Abstract: A method has been developed to estimate Origin Destination (OD) matrices using a neural network (NN) model and loop traffic data collected from a UK motorway site (M42) as the primary input. The estimated ODs were validated against matched vehicle number plate data derived from the ANPR (automatic number plate recognition) cameras which were installed at all the slip roads between junctions 3a and 7 of the motorway. Key research questions were: whether it is realistic to use the full loop data, whether particular features of the data influenced modelling success, whether data transformation could improve modelling performance through variance stabilization and whether individual ODs should be estimated separately or simultaneously. The method has been shown to work well and the best results were obtained using a square root transformation of the training data and individual models for each OD.

Key words: intelligent transportation, neural networks, time series, ANPR data, loop traffic data, origin destination matrix

摘要： 以英国M42高速公路线圈交通数据为输入，建立预测模型，提出神经网络OD矩阵预测方法. 将预测结果与该公路3a号至7号交叉口间各支路上自动车牌识别装置测得的车辆数据进行对比，验证其有效性. 解决了以下关键问题：利用线圈数据实现OD矩阵预测的可操作性，该类数据的特殊性是否影响模型构建，通过变异数稳定数据转换能否改善模型性能，能否同时进行单个OD预测. 得到基于训练数据平方根代换的最佳计算结果和单个OD预测模型.

关键词: 智能交通, 神经网络, 时间序列, 自动车牌识别(ANPR)数据, 线圈交通数据, OD矩阵