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首页> 外文期刊>Expert Systems with Application >Queuing theory guided intelligent traffic scheduling through video analysis using Dirichlet process mixture model
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Queuing theory guided intelligent traffic scheduling through video analysis using Dirichlet process mixture model

机译：排队论通过Dirichlet过程混合模型通过视频分析指导智能交通调度

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摘要

Intelligent traffic signaling is an important part of city road traffic management systems. In many countries, it is done through supervised/semi-supervised ways. With the advances in computer vision and machine learning, it is now possible to develop expert systems guided intelligent traffic signaling systems that are unsupervised in nature. In order to schedule traffic signals, it is essential to learn the traffic characterization parameters such as the number of vehicles, their arrival and departure rates, etc. In this work, we use unsupervised machine learning with the help of a modified Dirichlet Process Mixture Model (DPMM) to measure the aforementioned traffic parameters. This has been done using a new feature, named temporal clusters or tracklets extracted using DPMM. Detailed analysis on tracklet behavior during signal on/off period has been carried out to derive a queuing theory-based method for signal duration prediction. The queuing behavior at a junction is analyzed using tracklets for understanding their applicability. Queue clearance time at the junction has been used for predicting the signal duration with the help of Gaussian regression of historical data.Two publicly available video datasets, namely QMUL and MIT have been used for verification of the hypothesis. The mean absolute error (MAE) of the proposed method using tracklets has been reduced by a factor of 2.4 and 6.3 when compared with the tracks generated using Kernel Correlation Filters (KCF) and Kanade-Lucas-Tomasi (KLT), respectively. Through experiments, we are also able to establish that KCF and KLT tracks do not consider spatial occupancy of the vehicles on roads, leading to error in the estimation. The results reveal that the proposed queuing theory-based approach predicts the signal duration for the next cycle more accurately as compared to the ground truths. The method can be used for building intelligent traffic control systems for roadway junctions in cities and highways. (C) 2018 Elsevier Ltd. All rights reserved.
机译：智能交通信号是城市道路交通管理系统的重要组成部分。在许多国家，这是通过监督/半监督的方式完成的。随着计算机视觉和机器学习的发展，现在有可能开发出专家系统指导的智能交通信号系统，这些系统本质上不受监督。为了调度交通信号，必须学习交通特征参数，例如车辆数量，其到达和离开速度等。在这项工作中，我们借助改进的Dirichlet过程混合模型使用无监督机器学习（DPMM）来测量上述流量参数。这是通过使用一项新功能完成的，该功能使用DPMM提取了名为时间簇或小轨迹。已经对信号开/关期间的小波行为进行了详细的分析，以推导基于排队论的信号持续时间预测方法。使用小轨迹来分析​​结点处的排队行为，以了解其适用性。在历史数据的高斯回归的帮助下，在交叉路口的队列清除时间已用于预测信号持续时间。已使用两个公开可用的视频数据集，即QMUL和MIT来验证该假设。与使用核相关滤波器（KCF）和Kanade-Lucas-Tomasi（KLT）生成的轨迹相比，使用轨迹轨迹的拟议方法的平均绝对误差（MAE）分别降低了2.4和6.3倍。通过实验，我们还可以确定KCF和KLT轨道未考虑道路上车辆的空间占用，从而导致估计误差。结果表明，与基本事实相比，所提出的基于排队论的方法可以更准确地预测下一个周期的信号持续时间。该方法可用于为城市和高速公路的道路交叉口建立智能交通控制系统。 （C）2018 Elsevier Ltd.保留所有权利。

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