Intelligent transportation systems: A global perspective

Wootton, John; García‐Ortiz, A.; Amin, Saurabh

doi:10.1016/0895-7177(95)00137-q

Cited by 53 publications

(35 citation statements)

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“…Since Avi may be unknown and nonlinear, free traffic model must be determined before it is used to predict the traffic flows or to adjust traffic signals. To get high accuracy of traffic prediction, equation (1) has been improved as follows.…”

Section: Sectionmentioning

confidence: 99%

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Robust identification for multi-section freeway traffic models

Shi

2005

J. Control Theory Appl.

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Since it is difficult to fit measured parameters using the conventional traffic model, a new traffic density and average speed model is introduced in this paper. To determine traffic model structures accurately, a model identification method for uncertain nonlinear system is developed. To simplify uncertain nonlinear problem, this paper presents a new robust criterion to identify the multi-section tra~c model structure of freeway efficiently. In the new model identification criterion, numerically efficient U-D factotization is used to avoid computing the determinant values of two complex matrices. By estimating the values of U-D factor of data matrix, both the upper and lower bounds of system uncertainties are described. Thus a model structure identification algorithm is proposed. Comparisons between identification outputs and simulation outputs of traffic states show that the traffic states can be accurately predicted by means of the new traffic models and the structure identification criterion.

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Section: Sectionmentioning

confidence: 99%

“…For decades, many research work have been done to estimate traffic density, traffic volume, average speed, and other parameters [ 1,2]. The problem of estimating dynamic tragic has been involved in parts of those research work [ 1 -4].…”

Section: Introductionmentioning

confidence: 99%

Robust identification for multi-section freeway traffic models

Shi

2005

J. Control Theory Appl.

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“…It is widely believed (for instance Shen, 1997;Nozick et al, 1998;Papageorgiou, 2003;Loukopoulos et al, 2005) that in recent decades increased travel demand has led to a rise in congestion especially in urban contexts and that there is a growing need to implement suitable measures to improve traffic conditions. Some of the measures to improve transportation system conditions (see, for instance, Wootton et al, 1995;Dia, 2002;Vianna et al, 2004;Gao and Chabini, 2006) are based on optimal resource management by means of the application of Intelligent Transportation Systems (ITS). In this context, amongst others, two groups of tools have been developed to tackle the above problems: Advanced Traveller Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS).…”

Section: Introductionmentioning

confidence: 99%

Estimation of urban traffic conditions using an Automatic Vehicle Location (AVL) System

D’Acierno

Cartenì

Montella

2009

European Journal of Operational Research

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“…weather condition, presents of other vehicles in the vicinity etc. [9,16]. Generally the application of GPS together with GIS gives perspectives in the area of detection, prediction and recognition of accidents, control and management of traffic and traffic organization [6][7][8][9][10][11][13][14][15][16][17][18][19][20].…”

Section: Registration and Analysis Of Signals Representing Vehicle Statementioning

confidence: 99%

Monitoring of combustion locomotive state with the use of an on-board device

Boguś¹,

Merkisz²,

Grzeszczyk³

2014

WIT Transactions on the Built Environment

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This paper presents the current state of the research on the on-board recording system application for the detection of critical situations in railways. The estimation of the rail vehicle state is based on signals registered by using a specialized on-board device. The critical situation of a rail vehicle can consist of diesel locomotive engine dysfunctions, high vibrations, exceeding of limit velocity, acceleration or inclination. The monitoring of the vehicle state needs a set of sensors. The movement parameters are acquired from a GPS device, accelerometers and gyroscopic MEMS sensors. Especially interesting here is the application of GPS signals, because GPS not only gives a geographical position, but also the vehicle velocity. The accelerometer sensors which measured the acceleration in three perpendicular directions were assigned to operate together with GPS in order to establish the vehicle position and for the detection of exceeding any limit acceleration value. The gyroscopic MEMS sensors support the position and route detection and also show any exceeding in vehicle limit inclinations. Additionally, some accelerate sensors record the vibroacoustic signals taken from chosen places on the engine body or other vehicle parts for the assessment of the engine state, particularly in terms of potential failures. The analysis of the additional acceleration signals was performed using specialized signal processing methods and some specialized algorithms based on artificial intelligence (e.g. Fourier analysis, wavelets, nonlinear methods based on deterministic chaos theory, short time methods and support vector machines). The first results are promising and show the possibility of using the above described approach to detect the combustion locomotive state.

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Intelligent transportation systems: A global perspective

Cited by 53 publications

References 1 publication

Robust identification for multi-section freeway traffic models

Robust identification for multi-section freeway traffic models

Estimation of urban traffic conditions using an Automatic Vehicle Location (AVL) System

Monitoring of combustion locomotive state with the use of an on-board device

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