ST-GCN：深度学习框架解决交通流量预测
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Spatio-temporal Graph Convolutional Neural Network (ST-GCN) 是一种深度学习框架，特别设计用于交通流量预测。论文由Bing Yu等人提出，他们隶属于北京大学的数学科学学院、高等交叉学科研究院、数据科学中心以及北京大数据研究院。作者们意识到传统的交通模拟实验和经典统计方法在应对中期和长期的交通流量预测任务时存在局限性，因为交通网络流具有动态性和复杂性。
ST-GCN的主要创新之处在于它不依赖于传统的循环模型（如RNN）来处理序列学习，而是完全基于卷积神经网络（CNN）构建。这种方法利用了图形结构来捕捉空间和时间上的依赖关系。在交通预测场景中，交通网络可以被视为一个空间图，其中节点代表路网中的交通观测点，边则表示它们之间的关联，如道路连接或交通流的影响范围。通过在图上应用卷积操作，ST-GCN能够有效地提取空间邻域特征，并结合时间维度进行信息融合。
该模型的核心思想是将空间和时间特征映射到一个共享的特征空间，然后通过一系列的卷积层捕获不同尺度的空间和时间特征。这有助于保留局部和全局信息，使得模型能够更好地理解交通流量随时间和地理位置的变化规律。此外，ST-GCN还可能包含池化层来减小特征维数并保留最重要的模式，以及全连接层用于最终的预测。
ST-GCN的优势在于其高效的学习能力和对大规模数据的适应性，它能够在训练过程中自动学习复杂的交通流模式，从而提高预测的准确性和可靠性。与传统方法相比，ST-GCN在处理非线性、复杂时空关系时表现出更好的性能，这对于交通规划、智能交通系统和城市基础设施优化等领域具有重要的实际应用价值。
Spatio-temporal Graph Convolutional Neural Network是一种创新的深度学习工具，它革新了交通流量预测的方法，利用图形卷积处理空间和时间序列数据，为解决实际交通问题提供了一种强大且有效的解决方案。
Spatio-temporal Graph Convolutional Neural Network: A Deep Learning
Framework for Traffic Forecasting
Bing Yu,
∗1
Haoteng Yin,
∗2,3
Zhanxing Zhu
†3,4
1
School of Mathematical Sciences, Peking University, Beijing, China
2
Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
3
Center for Data Science, Peking University, Beijing, China
4
Beijing Institute of Big Data Research (BIBDR), Beijing, China
{byu, htyin, zhanxing.zhu}@pku.edu.cn
Abstract
The goal of traffic forecasting is to predict the future vital
indicators (such as speed, volume and density) of the local
traffic network in reasonable response time. Due to the dy-
namics and complexity of traffic network flow, typical sim-
ulation experiments and classic statistical methods cannot
satisfy the requirements of mid-and-long term forecasting.
In this work, we propose a novel deep learning framework,
Spatio-Temporal Graph Convolutional Neural Network (ST-
GCNN), to tackle this spatio-temporal sequence forecasting
task. Instead of applying recurrent models to sequence learn-
ing, we build our model entirely on convolutional neural net-
works (CNNs) with gated linear units (GLU) and highway
networks. The proposed architecture fully employs the graph
structure of the road networks and enables faster training. Ex-
periments show that our ST-GCNN network captures com-
prehensive spatio-temporal correlations throughout complex
traffic network and consistently outperforms state-of-the-art
baseline algorithms on several real-world traffic datasets.
Introduction
Traffic forecasting is one of the most challenging studies
of Intelligent Transportation System (ITS). Accurate and
timely forecasting of multi-scale traffic conditions is of
paramount importance for road users, management agencies
and private sectors. Widely used transportation services pro-
vided by ITS such as dynamic traffic control, route planning
and navigation service also rely on a high-quality assessment
of future traffic network conditions under reasonable cost.
Indicators such as speed, volume and density gathered by
various sensors reflect the general status of road traffic con-
ditions. Thus, those measurements are typically chosen as
the target of traffic prediction. Based on the length of pre-
diction, traffic forecasting can be divided into three scales:
short-term (5 ∼ 30 min), medium-term (30 ∼ 60 min) and
long-term (over an hour). Most prevalent approaches are
able to perform well on short forecasting interval. Inherently,
because of the uncertainty and complexity of traffic flow,
those methods are unsatisfying on long-term time-series pre-
diction.
Previous studies on traffic prediction can be roughly di-
vided into two different categories, namely, traditional sim-
∗
Equal contributions.
†
Corresponding author.
ulation approaches and data-driven methods. For the sim-
ulation approaches, making traffic flow prediction requires
comprehensive and meticulous systemic modeling based on
physical theories and prior knowledge (Vlahogianni 2015).
Even though, the analog system and simulation tools still
consume massive computational power and skillful parame-
ter settings to achieve steady state. Nowadays, with the rapid
development of real-time traffic data collection methods and
forms, researchers are transferring their attention to explor-
ing data-driven methods through enormous historical traffic
records which are gathered by the advanced ITS.
Classic statistical models and machine learning models
are two major representative categories of data-driven meth-
ods. In time-series analysis, autoregressive integrated mov-
ing average (ARIMA) is one of the most consolidated ap-
proaches. It has been applied into various study fields and
firstly introduced into traffic forecasting as early as 1970s
(Ahmed and Cook 1979). ARIMA model can be applied
to non-stationary data, which require an integrated term
to make the time series stationary. Extensive variants of
ARIMA model have been proposed to improve the ability on
pattern capturing and prediction accuracy, such as seasonal
ARIMA (SARIMA) (Williams and Hoel 2003), ARIMA
with the Kalman filter (Lippi, Bertini, and Frasconi 2013).
However, models mentioned above highly rely on the sta-
tionary assumption of the time series and ignore the spa-
tial correlation among traffic network. Therefore, time-series
models have partially limited representability of highly dy-
namic and inconstant traffic flow.
Recently, machine learning methods have shown promis-
ing development in traffic study. Higher prediction accuracy
can be acquired by these non-parametric methods, includ-
ing k-nearest neighbors algorithm (KNN), support vector
machine (SVM), and neural network (NN) models (also re-
ferred as deep learning models).
Deep Learning Approaches Nowadays, deep learning
techniques, deep architectures in particular, have drawn lots
of academic and industrial interest and attention. Deep learn-
ing methods have been widely and successfully employed
in various tasks such as classification, pattern recognition
and object detection. In traffic prediction research, the deep
belief network (DBN) has been proved the capability of
capturing the stochastic features and characteristics of traf-
arXiv:1709.04875v2 [cs.LG] 25 Sep 2017
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