SCATS线圈数据短时多步双重预测方法

Bi-level method of multi-step forecasting for short-term data of loop in SCATS

摘要:

为了进一步改善悉尼自适应交通控制系统(Sydney coordinated adaptive traffic system, SCATS)线圈数据短时多步预测的效果,在对SCATS线圈数据进行预处理的基础上,将当前与之前若干时间间隔的交通数据及对应的时间点作为交通模式特征向量的构成要素,用欧式距离作为当前交通模式特征向量和历史交通模式特征向量相似性的测度指标,以多步预测结果的误差最小为目标选取近邻数,通过对交通模式之间距离的倒数正规化处理,确定了所选相似交通模式的未来交通参数的权重,设计了一种基于k近邻(k nearest neighbor, k-NN)算法的短时多步双重预测方法,包括SCATS线圈数据的多步预测方法以及可预测步数在线估计方法,并采用某特大城市SCATS线圈实测数据进行了验证和对比分析．结果表明,所提出的新方法能够进一步降低SCATS线圈数据短时多步预测的误差．

Abstract:

For the improvement of the effect of traffic multi-step forecasts using short-term data of loop in Sydney coordinated adaptive traffic system(SCATS), on the basis of data preprocessing, traffic data and time points at time t over a sampling period of n intervals were included in the traffic state feature vector, Euclidean distance was used to measure the closeness between current traffic state and historical traffic state, the number of nearest neighbors corresponding to the minimum error of travel multi-step forecasts was selected, and the weights of k-nearest neighbors were identified by normalizing the reciprocal of the distance between traffic states, a new bi-level method of multi-step forecasting using k Nearest Neighbor(k-NN) algorithm was designed, including a multi-step forecasting method and a predictable steps on-line estimation method. The validity of the proposed method was tested with data measured from a megacity. The results indicate that the proposed method can further improve the effect of short-term traffic multi-step forecasts.