Traffic Light Management Research Articles

Real Time Traffic Light Controlling System Using Morphological Operators and Fuzzy Logic

There is a need for development of advanced smart traffic controlling schemes due to the enhancement in urban traffic congestion. Currently, there are some traffic controlling methods based on timers or controlled by human. However, due to these systems there must be wastage of power in the night times and early morning hours. In order to address this issue, this paper introduces a novel and Traffic light management scheme in real-time with digital image recognition using contrast improvement and fluctuating logic systems for morphological operators. The proposed traffic control system provides an upgrade over traditional systems in terms of time of response, automation, stability and overall performance.

Smart Traffic Light and Street Light Management System

Smart Traffic Light and Street Light Management System

Intelligent Traffic Monitoring Systems: Vehicle Type Classification Using Support Vector Machine

In the automation of vehicle traffic monitoring system, information about the type of vehicle, it is essential because used in the process of further analysis as management of traffic control lights. Currently, calculation of the number of vehicles is still done manually. Computer vision applied to traffic monitoring systems could present data more complete and update.In this study consists of three main stages, namely Classification, Feature Extraction, and Detection. At stage vehicle classification used multi-class SVM method to evaluate characteristics of the object into eight classes (LV-TK, LV-Mobil, LV-Mikrobis, MHV-TS, MHV-BS, HV-LB, HV- LT, MC). Features are obtained from the detection object, processed on the feature extraction stage to get features of geometry, HOG, and LBP in the detection stage of the vehicle used MOG method combined with HOG-SVM to get an object in the form of a moving vehicle and does not move. SVM had the advantage of detail and based statistical computing. Geometry, HOG, and LBP characterize complex and represents an object in the form of the gradient and local histogram.The test results demonstrate the accuracy of the calculation of the number of vehicles at the stage of vehicle detection is 92%, with the parameters HOG cellSize 4x4, 2x2 block size, the son of vehicle classification 9. The test results give the overall mean recognition rate 91,31 %, mean precision rate 77,32 %, and mean recall rate 75,66 %.

Smart City: A new phase of sustainable development using fog computing and IoT

Fog computing is a distributed technology which performs computations at the edge of the network. IoT devices are generating large amount of data that too Heterogeneous in nature. In order to handle applications which, require time sensitive data and quick decision making, a reliable platform is required which can handle all the tasks in a robust manner considering fault tolerance and state migration. To process request originating from end user, an efficacious and pragmatic approach is to get the request served at the edge of the network rather than cloud envisioned with the aim to minimize the latency. Quick decision-making characteristic of fog computing makes it a smart choice for smoother processing and execution of smart city applications. As utilization of internet and IoT devices has boomed the industry, it is important to have reliable applications for day to day activities like payment of bills, traffic light management, health support system etc. The research study conducted explores multi-dimensional abstractions that can extend technological contributions in current epidemic situation.

A cellular automation traffic flow model for traffic light signal management in intersection system with and without random driving behaviour

In this study, traffic light management in intersection system was simulated based on cellular automata traffic flow model. Characteristic of vehicles flow is defined according to the NS model. Our model was developed by adding driving behaviours, which are randomly defined in different maximum vehicle speed of mixed flow for two-lane traffic flow without lane-changing. The relation between time duration of traffic light signal and the traffic flow by analyzing vehicle density and averaged speed were investigated. The results show that the time duration of traffic light signal affects the traffic flow. Our model can be used to find the appropriated time duration to handle the traffic and can eventually reduce traffic jam. Additionally. Our result also presents an effect of the driving behaviour on the traffic flow.

Deep learning support for intelligent transportation systems

AbstractIntelligent Transportation Systems (ITS) help improve the ever‐increasing vehicular flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation of massive amounts of data generated by all the digital devices connected to the transportation network enables the creation of datasets to perform an in‐depth analysis of the data using deep learning methods. Such methods can help predict traffic performance, automated traffic light management, lane detection, and identifying objects near vehicles to increase the safety and efficiency of ITS. We discuss some of the challenges that need to be solved to achieve seamless integration between ITS and deep learning methods to address issues such as (1) improving traffic flow/transportation logistics, (2) predicting best routes for the transportation of goods, (3) optimal fuel consumption, (4) intelligent environmental conditions perception, (5) traffic speed management, and accident prevention.

An Analysis of A Three-Phase Versus A Four-Phase Traffic Lights Regulation for A Four-Leg Intersection

Traffic management of intersections is an important factor that can determine traffic density at the intersection, as well as its surrounding. Long traffic queues we encounter in daily life, were often caused by ineffectiveness of traffic lights management of the cross sections.In this article, an analytic study of traffic light management of a four-leg intersection, based on the kinematic LWR model, was presented. Comparison was based on observing the end of queues over three cycles of red-green lights, under the assumption of a constant traffic flux. On every leg of the intersection, the end of the queues were obtained from characteristic lines of the shock waves.From these observations, the three phase regulation was preferred over the four-phase one. Finally, a case study of Taman Sari - Baltos intersection located in Bandung City, Indonesia, was discussed. Parameter values used in these simulations were obtained from direct observation.

Smart Traffic Light Management Systems

Traffic congestion is a major concern in many cities. Failure to heed signals, poor law enforcement, and bad traffic light management are main factors that have led to traffic congestion. One of the most important problems in cities is the difficulty of further expanding the existing infrastructures. Having that in mind, the main accessible and available alternatives that could provide better management of the traffic lights is to use technological systems. There are many methods available for traffic management such as video data analysis, infrared sensors, inductive loop detection, wireless sensor networks, and a few other technologies. This research is focused on reviewing all these existing methods and studies using a systematic literature review (SLR). The SLR was intended to improve the synthesis of research by introducing a systematic process. This article aims at analyzing and assessing the existing studies against selected factors of comparison. The study achieves these aims by analyzing 78 main studies. The research outcomes indicated that there are decent numbers of studies that have been proposed in the area of smart traffic light management. However, less attention has been paid on the possibility of investigating the use of live traffic data to improve the accuracy of traffic management.

Information system for the management of intelligent traffic lights

Derived from the population increase and urban growth, vehicle traffic has increased in the cities of Colima and Villa de Álvarez located in Mexico, and with it the problems of road safety and traffic management; the increasing number of roads and traffic lights implies recording the information of each one of these; Therefore, this article presents an information system for the management of intelligent traffic light infrastructure. In the software engineering process, the Agile Unified Process was used to manage the main risks early and guarantee the quality of the product during its life cycle. The system was tested at a prototype level with satisfactory results, as a result a web system contributes to improving road and citizen safety, since based on two vehicle data it connects with web services to other databases, and identifies Immediately form the incidents of vehicles that pass through the roads, including stolen vehicle, speeding and a panic button. The expectations are to scale it to the real environment, and make available to the corresponding authorities the information collected to favor decision-making.

Machine Learning and Answer Set Program Rules towards Traffic Light Management

Machine Learning and Answer Set Program Rules towards Traffic Light Management

Injection-Induced Seismic Risk Management Using Machine Learning Methodology – A Perspective Study

Effective identification of induced seismicity and real-time management of seismic risks are hot topics due to increasing induced seismicity in areas related to energy exploitation. Existing decision-making tool for managing seismic risks, known as the traffic light system, is not robust enough. To meet the increasing needs for safe mining of energy at production sites, finding an advanced and efficient method to improve the traffic light system is essential. In recent years, machine learning, an advanced inductive and analytical method, has been widely used in seismology. In this context, research gaps associated with the identification and management of induced seismicity, as well as the current achievements of machine learning in addressing induced seismicity problems, are reviewed. A basic framework of using machine learning method to optimize the traffic light system in the industrial production process is first proposed. Then, its feasibility and rationality are demonstrated by similar cases. This framework may provide a reference for the development of a risk-based adaptive traffic light management system.

Impact of variable physical conditions and future increased aquaculture production on lice infestation pressure and its sustainability in Norway

Salmon lice infestation is a challenge for wild post-smolt salmon during migration from their natal river to the sea in several regions of Norway. The traffic-light management system regulates growth in the aquaculture industry, where growth in production (6%) is only allowed if the impact of salmon lice on wild fish can be kept at a minimum and up to 10% mortality of wild salmonids are considered within the sustainability goal. We used a numerical ocean model, combined with an individual-based model for salmon lice, to evaluate the interannual variability in salmon lice concentrations in Production Zone 7, which was granted permission for production growth in 2017. Salmon lice releases were kept constant for 3 yr, while the physical conditions, e.g. wind and ocean circulation, varied. The total area of elevated lice infestations varied between 3.2 and 8.4% of the area within 5 km from the coast, due only to interannual physical variability mainly caused by variable wind patterns. Modeled post-smolts migrating out from the Namsen River (64.5°N, central Norway) towards the open ocean experienced mortality between 5 and 9%. Since Production Zone 7 was granted growth, we have simulated increased production and corresponding increases in lice releases. After 5 to 8 increments of 6% increase in production, the increase in salmon post-smolt mortality was of the same order of magnitude as the interannual variability. Information regarding migration route and time is crucial input to the model calculating post-smolt mortality, and inadequate information can affect the results significantly. These 2 methods (determining area of elevated lice infestations and estimating post-smolt mortality) provide complementary information and should be used in combination when the overall assessment of a production zone is made.

Design and Implementation of a Smart Traffic Light Management System Controlled Wirelessly by Arduino

<p>In Iraq, the number of people who own vehicles has grown up significantly. However, this increment in vehicles number doesn’t accomplished by a study of roads and intersections expansion. As a result, traffic jams became a big problem that led to long waiting time at each intersection, increased car accidents, pollution, and economic problems. To solve this problem a Smart Traffic Light System (STLS) has been implemented using Arduino, camera, IR sensor to overcome traffic jams problems in Kut city – Iraq.</p>

Regulation Models of Crossroad Based on Wireless Sensor Networks and Notions of the Fluid Mechanics

The permanent growth of the population in smart cities has increased the number of vehicles. Consequently the problem of traffic congestion has become one of the main problems to be solved by today's traffic control systems, especially at traffic intersections. In fact, the traditional method which avoids the congestion in a crossroads is the classic command (Timing) by means of traffic lights. However, the traffic light management modes are sometimes based on classic models which make them unsuitable for the treatment of different experienced situations in traffic (either dense or fluid traffic). Fortunately, thanks to the significant progress made, especially the use of New Information Technologies and Communications for example Wireless Sensor Network, for the regulation of traffic, are solutions become central in the field of urban traffic management. They have made it possible to propose more effective control mechanisms to reduce the effects of traffic congestion. In this article, we will present the continuation of our work [1], the objective is to offer to the users of the road a crossing time as long as possible, while preventing the car cap to propagate over a distance that is set between two wireless sensors, to do this, we can act on the setting of the traffic light to regulate traffic in intersections.

Internet of things-based fog and cloud computing technology for smart traffic monitoring

Internet of Things (IoT) is changing the world by connecting billions of physical and virtual objects with distinctive identities to the Internet. This fusion results in generating huge volumes of data that might not be manageable using today's storage and data analytics technologies. Although cloud computing offers services to tackle this issue at infrastructural level, its efficiency for time sensitive applications (e.g. oil, gas, and traffic monitoring) is still questionable. Arguably, transferring massive amount of data to the cloud for storage and processing may lead to cloud overloading and saturation of network bandwidth. In this study, an integrated fog and cloud computing framework is introduced to overcome the limitations of real-time analytics, latency and network congestion of basic cloud services for traffic monitoring. The proposed approach is implemented to prototype a smart traffic monitoring system (STMS). The proposed monitoring system is designed for congestion monitoring and traffic light management. It can also be tuned to detect traffic incidents that requires immediate assistance during congestion. In this framework, a tiny computer-on-module serves as a fog node to collect real-time data from geographically distributed sensors and to transfer it to the cloud for storage and processing. The results show the efficiency of the fog network in improving the performance of the cloud platform in terms of reducing the response time and increasing the bandwidth. Furthermore, the proposed integrated fog and cloud framework is interfaced with Tweeter to send alerts about traffic congestion to be subscribed users in the form of Tweet messages .

Intelligent Traffic-Lights Management by Exploiting Smart Antenna Technology (ITSAT)

Existing traffic lights’ timings and switching patterns do not operate with real-time traffic conditions. This results in wastage of time, fuel, and higher emission of CO2. This paper proposes an intelligent traffic-lights management system by exploiting Smart Antenna Technology (already installed on cellular Base Stations). With the proposed approach, the traffic lights’ duration adapts to the real-time traffic flow. Our proposed approach does not delve into improvements of Smart Antenna Technology but suggests to extract cell-phones’ location information, which translates it into traffic density and use the information to control traffic lights’ timings. The work is based on the assumption that the cell-phones density corresponds to the vehicles’ density on the roadways. We also propose a Notifier integrated into the TDC. It alerts the driver about the signal transition from red to green, and thus helps drivers use their cell phones serenely for the transitory duration the light stays the red. The analysis shows that the proposed approach not only improves the Average Waiting Time (AWT) by 59.3% but also saves 3.42 gallons of fuel, and reduces CO2 emission by 67 pounds per year for each commuter.

Road traffic optimisation based on a learning approach

Road traffic management is an important issue for authorities, road operators and users. Many researchers have worked since many years in order to propose smart solutions. Some of them have been adopted by road operators and applied on road signalling or on traffic light management. During the last decade, cooperative intelligent transport systems (C-ITS) have emerged and are being deployed. They permit on one hand to increase the number of connected vehicles and on another hand to deploy road side units (RSU) along roads and junctions. RSUs together with central road operator servers compose the road infrastructure used for infrastructure to vehicle communications (I2V). RSUs disseminate continuously messages about road safety and traffic flows to vehicles. Vehicles and RSUs are able to measure, to compute, and to disseminate relevant information. In this study, we add to these actors the ability to learn road behaviours in order to be able to provide the optimal path in terms of travel time for vehicles seeking to reach their destination. The solution is based on a reinforcement learning technique, in particular the Q-learning, that is used to learn the best action to take into account according to various situations. The transit delay from one location to another is used to evaluate the rewards. The simulation results confirm that the proposed Q-learning approach outperforms the existing greedy algorithm with better performances in terms of a transit delay.

Road traffic optimisation based on a learning approach

Road traffic management is an important issue for authorities, road operators and users. Many researchers have worked since many years in order to propose smart solutions. Some of them have been adopted by road operators and applied on road signalling or on traffic light management. During the last decade, cooperative intelligent transport systems (C-ITS) have emerged and are being deployed. They permit on one hand to increase the number of connected vehicles and on another hand to deploy road side units (RSU) along roads and junctions. RSUs together with central road operator servers compose the road infrastructure used for infrastructure to vehicle communications (I2V). RSUs disseminate continuously messages about road safety and traffic flows to vehicles. Vehicles and RSUs are able to measure, to compute, and to disseminate relevant information. In this study, we add to these actors the ability to learn road behaviours in order to be able to provide the optimal path in terms of travel time for vehicles seeking to reach their destination. The solution is based on a reinforcement learning technique, in particular the Q-learning, that is used to learn the best action to take into account according to various situations. The transit delay from one location to another is used to evaluate the rewards. The simulation results confirm that the proposed Q-learning approach outperforms the existing greedy algorithm with better performances in terms of a transit delay.

Modular design for an urban signalized intersections network using synchronized timed Petri nets and responsive control

Abstract Traffic flow at urban intersections fluctuates randomly throughout the day. It depends on several dynamic factors and requires adequate regulation and adaptable control strategies in particular for traffic signals regulation. This paper deals with this problem, and our proposal focuses on the adaptive management of traffic lights within urban intersections. To do so, Timed Synchronized Petri Nets (TSPNs) models are first developed to model and study the system behaviour. Then a real-time adaptive control strategy is proposed for traffic regulation within urban signalized intersections. The control logic is shared between two communicating components of the system. The slaves (TSPNs sub-models) control the traffic signals displays, phases transitions, and traffic flow fluctuations. The master (controller) decides and selects the next phase that should be serviced and determines its green light duration. Due to the used modularity approach, the developed models decrease the modelling complexity, and more importantly, they can be adapted easily to a cluster of intersections. Furthermore, various traffic signal control strategies could be implemented using these models. Moreover, some simulations are performed, and the obtained results are analysed and discussed. Our control strategy is validated through these simulation results.

Optimised Traffic Light Management Through Reinforcement Learning: Traffic State Agnostic Agent vs. Holistic Agent With Current V2I Traffic State Knowledge

Traffic light control falls into two main categories: Agnostic systems that do not exploit knowledge of the current traffic state, e.g., the positions and velocities of vehicles approaching intersections, and holistic systems that exploit knowledge of the current traffic state. Emerging fifth generation (5G) wireless networks enable Vehicle-to-Infrastructure (V2I) communication to reliably and quickly collect the current traffic state. However, to the best of our knowledge, the optimized traffic light management without and with current traffic state information has not been compared in detail. This study fills this gap in the literature by designing representative Deep Reinforcement Learning (DRL) agents that learn the control of multiple traffic lights without and with current traffic state information. Our agnostic agent considers mainly the current phase of all traffic lights and the expired times since the last change. In addition, our holistic agent considers the positions and velocities of the vehicles approaching the intersections. We compare the agnostic and holistic agents for simulated traffic scenarios, including a road network from Barcelona, Spain. We find that the holistic system substantially increases average vehicle velocities and flow rates, while reducing CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions, average wait and trip times, as well as a driver stress metric.