田俊山, 曾俊铖, 丁峰, 徐劲, 江龑, 周成, 李英达, 王歆远. 基于时空关系的高速公路交通流量预测[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.10.24.004
引用本文: 田俊山, 曾俊铖, 丁峰, 徐劲, 江龑, 周成, 李英达, 王歆远. 基于时空关系的高速公路交通流量预测[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.10.24.004
TIAN Junshan, ZENG Juncheng, DING Feng, XU Jin, JIANG Yan, ZHOU Cheng, LI Yingda, WANG Xinyuan. Highway traffic flow forecasting based on spatiotemporal relationship[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.10.24.004
Citation: TIAN Junshan, ZENG Juncheng, DING Feng, XU Jin, JIANG Yan, ZHOU Cheng, LI Yingda, WANG Xinyuan. Highway traffic flow forecasting based on spatiotemporal relationship[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.10.24.004

基于时空关系的高速公路交通流量预测

Highway traffic flow forecasting based on spatiotemporal relationship

  • 摘要: 高速公路交通流量预测对于交通拥堵预警、分流诱导和智慧高速公路建设具有重要意义. 交通流具有复杂的时空依赖性,各个交通节点之间的空间关系随时间动态变化,时空关系的融合也缺乏高效的手段,因此对交通流量进行准确的预测具有挑战性. 对此,提出一种基于动态图卷积网络与时空特征提取模块的高速公路交通流量预测方法. 首先,通过动态图调节模块,提取交通流量序列的空间关系,根据提取到的空间特征,计算不同路网节点的道路相似性,并调整交通路网图结构;其次,通过时空特征提取模块,利用更新后的空间结构,结合时序处理方法,对交通流量数据的时空依赖关系进行建模. 为检验模型效果,在美国加州高速公路性能测量系统 (Performance measurement system, PeMS)所制作的数据集PeMS03、PeMS04、PeMS08和福州京台线高速公路数据集中进行实验对比,平均绝对误差分别为15.6、19.7、16.8和5.21,结果表明,本文提出的方法在高速公路交通流量预测中具有较好的表现.

     

    Abstract: With the continuous advancement in socioeconomic development and transportation infrastructure, the daily traffic volume on highways has been steadily increasing, resulting in the growing frequency of traffic congestion incidents. Thus, the accurate prediction of highway traffic flow is of great significance for implementing traffic congestion warnings, guiding traffic diversion, and developing the concept of intelligent highways. Traffic flow exhibits intricate spatial and temporal dependencies: in the spatial dimension, the relationships between various traffic nodes are not fixed, changing dynamically over time; in the temporal dimension, multiple temporal patterns of traffic flow sequences are entangled with each other. In addition, an efficient method for fusing spatiotemporal relationships is lacking, making the accurate prediction of traffic flow a challenging endeavor. In this regard, a methodology for forecasting highway traffic flow is proposed based on dynamic graph convolutional networks and spatiotemporal feature extraction modules. Given the challenge posed by the static nature of predefined graph structures in capturing dynamic spatial relationships among traffic nodes, a dynamic graph adjustment module is introduced. Initially, the spatial features of each traffic node are extracted. Subsequently, utilizing these extracted spatial features, spatial similarity scores between traffic nodes are computed. Based on these scores, a traffic network graph structure is adapted: connections between nodes with high similarity scores, previously unlinked, are established with a certain probability, while connections between nodes with low similarity scores, previously linked, are severed with a certain probability. Furthermore, by employing the spatiotemporal feature extraction module and leveraging the updated graph structure, spatial relationships are extracted through graph convolution. This is complemented by integrating a patch concept from temporal processing methodologies. Herein, a one-dimensional traffic flow sequence is decomposed and transformed into two-dimensional data. Through convolutional operations, temporal features within and between periods are simultaneously extracted before reverting the data back to its original dimensionality. This comprehensive approach enables the modeling of spatiotemporal dependencies within the traffic flow data. To validate the effectiveness of the proposed model, experiments were conducted on four highway traffic datasets, contrasting its performance with baseline models. The proposed model achieved the mean absolute error (MAE) values of 15.6, 19.7, 16.8, and 5.21 on the PeMS03, PeMS04, PeMS08, and Fuzhou Jingtai highway datasets, respectively. These results show that the proposed method reaches an advanced level in traffic flow forecasting. Lastly, to assess the efficacy of individual model components, ablative experiments were conducted, and their results were compared. These experiments validate the effectiveness of each component, thereby affirming the efficacy of the proposed model in highway traffic flow forecasting.

     

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