A hybrid deep learning model for urban expressway lane-level mixed traffic flow prediction

计算机科学 稳健性(进化) 流量(计算机网络) 实时计算 人工智能 数据挖掘 模拟 计算机安全 生物化学 基因 化学
作者
Heyao Gao,Hongfei Jia,Qiuyang Huang,Ruiyi Wu,Jingjing Tian,Guanfeng Wang,Chao Liu
出处
期刊:Engineering Applications of Artificial Intelligence [Elsevier BV]
卷期号:133: 108242-108242 被引量:3
标识
DOI:10.1016/j.engappai.2024.108242
摘要

Precise real-time traffic flow prediction is crucial for route guidance and traffic fine control. With the development of autonomous driving, the mixed traffic flow state composed of Connected Automated Vehicles (CAVs) and Human-driven Vehicles (HVs) provides new insight into traffic flow prediction. In this paper, we innovatively consider the interaction between heterogeneous traffic flow as well as the mutual effect of traffic flow on different lanes and develop a hybrid model based on deep learning for urban expressway lane-level mixed traffic flow prediction, including three modules. First, the feature selection module is applied to screen the features with a high spatio-temporal correlation to the prediction object and construct the input matrix. Then, it is input to the feature attention module to quantify the importance of the input features on the prediction object, thereby assigning attention weights. Finally, the spatio-temporal information fusion module is adopted to capture the global spatio-temporal dynamics of traffic flow at horizontal and vertical spatial scales, as well as learn the complex coupling characteristics of heterogeneous traffic flow, thus obtaining predictions. An urban expressway mixed traffic flow simulation environment is built to collect experimental datasets for prediction accuracy evaluation. The results indicate that the proposed model outperforms the benchmarks in single-step and multi-step mixed traffic flow predictions on each lane. Furthermore, the proposed model shows the best performance and strong robustness under different penetration rates of connected automated vehicles.
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