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.

A Comprehensive Taxi Assessment Index Using Floating Car Data

With the expansion of urban area and development of taxi system,problems arise,such as low operation efficiency,high taxi idling rate,and long passenger waiting-time. Although various studies have been conducted,only limited overview of the factors towards urban taxi system has been provided. Consequently,a comprehensive evaluation of taxi system is essential for the urban planner to analyze the current situation and take effective measures. This paper,by using Floating Car Data( FCD),proposes a Comprehensive Taxi Assessment Index( CTAI) to quantify the quality of existing urban taxi system with the assistance of Geographic Information System( GIS) technology. The proposed index system extracts and classifies key factors,reflecting the taxi system from the perspectives of operation efficiency,customer and taxi-driver satisfaction. The system contributes to improving the organization and operation of urban taxi system. Based on the data obtained from the city of Shenzhen,Guangdong Province,China,for both weekday and weekends( Dec.,2011),the proposed CTAI was illustrated by using the Principal Component Analysis( PCA) with ArcGIS 10. 0 platform. The results indicate that the system provides a good multi-dimensional view to delve into the existing urban taxi operation, thus to point out the most sensitive indices towards the entire system,which consequently provides guidelines for future improvement and management of urban taxi system.

City Routing Velocity Estimation Model under theEnvironment of Lack of Floating Car Data

After introducing the principle of float car data (FCD), this paper gives the primary flow of pre-handing and map- matching of the FCD. After analyzing the percentage of coverage of FCD on the road network, large quantity of heritage database of routing status is used to estimate the routing velocity when lack of FCD on parts road segments. Multi liner regression model is then put forwarded by considering the spatial correlativity among the road network, and some model parameters are deduced when time series is classified in day and week. Besides, error of velocity probability and error of status probability are achieved based on the result from field testing while the feasibility and reliability of the velocity estimation model is obtained as well. Finally, as a case study in Shanghai center area, the whole routing velocity in the road network is estimated and published in real time.

Freight Vehicle Routing Optimization for Sporadic Orders Using Floating Car Data

The increasing popularity of e-commerce brings large volumes of sporadic orders from different customers,which have to be handled by freight trucks and distribution centers. To improve the level of service and reduce the total shipping cost as well as traffic congestions in urban area, flexible methods and optimal vehicle routing strategies should be adopted to improve the efficiency of distribution effort. An optimization solution for vehicle routing and scheduling problem with time window for sporadic orders (VRPTW- S) was provided based on time-dependent travel time extracted from floating car data (FCD) with ArcGIS platform. A VRPTW-S model derived from the traditional vehicle routing problem was proposed, in which uncertainty of customer orders and travel time were considered. Based on this model, an advanced vehicle routing algorithm was designed to solve the problem. A case study of Shenzhen, Guangdong province, China, was conducted to demonstrate the vehicle operation flow,in which process of FCD and efficiency of delivery systems under different situations were discussed. The final results demonstrated a good performance of application of time-dependent travel time information using FCD in solving vehicle routing problems.

考虑载客状态的改进孤立森林浮动车异常数据检测算法

为提高浮动车数据中异常数据检测能力及不同载客状态下的模型检测分析能力,提出基于S-DTA-IIForest(Summation&Difference Third Order Average&Improvement-Isolation Forest)的浮动车数据异常检测算法。构建由相邻两项求和(S)、三阶求和平均差分(DTA)的二维度空间SDTA特征向量;提出差额累计更新和动态区分辨识的改进孤立森林IIForest算法,通过设置停止阈值参数,避免当出现新样本异常值分数大于停止阈值时,仅更新样本不更新孤立森林模型的问题,设计每个二叉树区分辨识度参数,区分辨识度位于停止区间时停止二叉树生长,提高算法收敛性能,以ROC(Receiver Operating Characteristic)曲线下面积AUC(Area Under ROC Cure)、F1-score为指标对模型精度进行对比分析,并以重庆市中心城区学府大道开展实例验证。结果表明:本文S-DTA-IIForest组合算法AUC、F1-score分别为86.63%、0.89,AUC较传统孤立森林IForest(Isolation Forest)提高32.4%,运行效率提高1.29%,具有收敛速度更快、精度更高的优势,载客条件下模型AUC、F1-score较未载客分别提高7.7%、10.8%,组合算法对载客数据有更高的检测精度,且未载客状态数据异常率较载客状态增加71.4%,未载客数据异常率更高。

基于迁移学习的GOCI超分辨率重建与海洋漂浮藻类探测

遥感技术是进行海洋漂浮藻类目标识别与变化监测的重要手段。GOCI遥感卫星影像具有高时间分辨率、低空间分辨率的特点,其低空间分辨率影响了海洋漂浮藻类遥感探测的效果。本研究通过对具有较高空间分辨率的Sentinel-2遥感卫星影像结构特征的迁移学习,应用ESRGAN超分辨率重建技术,将GOCI影像的空间分辨率提升至125 m;在此基础上,构建了基于超分辨率重建GOCI遥感影像的U-Net深度学习语义分割网络,实现了海洋漂浮藻类的较高精度探测。实验结果表明:超分辨率重建的GOCI影像显著提升了影像的空间细节清晰度,基于此实现的海洋漂浮藻类探测结果取得了较高的精度,其中面积相对误差下降了51.87%,F1值提高了2.41%。本研究是应用GOCI遥感影像进行海洋漂浮藻类高精度探测的一次成功实践,为实现海洋目标的动态精细化监测提供有益的参考。

基于GPS数据的高速公路行程时间估计与可靠性分析

准确地进行高速公路行程时间估计与可靠性分析对交通规划与缓解交通拥堵具有重要意义。针对目前行程时间估计准确性不高、可靠性分析不够全面的问题,提出基于BP神经网络的高速公路行程时间估计模型,并利用该模型计算的行程时间分析高速公路上车辆行驶的行程时间可靠性。以广州机场高速公路GPS浮动车数据为例进行实例验证,结果表明,与速度-时间积分法和位置-时间内插法相比,本文提出的模型提高了行程时间估计的准确度,同时能多方面地分析车辆行程时间的可靠性。

国医大师颜正华治疗呼吸系统疾病用药升降浮沉药性规律分析

目的分析国医大师颜正华教授治疗呼吸系统疾病用药的升降浮沉药性规律,传承其宝贵学术经验。方法以4本颜正华教授弟子为主编的著作为主要来源,系统收集、分析颜正华教授临证治疗呼吸系统疾病的处方,对患者的性别、年龄,处方的辨病、证型,中药的升降浮沉药性、用量及常用药对等进行统计分析。结果共纳入处方208首,涉及中药178味、君药64味,药性多趋向沉降,全方以沉降方居多;处方平均用药13.2味,且大部分用量点在常规用量范围内;后下药中使用频数最高的中药为鱼腥草。常用药对有紫菀-白前、白前-百部、紫菀-百部,沉降方中常用苦杏仁、浙贝母、紫菀等药。结论颜正华教授治疗呼吸系统疾病时喜用沉降药,配以趋向升浮和具有双重趋向的药物调理气机的升降,且具有孟河医派“用药轻灵”的特点。

Modbus-RTU协议的浮点数传输设计

为解决Modbus-RTU通信协议中保持寄存器不能直接传递高精度浮点数的问题,设计了一种共用体数据结构,定义联合变量,将浮点数和对应的存储数组共享同一内存空间,发送端用浮点数二进制码的方式传输浮点数,接收端再用浮点数的格式读取这4个字节内的数值,实现Modbus-RTU协议的浮点数传输设计。该方法经多次数据传输测试,可同时发送多个不同精度的浮点数,数据传输简单、高效。该方法可应用于需要采用Modbus-RTU协议进行多个浮点数数据传输且精度不同的通信系统中。

China Argo project:progress in China Argo ocean observations and data applications

This paper reviews the current achievements of the China Argo project. It considers aspects of both the construction of the Argo observing array, float technology, and the quality control and sharing of its data. The developments of associated data products and data applications for use in the fields of ocean, atmosphere, and climate research are discussed, particularly those related to tropical cyclones （typhoons）, ocean circulation, mesoscale eddies, turbulence, oceanic heat/salt storage and transportation, water masses, and operational oceanic/atmospheric/climatic forecasts and predictions. Finaliy, the challenges and opportunities involved in the long-term maintenance and sustained development of the China Argo ocean observation network are outlined. Discussion also focuses on the necessity for increasing the number of floats in the Indian Ocean and for expanding the regional Argo observation network in the South China Sea, together with the importance of promoting the use of Argo data by the maritime countries of Southeast Asia and India.

预制钢弹簧浮置板道床顶升精度探析

城市轨道交通线路在经过密集居民区、医院、精密实验室、剧院等特殊路段时,车辆运行发出的振动和噪音会严重影响周边环境和居民生活,因此在轨道工程轨道施工中,常采用钢弹簧浮置板道床,其减振、降噪效果最为显著,而在钢弹簧浮置板道床顶升施工中,浮置板顶升后的精度一直是控制难点。本文以苏州市轨道交通6号线预制钢弹簧浮置板道床为例,探讨了城市轨道交通预制钢弹簧浮置板道床曲线段顶升后的精度,结合实践,分析影响顶升后精度的主要原因,给出解决对策,规避施工难点,有效提高施工精度,总结施工经验并为后续同类型施工提供参考。

兼容bfloat16的高速浮点加法器设计

为了提高人工智能、深度学习等领域对于浮点数计算的速度,介绍了一种兼容bfloat16格式的高速浮点加法器,在可以完成正常格式的16、32、64位浮点数计算,同时兼容bfloat16格式浮点数进行计算,利用对应的浮点加法指令编写定向测试激励进行功能验证,对设计结果利用软件综合验证。设计使用主流的双通路TWO-PATH算法,即根据阶码差值大小将计算转化为不同路径计算,首先为减少计算绝对延时,调整计算步骤缩减流水线拍数;然后在半精度加法中实现兼容bfloat16格式。相比于初始设计频率下降1.36%,为2.16 GHz,面积增加14.01%,功率增加53.31%。

Rapid prediction of floating and sinking components of raw coal

A model that rapidly predicts the density components of raw coal is described.It is based on a threegrade fast float/sink test.The recent comprehensive monthly floating and sinking data are used for comparison.The predicted data are used to draw washability curves and to provide a rapid evaluation of the effect from heavy medium induced separation.Thirty-one production shifts worth of fast float/sink data and the corresponding quick ash data are used to verify the model.The results show a small error with an arithmetic average of 0.53 and an absolute average error of 1.50.This indicates that this model has high precision.The theoretical yield from the washability curves is 76.47% for the monthly comprehensive data and 81.31% using the model data.This is for a desired cleaned coal ash of 9%.The relative error between these two is 6.33%,which is small and indicates that the predicted data can be used to rapidly evaluate the separation effect of gravity separation equipment.

基于协调影响流量的交叉口群主要流线识别

为了确定交叉口群的主要流线,以更好地进行交叉口群协调控制,建立基于协调影响流量交叉口群主要流线识别算法。首先分析路径协调影响流量成分,通过对交叉口群范围内的浮动车轨迹路径进行统计分析,确定备选主要流线并计算流线协调影响浮动车数量;然后利用交叉口流量和转向比例估计备选主要流线的协调影响流量;最后根据备选主要流线的统计协调影响浮动车数量和估计协调影响流量来计算流线权重指标,通过权重指标确定交叉口群的主要流线。以西安雁塔区划分的一个交叉口群为例,对交叉口群主要流线进行识别,验证本文算法的效果。实验结果表明,本文算法能利用浮动车数据和流量数据对交叉口群主要流线进行实时识别,为交叉口群信号协调控制提供支撑。

基于公交浮动车数据的城市主干道交通拥堵预测

交通拥堵预测是解决交通拥堵问题的前提。针对速度特性分析不全面的问题,基于公交浮动车数据,在速度时间相关性和空间相关性分析的基础上,加入了公交流量和时间占有率两个特征,提出了考虑时空特性和公交车流特性的改进粒子群优化的径向基函数神经网络(particle swarm optimization-radial basis function,PSO-RBF)速度预测模型。通过比较预测结果与速度阈值,得到城市主干道的交通拥堵情况。结果表明,与只考虑时空特性的预测结果相比,所提出的基于时空特性和公交车流特性的预测方法,可使模型预测的均方根误差(root mean square error,RMSE)、平均绝对误差(mean absolute error,MAE)分别降低13.58%、12.63%,决定系数达92.39%。同时,实例验证了改进的PSO-RBF神经网络模型的预测精度要优于标准的PSO-RBF神经网络。

台风利奇马(1909)海温变化特征分析

本文采用多源卫星融合数据、海洋气象漂流观测仪直接测量数据、Argo浮标的数据以及分析数据集,分析2019年超强台风利奇马过境时海面温度(SST)以及海温垂直剖面的时空变化。结果表明,“利奇马”过境前后,SST最大降温可达5℃以上,并且降温区域集中在台风路径的右侧;“利奇马”过境导致近表层与次表层水体的垂直混合,次表层水体被加热,使混合层深度增加,深层海水温度升高。通过各类数据分析,发现“利奇马”过境后引起的SST降温核心区域的形成滞后于“利奇马”台风中心区域约1~2天。在各类数据的对比分析中,由于海洋气象漂流观测仪的数据是直接测量的,且采样频次高,能够更加细致和准确的捕捉到温度的变化,且可以作为用于卫星数据校正的重要参考。另外,不同海洋区域的降温还与海洋环境有关,台风靠近黑潮,降温主要集中在混合层,台风远离黑潮,降温可以延伸到温跃层。

大数据时代下流动人员人事档案管理信息化建设研究

我国现阶段经济建设和产业发展正处在重要阶段,各地区城市对于人力资源需求急剧增长,这也使得我国各地区城市中出现了规模十分庞大的流动人口,在这一背景下,如何高效率且高质量开展流动人员人事档案管理工作就显得至关重要。大数据时代下运用信息技术开展流动人员人事档案管理能够充分发挥大数据技术、计算机技术、现代通信技术先进功能优势,提高流动人员人事档案信息管理效率和利用的便利性,降低人为因素所导致管理失误发生的概率。因此,各地区政府相关部门要不断加大投入力度,加快流动人员人事档案管理信息化建设进程。文章将在明确流动人员人事档案管理特点基础上,阐述大数据时代下流动人员人事档案管理信息化的价值优势,并提出几点实现档案管理信息化的思路和要点。

“新冠”疫情对私家车出行特征的影响

受新冠肺炎疫情的影响,北京市私家车辆在2020年的出行特征,包括出行频率,出行时间以及出行地点均表现出了不同于往年的变化.使用安装在志愿者车辆上的车载盒子收集车辆行驶的浮动车数据,通过对比2020年6月与2019年6月同期的私家车辆出行特征,在不同功能的出行区域内、以出行终点为研究对象,对私家车辆的出行特征进行分析.经过对数据的筛选和处理,进而从出行目的及出行意愿角度对“新冠”疫情影响私家车辆出行特征的改变进行总结.

城市绿色开敞空间可达性研究——以南京市中心城区为例

城市绿色开敞空间是城市公共空间的重要组成部分,合理配置城市绿色开敞空间对城市宜居环境建设至关重要。空间可达性常用于评价公共服务设施空间布局的合理性,两步移动搜索法即是一种直观、运算简便、应用广泛的可达性计算方法,但该方法未考虑需求主体之间的差异性。本研究以南京市中心城区为例,利用手机信令数据提取需求者属性特征以及空间分布,通过分类细化需求主体改进搜索策略,改善两步移动搜索法传统研究未考虑不同群体差异性、统计数据滞后、空间尺度粗等弊端。结果表明,南京市中心城区绿色开敞空间可达性呈现出明显的空间分异特征,江南主城可达性总体比江北新区高,低值区集中在人口密度高、绿色开敞空间资源规模小的中心城区周边;不同人群的绿色开敞空间可达性总体格局相似,但在数值上老年人可达性均值是其他年龄段的14.60%,工作人群可达性均值是居住人群的86.02%,老年人作为弱势群体在享受绿色开敞空间服务中被边缘化。研究结果可为优化南京市绿色开敞空间布局提供科学依据,也可为其他公共服务设施可达性评价提供借鉴。