Evaluating Traffic Congestion Using the Traffic Occupancy and Speed Distribution Relationship: An Application of Bayesian Dirichlet Process Mixtures of Generalized Linear Model

Accurate classification and prediction of future traffic conditions are essential for developing effective strategies for congestion mitigation on the highway systems. Speed distribution is one of the traffic stream parameters, which has been used to quantify the traffic conditions. Previous studies have shown that multi-modal probability distribution of speeds gives excellent results when simultaneously evaluating congested and free-flow traffic conditions. However, most of these previous analytical studies do not incorporate the influencing factors in characterizing these conditions. This study evaluates the impact of traffic occupancy on the multi-state speed distribution using the Bayesian Dirichlet Process Mixtures of Generalized Linear Models (DPM-GLM). Further, the study estimates the speed cut-point values of traffic states, which separate them into homogeneous groups using Bayesian change-point detection (BCD) technique. The study used 2015 archived one-year traffic data collected on Florida’s Interstate 295 freeway corridor. Information criteria results revealed three traffic states, which were identified as free-flow, transitional flow condition (congestion onset/offset), and the congested condition. The findings of the DPM-GLM indicated that in all estimated states, the traffic speed decreases when traffic occupancy increases. Comparison of the influence of traffic occupancy between traffic states showed that traffic occupancy has more impact on the free-flow and the congested state than on the transitional flow condition. With respect to estimating the threshold speed value, the results of the BCD model revealed promising findings in characterizing levels of traffic congestion.

A parallel algorithm for detecting traffic patterns using stay point features and moving features

In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the features of the stay points in different traffic patterns are extracted, that is, the stay points of various traffic patterns are identified, respectively, and the clustering algorithm is used to mine the unique features of the stop points to different traffic patterns. Then, the moving features in different traffic patterns are extracted from a trajectory of a moving object, including the maximum speed, the average speed, and the stopping rate. A classifier is constructed to predict the traffic pattern of the trajectory using the stay points and moving features. Finally, a parallel algorithm based on Spark is proposed to detect traffic patterns. Experimental results show that the stay points and moving features can reflect the difference between different traffic modes to a greater extent, and the detection accuracy is higher than those of other methods. In addition, the parallel algorithm can increase the speed of identifying traffic patterns.

轻量级的全息道路交通状态视觉检测的研究

针对道路拥堵检测难、交通基本参数获取计算复杂度高等问题,提出了一种轻量级的全息道路交通状态视觉检测方法。为了能在嵌入式系统上同时实现道路拥堵状态和各种交通基本参数的视觉自动化检测,首先通过定制道路区域并自动生成均匀分布的采样点,采用以点代面的设计思想,来减少图像处理的计算资源和存储资源;其次,采用背景差法和帧间差法相结合的处理方法分别得到非存在采样点、存在采样点、移动存在采样点和静止存在采样点;接着,采用非存在采样点实现精准快速的道路背景建模,根据存在采样点的空间分布情况获取一些重要的交通基本参数,并利用静止存在采样点的空间排列情况进行拥堵分析。实验结果表明,文中提出的检测算法具有计算效率高、耗费资源少、检测范围广、鲁棒性强等优点,能快速并准确地检测出各种交通基本参数和道路拥堵状态。

一种基于虚拟线圈的交通信息流分析方法

基于视频的交通信息流分析方法主要包含背景建模、阴影消除、车速提取、车辆统计与分类、道路交通拥堵分析等五个环节。传统方法对于每一个环节分析方法较多,但缺乏有机整合,无法满足系统的实时性需求。鉴于以上原因,现提出一种虚拟线圈的方法。该法通过在场景中设置两条虚拟线,当动目标通过线圈时开始检测,根据设定的面积比例值消除阴影,并得到该目标在同一帧内经过两线圈时的质心,由质心和帧差求得动目标的速度;再根据同一线圈内不同车型的比例阈值作出分析,最后综合以上数据得出交通拥堵情况。实验表明,本方法具有实时性高和鲁棒性强的优点。

深度学习在交通拥堵检测中的应用

随着全球汽车保有量的不断增加,人们在出行中遇到的交通拥堵问题日益严重,这对相关部门的管理效率提出较高要求。本文通过阐述深度学习领域中图像分类技术和目标检测技术的原理以及他们各自在交通拥堵检测中的应用,为相关部门在解决交通拥堵这一实际问题时提供应对方法,具有一定的参考价值。

基于多线索混合的交通标志检测与跟踪

提出一种基于多特征融合的交通标志检测、识别和跟踪算法.在检测阶段,先利用颜色信息提取出感兴趣的区域;然后利用角点、几何特征等信息检测出交通标志.在识别阶段,首先根据颜色和形状的对应关系进行粗分类;然后针对每一类标志建立一个二叉树结构的支持向量机多分类器用于识别其具体含义.为了减少误识别率,在跟踪阶段采用Lucas-Kanade的特征点跟踪算法跟踪交通标志.实验结果表明,该方法具有很好的鲁棒性.

利用CART分类树分类检测交通拥堵点

交通拥堵检测是城市交通管理工作的重点和难点之一,现有的拥堵检测以路段为单位,不利于拥堵时空演变规律信息的提取,且检测内容大多只涉及拥堵程度,缺少对拥堵类型的识别。基于CART(classification and regression tree)分类树算法,提出一种以路段点为检测单元的拥堵点分类检测方法,该方法可根据路段平均行驶速度实时检测拥堵点及其类型。首先,将路段等距离划分后映射为路段点,根据时空维路况异常规则和异常模式,以路段点为单元分析了4种拥堵类型的时空演变模式;其次,在路段路况检测的基础上,提取路段点路况时空序列,根据不同类型的拥堵模式对路况时空序列进行分类标记;然后,选取4种速度指标作为样本属性集合,按照属性集合提取各路段点在各时段的速度,以此作为决策树学习的数据集;最后,基于CART分类树算法,采用交叉验证的方式训练出最优模型,使其达到最佳的泛化能力。与支持向量机(support vector machine,SVM)分类模型进行比较,实验结果表明,该方法在分类检测交通拥堵点时具有较高的正确率和召回率,且分类检测时效性较好。