A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.

基于模式识别的非参数回归在短时交通流上的预测应用

针对非参数回归在短时交通流预测上的局限性,改进传统K近邻方法,加入模式识别功能(通过匹配数l实现)和变K和l搜索算法,得到最优K和l值及相应的预测结果。通过实验发现:改进的K近邻方法在误差范围为5%、9%时对应的预测准确率为84.4%、96.10%。将其与传统K近邻方法进行对比,通过计算两者预测效果的各方面指标,发现改进的K近邻方法在精度和实时性上都有了很大的提高。

短时交通流交通状态转变及其特性分析

针对短时交通流预测中忽视交通流所处交通状态的转变及所处的状态。首先简要介绍了相干递归图及相干定量递归分析,以1分钟为间隔的实测数据为例,通过相干递归图分别可视化常发性拥挤和偶发性拥挤中时间占有率和流量的递归特性,然后运用相干定量递归分析分别确定了常发性拥挤和偶发性拥挤交通状态的转变时刻,得到了不同交通状态的统计特征值,并做出分析。结果表明将交通流划分为四个状态更具合理性,同时各状态的统计特性分析对短时交通量预测及交通拥堵机理研究都有重要的意义。

短时交通流预测的改进K近邻算法

分析了原有的短时交通流预测的K近邻算法,用模式距离搜索方法代替原有的欧氏距离搜索方法,引入多元统计回归模型,建立了一种改进的短时交通流预测的K近邻算法,并以北京市某路段进行实例验证。试验结果表明:当K取23时,利用改进的K近邻算法,预测结果的均方误差、平均相对误差、平均绝对误差分别为31.43%、4.17%、0.27%;利用原有的K近邻算法,预测结果的均方误差、平均相对误差、平均绝对误差分别为33.33%、4.40%、0.28%;利用历史平均模型,预测结果的均方误差、平均相对误差、平均绝对误差分别为46.20%、11.40%、0.48%。可见,改进的K近邻算法的预测精度明显高于其他2种方法,在提高搜索效率的同时准确地刻画了交通流的真实情况。