基于SAS统计软件的电梯交通流预测分析

摘要：为实现电梯的合理选型配置,须对大楼内电梯交通流做出准确预测。为避免传统的预测方法误差率较大的问题．本文对电梯交通流构建了ARIMA预测模型．并结合SAS软件应用．对电梯交通流做出预测。应用预测模型对天津某大厦的电梯交通流数据进行预测．通过比较模型预测值与实测数据验证该预测模型能较准确地预测电梯的交通流。仿真和实验结果验证了该模型的有效性。     

上行高峰时段蒙特卡罗电梯交通流预测方法研究

摘要：为实现电梯的合理选型配置,需对大楼内上行高峰时段的交通流做出准确预测。通过改进传统蒙特卡罗交通流模型．计算累积概率值可以提高预测精确度。理论分析和仿真结果表明在上行高峰时段．改进的蒙特卡罗交通流预测方法比传统的方法预测精度更高。     

电梯群控系统调度方法研究

提出了一种基于改进L—M算法的电梯群控系统（EGCS）交通模式的判别方法,并介绍了基于模糊神经网络的电梯群控系统调度方法的设计思路。试验结果表明,用文中所介绍的方法可以获得最佳的派梯方案。     

双层轿厢电梯在高层办公建筑中的应用

探讨了双层轿厢电梯在高层办公建筑中的应用,着重介绍双层轿厢电梯设置方法、系统特点、运行模式、应用前景等,对传统双层轿厢电梯运行模式与目的选层运行模式进行了比较分析,并结合某工程电梯系统实例,详细阐述电梯交通配置分析步骤,提出合理化建议。     

电梯选型配置与优化设计研究

针对高层建筑电梯垂直交通的特点,应用专家系统建立电梯交通流量预测模型,研究电梯智能交通理论和流量预测方法,为电梯最佳选型配置提供理论依据。通过在预测模型中设置映射变量,将预测模型推广到其它不同用途的高层建筑电梯交通流量预测中。提出外呼信号智能目的选层电梯群控策略;结合预测静态分区和智能动态分区,应用前向神经网络技术,以平均候梯时间、平均乘梯时间和目的楼层重复度为控制目标,寻找不同交通模式的最佳动态调度方法。分别对上行高峰、下行高峰、午餐上／下行高峰、随机层间交通及空闲交通5种模式进行建模,并实现预测模型与5种交通模式的模型嵌套。应用数据挖掘和小波分析技术,通过实时模拟运行,对电梯运行数据进行融合处理,对系统参数进行定时不定期在线自动整定修改,提高电梯运行效率和服务质量。研究电梯在风摆、火灾、地震等特殊情况下的运行模式,提出相应控箭和减灾措施,提高电梯运行安全和舒适性。     

电梯智能群控系统研究概况

电梯是高楼大厦中不可缺少的垂直运输工具。为了提高电梯服务的性能,采用优质的电梯群控系统统一管理电梯运行是一个值得研究的问题。本文介绍了电梯群控系统的起源和现状,着重对智能群控技术发展进行了概括和分析。对部分电梯公司的群控技术应用情况进行了论述。最后对群控技术的发展进行了展望。     

Predicting Motorway Traffic Performance by Data Fusion of Local Sensor Data and Electronic Toll Collection Data

Abstract: This article proposes data fusion from different sources to improve estimation and prediction accuracy of traffic states on motorways. This is demonstrated in two case studies on an intraurban and an interurban motorway section in Austria. Data fusion in this case combines local detector data and speed data from the Electronic Toll Collection (ETC) system for heavy goods vehicles (HGV). A macroscopic model for open motorway sections has been used to estimate passenger car and HGV density, applying a standard state‐space model and a linear Kalman filter. The resulting historical database of 4 months of speed‐density patterns has been used as a basis for pattern recognition. A nonparametric kernel predictor with memory length of 9 and 18 hours has been used to predict HGV speed for a prediction horizon of 15 minutes to 2 hours. Results show good overall prediction accuracy. Correlation analysis showed little bias of predicted speed for free flow and congested time intervals, whereas transition states between free flow and congestion were frequently biased. Prediction accuracy can be improved by applying a combination of different prediction methods. On the other hand, computational performance of the prediction has to be further improved prior to implementation in a traffic management center.     

交通流量的混沌特性分析及预测模型研究

基于混沌动力系统的相空间重构和非线性系统的Volterra级数,分析交通流量的混沌特性,研究了一种交通流量的自适应预测模型。在合理选取嵌入维数和延滞时间实现交通流量时间序列相空间重构的基础上,应用小数据量法计算重构交通流量时间序列的最大Lyapunov指数,根据该指数值对交通流量的混沌特性进行分析,并采用庞卡莱截面法对分析结果进行验证;构建交通流量的Volterra预测模型,并采用LMS自适应算法对模型系数进行调整。通过对实际采集的高速公路交通流量数据的仿真研究表明,小数据量法能对交通流混沌特性进行准确判别,构建的二阶Volterra自适应预测模型能够有效地预测交通流量的变化。因此,在判定交通流量存在混沌特性时,可以应用论文构建的二阶Volterra自适应预测模型对其进行准确的预测。     

改进型蒙特卡罗算法的公寓楼电梯配置设计

针对公寓楼交通流的特点．结合电梯服务质量和节约资源两个因素。本文利用改进的蒙特卡罗算法对电梯配置设计中的相关参数进行优化,经过对比分析,给出最佳的配置结果。利用VB60进行界面设计,并增加了性能评价模块。     

提高本院住院大楼电梯运输能力解决方案研究

研究电梯运行方案,提高电梯运行能力。方法针对本院电梯运行特点，从运行路线，运行时间、乘客流量及优化方案的黄金分割特点．进行对本院电梯的运行方法进行改进,结果强化运行管理后患者及职工对电梯的运行满意度明显提高，明显提升了医院的服务水平。     

公路隧道滞留人员密度和疏散时间预测研究

为研究公路隧道突发火灾事故下滞留人员的密度与疏散时间,结合火灾应急响应时间与处置要求,引入交通波传播理论,考虑交通流状态、人员组成、车辆组成等因素,提出滞留人员密度计算方法,并以人员密度、大巴车疏散时间等为参数,构建人员疏散时间预测模型,分析不同交通流条件下的疏散时间,通过实例计算与数值仿真,验证了模型的有效性。结果表明：基于交通波理论提出的滞留人员密度计算方法,能准确地计算出隧道突发火灾事故后的人员密度分布,符合真实场景中的非均匀分布特征;采用人员疏散时间预测模型,能快速预测公路隧道人员疏散所需时间,预测结果与仿真结果基本一致。     

城市信号交叉口人行横道通行能力研究

通过对交叉口过街混合交通流过街特性、过街需求、过街效率指标的分析,得到了人行横道混合交通流相对于行人的通用折算方法,同时提出一种针对我国人行横道交通特征计算行人通行能力的新方法,具有一定的实用价值。     

汾酒文化商务中心写字楼的电梯设计

介绍了汾酒文化商务中心写字楼的地理位置及建筑功能,重点对电梯的设计进行了分析,依据电梯交通系统的评价指标,拟定了电梯的配置方案并作了分析计算,通过优化设计该配置方案,最终使电梯的设计满足了使用需求。     

基于视频识别的施工电梯效能分析

施工电梯作为工地主要的载人载货垂直运输工具,其运载人数的监管和运输效率的统计及分析,一直以来是现场安全及技术关注的重点.通过在施工电梯内加装监控设备,开发电梯人流统计系统,对电梯运行状态进行实时监控,并对监控数据进行了统计分析,为项目部的施工电梯运载监管及电梯数量配置优化提供了有力支持.     

An Approach to Dynamical Classification of Daily Traffic Patterns

This article proposes a prototype of an urban traffic control system based on a prediction‐after‐classification approach. In an off‐line phase, a repository of traffic control strategies for a set of (dynamic) traffic patterns is constructed. The core of this stage is the k‐means algorithm for daily traffic pattern identification. The clustering method uses the input attributes flow, speed, and occupancy and it transforms the dynamic traffic data at network level in a pseudo‐covariance matrix, which collects the dynamic correlations between the road links. A desirable number of traffic patterns is provided by Bayesian Information Criterion and the ratio of change in dispersion measurements. In an on‐line phase, the current daily traffic pattern is predicted within the repository and its associated control strategy is implemented in the traffic network. The dynamic prediction scheme is constructed on the basis of an existing static prediction method by accumulating the trials on set of patterns in the repository. This proposal has been assessed in synthetic and real networks testing its effectiveness as a data mining tool for the analysis of traffic patterns. The approach promises to effectively detect the current daily traffic pattern and is open to being used in intelligent traffic management systems.     

A model-based approach to natural ventilation

This paper presents an in situ calibrated model-based approach to the control of natural ventilation in buildings. Specifically, the potential of empirically-based and numeric air flow models is explored as part of a hybrid building controls scheme. Toward this end, two case studies are described. In one case, the underlying predictive model is based on the statistical treatment of data obtained from a set of in situ measurements in a typical office space. In the second case, a multi-zone air flow model is used to predict the fresh air volume flows into the building interior. The resulting air flow models can be used as an integrated part of a control method for natural ventilation devices to predict the implications of alternative control options.     

乘客电梯轿内通风的分析和研究

以载重量为1000kg的轿厢为例,考虑轿厢完全密闭、单个风扇工作及轿厢自然通风3种工况,对轿厢关人时乘客的呼吸安全性进行了分析。指出只要轿厢能够保证正常通风,则轿内乘客的呼吸是安全的;如果轿厢完全密闭,则在约1h内乘客的呼吸是安全的。据此,对电梯标准的修订及政府监管提出了建议。     

地铁站出入口客流分布及其影响因素分析

以上海人民广场地铁站为例,通过分析其出入口空间环境特征及群体交通行为,探究影响出入口客流分布的相关变量因素,为地铁站出入口的合理规划布局提供一定依据。首先,采用空间句法、兴趣点(POI)数据分析法结合传统实地调研分别定量分析人民广场地铁站18个出入口的外部空间特征(地面街道集成度、穿行度和POI分布现状)以及内部空间特征(地下空间出入口的集成度、穿行度和便捷度);同时利用行人计数法获取各出入口在特定时间内的现场人流截面穿行量;最后通过建立多元线性回归模型探讨各变量与出入口客流分布之间的相关性。结果表明,地铁站出入口客流分布主要受地铁站外部空间特征影响,并与出入口所在街道可达性和区域功能混合程度有较大关联。     

城市高架道路下匝道车流插入机制的Markov链模型

通过分析城市高架道路下匝道车流与地面车流相互影响的规律,引入随机动态系统,建立了时间齐次的M arkov链模型,能对各车道的车辆“顺畅驶入”、“以某概率驶入”、“减速停车等待”作实时预报,揭示了地面车流与下匝道车流相互影响的机制。     

Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition

Accurate traffic speed forecasting is one of the most critical tasks in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid forecasting approach named DeepEnsemble by integrating the three‐dimensional convolutional neural network (3D CNN) with ensemble empirical mode decomposition (EEMD). There are four steps in this hybrid approach. First, EEMD is adopted to decompose the complex traffic speed time series data with noise into several intrinsic mode functions (IMFs) and a residue. Second, a three‐dimensional tensor is established and fed into 3D CNN for prediction. Third, the output of 3D CNN prediction is obtained by a linear combination of the results of all components. Finally, the 3D CNN prediction output, external features, and historical features are fused to predict the network‐wide traffic speed simultaneously. The proposed DeepEnsemble approach is tested on the three‐month traffic speed series data of a real‐world large‐scale urban expressway network with 308 traffic flow detectors in Beijing, China. The experimental results indicate that DeepEnsemble outperforms the state‐of‐the‐art network‐wide traffic speed forecasting models. 3D CNN learns temporal, spatial, and depth information better than 2D CNN. Moreover, forecasting accuracy can be improved by employing EEMD. DeepEnsemble is a promising model with scalability and portability for network‐wide traffic speed prediction and can be further extended to conduct traffic status monitoring and congestion mitigation strategies.