Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749)最新文献_第2页

Automatic rail traffic control system for bi-directional sections 双向路段轨道交通自动控制系统

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1399021

V. Raghavan

引用次数: 1

The impact of multi-modal transportation on the evacuation efficiency of building complexes 多式联运对建筑群疏散效率的影响

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398987

J. Blum, A. Eskandarian

引用次数: 5

Dynamic reconfiguration for high level in-vehicle applications using IEEE-1394 使用IEEE-1394的高级车载应用的动态重新配置

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1399009

K. Chaaban, M. Shawky, P. Crubillé

引用次数: 6

Modeling advanced traffic signal control systems: a communication network prototype 先进交通信号控制系统建模:一个通信网络原型

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398926

E. M. Suwal, T. R. Cuff, B. Hamlett, B.K. Johnson, R. Wall, A. Abdel-Rahim

引用次数: 4

Car navigation with route information sharing for improvement of traffic efficiency 具有路线信息共享的汽车导航，提高交通效率

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398944

T. Yamashita, K. Izumi, K. Kurumatani

引用次数: 47

A robust chaos radar for collision detection and vehicular ranging in intelligent transportation systems 用于智能交通系统中碰撞检测和车辆测距的鲁棒混沌雷达

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398959

V. Venkatasubramanian, H. Leung

引用次数: 22

Grey model for asphalt pavement performance prediction 沥青路面性能预测的灰色模型

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398981

D. Shen, Jia-Chong Du

{"title":"Grey model for asphalt pavement performance prediction","authors":"D. Shen, Jia-Chong Du","doi":"10.1109/ITSC.2004.1398981","DOIUrl":"https://doi.org/10.1109/ITSC.2004.1398981","url":null,"abstract":"Most existing rutting models are either based on an empirical or mechanistic-empirical modeling approach. The mechanistic-empirical model can be used easily and effectively on the relationship between rutting and the pavement response such as vertical strain, asphalt mix properties, and ambient condition. However, the cause of rutting is sophisticated and is the function of molding variables, which results in the poor performance of many of the existing models. In addition, some data or properties for pavement evaluation relating to the environment and structure may not be very clear, and so the pavement system is grey in its nature. Due to the advent of laboratory facilities and pavement management systems, a large number of pavement databases have become available. Thus, in this paper, the prediction-modeling approach is to simplify with two components and to emphasize the phenomenon of cause and effect by the data of rut depth versus traffic loadings. The model based on grey system theory is, then, developed to predict the rut depth of the asphalt pavement performance. The algorithm of model represented GM (1, 2) is presented and the applicability of the model is the data of rut depth obtained from the wheel-tracking device test. Field test data of rutting performance is collected from FHWA research data to evaluate the accuracy and effectiveness of the new prediction model and to confirm the model's validity. A statistical linear regression analysis was conducted for testing the accuracy of prediction. The comparison between the measured and predicted rut depth is within the tolerance level of /spl plusmn/2.5 mm. It is to believe that GM (1, 2) is useful for making a prediction of rut depths.","PeriodicalId":239269,"journal":{"name":"Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115108416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Management of intermodal container terminals using feedback control 采用反馈控制的多式联运集装箱码头管理

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1399020

A. Alessandri, S. Sacone, S. Siri

引用次数: 2

Planning for on-road driving through incrementally created graphs 通过增量创建的图形规划道路驾驶

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398894

S. Balakirsky

引用次数: 6

EM-IMM based land-vehicle navigation with GPS/INS 基于EM-IMM的陆地车辆GPS/INS导航

Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749) Pub Date : 2004-10-03 DOI: 10.1109/ITSC.2004.1398973

Dongliang Huang, H. Leung

{"title":"EM-IMM based land-vehicle navigation with GPS/INS","authors":"Dongliang Huang, H. Leung","doi":"10.1109/ITSC.2004.1398973","DOIUrl":"https://doi.org/10.1109/ITSC.2004.1398973","url":null,"abstract":"Integration of the global positioning system (GPS) with the inertial navigation system (INS) is favorable since it provides enhanced positioning accuracy. Its implementation is essentially based on the standard Kalman filter techniques. However, the estimation accuracy is degraded if unknown parameters present in the system model or the model changes with the environment as in the case of intelligent transportation systems (ITS). We propose an expectation-maximization (EM) based interacting multiple model (IMM) method, namely, EM-IMM algorithm, to jointly identify the unknown parameters and to estimate the position information. The IMM is capable of identifying states in jumping dynamic models corresponding to various vehicle driving status, while the EM algorithm is employed to give the maximum likelihood (ML) estimates of the unknown parameters. Compared to the conventional single model Kalman filter based navigation, the proposed algorithm gives improved estimation performance when the land-vehicle drives with changing conditions. Simulation results demonstrate the effectiveness of the proposed method.","PeriodicalId":239269,"journal":{"name":"Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126004205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 32