Gaussian Mixture Model-Based Speed Estimation and Vehicle Classification Using Single-Loop Measurements
Gaussian Mixture Model-Based Speed Estimation and Vehicle Classification Using Single-Loop Measurements
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DOI：
10.1080/15472450.2012.706196
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发表时间：
2012-06
影响因子：
3.6
通讯作者：
Yunteng Lao;Guohui Zhang;Jonathan Corey;Yinhai Wang
中科院分区：
文献类型：
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作者：
Yunteng Lao;Guohui Zhang;Jonathan Corey;Yinhai Wang
交通速度和基于长度的车辆分类数据是交通运营、路面设计和维护以及交通规划的关键输入。然而，它们不能通过单回路探测器直接测量，单回路探测器是现有道路基础设施中部署最广泛的交通传感器类型。在本研究中，开发了一种基于高斯混合模型（GMM）的方法，使用单回路输出来估计更准确的交通速度和分类车辆数量。估计过程包括参数修正和验证的多次迭代。在建立GMM对单回路检测器测量的车辆正点率进行经验建模后，可以初步寻求最优解来分离基于长度的车辆体积数据。基于GMM分离的短车辆的准点率，迭代改进交通速度和分类体积估计，直到估计结果在统计上趋于稳定和收敛。该方法简单，计算效率高。使用从西雅图地区90号州际公路上的几个环线站收集的数据来检验拟议方法的有效性。基于该方法对三类车辆的交通量数据进行了分类。实验结果表明，本文提出的GMM方法优于传统的恒g因子法、序列法和移动中位数法，能够更可靠、准确地估计各种交通条件下的交通速度和分类车辆数量。
Traffic speed and length-based vehicle classification data are critical inputs for traffic operations, pavement design and maintenance, and transportation planning. However, they cannot be measured directly by single-loop detectors, the most widely deployed type of traffic sensor in the existing roadway infrastructure. In this study, a Gaussian mixture model (GMM)-based approach is developed to estimate more accurate traffic speeds and classified vehicle volumes using single-loop outputs. The estimation procedure consists of multiple iterations of parameter correction and validation. After the GMM is established to empirically model vehicle on-times measured by single-loop detectors, the optimal solution can be initially sought to separate length-based vehicle volume data. Based on the on-time of the separated short vehicles from the GMM, an iterative process will be conducted to improve traffic speed and classified volume estimation until the estimation results become statistically stable and converge. This method is straightforward and computationally efficient. The effectiveness of the proposed approach was examined using data collected from several loop stations on Interstate 90 in the Seattle area. The traffic volume data for three vehicle classes are categorized based on the proposed method. The test results show the proposed GMM approach outperforms the previous models, including conventional constant g-factor method, sequence method, and moving median method, and produces more reliable, accurate estimates of traffic speeds and classified vehicle volumes under various traffic conditions.