基于粗糙集交通信息提取计算的城市道路行程时间预测

针对城市道路的行程时间预测问题进行研究。由于城市道路交通问题具有不确定性和不精确性,故采用基于粗糙集的交通信息提取计算理论建立城市道路行程时间预测模型。模型建立后,利用在荷兰代尔夫特市采集到的实际数据,对该预测模型进行检验。检验结果表明:如果不进行原始数据的前期处理,那么得到的预测误差在35%左右;而在剔除了质量较差的数据后,预测精度明显提高;同时,条件属性和决策属性的分类,显著影响到预测的精度。通过计算得到分类范围值,该模型能够较好的对交通状态进行物理解释同时预测精度能够达到可以接受的范围。

Travel Time Prediction for Urban Arterials Based on Rough Set

Travel time prediction of urban networks was studied.Since urban travel times are stochastic and uncertain,a model for addressing urban travel time prediction by using transport information granular computing theory based on rough set was proposed.An urban route of Delft,the Netherlands,was selected as the test bed to test the proposed model.The results show that(1) feed with raw data,the model produces error of 35%;(2) with data pre-processing,the model improves performance significantly;(3) the classifications of condition and decision attributes significantly influence the accuracy.With the optimal setting of the ranges,the proposed model is able to describe traffic phenomena with physical meaning.Overall,the accuracy is acceptable.