scholarly journals A real-time system for vehicle detection with shadow removal and vehicle classification based on vehicle features at urban roads

2020 ◽  
Vol 11 (4) ◽  
pp. 2091
Author(s):  
Issan Atouf ◽  
Wahban Yahya Al Okaishi ◽  
Abdelmoghit Zaaran ◽  
Ibtissam Slimani ◽  
Mohamed Benrabh
Keyword(s):  
Traffic Flow ◽  
Urban Areas ◽  
Vehicle Detection ◽  
Vehicle Classification ◽  
Traffic Density ◽  
Light Cycle ◽  
Specific Class ◽  
Traffic Light ◽  
Real Time System ◽  
Moving Vehicles

Monitoring traffic in urban areas is an important task for intelligent transport applications to alleviate the traffic problems like traffic jams and long trip times. The traffic flow in urban areas is more complicated than the traffic flow in highway, due to the slow movement of vehicles and crowded traffic flows in urban areas. In this paper, a vehicle detection and classification system at intersections is proposed. The system consists of three main phases: vehicle detection, vehicle tracking and vehicle classification. In the vehicle detection, the background subtraction is utilized to detect the moving vehicles by employing mixture of Gaussians (MoGs) algorithm, and then the removal shadow algorithm is developed to improve the detection phase and eliminate the undesired detected region (shadows). After the vehicle detection phase, the vehicles are tracked until they reach the classification line. Then the vehicle dimensions are utilized to classify the vehicles into three classes (cars, bikes, and trucks). In this system, there are three counters; one counter for each class. When the vehicle is classified to a specific class, the class counter is incremented by one. The counting results can be used to estimate the traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The system is applied to videos obtained by stationary cameras. The results obtained demonstrate the robustness and accuracy of the proposed system.

scholarly journals Recent development of smart traffic lights

2021 ◽  
Vol 10 (1) ◽  
pp. 224
Author(s):  
A’isya Nur Aulia Yusuf ◽  
Ajib Setyo Arifin ◽  
Fitri Yuli Zulkifli
Keyword(s):  
Traffic Flow ◽  
Time Allocation ◽  
Traffic Density ◽  
Light Cycle ◽  
Traffic Light ◽  
Large Cities ◽  
Traffic Lights ◽  
Smart Traffic ◽  
Very High ◽  
Signal Time

<span id="docs-internal-guid-288f4dcc-7fff-1e8c-0350-5032593b6e4f"><span>Increased traffic flow causes congestion, especially in large cities. Even though congestion is not unusual, traffic jams still result in very high economic and social losses. Several factors cause congestion, one of which is traffic lights. Therefore, a mechanism is needed so that traffic lights can intelligently and adaptively manage signal time allocation according to traffic flow conditions. A traffic light with this type of mechanism is known as a smart traffic light. Smart traffic light cycle settings can be grouped based on the traffic density, scenarios for emergency vehicles, and the interests of pedestrians. This paper analyzes the methods and technologies used in the development of smart traffic light technology from the perspective of these three situations as well as the development of smart traffic light technology in the future.</span></span>

Design of Lighting Intelligent Control System Based on OpenCV Image Processing Technology

2021 ◽  
Vol 29 (Supp01) ◽  
pp. 119-139
Author(s):  
Suning Gong ◽  
Rakesh Kumar ◽  
D. Kumutha
Keyword(s):  
Image Processing ◽  
Control System ◽  
Traffic Flow ◽  
Public Transportation ◽  
Traffic Control ◽  
Urban Areas ◽  
Light Signal ◽  
Image Processing Technique ◽  
Traffic Density ◽  
Traffic Light

The high growth of vehicular travel in urban areas, in particular, requires a traffic control system that optimizes traffic flow efficiency. Traffic congestion can also occur by large de-lays in Red Light etc. The delay in lighting is difficult to code and does not rely on real traffic density. It follows that traffic controls are simulated and configured to better meet this rising demand. So, in order to avoid the traffic control problem, the Adaptive Intelligent Traffic Light control system (AITLCS) has been proposed based on OpenCV and Image processing technique. The system proposed is designed to ensure smooth and efficient traffic flow for daily life as well as emergency and public transportation safety. Based on the road density instead of the levels set the proposed system provides the timing for the traffic light signal so that a highly loaded side switched on over long periods compared with the other lanes. It can also be used at an intersection with traffic signs, which controls the traffic light signal at the intersection. If timers are smart to predict the exact time, the system is more efficient because it reduces the time spent on unintended green signal significantly. With the help of OpenCV software, this paper aims to have a signal management SMART solution that will be cost-effective at the end. The system consists of a camera facing a lane taking pictures of the route we want to travel and then the density of the pedestrian and vehicle is taken and compared with each image employing image processing. Such images are processed effectively to learn the density of traffic.

scholarly journals Developing Hybrid Simulation Model to Improve Road Traffic Management

2020 ◽  
Vol 4 (01) ◽  
pp. 56-65
Author(s):  
Hayati Mukti Asih
Keyword(s):  
Simulation Model ◽  
Urban Areas ◽  
Traffic Management ◽  
Road Traffic ◽  
Hybrid Simulation ◽  
Simulation Method ◽  
Vital Role ◽  
Light Cycle ◽  
Traffic Light ◽  
Traffic Lights

Yogyakarta has increasing trends in the number of vehicles and consequently intensifying the traffic volume and will effect to higher emission and air pollution. Traffic lights duration plays a vital role in congestion mitigation in the critical intersections of urban areas. This study has objective to minimize the number of vehicles waiting in line by developing the hybrid simulation method. First of all, the MKJI and Webster method were calculated to determine the green traffic light. Then, the simulation model was developed to evaluate the number of vehicles waiting in line according to different duration of green traffic lights from MKJI and Webster method. A case study will then be provided in Pelemgurih intersection located in Yogyakarta, Indonesia for demonstrating the applicability of the developed method. The result shows that the duration of green traffic lights calculated by Webster method provides lower number of vehicles waiting in line. It is due to the short duration of green traffic light resulted by Webster method so that the traffic light cycle becomes shorter and it effects the number of vehicles waiting in line which is lower than MKJI method. The results obtained can help the generating desired decision alternatives that will important for Department of Transportation, Indonesia to enhance the road traffic management with low number of vehicles waiting in line.

scholarly journals Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6218
Author(s):  
Rodrigo Carvalho Barbosa ◽  
Muhammad Shoaib Ayub ◽  
Renata Lopes Rosa ◽  
Demóstenes Zegarra Rodríguez ◽  
Lunchakorn Wuttisittikulkij
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Detection System ◽  
Lightweight Design ◽  
Vehicle Detection ◽  
Activation Function ◽  
Urban Traffic ◽  
Vehicle Classification ◽  
Traffic Light ◽  
Traffic Lights

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

scholarly journals Optimization of Traffic Flow On KS Tubun Street using Greenberg Model

2019 ◽  
Vol 8 (2S7) ◽  
pp. 83-86
Keyword(s):  
Traffic Flow ◽  
Red Light ◽  
Green Light ◽  
Light Cycle ◽  
Traffic Light ◽  
Traffic Jam ◽  
Traffic Lights ◽  
The Road ◽  
Road Users ◽  
Time Parameters

KS Tubun Street is a street in Bogor, which has a fairly high vehicle volume and become one of a high-traffic jam area. This is caused by KS Tubun Street is the main road for road users from Jakarta and Bogor. Traffic jam problem that occurs due to the confluence interchange of traffic flow and traffic lights settings that are not proportional to the volume of vehicles across the road. Optimization of traffic flow at KS Tubun Street performed by the stages of forming a model of traffic flow, determining the density and velocity of the vehicle is based on the Greenberg model, and determining the length of the traffic lights to avoid a buildup of vehicles. The result is a traffic flow model with distance and time parameters. The density of vehicles that occurs on the streets of KS. Tubun street based on the Greenberg model between 180 to 240 unit car of passanger (ucp) with the average velocity of vehicles 15 to 19.5 km per hour. The density of vehicles on KS. Tubun street can be break down by increasing time. Traffic light cycle time can be reduced for 8 seconds with the red light glowing time is 80 seconds and the green light glowing time is 62 seconds.

Proposed Traffic Light Control Mechanism Based on Multi-Agent Coordination

2016 ◽  
Vol 20 (5) ◽  
pp. 803-812
Author(s):  
Satoshi Kurihara ◽  
◽  
Ryo Ogawa ◽  
Kosuke Shinoda ◽  
Hirohiko Suwa ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Urban Areas ◽  
Transport Systems ◽  
Light Control ◽  
Traffic Light ◽  
Agent Based ◽  
Traffic Lights ◽  
Control Framework ◽  
Traffic Light Control ◽  
Multi Agent

Traffic congestion is a serious problem for people living in urban areas, causing social problems such as time loss, economical loss, and environmental pollution. Therefore, we propose a multi-agent-based traffic light control framework for intelligent transport systems. Achieving consistent traffic flow necessitates the real-time adaptive coordination of traffic lights; however, many conventional approaches are of the centralized control type and do not have this feature. Our multi-agent-based control framework combines both indirect and direct coordination. Reaction to dynamic traffic flow is attained by indirect coordination, whereas green-wave formation, which is a systematic traffic flow control strategy involving several traffic lights, is attained by direct coordination. We present the detailed mechanism of our framework and verify its effectiveness using simulation to carry out a comparative evaluation.

scholarly journals Device and Algorithm for Vehicle Detection and Traffic Intensity Analysis

2021 ◽  
Vol 17 (1) ◽  
pp. 83-92
Author(s):  
Mikhail Gorobetz ◽  
Andrey Potapov ◽  
Aleksandr Korneyev ◽  
Ivars Alps
Keyword(s):  
Traffic Flow ◽  
Internal Combustion Engines ◽  
Radio Channel ◽  
Vehicle Detection ◽  
Detection Methods ◽  
Traffic Intensity ◽  
Traffic Light ◽  
Traffic Lights ◽  
Advantages And Disadvantages ◽  
Sensor Module

Abstract To effectively manage the traffic flow in order to reduce traffic congestion, it is necessary to know the volumes and quantitative indicators of this flow. Various detection methods are known for detecting a vehicle in a lane, which, in turn, have their own advantages and disadvantages. To detect vehicles and analyse traffic intensity, the authors use a pulse coherent radar (PCR) sensor module. Testing of various modes of operation of the radar sensor was carried out to select the optimal mode for detecting vehicles. The paper describes a method for fixing vehicles of different sizes, filtering and separating the vehicle from the traffic flow. The developed vehicle detection device works in conjunction with signal traffic lights, through which traffic control takes place. The signal traffic lights, which have their own sensors and control units, communicate with each other via a radio channel; there is no need for cable laying. The system is designed to work on road maintenance sites. The paper describes the experimental data when testing on a separate section of the road. The experiment showed the advantage of traffic lights (cars passed the regulated traffic light faster) from the point of view of calculating the traffic flow over the normal traffic light operation. Reducing downtime in traffic jams, in turn, has a beneficial effect on the environmental situation, since at the moment internal combustion engines prevail in vehicles.

scholarly journals A Cellular Automaton Model for Traffic Flow -Investigating the Effect of Turning

2014 ◽  
Vol 12 (1) ◽  
Author(s):  
Tracy Finner ◽  
Matthew Beauregard
Keyword(s):  
Cellular Automaton ◽  
Traffic Flow ◽  
Power Law ◽  
Average Velocity ◽  
Cellular Automaton Model ◽  
Light Cycle ◽  
Vehicular Traffic ◽  
Traffic Light ◽  
Vehicular Traffic Flow

A cellular automaton model is proposed, modeling vehicular traffic flow on a two dimensional lattice in which the vehicles turn at an intersection with a given probability. It is shown that the introduction of turning reduces the long-term average velocity, and can be predicted by a power law depending on the probability of a vehicle turning and the density of cars. The reduction in speed decreases rapidly once the light cycle length surpasses a certain threshold, the value of which can be predicted from the observed power law. Keywords: cellular automaton, traffic flow, traffic light strategy, turning, dynamical systems, power law

scholarly journals Density Controlled Traffic Lights System using Aurdino Mega 2560

2020 ◽  
Vol 9 (5) ◽  
pp. 392-394
Keyword(s):  
Traffic Control ◽  
Urban Areas ◽  
Red Light ◽  
Green Light ◽  
Fixed Time ◽  
Traffic Density ◽  
Traffic Light ◽  
Traffic Lights ◽  
Choice Making ◽  
Control Authority

We have proposed the enhancement of Traffic Light Controller utilizing ultrasonic sensor and microcontroller. The Paper is planned for structuring a thickness based dynamic traffic signal framework where the planning of signal will change consequently on detecting the traffic density at any road junction. Traffic jams are an extreme issue in many urban areas over the world and thusly the time has come to move progressively manual mode or fixed clock mode to a robotized framework with choice making abilities. Present day traffic control framework is fixed time based which may render wasteful on the off chance that one path is operational than the others. To solve this issue, we have made a structure for a clever traffic control system. Some of the time higher traffic density at one side of the intersection requires longer green light time when compared with standard green light time. We, consequently propose here a component where the time of green light and red light is allotted based on the thickness of the traffic present around then. This is accomplished by utilizing ultrasonic sensors which are available on Top of the street.Sometime, in specific intersection of the street junctions extended periods of Red Traffic Light. In instance of any vehicle in crisis circumstance or on the other hand in emergency like VVIPs,a SMS is send to Traffic Control Authority, who has the control of microcontroller empowers microcontroller to change traffic light green for specific time on need premise.

scholarly journals Measures to ensure road traffic safety on controlled intersections

2019 ◽  
Vol 298 ◽  
pp. 00046 ◽  
Author(s):  
Aleksandr Novikov ◽  
Ivan Novikov ◽  
Anastasiya Shevtsova
Keyword(s):  
Traffic Flow ◽  
Traffic Safety ◽  
Road Traffic ◽  
Weather Conditions ◽  
Transport Systems ◽  
Light Cycle ◽  
Traffic Light ◽  
Intelligent Transport ◽  
Road Traffic Safety ◽  
Rational Distribution

The paper discusses a new approach to ensuring the safety of controlled intersections. In the course of research, the authors found a change in the main characteristics of the traffic flow, which requires constant monitoring in the implementation of the intersection control using a traffic light. The heterogeneity of the composition of the traffic flow is determined and the influence of this parameter on the duration of the resolving signal is established. In addition, the authors determined the influence of weather conditions on the duration of the traffic light cycle and developed an approach to take into account changes in this factor in the calculation of the mode of operation of the traffic light object. As a result of the model experiment, the economic and environmental efficiency of the developed approach is determined. The results obtained in the course of the study are an integral part in the implementation of intelligent transport systems, which confirms the relevance and feasibility of the study. The use of this approach, in addition to integration into the its system, allows to reduce the accident rate of controlled intersections due to the rational distribution of the time of the traffic light object and reduce vehicle delays on the approaches, which together ensures the safety of regulated areas.

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