考虑驾驶员行为特性的模糊推理跟驰模型改进研究

基于模糊推理的车辆跟驰模型实现了用数学方法描述驾驶员的主观判断、推理和执行的过程,但现有的这类模型通常未考虑驾驶员行为特性的差异。针对这一问题,在基于模糊推理的跟驰模型中引入期望车头时距,不同行为特性的驾驶员具有不同的期望车头时距,在此基础上建立了改进的车辆跟驰模型。仿真结果表明,改进的跟驰模型能准确描述不同类型驾驶员的跟驰行为及其差异。     

A decision support approach for condition-based maintenance of rails based on big data analysis

In this paper, a decision support approach is proposed for condition-based maintenance of rails relying on expert-based systems. The methodology takes into account both the actual conditions of the rails (using axle box acceleration measurements and rail video images) and the prior knowledge of the railway track. The approach provides an integrated estimation of the rail health conditions to support the maintenance decisions for a given time period. An expert-based system is defined to analyse interdependency between the prior knowledge of the track (defined by influential factors) and the surface defect measurements over the rail. When the rail health conditions is computed, the different track segments are prioritized, in order to facilitate grinding planning of those segments of rail that are prone to critical conditions. In this paper, real-life rail conditions measurements from the track Amersfoort-Weert in the Dutch railway network are used to show the benefits of the proposed methodology. The results support infrastructure managers to analyse the problems in their rail infrastructure and to efficiently perform a condition-based maintenance decision making.     

基于可信度的不确定性推理在公路软基处理专家系统中的应用

为提高公路软基处理决策的科学性,分析了公路软基处理知识的不确定性、模糊性和多因素影响的特点与专家系统的思维方式和基本原理,将基于可信度表示的不确定性推理引入公路软基处理专家系统中,采用产生式规则表示公路软基处理知识,采用正向推理和逆向推理相结合的混合推理方法,进行软土鉴别、路堤稳定与沉降计算方法选择、地基处理方案决策、软土地基处理设计与施工问题分析。实例分析表明:基于可信度的不确定性推理的应用提高了专家系统的交互化和智能化程度以及公路软土地基处理设计与施工技术的科学性。     

Multi-criteria route planning based on a driver’s preferences in multi-criteria route selection

In this study, some different approaches were designed, implemented, and evaluated to perform multi-criteria route planning by considering a driver’s preferences in multi-criteria route selection. At first, by using a designed neuro-fuzzy toolbox, the driver’s preferences in multi-criteria route selection such as the preferred criteria in route selection, the number of route-rating classes, and the routes with the same rate were received. Next, to learn the driver’s preferences in multi-criteria route selection and to classify any route based on these preferences, a methodology was proposed using a locally linear neuro-fuzzy model (LLNFM) trained with an incremental tree based learning algorithm. In this regard, the proposed LLNFM-based methodology reached better results for running-times, as well as root mean square error (RMSE) estimations in learning and testing processes of training/checking data-set in comparison with those of the proposed adaptive neuro-fuzzy inference system (ANFIS) based methodology. Finally, the trained LLNFM-based methodology was utilized to plan and predict a driver’s preferred routes by classifying Pareto-optimal routes obtained by running the modified invasive weed optimization (IWO) algorithm between an origin and a destination of a real urban transportation network based on the driver’s preferences in multi-criteria route selection.     

基于票务数据的团体旅客出行目的推断

为推进城际交通大数据的应用，需要补全出行目的信息，将团体旅客出行目的决策与文本主题生成类比，开发基于无监督学习框架的出行目的推断方法.提出嵌入出发时间生成模块的主题模型，以及团体旅客重建和语义化特征设计方法，并通过吉布斯采样估计参数. 基于调查数据的模型对比研究发现，模型对一般私务辨识性能提升7.7%；基于票务数据的案例研究发现，模型对出发时间预测精度达到90.9%，间接验证了模型的可靠性.主题标注表明，模型不仅推断出4种与典型模式相符的出行目的，还辨识出既有认识外的非常规模式.对道路客运分析表明，出行目的构成呈现地区差异，高铁开通对不同出行目的出行量的负向影响程度不一.     

Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model.     

基于FNN与规则推理的轿车故障诊断系统

简要分析了模糊推理与规则推理以及这两者相结合的故障诊断方法，这三种方法在处理多症状多原因的机械故障诊断问题上都存在缺陷，基于此引出了模糊神经网络代替模糊推理并与规则推理相结合的先进诊断方法，最后给出了一个轿车诊断实例。实例表明该方法是有效的。     

ZPW-2000A轨道电路智能故障诊断算法应用研究简

提出一种基于模糊推理的轨道电路智能故障诊断算法,通过对ZPW-2000A轨道电路监测子系统实时采集的信息进行智能分析,实时故障诊断,该诊断算法对于诊断轨道电路模拟元件的短路、开路等硬故障有较好的效果.在ZPW-2000A轨道电路监测子系统平台进行了工程化实现,在现场真实环境下,进行多种故障的测试,测试结果达到预期效果.     

电气集中联锁设备故障混合诊断的研究

在铁路电气集中联锁设备的故障诊断应用中,传统人工故障检测异常繁杂和困难,介绍将基于规则推理和CBR推理应用到其故障诊断的方法.首先分析基于规则推理和基于事例推理各自的优势,然后根据6502电气集中联锁设备各个不同检查点的特点,采用以基于规则和基于事例推理的混合推理系统,并建立起各自推理模型.     

基于SFCE与规则推理的发动机集成故障诊断方法

针对发动机磨损过程的复杂性以及故障症状(磨粒信息)与故障原因之间对应关系的不确定性,首先对磨粒数量特征信息进行了模糊化,建立了某发动机典型磨损故障的模糊规则库。针对单一故障诊断方法的局限性,引入了对称模糊交互熵的故障推理方法,在此基础上建立了基于对称模糊交互熵和规则推理相结合的发动机故障诊断模型。本模型首先采用对称模糊交互熵对故障原因进行"过滤",滤除出现可能性较小的原因,然后利用规则推理进一步确认最后的故障原因。实例表明,该方法在发动机多症状多原因的复杂情况下能够取得较好的诊断结果。