基础交通信息融合方法综述

Review of Basic Traffic Data Fusion Methods

文章介绍了几种经典算法在基础交通信息融合中的应用,包括卡尔曼滤波、人工神经网络、统计分析(加权平均,指数平滑法,平均值的递推估计算法),以及交通流量和行程时间预测方法。各种方法均通过实际验证其有效性及可靠性。融合后的数据能够满足智能交通系统的子系统ATMS相关应用领域对信息的精度要求,为下一步的交通状态估计提供可靠信息。

The paper introduces several classical algorithms and their application in basic traffic data fusion.These algorithms include Kalman filtering, artificial neural network, statistic analyses （mean weight, exponential smoothing, reasoning estimate of mean value）, and prediction methods of traffic volume and road network travel time.Their validity and reliability were validated by practice.Data after fusing can meet the demands for information precision in ATMS and provide reliable information for estimating traffic state.