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Traffic-incident detection-algorithm based on nonparametric regression

机译:基于非参数回归的交通事件检测算法

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This paper proposes an improved nonparametric regression (INPR) algorithm for forecasting traffic flows and its application in automatic detection of traffic incidents. The INPRA is constructed based on the searching method of nearest neighbors for a traffic-state vector and its main advantage lies in forecasting through possible trends of traffic flows, instead of just current traffic states, as commonly used in previous forecasting algorithms. Various simulation results have indicated the viability and effectiveness of the proposed new algorithm. Several performance tests have been conducted using actual traffic data sets and results demonstrate that INPRs average absolute forecast errors, average relative forecast errors, and average computing times are the smallest comparing with other forecasting algorithms.
机译:提出了一种改进的非参数回归(INPR)算法,用于交通流量预测及其在交通事件自动检测中的应用。 INPRA是基于对交通状态向量的最近邻搜索方法构造的,其主要优势在于可以通过交通流量的可能趋势进行预测,而不仅仅是以前的预测算法中常用的当前交通状态。各种仿真结果表明了该算法的可行性和有效性。使用实际的交通数据集进行了一些性能测试,结果表明,与其他预测算法相比,INPR的平均绝对预测误差,平均相对预测误差和平均计算时间最小。

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