Transit smart card data mining for passenger origin information extraction

The automated fare collection(AFC) system,also known as the transit smart card(SC) system,has gained more and more popularity among transit agencies worldwide.Compared with the conventional manual fare collection system,an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival.Although it is possible to collect highly valuable data from transit SC transactions,substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection.This is true especially for the Beijing AFC system,where a passenger’s boarding stop(origin) on a flat-rate bus is not recorded on the check-in scan.To extract passengers’ origin data from recorded SC transaction information,a Markov chain based Bayesian decision tree algorithm is developed in this study.Using the time invariance property of the Markov chain,the algorithm is further optimized and simplified to have a linear computational complexity.This algorithm is verified with transit vehicles equipped with global positioning system(GPS) data loggers.Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers’ origin information from SC transactions with a relatively high accuracy.Such transit origin data are highly valuable for transit system planning and route optimization.     

数据挖掘技术在学校管理中的应用

数据挖掘技术是一门多学科相互交叉融合而形成的新兴学科。目前,该技术已在商业、金融业、农业、互联网、医药业等多个领域中得到广泛应用。而将数据挖掘技术与学校管理相结合,可以从大量事务管理数据中提取出了隐藏在其中的有用信息,因此可以帮助教学人员合理安排教学工作,协助辅导员对学生的管理,从而促进教育体制的进一步完善与发展。本文由数据挖掘技术概述入手,论述了数据挖掘技术在学校管理中的作用,最后,将数据挖掘技术应用在学生成绩管理中,可以实现透过现象看本质,提炼有价值的信息。     

船舶抗沉辅助决策系统的研究与设计

抗沉辅助决策系统是船舶常规和紧急状态下进行损管行动的关键性依据。目前抗沉计算分析系统,可对船舶基础状态、浮稳性等信息进行管理,并进行仿真和辅助决策,但大多存在算法效率低,决策生成速度和精度不足、人机交互接口脱离实际等问题。本文从船舶抗沉操作的实际执行过程出发,制定抗沉算法,根据实时装载和破损情况,对液舱采用注入水或排水的损管操作,生成调整措施,使船舶恢复良好的浮态与稳性。系统基于面向对象过程控制平台设计和实现,仿真结果验证了系统的有效性和高效性。     

邻域等价关系诱导的改进ID3决策树算法

经典ID3决策树算法适用于离散型数据分类,但用于连续处理时需要数据离散化容易导致信息损失。提出邻域等价关系从而诱导邻域ID3(NID3)决策树算法,NID3算法改进了ID3决策树算法,能够直接实施连续预测并获取更好的分类效果。在邻域决策系统中,挖掘一种邻域等价关系;基于邻域等价粒化,构建邻域信息度量;基于邻域信息增益,设计NID3决策树算法。实例分析与数据实验均表明,NID3算法具有连续数据分类预测有效性,在分类机器学习中优于ID3算法。     

考虑多样化需求的诊疗设备维护和患者调度联合优化研究

针对诊疗设备维护和住院患者候诊难以有效协调的问题,建立了联合优化模型。首先,假设设备具有离散的劣化状态,将设备的劣化过程建模为连续时间马尔可夫链;其次,考虑到患者对诊疗设备的不同功能频率需求,以及不同劣化状态对患者治疗时间、费用的影响,以患者就诊顺序、检查策略、修复策略为决策变量建立了设备维护和患者调度模型;最后,采用改进后的非支配排序遗传算法对多目标问题进行了求解。实验结果验证了设备维护与患者调度联合优化模型的有效性。改进后的算法提高了整体和局部的搜索能力,且具有鲁棒性。     

基于改进的NSGA2算法考虑病患公平性及医院运作成本的病床配置优化研究

针对目前医院病床调度存在运营成本较大以及医患关系之间公平性的问题,提出一个考虑医院运作成本和病患公平性下单科室病床分配的多目标随机规划模型。首先,根据基于医院的相关政策,提出一个考虑响应性与准入性的权重测度指标来反映医患关系的公平性,并考虑医院的运作成本建立多目标随机规划模型;其次,为方便算法求解,引入线性化方法将复杂模型处理成混合整数线性模型;最后,采用改进后的NSGA2算法对多目标问题求解,并对算例进行不同的数值实验。通过调整不同的参数进行相对应的灵敏度分析,改进后的算法提升了算法的收敛性与多样性,实验结果验证了模型的有效性和适用性。     

改进蚁群算法在路径规划中的应用

针对传统蚁群算法在机器人路径规划时存在收敛速度慢、易陷入局部最优等问题,提出了一种基于自适应归档更新的蚁群算法。根据路径性能指标建立多目标性能评估模型,对最优路径进行多指标优化;采用路径方案归档更新策略进行路径方案的更新和筛选,提高算法的收敛速度;当搜索路径进入不可行区域时,采用自适应路径补偿策略转移不可行路径节点,构造可行路径,减少死锁蚂蚁数量;若算法无法避开障碍或者进入停滞状态,则进行种群重新初始化,增加物种多样性,避免算法陷入局部最优。仿真实验表明,改进后的算法收敛速度更快、收敛精度更高、稳定性更好。     

洪水预报智能模型在中国半干旱半湿润区的应用对比

为探究不同洪水预报智能模型在我国半干旱半湿润区的应用效果,选用决策树、多层感知器、随机森林和支持向量机4种模型在陕西省3个典型流域进行逐时洪水预报;选择相关系数、纳什效率系数、均方根误差、平均绝对误差和相对误差等评价指标,比较不同预见期下4种模型在半干旱半湿润典型流域洪水预报的适用性。结果表明:在短预见期预报中,4种模型在半湿润区典型流域均可获得较高的预报结果,在半干旱典型流域模拟精度相对偏低,仅支持向量机模型满足预报要求;随着预见期延长,不同模型性能变化差异大,支持向量机模型整体稳定,在小流域实时洪水预报中具有明显优势;随机森林模型与决策树模型精度随预见期延长而缓慢下降,前者适应性更好;多层感知器模型精度随预见期延长而骤减,模型稳定性较差。     

基于IFMOP的玉环市区域水资源配置

引入无概率分布的区间数表示不确定性影响范围,选取经济效益、供需水效率和COD排放量分别构建经济目标、社会目标和环境目标优化函数,构建了玉环市不确定性模糊多目标规划(IFMOP)水资源配置模型,并计算了不确定性解集。结果表明:IFMOP配置计算成果可实现玉环市区域水量水质协调高效利用和社会-经济-生态系统均衡发展;在75%来水保证率下各行业配置总水量不确定性范围为14 375万~15 154万m³。IFMOP水资源配置模型在信息不充分的条件下能够全面使用各类数据信息进行水资源配置,更好地满足水资源在各行业配置过程中的动态管理需求。     

Probabilistic heuristic estimates

Though they constitute the major knowledge source in problem-solving systems, no unified theory of heuristics has emerged. Pearl [15] defines heuristics as criteria, methods, or principles for deciding which among several alternative courses of action promises to be the most effective in order to achieve some goal. The absence of a more precise definition has impeded our efforts to understand, utilize, and discover heuristics. Another consequence is that problem-solving techniques which rely on heuristic knowledge cannot be relied upon to act rationally — in the sense of the normative theory of rationality.To provide a sound basis for BPS, the Bayesian Problem-Solver, we have developed a simple formal theory of heuristics, which is general enough to subsume traditional heuristic functions as well as other forms of problem-solving knowledge, and to straddle disparate problem domains. Probabilistic heuristic estimates represent a probabilistic association of sensations with prior experience — specifically, a mapping from observations directly to subjective probabilities which enables the use of theoretically principled mechanisms for coherent inference and decision making during problem-solving. This paper discusses some of the implications of this theory, and describes its successful application in BPS.This research was made possible by support from Heuristicrats, the National Aeronautics and Space Administration, and the Rand Corporation.