基于TAN分类器的交通灯时间智能动态估计

针对目前交通灯智能化程度低，容易造成交通拥堵的问题，提出一种基于TAN分类器的交通灯时间智能动态估计方法。首先，分析影响交通灯时间的主要因素，并对采集到的数据用模糊分类函数进行离散化处理；其次，依据K2算法学习TAN分类器结构；接着，使用最大似然估计法学习TAN分类器的参数；最后，通过基于时间窗的前向后向算法在线估计出最佳交通灯时间。仿真实验结果表明：本文提出的方法能够根据实时交通路况信息动态估计出最佳交通灯时间，当路口畅通时，交通灯时间短；反之，交通灯时间长。对有效缓解交通拥堵，减少环境污染有着重要的现实意义。

Intelligent Dynamic Estimation of Traffic Light Time Based on TAN Classifier

Aiming at the problem that traffic lights are weak in intelligence and easy to cause traffic jam, a time intelligent dynamic estimation method for traffic lights based on TAN classifier is proposed. Firstly, this paper analyzes the main factors which affect the traffic light time, and discretizes the collected data by fuzzy classification function. Then, it learns the structure of the TAN classifier by K2 algorithm. Next, it learns the parameters of TAN classifier with maximum likelihood estimation. Finally, the best traffic light time is estimated online by forwards-backwards algorithm based on the sliding window. The experimental simulation shows that the proposed method can dynamically estimate the optimal traffic light time according to real-time traffic information. When the traffic light is free, the traffic light time is short, otherwise, time is long. It is effective to alleviate traffic jam and reduce environment pollution.