School of Transportation, Fujian University of Technology, Fuzhou, Fujian, China.
Fujian Provincial Expressway Information Technology Co., Ltd, Fuzhou, Fujian, China.
PLoS One. 2023 Jan 6;18(1):e0279966. doi: 10.1371/journal.pone.0279966. eCollection 2023.
Identifying traffic congestion accurately is crucial for improving the expressway service level. Because the distributions of microscopic traffic quantities are highly sensitive to slight changes, the traffic congestion measurement is affected by many factors. As an essential part of the expressway, service areas should be considered when measuring the traffic state. Although existing studies pay increasing attention to service areas, the impact caused by service areas is hard to measure for evaluating traffic congestion events. By merging ETC transaction datasets and service area entrance data, this work proposes a traffic congestion measurement with the influence of expressway service areas. In this model, the traffic congestion with the influence of service areas is corrected by three modules: 1) the pause rate prediction module; 2) the fitting module for the relationship between effect and pause rate; 3) the measurement module with correction terms. Extensive experiments were conducted on the real dataset of the Fujian Expressway, and the results show that the proposed method can be applied to measure the effect caused by service areas in the absence of service area entry data. The model can also provide references for other traffic indicator measurements under the effect of the service area.
准确识别交通拥堵对于提高高速公路服务水平至关重要。由于微观交通量的分布对微小变化非常敏感，因此交通拥堵测量受到许多因素的影响。服务区作为高速公路的重要组成部分，在测量交通状态时应予以考虑。尽管现有研究越来越关注服务区，但由于难以衡量服务区造成的影响，因此难以评估交通拥堵事件。通过合并 ETC 交易数据集和服务区入口数据，本工作提出了一种考虑高速公路服务区影响的交通拥堵测量方法。在该模型中，通过三个模块来修正受服务区影响的交通拥堵：1）停顿率预测模块；2）影响与停顿率之间关系的拟合模块；3）带有修正项的测量模块。在福建高速公路的真实数据集上进行了广泛的实验，结果表明，所提出的方法可以在没有服务区入口数据的情况下应用于测量服务区造成的影响。该模型还可以为其他在服务区影响下的交通指标测量提供参考。
