An efficient intelligent traffic light control and deviation system for traffic congestion avoidance using multi-agent system

An efficient and intelligent road traffic management system is the corner stone for every smart cities. Vehicular Ad-hoc NETworks (VANETs) applies the principles of mobile ad hoc networks in a wireless network for Vehicle-to-vehicle data exchange communication. VANETs supports in providing an efficient Intelligent Transportation System (ITS) for smart cities. Road traffic congestion is a most common problem faced by many of the metropolitan cities all over the world. Traffic on the road networks are widely increasing at a larger rate and the current traffic management systems is unable to tackle this impediment. In this paper, we propose an Efficient Intelligent Traffic Light Control and Deviation (EITLCD) system, which is based on multi-agent system. This proposed system overcomes the difficulties of the existing traffic management systems and avoids the traffic congestion problem compare to the prior scenario. The proposed system is composed of two systems: Traffic Light Controller (TLC) system and Traffic Light Deviation (TLD) system. The TLC system uses three agents to supervise and control the traffic parameters. TLD system deviate the vehicles before entering into congested road. Traffic and travel related information from several sensors are collected through a VANET environment to be processed by the proposed technique. The proposed structure comprises of TLC system and makes use of vehicle measurement, which is feed as input to the TLD system in a wireless network. For route pattern identification, any traditional city map can be converted to planar graph using Euler’s path approach. The proposed system is validated using Nagel–Schreckenberg model and the performance of the proposed system is proved to be better than the existing systems in terms of its time, cost, expense, maintenance and performance. First published online 26 September 201

A Study on Vehicle Trajectory Analysis

Successful developments of effective real-time traffic management and information systems demand high quality real time traffic information. In the era of intelligent transportation convergence, traffic monitoring requires traffic sensory technologies. The present analysis extracted data from Mobile Century experiment. The data obtained in the experiment was pre-processed. Based on the pre processed data experimental road map has generated. Individual vehicle tracking has done using trajectory analysis. Finally an attempt has been made for extracting association rules from mobile century dataset using Apriori algorithm

Deep Learning Based Automatic Vehicle License Plate Recognition System for Enhanced Vehicle Identification

An innovative Automatic Vehicle License Plate Recognition (AVLPR) system that effectively identifies vehicles using deep learning algorithms. Accurate and real-time license plate identification has grown in importance with the rise in demand for improved security and traffic management.The convolutional neural network (CNN) architecture used in the AVLPR system enables the model to automatically learn and extract discriminative characteristics from photos of license plates. To ensure the system's robustness and adaptability, the dataset utilized for training and validation includes a wide range of license plate designs, fonts, and lighting situations.We incorporate data augmentation approaches to accommodate differences in license plate orientation, scale, and perspective throughout the training process to improve recognition accuracy. Additionally, we use transfer learning to enhance the system's generalization abilities by refining the pre-trained model on a sizable dataset.A trustworthy and effective solution for vehicle identification duties is provided by the Deep Learning-Based Automatic Vehicle License Plate Recognition System. Deep learning approaches are used to guarantee precise and instantaneous recognition, making it suitable for many uses such as law enforcement, parking management, and intelligent transportation systems

IMU-based Modularized Wearable Device for Human Motion Classification

Human motion analysis is used in many different fields and applications. Currently, existing systems either focus on one single limb or one single class of movements. Many proposed systems are designed to be used in an indoor controlled environment and must possess good technical know-how to operate. To improve mobility, a less restrictive, modularized, and simple Inertial Measurement units based system is proposed that can be worn separately and combined. This allows the user to measure singular limb movements separately and also monitor whole body movements over a prolonged period at any given time while not restricted to a controlled environment. For proper analysis, data is conditioned and pre-processed through possible five stages namely power-based, clustering index-based, Kalman filtering, distance-measure-based, and PCA-based dimension reduction. Different combinations of the above stages are analyzed using machine learning algorithms for selected case studies namely hand gesture recognition and environment and shoe parameter-based walking pattern analysis to validate the performance capability of the proposed wearable device and multi-stage algorithms. The results of the case studies show that distance-measure-based and PCA-based dimension reduction will significantly improve human motion identification accuracy. This is further improved with the introduction of the Kalman filter. An LSTM neural network is proposed as an alternate classifier and the results indicate that it is a robust classifier for human motion recognition. As the results indicate, the proposed wearable device architecture and multi-stage algorithms are cable of distinguishing between subtle human limb movements making it a viable tool for human motion analysis.Comment: 10 pages, 12 figures, 28 reference

Space power distribution system technology. Volume 2: Autonomous power management

Electrical power subsystem requirements, power management system functional requirements, algorithms, power management subsystem, hardware development, and trade studies and analyses are discussed

Emergency first response to a crisis event a multi-agent simulation approach

Homeland Security Presidential Directive #8 led to the establishment of the National Exercise Program and the Top Officials exercise series to test and evaluate first response agency integration and effectiveness. The last TOPOFF exercise cost $16M and involved over 10,000 people, but did not effectively leverage simulation techniques to make efficient use of resources. This research adapts an existing organizational learning process, integrating low- and high resolution simulation to provide decision support. This process led to the development of a multi-agent simulation methodology for emergency first response, specifically applied to analyze a notional vehicle bomb attack during a festival in the Baltimore Inner Harbor. This simulation demonstrates the potential benefits of low resolution simulation, using efficient experimental design and high-performance computing. Combined, these two ideas result in examining a 48-dimensional response surface and using over 156 CPU centuries of computer time. All experiments were completed in less than three weeks. The analysis of this data set provided insight into several areas, including the importance of standing operating procedures in the early moments of a crisis. Analysis showed that effective procedures may even be more important than the effectiveness of communications devices early in a first response operation.http://archive.org/details/emergencyfirstre109452800Outstanding ThesisUS Army (USA) author.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

Power line communication systems for industrial control applications

For almost as long as the electricity distribution industry itself has existed, so also has the idea of utilising the transmission grid, be it over a wide area or on a local basis, for the transmission of 'intelligence'. This might be in the form of voice transmissions, or for the purposes of monitoring or controlling electrical devices attached to the network. This thesis specifically concerns itself with the potential applications of power-line-carrier (PLC) communications technology within the field of industrial plant/equipment control, as it is within this field that the author works. We look at the entire subject area of industrial control, starting from a historical viewpoint, and consider the special needs and requirements that a proposed PLC solution must offer for this application, especially based on the noise conditions likely to be experienced on a `real' power line. A proposal is made for a `Power Bus', intended for use within certain areas of industrial control, and decisions are made based on the projected link response times for such applications. The experimental phase of the research is practical in nature and consists of a raft of tests and evaluations of the performance of power line modem technologies, under controlled and repeatable noise conditions. To complement these results, further tests are carried out under `real world' conditions, within an actual factory environment. Based on the results of all of these tests, the suitability of a PLC solution for this type of industrial control application is considered. The Thesis concludes with a look at recent developments in, as well as the future of, Power Line Communication techniques

Electronics for Sensors

The aim of this Special Issue is to explore new advanced solutions in electronic systems and interfaces to be employed in sensors, describing best practices, implementations, and applications. The selected papers in particular concern photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs) interfaces and applications, techniques for monitoring radiation levels, electronics for biomedical applications, design and applications of time-to-digital converters, interfaces for image sensors, and general-purpose theory and topologies for electronic interfaces

Knowledge-based processing for aircraft flight control

This Contractor Report documents research in Intelligent Control using knowledge-based processing in a manner dual to methods found in the classic stochastic decision, estimation, and control discipline. Such knowledge-based control has also been called Declarative, and Hybid. Software architectures were sought, employing the parallelism inherent in modern object-oriented modeling and programming. The viewpoint adopted was that Intelligent Control employs a class of domain-specific software architectures having features common over a broad variety of implementations, such as management of aircraft flight, power distribution, etc. As much attention was paid to software engineering issues as to artificial intelligence and control issues. This research considered that particular processing methods from the stochastic and knowledge-based worlds are duals, that is, similar in a broad context. They provide architectural design concepts which serve as bridges between the disparate disciplines of decision, estimation, control, and artificial intelligence. This research was applied to the control of a subsonic transport aircraft in the airport terminal area

Advanced techniques for traffic monitoring using inductive sensors

Programa Oficial de Doutoramento en Tecnoloxías da Información e Comunicación en Redes Móbiles. 553V01[Resumen] El objetivo principal de este proyecto es el desarrollo de técnicas avanzadas para gestión del tráfico de vehículos usando un Detector de Bucles Inductivos (ILDs). Para ello, en primer lugar se desarrolla e implementa un ILD que va a proporcionar huellas inductivas de los vehículos que transitan por una vía. Además de las funciones tradicionales de medida de aforamientos de tráfico, tales como densidad, ocupación y clasificación de vehículos, se pretende conseguir el reconocimiento de los mismos mediante el análisis de la señal de su huella. Basándose en la infraestructura existente en las carreteras para realizar los aforamientos de tráfico que usa fundamentalmente bucles inductivos, modificaciones de los equipos detectores van a permitir incluir además la función de re-identificación, para su uso en aplicaciones de control y supervisión de tráfico de vehículos. Por lo tanto, y aunque la tecnología de los detectores de bucles inductivos está totalmente extendida y en uso en este momento, se le añade una función de captura de las huellas inductivas del vehículo que permite aplicaciones adicionales de reconocimiento de los mismos para mejorar la clasificación, detección de velocidad con una sola espira, y re-identificación para aplicaciones de control y supervisión del tráfico rodado. Este trabajo presenta un sistema completo para clasificación de vehículos compuesto de un detector de bucles inductivos y los correspondientes algoritmos o.ff-line. El sistema detecta la presencia de vehículos mediante un desplazamiento en el periodo de oscilación del bucle de forma que las huellas de los vehículos detectados se registran mediante la duración de un número prefijado de pulsos de oscilación. En este trabajo nos centraremos en la cuestión, todavía no resuelta a día de hoy, de contar el número de vehículos (clasificándolos en tres tipos, coches, furgonetas y camiones) que circulan por una carretera. El método clásico para tal propósito consiste en la estimación de la longitud del vehículo usando las huellas inductivas obtenidas en dos bucles y, a continuación, las clasifica de acuerdo con un umbral preestablecido. Para la clasificación de los vehículos que circulan por una vía, presentamos un sistema bastante simple que usa esas huellas inductivas y la transformada discreta de Fourier (OFf, del inglés Discrete Fourier Transfonn). Para abordar el problema de clasificación en tres tipos de vehículos (como mencionábamos antes, coches, furgonetas y camiones) se propone un algoritmo heurístico basado en decisión por umbrales y en la magnitud del primer máximo espectral obtenido aplicando el análisis DFf a la huella inductiva del vehículo obtenida a partir de un único blucle. Además, el método aquí desarrollado puede aplicarse a huellas de vehículos capturadas con otros tipos de sensores. En este trabajo compararemos nuestro sistema con métodos de clasificación clásicos basados en la estimación de la longitud del vehículo obtenida a partir de dos bucles. Los resultados experimentales muestran que el criterio basado en la magnitud de la DFT exhibe un error de clasificación más bajo que el alcanzado con dichos métodos, con la enorme ventaja de la utilización de un único bucle. Por último, dado el elevado coste de estas pruebas en escenarios reales cada vez que una nueva técnica está siendo estudiada, hemos desarrollado un modelo avanzado del detector de bucles inductivos que claramente supera los modelos que se han usado tradicionalmente con unos resultados muy similares a los obtenidos directamente usando el prototipo de ILD que hemos desarrollado.[Resumo]O obxetivo principal deste proxecto é o desenvolvemento de técnicas avanzadas para a xestión do tráfico de vehículos usando un Detector de Bucles Inductivos (ILD). Así, desenvólvese e impleméntase un ILD que vai a proporcionar pegadas inductivas dos vehículos que transitan por unha vía. Ademáis das funcións tradicionais de medidas de aforamentos de tráfico, tales como densidade, ocupación e clasificación de vehículos, preténdese conseguir o recoñecemento dos mesmos mediante a análise do sinal da pegada. Baseándose na infraestrutura existente nas estradas para realizar os aforamentos de tráfico que usa fundamentalmente bucles inductivos, modifi.cacións dos equipos detectores permiten incluir ademáis a función de re-identificación, para o seu uso en aplicacións de control e supervisión de tráfico de vehículos. Polo tanto, e aímla que a tecnoloxfa dos detectores de bucle inductivos está totalmente extendida e en uso neste momento, engádese unha función de captura das pegadas inductivas do vehículo que pennite aplicacións adicionais de recoñecemento dos mesmos para mellorar a clasificación, detección de velocidade cunha soa espira, e re-identificación para aplicaci6ns de control e supervisión do tráfico rodado. Este traballo presenta un sistema completo para clasificación de vehículos composto dun detector de bucles inductivos e dos correspondentes algoritmos off-line. O sistema detecta a presenza de vehículos mediante un desprazamento no periodo de oscilación do bucle de xeito que as pegadas dos vehículos detectados se rexistran mediante a duración dun número prefixado de pulsos de oscilación. Neste traballos imos focalizarnos na cuestión, aínda non resalta a día de hoxe, de contar o número de vehículos (clasificándoos en coches, furgonetas e camións) que circulan por unha estrada. O método clásico para este propósito consiste na estimación da lonxitude do vehículo usando as pegadas inductivas obtidas en dous bucles e, a continuación, clasificalas dacordo a un umbral preestablecido. Para a clasificación dos vehículos que circulan por unha vía, presentamos un sistema bastante sinxelo que usas esas pegadas inductivas e a transformada discreta de Fourier (OFf, do inglés Discrete Fourier Transform). Para abordar o problema de clasificación en tres tipos de vehículos (como comentabamos antes, coches, furgonetas e camións) proponse un algoritmo heurístico baseado en decisión por umbrais e na magnitude do primeiro máximo espectral obtido aplicando a análise DFf á pegada inductiva do vehículo obtida a partir dun único bucle. Ademáis, o método proposto pode aplicarse a pegadas de vehículos capturadas con outros tipos de sensores. Neste traballo compararemos o noso sistema a métodos de clasificación clásicos baseados na estimación da Jonxitude do vehfculo obtida a partir de dous bucles. Os resultados experimentais amasan que o criterio baseado na magnitude da OFf presenta un erro de clasificación máis baixo que o que acadan estos métodos, coa enorma avantaxe da súa utilización dun único bucle. Por último, dado o elevado custo das probas realizadas en escearios reais cada vez que unha nova técnica está baixo estudo, desenvolvemos tamén un modelo avanzado de detector de bucles inductivos que claramente supera os modelos que se están a usar tradicionalmente con esta finalidade cuns resultados moi similares aos obtidos directamente usando o prototipo de ILD proposto neste traballo.[Abstract] The main goal of this work is the development of advanced techniques for vehicle traffic monitoring using Jnductive Loop Detectors (ILD). Thus, we develop an implementation of an ILD that will provide vehicle inductive signatures passing on a road. Severa! traditional functions of traffic monitoring are intensity, density or vehicle classification, but mon:over we want to identify those vehicles using their inductive signatures. Based on the infrastructure already available under the road pavements for traffic applications using inductive sensors, sorne modifications on the detector equipments allow us to include re-identification functions to be used for vehicle traffic control and management. Therefon:, although the technology of inductive loop detectors is widely used in many countries, we will add a module for capturing the inductive signatun:s leading to additional applications of vehicle recognising to improve the classification, the vehicle detection, and their re-identification useful for vehicular traffic control and surveillance tasks. This work presents a complete system for vehicle classification composed by an inductive-loop detector and the corresponding off-line algorithms. The system detects the presence of vehicles by means of a shift in the loop oscillation period so that the signature of the detected vehicles is registered by measuring the duration of a fixed number of oscillator pulses. We focus on the open issue of counting the number of vehicles (classified into cars, vans and trucks) on a roadway. The classical method for such purpose consists of estimating the vehicle length using the inductive signatures obtained from two loops and, subsequently, it classifies them taking into account a prefixed threshold. We presenta simple system to classify vehicles travelling along a road using inductive signatures and the Discrete Fourier Transform (DFf). We focus on the problem of classifying those vehicles into three types (cars, vans, and trucks) using a heuristic algorithm based on threshold decision and on the magnitude of the first spectral maximum obtained applying the DFT analysis to the vehicle inductive signature from only one loop. Moreover, the method here developed can be applied to vehicle signatures captured with other types of sensors. In this dissertation we will compare our system to classical methods based on estimating the vehicle length obtained from two loops. Experimental results show that the magnitude of the DFT exhibits a lower classifying error rate than that achieved using the lenglh-based rnethod, with the enormous advantage of requiring only one loop. Finally, due to the high cost of testing in real scenarios each new technique under study, we also develop an advanced model of an ILD that clearly outperforms the traditional ones with similar results to those directly obtained from the hardware prototype of ILD proposed in this work