TRAFFIC CONDITION FORECASTING SYSTEM BY FLOATING CAR DATA WITH DATA MINING METHOD

机译：数据挖掘浮动数据的交通状况预测系统

获取原文

获取原文并翻译 | 示例

页面导航

摘要
著录项
相似文献
相关主题

摘要

A traffic condition forecasting system using floating car data in conjunction with a datarnmining method is proposed. A register network is used to describe the congestion model. Thernregister network denotes the dual graph of the actual road link connections. In this forecastingrnsystem, estimation and learning agents alternately calculate the results to improve thernforecasting accuracy. To forecast future traffic conditions, it is essential to interpolate presentrntraffic conditions. The forecast results for an hour ahead are used as the interpolation resultsrnafter an hour. The standard deviation of the forecast velocity error is 14.03 km/h.

机译：提出了一种结合浮动汽车数据和数据挖掘方法的交通状况预测系统。寄存器网络用于描述拥塞模型。寄存器网络表示实际道路连接的对偶图。在此预测系统中，估计和学习代理会交替计算结果以提高预测准确性。为了预测未来的交通状况，必须对当前的交通状况进行插值。一个小时前的预测结果用作一个小时后的插值结果。预测速度误差的标准偏差为14.03 km / h。

著录项

来源
《18th world congress on intelligent transport systems, 2011 ITS America's annual meeting and exposition.》|2011年|p.1-12|共12页
会议地点 Orlando FL(US);Orlando FL(US)
作者
Tetsuo Morita;
展开▼
作者单位

Information Communication Laboratories, Sumitomo Electric Industries, Ltd. 1-1-3, Shimaya, Konohana-ku Osaka, Japan 554-0024 +81-6-6466-5607, morita-tetsuo@sei.co.jp;

展开▼
会议组织
原文格式 PDF
正文语种 eng
中图分类交通工程与交通管理;自动化系统理论;
关键词
traffic forecasting; floating car; multivariate analysis; data mining; coefficientrnof determination; mean square error;

机译：交通预测;浮动汽车;多元分析;数据挖掘;系数确定;均方误差;

相似文献

外文文献
中文文献
专利

1. A Comparison of Deep Learning Methods for Urban Traffic Forecasting using Floating Car Data [J] . Juan José Vázquez, Jamie Arjona, MaPaz Linares, Transportation Research Procedia . 2020,第1期

机译：使用浮动车数据进行城市交通预测深层学习方法的比较
2. High Performance Spatial Interpolation System for Traffic Conditions by Floating Car Data [J] . Tetsuo MORITA SEI Technical Review . 2011,第72期

机译：浮动汽车数据的交通状况高性能空间插值系统
3. High Performance Spatial Interpolation System for Traffic Conditions by Floating Car Data [J] . Tetsuo MORITA SEI Technical Review . 2011,第73期

机译：通过浮动汽车数据实现交通状况的高性能空间插值系统
4. TRAFFIC CONDITION FORECASTING SYSTEM BY FLOATING CAR DATA WITH DATA MINING METHOD [C] . Tetsuo Morita ITS world congress on intelligent transport systems . 2011

机译：数据挖掘浮动数据的交通状况预测系统
5. Design recovery and data mining: A methodology that identifies data-cohesive subsystems based on mining association rules. [D] . Montes de Oca, Carlos. 1999

机译：设计恢复和数据挖掘：一种基于挖掘关联规则识别数据内聚子系统的方法。
6. Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data [O] . Bin Ran, Li Song, Jian Zhang, -1

机译：使用张量补全法从稀疏的浮动车数据中获得更好的交通状态估计覆盖率
7. ABSTRACT Various body parts or organs can be analysed to identify the different diseases in the human body. Fingernail analysis is one of the ways to identify disease in the human body. Nails are the body part which are farthest from the heart and therefore receive oxygen at last. As a result the nails are the first who show the symptoms of a disease in the human body. Fingernails can be easily captured for diagnosis and there are no heavy equipment or no specific conditions required to use nail image for disease diagnosis, like in other tests and scanning processes. Human nails deliver beneficial information about complaints or any nutritive imbalances in the human body depending upon their shape, texture and colour. In human beings, numerous systemic and skin diseases can be easily analyzed through careful examination of nails of both the limbs. A lot of nail illnesses have been found to be primary signs of numerous underlying systemic illnesses. The colour, texture or shape changes in nails are signs of many diseases mainly affecting nails. Considering all these properties of nails a system is proposed that uses digital image processing (DIP) methods for identifying such changes in the human nail to get more precise results and predict numerous diseases effortlessly. With the emerging Internet of Things (IOT) concept the generated report is made available remotely, this will help users to reduce transportation efforts. As the system has to deal with large and private data, the security of data must be ensured. To keep the data confidential, the Blockchain concept which is one of the most emerging concepts in the field of data management is used. The paper contains the implementation of the digital image processing for feature extraction of nail images, usage of IOT (ThingSpeak cloud) for data storage and implementation of Blockchain to keep the system secured and theft free. KEY WORDS: Int ernet of thin gs (IOT), Image proc essin g, Thin gSpeak, RG B vavalues, Mean pi xel vavalues, Bloc kchain , Hash key. Disease Diagnostic System: Abnormalities in Human Nail [O] . Pranav S. Wazarkar 2020

机译：摘要的各个身体部位或器官可被分析以识别在人体内的不同的疾病。指甲分析来识别人体疾病的方法之一。指甲是身体一部分是离心脏最远，因此在最后接受氧气。作为结果，指甲是第一谁表现出人体疾病的症状。指甲可以容易地捕获用于诊断和没有重装或需要使用指甲图像用于疾病诊断，比如在其他测试和扫描过程没有特定的条件。人的指甲提供有关投诉或取决于它们的形状，纹理和色彩在人体内的任何营养失衡有益的信息。在人类中，许多全身性皮肤疾病是可以很容易地通过两个四肢指甲的仔细检查分析。很多指甲病已发现众多潜在系统性疾病的主要症状。在指甲的颜色，质地和形状的变化是许多疾病主要影响指甲的迹象。考虑到所有的指甲的这些性能的系统被提出，用于识别人指甲这样的变化以获得更精确的结果，并毫不费力预测许多疾病用途的数字图像处理（DIP）方法。随着物联网（IOT）的概念，新兴的互联网将生成的报告提供远程，这将帮助用户降低运输工作。由于系统必须处理大量的私人数据，数据的安全性必须得到保证。为了保持数据的机密性，使用Blockchain的概念，它是在数据管理领域的大多数新兴的概念之一。本文包含了数字图像处理的指甲图像，IOT（ThingSpeak云）的使用为数据存储和执行Blockchain的特征提取的执行，以保持固定的系统和盗窃免费。关键词：诠释薄GS（IOT），图像的ERNET PROC essin克，薄型gSpeak，RG乙vavalues，平均数PI XEL vavalues，阵营kchain，哈希密钥。疾病诊断系统：在人类指甲异常
8. Mining and Analysis of Traffic Safety and Roadway Condition Data Final rept. (January 1, 2004-February 1,2005) [R] . Graves, S. J., Rochowiak, D. 2005

机译：交通安全与道路状况数据的挖掘与分析。（2004年1月1日 - 2005年2月1日）

TRAFFIC CONDITION FORECASTING SYSTEM BY FLOATING CAR DATA WITH DATA MINING METHOD

摘要

著录项

相似文献

相关主题

期刊订阅