A Data Mining Approach to Creating Fundamental Traffic Flow Diagram
A Data Mining Approach to Creating Fundamental Traffic Flow Diagram
复制标题
DOI：
10.1016/j.sbspro.2013.11.136
复制
发表时间：
2013-12
期刊：
影响因子：
--
通讯作者：
J. Kianfar;P. Edara
中科院分区：
文献类型：
--
作者：
J. Kianfar;P. Edara
本文研究了聚类技术在交通流数据划分中的应用。聚类技术识别数据之间的相似性和差异性，并将数据分成具有相似特征的组。这些技术已经成功地应用于市场研究、天文学、精神病学和交通运输。提出了一种基于基本交通流变量的交通数据聚类框架。研究了三种聚类技术：1)基于连通性的聚类，2)基于质心的聚类和3)基于分布的聚类。具体而言，研究了层次聚类、k -均值聚类和一般混合模型（GMM）。该研究使用了美国两个主要大都市——密苏里州的圣路易斯和明尼苏达州的双子城——的三个高速公路瓶颈位置的交通传感器数据。研究了所有三种聚类技术中流量变量的各种组合。结果表明，聚类是将交通数据划分为自由流和拥挤流的有效方法。划分的交通数据可用于创建基本交通流图和宏观交通流模型。使用速度，或者同时使用速度和占用率作为输入变量，可以产生最佳的聚类结果。K-means和分层聚类技术的性能具有可比性，优于GMM聚类。
This paper investigates application of clustering techniques in partitioning traffic flow data to congested and free flow regimes. Clustering techniques identify the similarities and dissimilarities between data, and classify the data into groups with similar characteristics. Such techniques have been successfully used in market research, astronomy, psychiatry, and transportation. A framework is proposed for clustering traffic data based on fundamental traffic flow variables. Three types of clustering techniques are investigated: 1) connectivity-based clustering, 2) centroid-based clustering, and 3) distribution-based clustering. Specifically, hierarchical clustering, K-means clustering and general mixture model (GMM) were investigated.Traffic sensor data from three freeway bottleneck locations in two major U.S. metropolitan areas, St. Louis, Missouri, and Twin Cities, Minnesota, were used in the study. Various combinations of traffic variables were investigated for all three clustering techniques. The results indicated that the clustering is an effective way to partition traffic data into the free flow and congested flow regimes. Partitioned traffic data can be used to create fundamental traffic flow diagrams and macroscopic traffic stream models. Using speeds, or both speeds and occupancies as input variables produced the best clustering results. The performance of K-means and hierarchical clustering techniques were comparable to each other and they outperformed GMM clustering.