改进的GM(1,1)模型公路货物运输量预测

货物运输量预测对交通运输基础设施建设和区域经济发展具有重要作用。针对传统的GM(1,1)模型存在精度低的问题,结合连续9组时间序列货运量数据,通过二次函数对背景值重构,从而修正参数向量,构建改进的GM(1,1)预测模型。并利用2017~2019年货运量对模型预测精度进行检验。结果表明:最大相对误差≯1%,后验残差比C为0.18,小概率误差P为1,模型预测精度高。     

基于组合预测模型的广州市物流需求量预测

选取2004—2014年广州市物流需求量数据,分别建立三次指数平滑预测模型和回归分析预测模型,加权组合后构建组合预测模型,并对广州市"十三五"规划期末2020年物流需求量进行预测,结果表明:货运量将大幅增长,超过15亿吨。提出以下建议:通过提高物流技术、增加物流基础设施投入、发展特色物流园区等多种举措推动广州市物流业持续健康有序发展。     

兰渝铁路货物运量分析

从西部地区的资源、产业布局、沿线的社会经济特点等方面对兰渝铁路区域货运量进行了综合分析,并根据区域经济发展规划,结合铁路运量历史变化趋势和发展规划对川渝地区与西北地区的货物交流分年度进行了预测,并对线路沿线的地方货运量进行了预测,为论证兰渝铁路的修建提供了强有力的依据。     

灰色理论在高层建筑沉降监测中的应用

根据灰色系统理论中的数列预测理论 ,针对高层建筑沉降监测 ,建立了灰色预测模型 ,并采用绝对关联度分析了模型的预测精度。最后根据预测模型对成都市某高层商住楼沉降量进行了预测 ,并根据预测结果与沉降实际测量数据的对比 ,分析了模型预测结果的合理性 ,并讨论了所用灰色预测模型的特点及其在高层建筑沉降监测的应用     

城市道路信息管网需求量预测分析

城市道路信息管网是一种新兴的管网技术。通过对其需求量影响因素进行分析,建立了城市道路信息管网需求量的预测步骤及模型。并以运营商——中国电信为例,验证了预测模型的正确性;在此基础上,给出了其它运营商信息管网需求量预测的具体建议,供各城市发展道路信息管网时参考。     

非饱和土地区高速铁路路基沉降预测模型

准确、合理地预测线路工后沉降是高速铁路建设的关键,现场沉降观测数据表明不同饱和度地区路基沉降曲线线型变化较大,基于饱和理论的沉降预测方法在工后沉降预测中存在不能准确描述沉降规律,且存在预测值偏小的风险。基于实测沉降规律,提出了一个适用于非饱和土地区路基的沉降预测曲线模型。分析了预测曲线模型的特点,并基于最小二乘法给出模型参数的求解方法。结合兰新铁路第二双线LXS-15标段沉降数据,提出相关系数、偏差度、稳定度为模型有效性检验指标,结合施工完成2 a的实测资料,进行了在施工完成3个月及6个月所提模型与规范要求3种预测模型的对比研究;对3条不同饱和程度高铁路基沉降预测结果表明所提出的非饱和土预测模型具有较好的精度和广泛的适用性,为非饱和土地区高速铁路建设合理判断工后沉降提供参考。     

基于GIS的滑坡预测模型的预测率及其作用

滑坡预测模型的选择直接影响到滑坡预测的准确性,是滑坡预测的关键所在。该研究利用意大利Alpago地区的滑坡数据和其他相关地理空间数据,以模糊伽马模型、模糊代数积模型、模糊代数和模型以及模糊最小模型等4个定量滑坡预测模型为例,探讨滑坡预测模型的预测率在对比、评价和选择不同模型方面的作用。滑坡预测模型的预测率是,模型预测结果图的各个级别类型中,未用于建模的滑坡面积百分比的累积分布函数。在地理信息系统中,利用已知的滑坡分布数据和模型的预测结果图,可以计算滑坡预测模型的预测率。研究结果表明,滑坡模型的预测率是滑坡预测模型自身特性的度量,在输入图层和滑坡类型确定的条件下,滑坡预测模型的预测率可作为对比、评价和选择不同模型的定量指标,可以用来确定最合适的预测模型。     

基于组合预测模型的股票预测方法的研究

对股票预测问题进行了深入的研究,提出了一个新的预测方法.针对股票时间序列的高度非线性、高噪音的特点,采用小波变换方法有效的过滤噪音、约简数据,并对ARIMA模型和BP神经网络预测模型进行了研究和分析,提出了一个基于ARIMA模型和BP神经网络模型的模糊变权重组合预测模型,应用该模型对股票时间序列进行分析预测,取得了令人满意的效果.     

SD法在城市需水量预测和水资源规划中的应用研究

运用系统动力学方法进行了水资源系统动态模拟和水资源规划研究.以秦皇岛市为例,建立了秦皇岛市水资源系统动力学模型,并将其应用于社会-经济-环境复杂系统背景下的水资源需求量预测、水系统动态模拟和策略分析中.该模型考虑了社会-经济-环境复杂系统内各要素间的相互作用和影响,历史检验和灵敏度分析结果表明,其稳定性和强壮性良好.运用该模型考察了不同经济发展模式下的水资源供需情况,通过对模型模拟运行结果的比较和分析,得到了适合该地区的水资源规划方案.     

建筑业碳排放预测与减排策略研究

以行业视角下的平均碳排放量为依据,将我国30个省市划分为低、中、高三个建筑业碳排放区域。针对不同的区域,分别运用可拓展的随机性环境影响评估模型和灰色预测模型对直接和间接碳排放量进行预测,并结合区域碳排放特点,从建筑材料和经济发展目标方面对建筑业节能减排提出发展策略。     

城市地下物流的货物种类及需求预测方法研究

地下物流系统是通过自动运输技术与地下隧道或管道相结合,利用地下空间进行货物运输的新模式.在城市地下物流系统规划中,明确系统服务的货物品类,预测地下物流货运需求量十分重要,然而目前新模式下的运输单元、货物品类、货运量等尚不明确.本文首先是分析适应城市地下物流系统的运输制式,再根据运输制式与其他运输方式的接驳,制定城市地下...     

基于城市发展战略的深圳高铁枢纽规划研究——以深圳北站和福田站为例

由于需求与供应特征的不同,高铁车站的选址理念会不同,依托高速铁路车站建设的高铁枢纽在规划设计理念上也各有鲜明的特色.广深港客运专线深圳境内车站——深圳北站和福田站的综合交通枢纽就形成了两种截然不同的城市综合交通枢纽模式,前者选址于城市副中心,采用的是一般综合交通枢纽的选址、建设方式,属于典型的功能定位高、基础条件好、配...     

Large metropolitan water demand forecasting using DAN2, FTDNN,and KNN models: A case study of the city of Tehran,Iran

Efficient operation of urban water systems necessitates accurate water demand forecasting. We present daily, weekly, and monthly water demand forecasting using dynamic artificial neural network (DAN2), focused time-delay neural network (FTDNN), and K-nearest neighbor (KNN) models for the city of Tehran. The daily model investigates whether partitioning weekdays into weekends and non-weekends can improve forecast results; it did not. The weekly model yielded good results by using the summation of the daily forecast values into their corresponding weeks. The monthly results showed that partitioning the year into high and low seasons can improve forecast accuracy. All three models offer very good results for water demand forecasting. DAN2, the best model, yielded forecasting accuracies of 96%, 99%, and 98%, for daily, weekly, and monthly models respectively.     

Forecasting short-term air passenger demand using big data from search engine queries

Forecasting air passenger demand is a critical aspect of formulating appropriate operation plans in airport operation. Airport operation not only requires long-term demand forecasting to establish long-term plans, but also short-term demand forecasting for more immediate concerns. Most airports forecast their short-term passenger demand based on experience, which provides limited forecasting accuracy, depending on the level of expertise. For accurate short-term forecasting independent of the level of expertise, it is necessary to create reliable short-term forecasting models and to reflect short-term fluctuations in air passenger demand. This study aims to develop a forecasting model of short-term air passenger demand using big data from search queries to identify these short-term fluctuations. The suggested forecasting model presents an average forecast error of 5.3% and indicates that an increase of approximately 195,000 air passengers is to be expected 8 months later, as the key query frequencies increase by 0.1%.     

Assessment of predictive uncertainty within the framework of water demand forecasting using the Model Conditional Processor (MCP)

This paper presents an application of the Model Conditional Processor (MCP), originally proposed by Todini (2008) within the hydrological framework, to assess the predictive uncertainty in water demand forecasting related to water distribution systems. The MCP enables us to assess the probability distribution of the future water demand conditional on the forecasts provided by two or more deterministic forecasting models. In the numerical application described here, where two years of hourly water demand data for a town in northern Italy are considered, two forecasting models are applied in order to forecast hourly water demands from 1 to 24 hours ahead: the first model has a modular structure comprising a periodic component which reflects the long-term effects and a persistence component which represents the short-term memory of the process; the latter is based on neural networks. The results highlight the effectiveness of the approach, provided that the data set used for the MCP parameterization is properly selected so as to be actually representative of the accuracy of the real-time water demand forecasting models.     

组合预测方法在公路运输量预测中的应用研究

以吉林省公路货运量预测为例，在现有文献研究的基础上，引入了基于广义逆矩阵的变权组合预测模型，在经过理论验证的基础上，通过运用补充新息、二次循环迭代等方法克服了原有方法在实际预测出现的数据失真现象，开展了中长远期预测，并对比各特征年的实际发生数据，重新进行了检验．     

Using extreme learning machines for short-term urban water demand forecasting

This study explores the ability of various machine learning methods to improve the accuracy of urban water demand forecasting for the city of Montreal (Canada). Artificial Neural Network (ANN), Support Vector Regression (SVR) and Extreme Learning Machine (ELM) models, in addition to a traditional model (Multiple linear regression, MLR) were developed to forecast urban water demand at lead times of 1 and 3 days. The use of models based on ELM in water demand forecasting has not previously been explored in much detail. Models were based on different combinations of the main input variables (e.g., daily maximum temperature, daily total precipitation and daily water demand), for which data were available for Montreal, Canada between 1999 and 2010. Based on the squared coefficient of determination, the root mean square error and an examination of the residuals, ELM models provided greater accuracy than MLR, ANN or SVR models in forecasting Montreal urban water demand for 1 day and 3 days ahead, and can be considered a promising method for short-term urban water demand forecasting.     

联合方式划分/交通分配模型的扩展和修正研究

针对进行包括城市轨道交通在内的多方式(两种以上)交通需求预测时,方式划分和交通分配阶段模型存在参数不一致的问题,考虑各种交通方式在路网中的相互影响关系,提出一种交通方式分类方法。在此基础上,建立基于非集计模型的、多方式的、联合方式划分/交通分配的扩展模型,并给出该模型的等价性和解的唯一性证明及解法,最后通过一个算例验证模型的有效性。结果表明:提出的模型不仅能够满足三种及以上交通方式并存的网络的交通需求预测,而且能够有效地解决交通方式划分/交通分配两阶段参数不一致的问题。     

重庆市主城区停车位需求预测分析

该文通过介绍停车需求预测模型,从而选定预测重庆主城区停车需求的方法,并收集和整理相关资料,确定基础参数,最后根据模型公式推导出最终的预测结果。     

Forecasting construction industry demand,price and productivity in Singapore: the BoxJenkins approach

In academic research, the traditional Box-Jenkins approach is widely acknowledged as a benchmark technique for univariate methods because of its structured modelling basis and acceptable forecasting performance. This study examines the versatility of this approach by applying it to analyse and forecast three distinct variables of the construction industry, namely, tender price, construction demand and productivity, based on case studies of Singapore. In order to assess the adequacy of the Box-Jenkins approach to construction industry forecasting, the models derived are evaluated on their predictive accuracy based on out-of-sample forecasts. Two measures of accuracy are adopted, the root mean-square-error (RMSE) and the mean absolute percentage error (MAPE). The conclusive findings of the study include: (1) the prediction RMSE of all three models is consistently smaller than the model's standard error, implying the models' good predictive performance; (2) the prediction MAPE of all three models consistently falls within the general acceptable limit of 10%; and (3) among the three models, the most accurate is the demand model which has the lowest MAPE, followed by the price model and the productivity model.