temporal and spatial
anisotropy in traffic patterns).
Challenge (1)Therefore, in order to develop prediction methods with high accuracy and interpretability,
it is necessary to effectively separate and integrate the different characteristics of trafficconditions.
Characteristics (2)When accidents or congestion occur, traffic conditions become non-stationary, meaningthat they deviate from the normal state of traffic patterns.
Challenge (2)Therefore, it is necessary to develop prediction methods that are adaptive to changes intraffic conditions even after the occurrence of sudden events such as accidents andcongestion.
Research Overview

This research aims to develop a prediction method with higher accuracy, efficiency, andinterpretability compared to existing methods to address the challenges. The
effectiveness of the proposed method was verified by the real-world data. We expect it tohave broad applications, including its implementation in real services.
Methodology
To address challenge (1) in the traffic data, we designed a Meta-Graph
Convolutional Recurrent Network (MegaCRN) that incorporates a Meta-Graph
Learner and an Encoder-Decoder Graph Convolutional Recurrent Network. Byleveraging the Meta-Graph Learner, MegaCRN is able to distinguish between
patterns of roads and time with varying traffic conditions.
Furthermore, to address challenge (2) in the traffic data, we achieved adaptive
prediction capabilities by combining the Meta-Graph Learner and the EncoderDecoder Graph Convolutional Recurrent Network, which can handle nonstationary traffic conditions such as accidents and congestion.

Framework of Meta-Graph Convolutional Recurrent Network (MegaCRN)
Evaluation Results・High Accuracy
Using benchmark datasets such as METR-LA, PEMS-BAY, and our own Toyota
original dataset EXPY-TKY, we achieved higher prediction accuracy thanexisting methods in most evaluation metrics for road speed prediction. Our
proposal’s adaptive learning capabilities also make it possible to obtain highaccuracy predictions even after the occurrence of sudden events such asaccidents and congestion, where road speed decreases.
・High Efficiency
We were able to significantly reduce execution time compared to existing
methods by limiting the number of parameters to a reasonable range.
・High Interpretability① Using Meta-Graph Learner, we explicitly aggregate and visualize roadclusters based on their similarity in traffic conditions.
② By visualizing the local network structure of roads, we can display the extent
and severity of the impact of sudden events. This provides valuable
information for traffic management and control, allowing for effective
response and mitigation of the impact on traffic flow.

① Spatio-Temporal Disentangling Effect of Meta-Graph Learning by Clustering Results② (a)(b) Realization of adaptive speed prediction after traffic accidents(c)(d) Impact

Visualization of the area and severity of accidents using Meta-Graphs


Presentation Info・Date：

9th Feb 2023

・Conference：

AAAI2023 (The 37th AAAI Conference on Artificial Intelligence)

・Title：Spatio-Temporal Meta-Graph Learning for Traffic Forecasting・Pre-print:

https://arxiv.org/abs/2211.14701