Intelligent Traffic Control System Research Articles

Ingenious Traffic Control System with Green Signal Timings Using Image Processing

In metropolis, traffic congestion affects the daily routine of passengers and in the long run there will be a declination in productivity if such situation is left unaddressed. If an Ambulance, unfortunately, stuck in the middle of congested road, any delay can endanger the life of the patient and, such cases require intelligent, powerful and reliable traffic control system. In this paper, the Infra-Red (IR) Sensors keep track of vehicle density across the lane. The micro-controller in turn, generates the control signals to alter the traffic accordingly. During each transition phase, the Voice Recognition (VR) modules installed on lanes sense the emergency siren and thus temporarily allow passage by turning the signal green for the corresponding lane, while others, being remained at red. Using Image Processing analysis, the exact count of vehicles can be visualized in the Graphical User Interface (GUI) Tool and the green light timings for the consecutive turns can be estimated.

RETRACTED: Intelligent traffic control system based on open IoT and machine learning

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. The retraction notice is available at https://doi.org/10.3233/JIFS-219322.

СНИЖЕНИЕ НЕГАТИВНОГО ВОЗДЕЙСТВИЯ АВТОТРАНСПОРТА НА ОКРУЖАЮЩУЮ СРЕДУ ПУТЕМ ОПТИМИЗАЦИИ УПРАВЛЕНИЯ ДВИЖЕНИЕМ

The state’s transport system, which ensures the movement of goods and people, is an essential component of its economy, but at the same time one of the main sources of negative environmental impact. The main task of designing, creating and maintaining the transport systems’ sustainability in urban areas is to address their safety, reliability and environmental friendliness. The urbanized territory parameters (size, layout, etc.) have a significant impact on the any activity’s development, including transport and logistics. By analysing the influence of various factors on the transport system’s sustainability and identifying common patterns, it is possible to build rational management of the transport system and increase its reliability and security. The article is devoted to the motor transports’ environmental problems in small cities, where it is impossible to apply large and expensive solutions. The study’s goal is to develop tools for organizing traffic in the conditions of the vehicles fleet rapid growth. To this end, we have proposed an intelligent traffic control system, the intellectual core of which is simulation models of road network problem areas. Using specific examples, it is shown that in some cases there is no need to apply expensive solutions to reduce the negative vehicles impact on the environment’s ecological state, and, often, rational management can lead to the desired result. Performing a computer experiment on the constructed simulation models showed that there is considerable potential for improving the traffic flows parameters in these areas and reducing the negative impact on the environment.

Intelligent Traffic Control System Based on Cloud Computing and Big Data Mining

This article aims at discussing problems such as complex object types, large amount of data collection, high demand for transmission and calculation, and weak real-time scheduling and control ability in the construction of modern intelligent traffic information physical fusion network, cloud-based control system theory, modern intelligent traffic control network as the research object, and the physical design of the intelligent transportation information fusion cloud control system scheme. The scheme includes intelligent transportation edge control technology and intelligent transportation network virtualization technology. Based on intelligent traffic flow data, in the center of the cloud control management server using deep learning and overrun learning machine intelligence study methods, such as the forecast of traffic flow data for training, to predict urban road short-term traffic flow and congestion. Further up in the air by using intelligent optimization scheduling algorithm for real-time traffic flow control strategy, the simulation results show the effectiveness of the proposed method.

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.

Simulation of Dynamic Traffic Light Setting Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Traffic congestion is a common problem in so many big cities. The occurrence of traffic congestion can be caused by various reason, one of them is the increasing number of vehicles while the available facilities are not worth the amount. Congestion can cause many losses, including the amount of time wasted and spent fuel in vain. To solve the problem of congestion can be solved by building a new road. But the solution will make the environment more crowded and require more operational costs. Due to this reason, there is a good solution given that is, the renewal of traffic control system by building intelligent traffic light control system based on road density at each traffic intersection and the width of the road. The method used in this research is Adaptive Neuro-Fuzzy Inference System (ANFIS), this method will process the data of road density and road width to determine the duration of green light to be given. The road density will be calculated based on the video that will be captured by the system and will go through the pre-processing and processing stages until the labelling stage of the object on the road using the Connected Component Labelling (CCL) method, the image pixel of this CCL result will be compared to the image of the empty road condition (no vehicles) to get the percentage of road density. The system testing process used 36 images captured from the video and obtained the accuracy of 98,6%.

A Microcontroller Based Intelligent Traffic Control System

A new microcontroller based intelligent traffic control system is proposed in this paper. Instead of changing traffic lights automatically for a given interval, this system allows passing the vehicles considering the number of vehicles on the roads. An Atmega 32 microcontroller and infrared sensors (IR sensors) are used to implement the traffic system. An algorithm is also developed for this purpose. The system is simulated and implemented also by constructing a test rig. The results from the simulation and experimental test rig confirm its appropriateness of using in the roads of different cities. No further processing scheme is required for this intelligent traffic control system.

Towards an intelligent traffic control system using crowdsourcing, based on combined evaluation of weather information and accident statistics

Towards an intelligent traffic control system using crowdsourcing, based on combined evaluation of weather information and accident statistics

A fog-based security framework for intelligent traffic light control system

The world is facing many problems including that of traffic congestion. To highlight the issue of traffic congestion worldwide specially in urban areas and to make it more efficient, research community is working on Intelligent Transportation Systems (ITS). However, there is very limited work in security aspects of ITS which makes it less secure against increasing security threats. Most of the existing frameworks provide security services for ITS with many unrealistic assumptions. In this paper, we propose a Fog-based Security Framework for Intelligent Traffic Light Control System that provides security services with realistic assumptions. Moreover, the proposed framework is compared with a similar framework called secure intelligent traffic light control based on security, performance, and applicability in real world scenario. The results show that the proposed framework is more secure as compared to the existing secure intelligent traffic light control framework and realistic for real world scenario. The proposed framework possesses confidentiality, integrity, and authenticity features. The security features of the proposed framework are verified through the Automated Validation of Internet Security Protocols and Applications tool.

Intelligent Traffic Control System with Priority to Emergency Vehicles

Traffic is the most critical problem in metropolitan cities. Traffic control becomes more necessary to provide problem-less, peaceful driving and pollution-less life to people. The work is designed to develop a density based dynamic traffic signal system. The signal timing changes automatically on sensing the traffic density at the junction. The traffic system is structured with a camera. The image captured by the camera is processed in order to calculate the number of vehicles present in the image. The camera also identifies the siren and allows the green light for emergency vehicles like ambulance, fire engine etc. In this paper, masking algorithm is used to process the image and find the vehicle count on the road. This algorithm shows only the required portion of the image and masks the unnecessary details. The visual basic programming is used to control the glowing time of traffic light based on the vehicle count on the road. The Arduino controller is a single board microcontroller used for instructing the LED to glow. This method can be used to detect road accidents and identify violations of the spiral movements of cars. The whole system when installed on road, provides a way for easy traffic clearance for emergency vehicles without a need of sergeant.

A Rule based Fuzzy controlled Decision Support System for Intelligent Traffic Control System

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Density Based Traffic Control System

The project is aimed at designing a density based dynamic traffic signal system where the timing of signal will change automatically on sensing the traffic density at any junction. Traffic congestion is a severe problem in most cities across the world and therefore it is time to shift more manual mode or fixed timer mode to an automated system with decision making capabilities. Present day traffic signaling system is fixed time based which may render inefficient if one lane is operational than the others. To optimize this problem we have made a framework for an intelligent traffic control system. Sometimes higher traffic density at one side of the junction demands longer green time as compared to standard allotted time We, therefore propose here a mechanism in which the time period of green light and red light is assigned on the basis of the density of the traffic present at that time. This is achieved by using IR (Infrared sensors). Once the density is calculated, the glowing time of green light is assigned by the help of the microcontroller (Arduino). The sensors which are present on sides of the road will detect the presence of the vehicles and sends the information to the microcontroller where it will decide how long a flank will be open or when to change over the signal lights. In subsequent sections, we have elaborated the procedure of this framework.

A New Spectrum Management Scheme for Road Safety in Smart Cities

Traffic management in roads is one of the major challenges faced in developing efficient intelligent transportation systems. Recently, wireless networks have received significant attention for tackling this challenge. However, wireless technologies face the well-known spectrum scarcity problem due to the explosive demand for radio resources. To overcome this challenge, this study presents a novel intelligent traffic control system, utilizing the unused spectrum. Unlike existing works, the spectrum owners in this study hire free spectrum to drivers. The hired spectrum is deployed to build a short-range cost-effective wireless communication for monitoring traffic and enabling drivers to exchange warning messages, and thus enhancing road safety. Our objectives include minimizing crash probability, utilizing unused spectrum, and enabling spectrum owners to generate extra revenue. Numerical analysis demonstrates the capability of our approach to minimize the crash probability among vehicles under different operating road conditions.

Big data oriented intelligent traffic evacuation path fuzzy control system

Big data oriented intelligent traffic evacuation path fuzzy control system

Queuing theory guided intelligent traffic scheduling through video analysis using Dirichlet process mixture model

Intelligent traffic signaling is an important part of city road traffic management systems. In many countries, it is done through supervised/semi-supervised ways. With the advances in computer vision and machine learning, it is now possible to develop expert systems guided intelligent traffic signaling systems that are unsupervised in nature. In order to schedule traffic signals, it is essential to learn the traffic characterization parameters such as the number of vehicles, their arrival and departure rates, etc. In this work, we use unsupervised machine learning with the help of a modified Dirichlet Process Mixture Model (DPMM) to measure the aforementioned traffic parameters. This has been done using a new feature, named temporal clusters or tracklets extracted using DPMM. Detailed analysis on tracklet behavior during signal on/off period has been carried out to derive a queuing theory-based method for signal duration prediction. The queuing behavior at a junction is analyzed using tracklets for understanding their applicability. Queue clearance time at the junction has been used for predicting the signal duration with the help of Gaussian regression of historical data.Two publicly available video datasets, namely QMUL and MIT have been used for verification of the hypothesis. The mean absolute error (MAE) of the proposed method using tracklets has been reduced by a factor of 2.4 and 6.3 when compared with the tracks generated using Kernel Correlation Filters (KCF) and Kanade–Lucas–Tomasi (KLT), respectively. Through experiments, we are also able to establish that KCF and KLT tracks do not consider spatial occupancy of the vehicles on roads, leading to error in the estimation. The results reveal that the proposed queuing theory-based approach predicts the signal duration for the next cycle more accurately as compared to the ground truths. The method can be used for building intelligent traffic control systems for roadway junctions in cities and highways.

Ant colony optimization algorithm with Internet of Vehicles for intelligent traffic control system

Vehicles present on the Internet of Vehicles (IoV) can communicate with each other in order to determine the status of the road and vehicle in real time. These parameters are used to estimate the average speed and identify the optimal route to reach the destination. However, the government traffic departments are unable to use these valuable traffic data and thus more traffic jam, congestion and road accident occurs. In order to overcome this issue, this paper proposes an effective traffic control system with the help of IoV technology. The proposed method is demonstrated in the study are of Vellore district, Tamil Nadu, India. The street maps are segmented into number small number of distinct maps. Ant colony algorithm is applied to each map in order to find the optimal route. In addition, Fuzzy logic based traffic intensity calculation function is proposed in this paper to model the heavy traffic. The proposed IoV based route selection method is compared with the existing shortest path selection algorithms such as Dijikstra algorithm, Kruskal's algorithm and Prim's algorithm. The experimental results proved the good performance of the proposed IoV based route selection method.

Sampled-data controllability and stabilizability of Boolean control networks: Nonuniform sampling

Derived from a simplified intelligent traffic control system, sampled-data controllability and stabilizability of Boolean control networks are considered. Compared with the existing case of uniform (periodic) sampling in Boolean control networks, the nonuniform one is more general. Using linear span with integral coefficients, the distribution of sampling points can be obtained. Then by constructing novel systems, some necessary and sufficient conditions are proposed to determine sampled-data controllability and stabilizability. Finally, two illustrative examples, which are on apoptosis networks and traffic control systems, respectively, are worked out to show the effectiveness of the obtained results.

Image Processing Based Intelligent Traffic Control System by Using Raspberry Pi

Image Processing Based Intelligent Traffic Control System by Using Raspberry Pi

The simulation model on delay time of road accessibility based on intelligent traffic control system

After the Chinese government launched the policy of opening residential, the effects of easing traffic congestion have been widely discussed. In this paper, the impact of the open area on the surrounding road capacity is studied and analyzed quantitatively, and some feasible suggestions are put forward. First of all, we use the road congestion indicators to reflect the road traffic situation. And according to the three indexes to build an evaluation index system that can evaluate the influence of opening residential quarter on the surrounding road capacity. After determining the final evaluation indexes, the analytic hierarchy process is used to determine their standard weights. Second, build a mathematical model of vehicle traffic. The crossing time of vehicles is used to measure the traffic condition. Last but not least, suppose that after opening the residential quarter, there will be more vehicles, with the increase of the number of vehicles, the road saturation will increase, which will lead to the increase of traffic time. In the paper, three typical opening road structures are established to specific analyze the changes of the crossing time of vehicles, and then, use model one and model two to solve it. Opening residential quarter has a certain positive effect on the surrounding road capacity, on the condition of the number of connections between the opening residential quarter and the surrounding roads is not more than two. Otherwise, it will increase road congestion. In spite of a certain positive effect that opening residential quarter brings to the surrounding road capacity, blindly increase the number of opening residential quarter roads, road congestion may be increased.

Intelligent Traffic Control System for Over-Saturated Signalized Intersections in Kuwait..

This paper proposes a new intelligent traffic control (ITC) system which is more efficient than the traffic control system currently used in the state of Kuwait. The proposed ITC system is designed as a dynamic system by using a fuzzy expert system; the fuzzy rules are applied in the visual basic and computer-based program (Excel) to run the validation process. The developed control system applied on five intersections in the grid network at four periods. The results show that the number of vehicles passing the intersection phases is increased in most phases by an average of 12.9% at the first period, 23.3% at the second period, 10.4% at the third period and by 21.2% at the fourth period. For the same periods, the phases green time is increased by an average of 9.1%, 5.8%, 9.9% and 6.3%. And the number of intersection cycles remains constant at the most time which means that the developed control system distributes the phases green time dynamically based on the traffic situation.