基于手机运动传感器数据的交通流拥挤识别

准确的交通流状态识别是智能交通管理与控制的基础.通过所开发的手机端软件从手机中提取车辆的加速度与角加速度数据,在研究了其统计特征后,发现该数据可反应周围车辆对目标车辆运行环境的影响,从而与交通流状态的变化有着密切关系.利用支持向量机学习算法,以加速度与角加速度统计参数作为输入变量识别断面交通流状态.实验结果识别精度最高达到92%,表明加速度和角加速度指标可作为交通流状态的表征参数.该研究采用Lasso模型和最小角回归算法对输入参数进行变量选择,在降低计算成本的同时保证了良好的识别效果. 

Recognition of Traffic Congestion Based on Mobile Phone Sensor Data

Accurate recognition of traffic congestion is the basis of intelligent transportation system. This study develops a new method to evaluate the running environment surround the target vehicle based on two parameters:the real-time acceleration and angular acceleration collected from the smart phone. After analyzing the statistical characteristics of the parameters, the data is found to have a close relationship with the corresponding traffic flow states. Support vector machine and parametric optimization method are utilized to test these data. The experiments show a highest accuracy of 92%, indicating that acceleration and angular acceleration could be considered together as the characterization parameters of traffic flow states. To reduce the computation cost while maintaining the accuracy of the traffic state identification, strong explanatory variables of the support vector machine algorithm are recognized by the relative optimal regression model and the least angle regression method.