A genetic algorithm-based method for improving quality of travel time prediction intervals

The transportation literature is rich in the application of neural networks for travel time prediction. The uncertainty prevailing in operation of transportation systems, however, highly degrades prediction performance of neural networks. Prediction intervals for neural network outcomes can properly represent the uncertainty associated with the predictions. This paper studies an application of the delta technique for the construction of prediction intervals for bus and freeway travel times. The quality of these intervals strongly depends on the neural network structure and a training hyperparameter. A genetic algorithm–based method is developed that automates the neural network model selection and adjustment of the hyperparameter. Model selection and parameter adjustment is carried out through minimization of a prediction interval-based cost function, which depends on the width and coverage probability of constructed prediction intervals. Experiments conducted using the bus and freeway travel time datasets demonstrate the suitability of the proposed method for improving the quality of constructed prediction intervals in terms of their length and coverage probability.     

基于数据挖掘的汽车排放检测结果与使用特点关联性分析

本文利用SQLServer2008作为数据库管理及数据挖掘平台,并建立了一个基于约10万条在用汽车排放检测数据的决策树挖掘模型。数据挖掘结果指出,使用年限与车辆排放状态有着密切的关联性。随着使用年限的增加,在用汽车排放检测不合格率也逐步增加,排放检测的不合格率从使用年限区间(一)的2.96%逐渐增加至区间(五)的18.31%。另外,使用年限在6年以内,营运车的排放检测不合格率是非营运车的排放检测不合格率的2倍以上,因此应分别对这两种不同使用性质的车辆采用不同的检车周期,非营运车辆的检车周期应放宽于营运车辆,以减轻非营运车车主的检车负担以及节省相关费用;在使用年限大于6年,可以采用同一检车周期以便于车辆管理部门的管理工作。