The invention discloses a motor vehicle single trip trajectory identification method based on a Gaussian mixture model, belongs to the technical field of urban road traffic flow detection, and adopts a Bayesian information criterion to determine model selection, and the model selection is determined by a Bayesian information criterion. Solving model parameters by using an expectation maximization algorithm; on the basis of optimal fitting of Gaussian mixture distribution, considering the double-peak characteristic of urban road intermittent flow travel time distribution, selecting a linear combination form of a Gaussian component 1 and a Gaussian component 2 to characterize travel time distribution of directed intersection OD pairs, and determining a path travel travel completion time decision boundary of each directed intersection OD pair on the basis of a principle; by comparing the vehicle passing record time difference between the continuous track point pairs with the travel travel completion time decision boundary, the travel travel completion state of the vehicle is judged, and then the single travel track of the motor vehicle is identified, so that a traveler can avoid a congested road section, and the overall travel passing efficiency of the road condition is improved.
本发明公开了一种基于高斯混合模型的机动车单次出行轨迹辨识方法,属于城市道路交通流量检测技术领域,一种基于高斯混合模型的机动车单次出行轨迹辨识方法,采用贝叶斯信息准则确定模型选择,利用期望最大化算法对模型参数进行求解;在高斯混合分布最优拟合基础上,考虑城市道路间断流行程时间分布的双峰特性,选择高斯分量1和高斯分量2的线性组合形式表征有向交叉口OD对的行程时间分布,基于原则确定各有向交叉口OD对路径出行行程完成时间决策边界;通过比较连续轨迹点对间的过车记录时间差与出行行程完成时间决策边界,判断车辆出行行程完成状态,进而对机动车单次出行轨迹进行辨识,以实现出行者避开拥堵路段,提升路况整体的出行通行效率。
Motor vehicle single travel track identification method based on Gaussian mixture model
一种基于高斯混合模型的机动车单次出行轨迹辨识方法
2023-06-13
Patent
Electronic Resource
Chinese
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