基于贝叶斯组合模型的短期交通量预测研究

提出一种新的贝叶斯组合神经网络模型并将其应用于短期交通流量的预测。模型通过实时跟踪模型的预测表现,根据研究提出的分配算法不断调整模型的信用值,从而挑选并组合得到精度更高的预测模型。介绍了该模型的基本原理及在示范路网中的实际应用,通过选取反向传播神经网络和径向基函数神经网络,用以构造贝叶斯组合模型,并在测试数据集中进行了性能比较。计算结果表明：模型的预测性能整体上优于单一的神经网络模型,并且确保了模型预测的稳定性。     

基于RBF网络的小型车自由流车速模型的建立

充分考虑了自由流车速的主要影响因素,在30组观测数据的基础上,利用RBF网络建立了小型车自由流车速模型,并验证了该模型的可信性。     

基于反向传播神经网络的驾驶人人际与情绪危险性模型

为评价和研究驾驶人的人际关系质量对安全驾驶的影响,利用情绪状态可测定的特性,采用反向传播(BP)人工神经网络工具,建立人际-情绪危险性网络模型,并利用530个情绪与人际关系的有效样本作为建模样本占总样本量的80.7%,127个样本作为测试样本,反复对模型进行学习和训练,分别取得了建模样本68.3%的正确率,测试样本70.1%的正确率。研究结果表明:利用BP神经网络建立的人际与情绪危险性模型,在系统关系不明确的状态下仍能达到较为理想的评价结果,可以作为驾驶人尤其是职业驾驶人安全管理和自我检测的有效手段。     

A framework for short-term traffic flow forecasting using the combination of wavelet transformation and artificial neural networks

The main objective of this paper is to develop a framework for short-term traffic flow forecasting models with high accuracy. Due to flow oscillations, the real-time information presented to the drivers through variable message signs, etc., may not be valid by the time the driver reaches the location. On the other hand, not all compartments of the flow signal are of same importance in determining its future state. A model is developed to predict the value of traffic flow in near future (next 5–35?minutes) based on the combination of wavelet transformation and artificial neural networks. This model is called the hybrid WT-ANN. Wavelet transformation is set to denoise the flow signal, i.e., filtering the unimportant fluctuations of the flow signal. Unimportant fluctuations are those that have little or no effect on the future condition of the signal. The neural network is set and trained to use previous data for predicting future flow. To implement the system, traffic data of US-101 were used from Next Generation Simulation (NGSIM). Results show that removing the noises has improved the accuracy of the prediction to a great extent. The model was used to predict the flow in three different locations on the same highway and a different highway in a different country. The model rendered highly reliable predictions. The proposed model predicts the flow of next 5?min on the same location with 2.5% Mean Absolute Percentage Error (MAPE) and of next 35?min with less than 12% MAPE. It predicts the flow on downstream locations for next 5?min with less than 8% MAPE and for the different highway with 2.3% MAPE.     

基于BP神经网络轻型轿车排放模型的应用

本文利用BP神经网络理论,构造一个预测轻型轿车排放的BP神经网络模型,经验证,预测模型能较好地对一般轻型轿车排放结果进行定性预测,可以作为简易稳态工况法(ASM)尾气检测前的参考,对节约检测成本、提高一次检测通过率有着较高的实用价值;同时以南京市为例,研究了轻型汽油车行驶里程、使用年限等参数对排放的影响,为有针对性的维修治理提供可靠的依据。     

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.