Conventional Traffic Light Research Articles

A Two-Speed Synchronous Traffic Protocol for Intelligent Intersections: From Single-Vehicle to Platoon Crossing

With progress in cooperative and autonomous driving, there is an increasing interest in intelligent intersections to replace conventional traffic lights and, thereby, improve traffic efficiency. To avoid accidents in such safety-critical systems, a traffic protocol needs to be implemented. In this article, we are concerned with synchronous traffic protocols, i.e., those that synchronize the arrival time of vehicles at the intersection. In particular, such protocols are normally conceived for homogeneous vehicles of approximately the same size/length. However, these do not extend well to heterogeneous vehicles, i.e., they lead to unviable requirements on the road infrastructure. To overcome this limitation, based on the observation that large/overlength vehicles like buses and trams are less frequent than passenger vehicles, we propose an approach that treats them as exceptions (rather than the rule) leading to a much more efficient design. In contrast to approaches from the literature, we implement a two-speed policy—with a high speed for drive-through and a low speed for turn maneuvers—and analyze both single-vehicle as well as fairness-based platoon crossing. To conclude, we perform detailed comparisons illustrating the benefits by the proposed approach.

Renewable Energy-Powered Traffic Signalization as a Step to Carbon-Neutral Cities (The Case of Western Balkans)

Global warming and its negative effect on the environment pose a great threat to the future of mankind. In order to overcome these challenges, EU countries have set the target of becoming climate neutral by 2050, a path defined through the policies of the European Green Deal. The Republic of Serbia and Bosnia and Herzegovina also agreed upon the same goal through the endorsement of the Green Agenda for the Western Balkans. One of the means to achieve this goal is by increasing the share of renewable energy sources. In this paper, we analyze the feasibility, challenges, and potential issues of substituting conventional traffic light signalization with signalization based on renewable energy sources on a national scale. An ad hoc questionnaire was used to collect data from representatives of most of the municipalities in the Republic of Srpska entity in Bosnia and Herzegovina and representatives of the city of Novi Sad in Serbia. In the city of Novi Sad, personal interviews were also carried out to collect additional information. The results of this research show that the implementation of solar-powered traffic lights is economically and environmentally viable. These results will provide the basis for understanding the benefits and challenges in the case of the application of traffic light signalization based on renewable energy sources.

Perancangan Lampu Lalu Lintas Dengan Led Bicolor Menggunakan Arduino

The growth in the number of vehicles every year will have an impact on the number of vehicles on the road. Thus, road congestion will increasingly occur in big city areas. Traffic lights are one of the tools to prevent congestion at especially at road intersections by making motorists more orderly stop and walk according to the signal of the light on. The need for traffic lights will also increase along with the number of congestion points at road intersections. Conventional traffic lights use three sets of LED lights with each set of red, yellow and green lights. Along with the development of technology, a Bicolor LED lamp was created which has two basic colors, namely red and green. With these conditions, the author is interested in developing a traffic light system with a one-package light system using Bicolor LED lights using an Arduino UNO R3 mockup. One Bicolor LED used will have red, yellow, and green colors. In this development it is expected to produce a system that is cheaper in manufacture and operation.

PROTOTYPE SMART TIME SCHEDULER LAMPU LALU LINTAS MENGGUNAKAN ALGORITMA HAAR CASCADE

The traffic light functions as a traffic controller at crossroads and also as smoothness at intersections to avoid congestion. The current application of conventional traffic lights still has weaknesses in determining the duration of traffic lights which are not adjusted to the number of vehicle volumes which often change every time. Especially when there is a dense flow of vehicles at intersections, especially at certain times. Based on observations of traffic density at the intersection that connects Soekarno - Hatta road and Arifin Achmad road in Pekanbaru City. There is often a high density of vehicles from the direction of Jalan Soekarno-Hatta (Morning Market) to Jalan Jenderal Sudirman during working hours so that the duration of the traffic lights cannot reduce the amount of queue density in the light traffic. The implementation of the Smart Time Scheduler is a solution for adjusting the duration of traffic lights based on the level of traffic density, by building a tool to detect and calculate the number of vehicle queues at the Traffic Light and then enter the calculation results into 3 categories, namely Normal, Medium and Dense Density. The system uses Haar Cascade with the open cv library on the Raspberry pi, the results of the system testing that has been carried out can count the number of vehicles and instruct Arduino to set the duration of the traffic lights where in the category of normal vehicle queues, the green traffic light gets a waiting time of 10 seconds in countdown, Moderate category for 15 seconds, and Solid Category, for 20 seconds, while the calculation error value uses the Error Percentage equation formula, namely comparing the results of vehicle calculations by the system with Manual Calculation Results multiplied by 100% Then the System Calculation Error is 16% and the accuracy level of the tool is 100% - 16% = 84%, the size of the error value is influenced by the quality of the light intensity, and the distance of the camera's detection of the object when performing the detection.

Optimization-Based Approach for Resilient Connected and Autonomous Intersection Crossing Traffic Control Under V2X Communication

In this paper, we present an optimization-based approach for safe, efficient, and resilient autonomous intersection traffic control in realistic vehicle-to-everything (V2X) communication environment. The proposed framework produces the fastest discrete-time trajectory for vehicles who want to cross an intersection. Constraints for safety are designed carefully in the optimization problem formulation to prevent potential collisions during intersection crossings. A novel vehicle-to-intersection (V2I) interaction mechanism is designed to handle imperfect communication characteristics such as packet delivery delay and loss. The proposed intersection management framework is evaluated by running extensive simulations using an open source vehicular network and microscopic traffic simulation software, Veins. The results show that the overall traffic control performance of the proposed framework is substantially better than conventional traffic light control framework, in particular when traffic volume is light and medium, even in situations with a realistic wireless vehicular network setting where packet delivery delays and drops occasionally occur.

An Adaptive and Greener Traffic Signal Coordination Scheme for Transport 4.0

Traffic congestion has become a major concern, aroused as a result of increased population and urbanization. Hence, novel and innovative methods for controlling ever-increasing traffic volumes are essential. Conventional traffic light schemes are the most popular method of controlling traffic, and it is logical and economical to make research endeavors to optimize their existing performance. Despite numerous studies, the aforementioned problem has not been optimally and sufficiently solved. In this research, we introduce an adaptive traffic signaling scheme based on vehicle density to facilitate optimal traffic signal control as well as effective traffic management. We also propose effective coordination of the traffic amongst the junctions. Here, the live video is utilized as an input provided to a deep Q network to provide adaptive phase timings as the output. In the proposed scheme, we introduced per car unit (PCU) as a novel parameter to represent the effect of each vehicle type on traffic conditions. Numerous filed trials on real-time data amply prove that the proposed scheme enhances the average speed of traffic up to 5.597 km/h. The proposed scheme shows an average increment of 175.71% in average mean speed compared to the existing static schemes. Except for the high traffic scenario, for both mid traffic and low traffic scenarios, the proposed scheme shows a considerable improvement in both average densities and maximum densities. In the mid-traffic scenario, the average speed shows an improvement of 3.85 km/h, while in the low traffic scenario, the average mean speed shows an improvement of 7.96 km/h. A reduction in fuel consumption and average delay were also observed, which will lead to a greener Transport 4.0.

Design and development of portable smart traffic signaling system with cloud-artificial intelligence enablement

With increasing traffic, <span lang="EN-US">apart from the major traffic junctions, there are few smaller junctions which witness heavy traffic only during a certain period of the day. For such cases, deploying of conventional traffic lights are not a viable option. A cost-effective internet of things (IoT) enabled portable smart traffic signaling system is designed using ESP32 dual core microcontroller, to assist traffic personnel working at small traffic junctions. It uses a foldable mechanical structure which can be carried easily. The system is designed to work with and without internet connectivity depending on its functionality and place of deployment. The system can be pre-programmed with default time value to work without human intervention. Using an android application, the user can manually control the traffic signal by analysing the traffic density. System gathers the traffic density information based on the operations performed by the traffic personnel and stores it in the cloud. In Smart mode, system computes the mean value and also runs K-means clustering algorithm on the dataset to generate optimized time values. Comparison of the data generated using manual and automatic modes infer the credibility of the system in generating optimized time values and reducing human effort.</span>

Pedestrian Traffic Light Control with Crosswalk FMCW Radar and Group Tracking Algorithm.

The increased mobility requirements of modern lifestyles put more stress on existing traffic infrastructure, which causes reduced traffic flow, especially in peak traffic hours. This calls for new and advanced solutions in traffic flow regulation and management. One approach towards optimisation is a transition from static to dynamic traffic light intervals, especially in spots where pedestrian crossing cause stops in road traffic flow. In this paper, we propose a smart pedestrian traffic light triggering mechanism that uses a Frequency-modulated continuous-wave (FMCW) radar for pedestrian detection. Compared to, for example, camera-surveillance systems, radars have advantages in the ability to reliably detect pedestrians in low-visibility conditions and in maintaining privacy. Objects within a radar’s detection range are represented in a point cloud structure, in which pedestrians form clusters where they lose all identifiable features. Pedestrian detection and tracking are completed with a group tracking (GTRACK) algorithm that we modified to run on an external processor and not integrated into the used FMCW radar itself. The proposed prototype has been tested in multiple scenarios, where we focused on removing the call button from a conventional pedestrian traffic light. The prototype responded correctly in practically all cases by triggering the change in traffic signalization only when pedestrians were standing in the pavement area directly in front of the zebra crossing.

Traffic Signal Control System Using Deep Reinforcement Learning With Emphasis on Reinforcing Successful Experiences

In recent years, several studies have been conducted on the dynamic control of traffic signal durations using deep reinforcement learning with the aim of reducing traffic congestion. The unique advantages of independent control of traffic signals include reduction in the cost of information transmission and stable control without being affected by the failure of other traffic signals. However, conventional deep reinforcement learning methods such as Deep Q-Network may degrade the learning performance in a multi-agent environment where there are multiple traffic signals in the environment. Therefore, we propose a traffic light control system based on the dual targeting algorithm, which incorporates reinforcement of successful experiences in multi-agent environments, with the aim of realizing a better traffic light control system. The experimental results in a multi-agent environment using a traffic flow simulator based on simulation of urban mobility (SUMO) show that the proposed traffic light control system reduces the waiting time at traffic lights by 33% compared to a conventional traffic light control system using deep reinforcement learning. In the future works, we aim to apply this research to traffic light control systems in real environments.

Distant Traffic Light Recognition Using Semantic Segmentation

Traffic light recognition is an important task for automatic driving support systems. Conventional traffic light recognition techniques are categorized into model-based methods, which frequently suffer from environmental changes such as sunlight, and machine-learning-based methods, which have difficulty detecting distant and occluded traffic lights because they fail to represent features efficiently. In this work, we propose a method for recognizing distant traffic lights by utilizing a semantic segmentation for extracting traffic light regions from images and a convolutional neural network (CNN) for classifying the state of the extracted traffic lights. Since semantic segmentation classifies objects pixel by pixel in consideration of the surrounding information, it can successfully detect distant and occluded traffic lights. Experimental results show that the proposed semantic segmentation improves the detection accuracy for distant traffic lights and confirms the accuracy improvement of 12.8 % over the detection accuracy by object detection. In addition, our CNN-based classifier was able to identify the traffic light status more than 30 % more accurately than the color thresholding classification.

Real-time Dynamic Traffic Light ControlSystem with Emergency Vehicle Priority

Traffic light control systems are widely used to monitor and control vehicles’ flow to provide a smooth and orderly flow of traffic by controlling which lane should be granted to pass through the intersection point without compromising on safety standards. However, a conventional traffic light system handles neither dynamic traffic flow nor the prioritization of emergency vehicles (EVs). Consequently leads to congestion as the conventional traffic light grants underutilized road lanes to have the equal cycle length as a congested lane and possibly causes an accident and delay when multiple EVs are approaching the intersection. This paper proposes an intelligent system that dynamically adjusts the cycle length for each of the lanes at an intersection based on the vehicular density and grants the different types of EVs to pass through the intersection point by assigning different priorities. The system consists of three modules, the traffic light control module, the emergency Radio-frequency identification (RFID) moduleandtheinternetmodule. Thetrafficlightcontrolmoduledetectsvehiculardensityusing ultrasonicsensorsandassignsadynamicsetofcyclelengthbasedontheindividuallanedensity condition. TheemergencyRFIDsareinstalledon differenttypesofEVsbasedonthepreset priority weights. The internet module allows the dynamic traffic light system to be controlled by authorized personnel in a real-time application.

Adaptive Traffic Light Controller Based on Congestion Detection Using Computer Vision

The transportation sector plays an important role in realizing a smart city. The increase in the number of vehicles is currently not supported by an increase in road capacity. Traffic jams or congestion will occur in many places. Congestion will increase the accident rate, bad effect on economic growth, and increase gas emissions. Effective traffic management is necessary to reduce congestion levels and its side effects. A traffic light is one of traffic management methods. Traffic lights control the flow of traffic at road intersections, zebra crossings, and other traffic flow points. Conventional traffic lights work on a pre-programmed time sequence. This system is effective if the vehicle density is relatively constant. The density of vehicles from various directions fluctuates with time. To increase the effectiveness of using traffic light, an adaptive system is needed. In this study, a simple adaptive traffic light mechanism was developed based on congestion on the road using computer vision. Vehicle congestion is detected using the YOLOv3 object detection which detects the type of vehicle. The detection system used by YOLOv3 with pretrained weight COCO has a true positive value for motorbikes of 60%, cars (light vehicles) 93%, and trucks/buses (heavy vehicles) 100%. The processing speed of the Jetson Nano mini-PC with the OpenCV library on the GPU is 2 times faster than the process with the CPU.

Design and Implementation of an Emergency Traffic Light Pre-emption System Using PLC Automation

As traffic on the urban Ghanaian road network increases, congestion and delays are becoming more severe. Traffic signals are the most common method by which right of way is dynamically allocated to conflicting movements. Ghanaian traffic light control system is mainly designed by using devices such as timers, relays etc. The critical timing operation is required to be carried out under the existence of heavy traffic situations. This does not give this conventional traffic light control system the flexibility to control throughput to favor emergency vehicles. This conventional practice leads to many problems and subsequent delay for a long time. With the help of a PLC, the requirement of fast automation and effective optimization of traffic light control even in times of emergency can be achieved.At intersections, capacity bottlenecks are formed in urban road networks because at these locations, capacity must be shared by competing traffic movements. For this reason, this thesis introduces an execution and implementation of T-junction emergency traffic control system using SEIMENS S7-200 PLC. Programming in PLC is written in ladder logic with the help of STEP7 MICROWIN software.

Design and Implementation of Density-Based Traffic Management System

Traffic congestion and accidents caused by over speeding vehicles have been a major cause for concern in societies due to their negative effects such as stress to commuters, release of more toxic fumes into the atmosphere, accidents and loss of productive hours. The conventional traffic light uses a fixed logic of allocating the same “go time” to lanes at intersections without taking the density of traffic into consideration. This paper offers an approach for handling traffic congestion and over speeding offenders. This system uses infrared sensors as counters to measure the traffic density in each lane. The infrared sensors are also for speed detection with a bluetooth module and bluetooth serial monitor. These sensors are interfaced with the traffic light through a microcontroller. The microcontroller which was used to implement this is the Arduino Mega with the Atmega 2560 chip. The system was developed to alleviate traffic congestion and over-speeding offenders using a case study of an intersection in Uyo, Akwa Ibom State, Nigeria which usually has severe traffic congestion. This intersection consists of Abak Road, Udo Obio and Udo Eduok Streets. This system was tested and all objectives were accomplished. Results obtained from the implementation of the prototype design show that traffic control and speed detection using infrared sensors and Arduino Atmega 2560 give a better performance and the time taken to clear traffic at an intersection reduced significantly with 60% time saved.

Density Based Traffic System via IR Sensor

This paper presents an inventive prototype design of a traffic light which is purposely used at junctioned road since traffic light is the best way to supervise and manage traffic at any busy junction or intersection. This project is designed to develop a density based traffic signal system where its signal timing changes automatically after sensing the traffic density at the intersection. Traffic density is define as the amount of vehicle congested at a certain place, in this case an intersection. The more vehicles available, the higher the traffic density is. This increasing number of vehicles on the road will produce many problems such as traffic jammed thus will also drive to road accidents. Conventional traffic light uses fixed time which cause difficulties in controlling the traffic itself. In order to resolve this problem, design of this traffic light will help to reduce traffic jammed at a particular location. This traffic light uses an Arduino UNO microcontroller to create an automation function together with an Infrared sensor (IR sensor) to detect the density of the traffic. Each vehicle is set to 3 seconds. All vehicles pass through the traffic light is measured and processed accordingly to delays. If there is no vehicle on the road for at least 1 second, then it will change to another traffic light. By using this, all vehicles will be counted and interacted with the system thus increase delay period for the green light at each traffic light which may have high density of traffic. Thus, it will reduce problems which might rooted from traffic congestion at the intersection.

Augmenting Vehicle Localization by Cooperative Sensing of the Driving Environment: Insight on Data Association in Urban Traffic Scenarios

Precise vehicle positioning is a key element for the development of Cooperative Intelligent Transport Systems (C-ITS). In this context, we present a distributed processing technique to augment the performance of conventional Global Navigation Satellite Systems (GNSS) exploiting Vehicle-to-anything (V2X) communication systems. We propose a method, referred to as Implicit Cooperative Positioning with Data Association (ICP-DA), where the connected vehicles detect a set of passive features in the driving environment, solve the association task by pairing them with on-board sensor measurements and cooperatively localize the features to enhance the GNSS accuracy. We adopt a belief propagation algorithm to distribute the processing over the network, and solve both the data association and localization problems locally at vehicles. Numerical results on realistic traffic networks show that the ICP-DA method is able to significantly outperform the conventional GNSS. In particular, the analysis on a real urban road infrastructure highlights the robustness of the proposed method in real-life cases where the interactions among vehicles evolve over space and time according to traffic regulation mechanisms. Performances are investigated both in conventional traffic-light regulated scenarios and self-regulated environments (as representative of future automated driving scenarios) where vehicles autonomously cross the intersections taking gap-availability decisions for avoiding collisions. The analysis shows how the mutual coordination in platoons of vehicles eases the cooperation process and increases the positioning performance.

Crossroad control algorithm based on V2I-interaction

The importance of efficient traffic management problem grows every year. Constantly increasing number of vehicles and traffic volume requires improvement of methods and algorithms of traffic control. Such improvement becomes possible due to the spreading of global navigation and positioning systems, the development of wireless communication technologies and mobile internet, the advance in specialized technologies for interaction between vehicles and road infrastructure (V2I), the enhancement of machine learning models, computer vision algorithms, the emergence of driverless cars. This paper considers the novel approach to traffic management at intersection based on the use of V2I communication, describes general scheme of such approach and differences with conventional traffic light regulation. Developed algorithm of intersection management utilizes advantages of V2I communication to increase throughput of the intersection comparing to simple traffic light regulation and more advanced adaptive methods. The increase of throughput is achieved due to the dynamic construction of regulation phases which can be done because of more complete information about traffic flow. The comparence of various methods of regulation was performed in developed traffic simulation environment based on multi-agent approach.

IoT Green Corridor

IoT Green Corridor is a distributed sensor network system, a working prototype of which has been designed in-house and using low-cost hardware for economically-viable implementation. This paper outlines the development of the prototype and describes its details. This proposed system combines the existing conventional traffic signal lights with networked-sensors across length and breadth of connected and usually-congested city roads. Being connected to the internet, the sensors are better-enabled and capable of synchronizing with each other for sharing localized traffic information including the presence of emergency vehicles like ambulances. Having been equipped with the situational awareness data, the sensor network in an IoT framework is capable of collective decision to manage a given set of the traffic signal lights in tandem to achieve the singular aim of providing smooth passage to the arriving ambulances or in general to any Emergency Vehicle (EMV). Every traffic-signal junction is to be retrofitted with the Green Corridor system prototype, which is required to sense the presence of an Emergency Vehicle (EMV) within a specified distance in the field of view of the sensors. Based on a positive identification of such vehicles in distress, the traffic flow is designed to quickly enable the EMV to cross the junction smoothly in least time as possible. In the absence of a system of this kind, a manual intervention would have to be made by the concerned agency or valuable golden hour time to save lives would be spent in the traffic junctions. Besides, the prototype communicates with the inter-connected adjacent sensor-network elements through the cloud network to initiate similar action at each of the traffic signals enroute the desired destination of the emergency vehicle.

Design and implementation of a density-based traffic light control with surveillance system

Traffic congestion especially at road intersections is becoming an issue for which road traffic users contend with daily. The conventional traffic light applies a fixed logic of allocating equal “GO” time to lanes of traffic at road intersections irrespective of the density of traffic on each lane. Using the PIC18F4550 microcontroller interfaced with infrared sensors, a new traffic light control system was developed to ease the flow of traffic at a particular troubled spot in Uyo Metropolis (Abak Road, Udo Eduok, Udo Obio streets intersection) in Akwa Ibom State, Nigeria. Simulation of the proposed design was performed using the Proteus Software while implementation was carried out on a prototype. Performance evaluation of the prototype implemented showed that the 4 arrays (IR Transmitter and Receiver) of infrared sensors interfaced with the microcontroller using AND gates and strategically positioned to read the density of traffic on each lane at the intersection triggered when a vehicle comes between the transmitter and the receiver. Obtaining inputs from these sensors together with the logic from the microcontroller, a new traffic light control system was developed capable of controlling traffic based on the density of each lane of traffic. Results obtained from simulation and implementation of the design indicates that the traffic control system with the PIC18F4550 microcontroller and the infrared sensors gives a better performance compared to the conventional traffic light control system.Keywords: Road traffic density, Traffic light, Uyo metropolis, ‘+’ road intersection, Proteus

Design of a supervisory controller for Cooperative Intersection Control using Model Predictive Control

Abstract The Cooperative Intersection Control (CIC) methodology ensures a safe, smooth traffic flow through an automated intersection by means of virtual platooning. In this paper, we address the design of a centralised supervisory controller for CIC which optimises the crossing sequence of vehicles. We propose an approach in which the intersection as a whole is modelled as a hybrid system, which evolves in both continuous-time and in discrete-time. This hybrid system model resembles a queueing system, and relates the entry of vehicles into the intersection to a measure of the delay of their travel through the intersection. We design a supervisory controller using Model Predictive Control (MPC), which aims to minimise the vehicles’ average delay by controlling their access to the intersection. A simulation study based on real-life data demonstrates the effectiveness of the MPC approach compared to a first-come-first-served (FCFS) policy and a conventional traffic light controller. This study shows that MPC achieves a faster transient response and a lower average delay, thereby increasing the throughput of the intersection.