因地制宜,综合施治,务实解决街坊小区积水顽症

上海市杨浦区某些街坊小区一遇暴雨即产生严重的积水问题。通过调查研究，找出病症，提出改造方案．有效地解决了小区的积水顽症。本文结合实际，总结了若干综合技术措施和行政管理、协调手段。     

一种网格 k-近邻集的边界点识别算法

为了高效识别聚类边界,根据边界周围区域存在密度差异的特征,提出了一种网格 k‐近邻集的边界识别算法（BGN ）。在网格空间中,该算法根据网格单元和它最近邻居单元的 k‐近邻集的质量及其单元间中心距离确定边界度,由边界度和边界阈值判断每个网格单元是否为边界单元或噪声单元。通过从边界单元中提取更靠边缘的数据作为边界点的方式,使得边界更精细。实验结果表明,该算法能有效和快速识别出多密度数据集的聚类边界和噪声。     

基于非参数回归的快速路行程速度短期预测算法

基于北京市快速路上的检测器所采集的历史数据，经过数据筛选，剔除判别，小波滤噪平稳处理，聚类分析等过程，建立了交通状态演变系列的历史样本数据库。基于所构建的历史数据库，通过数值试验，确定了状态向量、距离匹配原则，K近邻值等参量，构建了一种基于K近邻的非参数回归短时交通预测模型，实现了对路段行程速度的短时预测。最后，利用随机选取的历史数据系列对预测模型的精度进行了检验。结果表明，预测算法的精度可以达到90％以上，可以很好地满足ITS应用系统对于交通预测数据的精度要求。     

城轨新线客流成长期进出站量短时预测研究

为准确掌握城轨新线开通初期客流演化态势、提高运输组织合理性,针对新线客流变化不稳定、缺乏历史客流数据等问题,提出城轨新线客流成长期进出站量短时预测方法。通过对新线站点进出站量变化规律的分析,基于改进模糊C均值聚类算法,对考虑客流趋势相似性的城轨站点类型划分方法进行研究,并提出城轨新线站点历史数据库构建方法;基于趋势距离对近邻匹配机制进行优化,并根据多元统计回归对K近邻算法进行改进,提出新线站点客流成长期进出站量短时预测方法;结合广州地铁客流数据,对预测方法的有效性进行验证。研究结果显示:新线站点客流成长期内短时进、出站量平均预测效率较既有方法增加了35.68%、32.23%,预测精度较既有方法增加了38.32%、25.80%。     

基于非参数回归的城轨实时进出站客流预测

为准确预测城轨实时进出站客流,构建基于非参数回归的实时进出站客流预测模型。首先,对不同特征日分时进出站客流量进行对比分析,据此构建历史数据库;其次,通过计算历史分时数据的相关系数,并设置阈值对分时客流数据间的相关性进行判断,从而确定合适的非参数模型状态向量;再次,根据K近邻样本与预测目标的客流量差异性,设计基于权重加权的预测算法;最后利用广州市城轨客流数据对预测模型进行精度分析,对全网站点多天的预测结果显示:全天平均绝对百分比误差均在2%以下,分时平均绝对百分比误差均在14%以下,表明模型具有较高的预测精度和良好的适用性。     

应急物流配送优化的改进最邻近算法研究

应急物流配送研究的核心是最短路径选择问题。以最邻近算法为基础,针对以往只能解决一个配送仓库对应多个救灾中心问题的局限性,提出一种多个配送仓库同时对应多个救灾中心的改进最邻近优化算法。通过仿真实验证明该算法具有良好的适用性。     

Monitoring Median Queries over Moving Objects

The k-median problem has attracted a number of researchers. However, few of them have considered both the dynamic environment and the issue of accuracy. In this paper, a new type of query is studied, called continuous median monitoring (CMM) query. It considers the k-median problem under dynamic environment with an accuracy guarantee. A continuous group nearest neighbor based (CGB) algorithm and an average distance medoid (ADM) algorithm are proposed to solve the CMM problem. ADM is a hill climbing schemed algorithm and achieves a rapid converging speed by checking only qualified candidates. Experiments show that ADM is more efficient than CGB and outperforms the classical PAM (partitioning around medoids) and CLARANS (clustering large applications based on randomized search) algorithms with various parameter settings.     

AN EFFICIENT FAST ENCODING ALGORITHM FOR VECTOR QUANTIZATION

IntroductionAs an effective technique for data compression,VQ[1,2 ] has been successfully used forvarious ap-plications involving VQ- based encoding and VQ-based recognition in both vocal and image pro-cessing.The k- dimensional,N- level vectorquantizer is defined as a mapping from a k- di-mensional Euclidean space Rkinto a certain finitesubset C={Ci,i=1 ,2 ,… ,N },which is called acodebook and whose elements Ci are called code-words.A distortion measure D( X,Ci) is a non-negative dissi…     

Floating Car Data Based Nonparametric Regression Model for Short-Term Travel Speed Prediction

A K-nearest neighbor (K-NN) based nonparametric regression model was proposed to predict travel speed for Beijing expressway. By using the historical traffic data collected from the detectors in Beijing expressways, a specically designed database was developed via the processes including data filtering, wavelet analysis and clustering. The relativity based weighted Euclidean distance was used as the distance metric to identify the K groups of nearest data series. Then, a K-NN nonparametric regression model was built to predict the average travel speeds up to 6 min into the future. Several randomly selected travel speed data series, collected from the floating car data (FCD) system, were used to validate the model. The results indicate that using the FCD, the model can predict average travel speeds with an accuracy of above 90%, and hence is feasible and effective.     

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针对我国当前在公交换乘问题上存在的问题，本文在对公交网络、公交系统新的认识基础上，以划分公交服务区，公交线路等级化的思想，提出了城市枢纽规划决策的几种方法:以交通分配预测为基础的会交客流分配数据，得到枢纽的集散客流量强度，进而提出基于公交客流预测的流量决定法；在城市公交网络结构和用地结构的基础上，利用最小控制集算法；利用城市规划中“邻里单元”的概念，作为规划枢纽的分区单位和依据，提出了基于城市布局结构的枢纽决策规划及修正方法等，并提出了以公交换乘枢纽为核心的规划新流程.