Title: Automatic Freeway Bottleneck Identification and Visualization using Image Processing Techniques Show Chinese title

Title: 基于图像处理技术的自动高速公路瓶颈识别与可视化

Abstract: This paper develops an automatic freeway bottleneck identification and visualization algorithm using a combination of image processing techniques and traffic flow theory. Unlike previous studies that are based solely on loop detector data, the proposed method can use traffic measurements from various sensing technologies. Four steps are included in the proposed algorithm. First, the raw spatiotemporal speed data are transformed into binary matrices using image binarization techniques. Second, two post-processer filters are developed to clean the binary matrices by filtering scattered noise cells and localized congested regions. Subsequently, the roadway geometry information is used to remove the impact of acceleration zones downstream of bottlenecks and thus locate bottlenecks more precisely. Finally, the major characteristics of bottlenecks including activation and deactivation points, shockwave speeds and traffic delay caused by bottleneck are automatically extracted and visualized. The proposed algorithm is tested using loop detector data from I-5 demonstrating that the proposed method outperforms the state-of-the-art methods for congestion identification. The second test using INRIX data from I-66 demonstrates ability of the proposed algorithm to accurately extract and visualize bottleneck characteristics. Show Chinese abstract

Abstract: 本文开发了一种使用图像处理技术和交通流理论相结合的自动高速公路瓶颈识别和可视化算法。 与仅基于环形检测器数据的先前研究不同，所提出的方法可以使用来自各种传感技术的交通测量数据。 所提出的算法包括四个步骤。 首先，原始时空速度数据通过图像二值化技术转换为二进制矩阵。 其次，开发了两个后处理器过滤器，通过过滤散乱的噪声单元和局部拥堵区域来清理二进制矩阵。 随后，利用道路几何信息去除瓶颈下游加速区的影响，从而更精确地定位瓶颈。 最后，自动提取并可视化瓶颈的主要特征，包括激活和解除点、激波速度以及瓶颈引起的交通延误。 所提出的算法使用I-5的环形检测器数据进行了测试，结果表明该方法在拥堵识别方面优于现有最先进的方法。 第二次测试使用I-66的INRIX数据，证明了所提出算法准确提取和可视化瓶颈特征的能力。