Optimization of smart traffic lights to prevent traffic congestion using fuzzy logic

One of the main causes of traffic congestion, especially at intersections, is because traffic lights have not been able to show the right time according to the existing traffic conditions. Time settings based on peak/off-peak traffic lights are not enough to handle unexpected situations. The fuzzy mamdani method makes decisions with several stages, the criteria used are the number of vehicles, the length of the queue and the width of the road to be able to optimize the time settings based on the real-time conditions required so that unwanted green signals (when there is no queue) can be avoided. The purpose of this research is to create a simulator to optimize traffic time management, so that the timers on each track have the intelligence to predict the right time, so that congestion at the intersection can be reduced by adding up to 15 seconds of green light from the previous time in the path of many vehicles

Introduction of programmable logic controller in industrial engineering curriculum

Recent trends in process control and industrial automation scenarios have resulted in the emergence of many pioneering techniques that have revolutionized the manufacturing industry. In order to maintain quality and precision, advances have been associated with the increasing use of microprocessors in process control applications. Most of the industrial process control systems utilize Programmable Logic Controllers (PLC). Also due to the increase in internet usage and recent innovations in PLC software, remote monitoring and PLC control of process through the internet is also a recent trend. This thesis presents course/lab material for integration in the Industrial Engineering curriculum. The course/lab content was designed to improve the student\u27s knowledge and to broaden the industrial engineering curriculum at West Virginia University (WVU). This thesis proposes the use of inexpensive T100MD+ PLCs. A traffic light control system was developed to introduce the fundamental concepts of Boolean algebra and real-time control. A series of control exercises can be carried on the traffic light system. A temperature sensitive system was also developed. Students can test various PID control strategies on this hardware/software platform. Students will also have the ability to control the process via the internet

Traffic Optimization Through Waiting Prediction and Evolutive Algorithms

Traffic optimization systems require optimization procedures to optimize traffic light timing settings in order to improve pedestrian and vehicle mobility. Traffic simulators allow obtaining accurate estimates of traffic behavior by applying different timing configurations, but require considerable computational time to perform validation tests. For this reason, this project proposes the development of traffic optimizations based on the estimation of vehicle waiting times through the use of different prediction techniques and the use of this estimation to subsequently apply evolutionary algorithms that allow the optimizations to be carried out. The combination of these two techniques leads to a considerable reduction in calculation time, which makes it possible to apply this system at runtime. The tests have been carried out on a real traffic junction on which different traffic volumes have been applied to analyze the performance of the system

New Solutions Based On Wireless Networks For Dynamic Traffic Lights Management: A Comparison Between IEEE 802.15.4 And Bluetooth

Abstract The Wireless Sensor Networks are widely used to detect and exchange information and in recent years they have been increasingly involved in Intelligent Transportation System applications, especially in dynamic management of signalized intersections. In fact, the real-time knowledge of information concerning traffic light junctions represents a valid solution to congestion problems. In this paper, a wireless network architecture, based on IEEE 802.15.4 or Bluetooth, in order to monitor vehicular traffic flows near to traffic lights, is introduced. Moreover, an innovative algorithm is proposed in order to determine dynamically green times and phase sequence of traffic lights, based on measured values of traffic flows. Several simulations compare IEEE 802.15.4 and Bluetooth protocols in order to identify the more suitable communication protocol for ITS applications. Furthermore, in order to confirm the validity of the proposed algorithm for the dynamic management of traffic lights, some case studies have been considered and several simulations have been performed

Driver monitoring system based on eye tracking

Dissertação de mestrado integrado em Engenharia Electrónica Industrial e ComputadoresRecent statistics indicate that driver drowsiness is one of the major causes of road accidents and deaths behind the wheel. This reveals the need of reliable systems capable of predict when drivers are in this state and warn them in order to avoid crashes with other vehicles or stationary objects. Therefore, the purpose of this dissertation is to develop a driver’s monitoring system based on eye tracking that will be able to detect driver’s drowsiness level and actuate accordingly. The alert to the driver may vary from a message on the cluster to a vibration on the seat. The proposed algorithm to estimate driver’s state only requires one variable: eyelid opening. Through this variable the algorithm computes several eye parameters used to decide if the driver is drowsy or not, namely: PERCLOS, blink frequency and blink duration. Eyelid opening is obtained over a software and hardware platform called SmartEye Pro. This eye tracking system uses infrared cameras and computer vision software to gather eye’s state information. Additionally, since this dissertation is part of the project "INNOVATIVE CAR HMI", from Bosch and University of Minho partnership, the driver monitoring system will be integrated in the Bosch DSM (Driver Simulator Mockup).Estatísticas recentes indicam que a sonolência do condutor é uma das principais causas de acidentes e mortes nas estradas. Isto revela a necessidade de sistemas fiáveis capazes de prever quando um condutor está sonolento e avisá-lo, de modo a evitar colisões com outros veículos ou objetos estacionários. Portanto, o propósito desta dissertação é desenvolver um sistema de monitorização do condutor baseado em eye tracking que será capaz de detetar o nível de sonolência do condutor e atuar em conformidade. O alerta para o condutor pode variar entre uma mensagem no painel de instrumentos ou uma vibração no assento. O algoritmo proposto para estimar o estado do condutor apenas requer a aquisição de uma variável: abertura da pálpebra. Através desta variável, o algoritmo computa alguns parâmetros utilizados para verificar se o condutor está sonolento ou não, nomeadamente: PERCLOS, frequência do pestanejar e duração do pestanejar. A abertura da pálpebra é obtida através de uma plataforma de hardware e software chamada SmartEye Pro. Esta plataforma de eye tracking utiliza câmaras infravermelho e software de visão por computador para obter informação sobre o estado dos olhos. Adicionalmente, uma vez que esta dissertação está inserida projeto: "INNOVATIVE CAR HMI", da parceria entre a Bosch e a Universidade do Minho, o sistema desenvolvido será futuramente integrado no Bosch DSM (Driver Simulator Mockup)

FUZZY BASED SECURITY ALGORITHM FOR WIRELESS SENSOR NETWORKS IN THE INTERNET OF THINGS PARADIGM

Published ThesisThe world is embracing the idea of Internet of Things and Industrial Revolution 4.0. However, this acceptance of computerised evolution is met with a myriad of challenges, where consumers of this technology are also growing ever so anxious about the security of their personal data as well as reliability of data collected by the millions and even billions of sensors surrounding them. Wireless sensor networks are the main baseline technology driving Internet of things; by their very inherent nature, these networks are too vulnerable to attacks and yet the network security tools designed for conventional computer networks are not effective in countering these attacks. Wireless sensors have low computational resources, may be highly mobile and in most cases, these networks do not have a central point which can be marked as an authentication point for the sensors, any node can join or leave whenever they want. This leaves the sensors and the internet of things applications depending on them highly susceptible to attacks, which may compromise consumer information and leave security breaches in situation that need absolute security such as homes or even the cars they drive. There are many possibilities of things that could go wrong when hackers gain control of sensors in a car or a house. There have been many solutions offered to address security of Wireless Sensor Networks; however, most of those solutions are often not customised for African context. Given that most African countries have not kept pace with the development of these underlying technologies, blanket adoption of the solutions developed for consumption in the developed world has not yielded optimal results. The focus of this research was the development of an Intrusion Detection System that works in a hierarchical network structured Wireless Sensor Network, where cluster heads oversee groups of nodes and relay their data packets all the way to the sink node. This is a reactive Intrusion Detection System (IDS) that makes use of a fuzzy logic based algorithm for verification of intrusion detections. This system borrows characteristics of traditional Wireless Sensor Networks in that it is hosted external to the nodes; that is, on a computer or server connected to the sink node. The rational for this is the premise that developing the system in this manner optimises the power and processing resource of nodes because no part of the IDS is found in the nodes and they are left to focus purely on sensing. The Intrusion Detection System makes use of remote Over The Air programming to communicate with compromised nodes, to either shut down or reboot and is designed with the ZigBee protocol in mind. Additionally, this Intrusion Detection System is intended to being part of a larger Internet of Things integration framework being proposed at the Central University of Technology. This framework is aimed at developing an Internet of Things adoption strategy customised for African needs and regionally local consumers. To evaluate the effectiveness of the solution, the rate of false detections being picked out by the security algorithm were reduced through the use of fuzzy logic systems; this resulted in an accuracies of above 90 %. The algorithm is also very light when asymptotic notation is applied, making it ideal for Wireless Sensors. Lastly, we also put forward the Xbee version of the Triple Modular Redundancy architecture, customised for Wireless sensor networks in order to beef-up on the security solution presented in this dissertation

A smart traffic light using a microcontroller based on the fuzzy logic

Traffic jam that is resulted from the buildup of vehicles on the road has become an important problem, which leads to an interference with drivers. The impacts it has on cost and time effectiveness may take the form of increased fuel consumption, traffic emissions, and noise. This paper offers a solution by creating a smart traffic light using a fuzzy-logic-based microcontroller for a greater adaptability of the traffic light to the dynamics of the vehicles that are to cross the intersection. The ATMega2560 microcontroller-based smart traffic light is designed to create a breakthrough in the breakdown of congestions at road junctions, thereby optimizing the real-time happenings in the road. Ultrasonic, infrared, and light sensors are used in this smart traffic light, resulting in the smart traffic light’s effectiveness in parsing jams. The four sets of sensors that are placed in four sections determine the traffic light timing process. When the length of vehicle queue reaches the sensor, a signal is sent as the microcontroller’s digital input. Ultrasonic and infrared sensors can reduce congestions at traffic lights by giving a green light time when one or all of the sensors are active so that the vehicle congestions can be relieved

A framework for smart traffic management using heterogeneous data sources

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.Traffic congestion constitutes a social, economic and environmental issue to modern cities as it can negatively impact travel times, fuel consumption and carbon emissions. Traffic forecasting and incident detection systems are fundamental areas of Intelligent Transportation Systems (ITS) that have been widely researched in the last decade. These systems provide real time information about traffic congestion and other unexpected incidents that can support traffic management agencies to activate strategies and notify users accordingly. However, existing techniques suffer from high false alarm rate and incorrect traffic measurements. In recent years, there has been an increasing interest in integrating different types of data sources to achieve higher precision in traffic forecasting and incident detection techniques. In fact, a considerable amount of literature has grown around the influence of integrating data from heterogeneous data sources into existing traffic management systems. This thesis presents a Smart Traffic Management framework for future cities. The proposed framework fusions different data sources and technologies to improve traffic prediction and incident detection systems. It is composed of two components: social media and simulator component. The social media component consists of a text classification algorithm to identify traffic related tweets. These traffic messages are then geolocated using Natural Language Processing (NLP) techniques. Finally, with the purpose of further analysing user emotions within the tweet, stress and relaxation strength detection is performed. The proposed text classification algorithm outperformed similar studies in the literature and demonstrated to be more accurate than other machine learning algorithms in the same dataset. Results from the stress and relaxation analysis detected a significant amount of stress in 40% of the tweets, while the other portion did not show any emotions associated with them. This information can potentially be used for policy making in transportation, to understand the users��� perception of the transportation network. The simulator component proposes an optimisation procedure for determining missing roundabouts and urban roads flow distribution using constrained optimisation. Existing imputation methodologies have been developed on straight section of highways and their applicability for more complex networks have not been validated. This task presented a solution for the unavailability of roadway sensors in specific parts of the network and was able to successfully predict the missing values with very low percentage error. The proposed imputation methodology can serve as an aid for existing traffic forecasting and incident detection methodologies, as well as for the development of more realistic simulation networks