Traffic Light Control Research Articles

Traffic Prediction for Intelligent Transportation Systems using Machine Learning

Over the past few decades, ITS have spiked an increasing research interest as a promising discipline for revolutionizing the transportation sector and solving common traffic and vehicle-related problems. ITS comprise a multitude of interconnected engineering feats that function as an entity for optimizing network-scale travel experiences from a technical, social, economic, and environmental aspect. Such optimizations necessitate the advancement of information and communication technologies, electronic sensors, control systems, and computers, which high-lights the data-driven nature of modern ITS. In this paper we design a system which uses machine learning algorithm using SVM, KNN and CNN algorithm which is a novel system which will provide intelligence to the current traffic control system present at a four-way junction. This ML technique is mainly aimed to replace the existing traffic light control system with artificial intelligence system. Nowadays most cities are equipped with CCTV cameras on the roads and the junctions, the basic idea is to collect the live video from the CCTV cameras and detect the number of vehicles on each lane and feed the data into another machine learning algorithm. according to the data of each lane changes into the light phase of the green signal. This system mainly aims to increase the traffic efficiency by increasing vehicle flow which will reduce waiting time for the vehicles. We are using HOG algorithm for feature extraction. In the implementation of the proposed architecture, we have achieved an accuracy of 86.34% for binary classification and 90.23% for multi-class classification

IoT Based Smart Ambulance Monitoring System with Traffic Light Control

Smart cities essentially require the state-of-the-art technologies that can provide smart service in various aspects, and robotic systems are one of the key solutions for such requirements. Time is a critical issue when dealing with people who experience a sudden cardiac arrest that unfortunately could Die due to inaccessibility of the emergency treatment. Therefore, an immediate treatment using automated external defibrillator (AED) must be administered to the victim within a few minutes after collapsing. Hence, we have designed and developed the ambulance robot, which brings along an AED in a sudden event of cardiac arrest and facilitates various modes of operation from manual to autonomous functioning to save someone’s lives in smart cities. The details of the design and development of such robot are presented in this paper.

Adaptive neuro-fuzzy enabled multi-mode traffic light control system for urban transport network

Adaptive neuro-fuzzy enabled multi-mode traffic light control system for urban transport network

Multi-Agent Deep Reinforcement Learning to Manage Connected Autonomous Vehicles at Tomorrow's Intersections

In recent years, the growing development of Connected Autonomous Vehicles (CAV), Intelligent Transport Systems (ITS), and 5G communication networks have led to the advent of Autonomous Intersection Management (AIM) systems. AIMs present a new paradigm for CAV control in future cities, taking control of CAVs in scenarios where cooperation is necessary and allowing safe and efficient traffic flows, eliminating traffic signals. So far, the development of AIM algorithms has been based on basic control algorithms, without the ability to adapt or keep learning new situations. To solve this, in this paper we present a new advanced AIM approach based on end-to-end Multi-Agent Deep Reinforcement Learning (MADRL) and trained using Curriculum through <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Self-Play</i> , called advanced Reinforced AIM ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adv.</i> RAIM). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adv.</i> RAIM enables the control of CAVs at intersections in a collaborative way, autonomously learning complex real-life traffic dynamics. In addition, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adv</i> .RAIM provides a new way to build smarter AIMs capable of proactively controlling CAVs in other highly complex scenarios. Results show remarkable improvements when compared to traffic light control techniques (reducing travel time by 59% or reducing time lost due to congestion by 95%), as well as outperforming other recently proposed AIMs (reducing waiting time by 56%), highlighting the advantages of using MADRL.

Design of VLSI Based Power-Saving Streetlight System and Automatic Traffic Controller with Automatic Transfer Switch

The number of road accidents is increasing due to the power outage of the traffic light system and the streetlight system's maintenance fees was too high. Therefore, a proposed solution that includes automatic traffic light controller, energy-saving streetlight controller and automatic energy transfer switch had been designed to overcome these issues. The proposed traffic light is on a four-way junction which has one sensor for each direction. This traffic light controller has 3 different conditions which will act differently when all directions had detected vehicle, or when vehicle is not detected, or when vehicle is detected in some directions. Apart from this, a streetlight controller is also designed to minimize the power usage and the streetlight will only turn on when vehicle is detected. The proposed solution was designed in Quartus 18 via Verilog Hardware Description Language (HDL) and functional simulation is used to verify the proposed circuit is functioning correctly The Verilog HDL files were input to the Synopsys Design Compiler for logical synthesis and IC Compiler for physical synthesis. After both logical and physical synthesis, the design was then analysed in terms of speed, area, and power consumption through the generate report commands. Besides, the proposed energy-saving streetlight was programmed into Field Programmable Gate Array (FPGA) and connected with AC Dimmer Module and LED bulb to measure the power consumption and the reduction of power consumption of the LED bulb. As for the result, the designed chip had a total area of 392.002199 μm^2, positive critical timing slack of 0.1 ns and power consumption of 0.1196 mW. The streetlight was able to achieve a reduction in power consumption by 77.87%.

Arduino Based Congestion Control System for Ambulance

Abstract: Congestion problem is a fact which contributed large impact on the transport system in the country. because many problems especially when there are emergency cases at traffic light junctions which are always busy with so many vehicles. A congestion control system is designed in order to solve all these problems. This system was designed in such a way that it should be activated when it receives a signal from an ambulance based on radio frequency (RF) transmission and uses a microcontroller to change the sequence back to the normal sequence before the emergency mode is activated. This system will lessen what often happens at the traffic light intersections because other vehicles have to gather together for giving a special route to an emergency vehicle. As a result, this project is successful and the implementation of wireless communication i.e. the radio frequency (RF) transmission in the traffic light control system for emergency vehicles like an ambulance is made possible. The main aim of this project is to design a system that is capable to identify an emergency situation in an ambulance and automatically controls the traffic at the signal. In the future, this system can be improved by controlling real traffic congestion, in fact improving the present traffic light system technology. The existing traffic control system has a lot of limitations because of its fixed time method used. The traffic signal will not alter based on the real-time traffic on road near the junction of two or more roads. Due to this, the traffic congestion cannot be controlled efficiently and the utilization of roads cannot be done to its maximum limit.

Intelligent Traffic Light Control System with Priority Lane Intervention

The current traffic light system uses a lot of fixed time signals. Fixed time signals cause traffic jams in one or several lanes due to traffic congestion. On the other hand, emergency vehicles cannot get their rights to the maximum due to the density of these vehicles. This study aims to build an intelligent traffic light with a vehicle density sensor device and a smartphone application to increase the accuracy of light timing and parse traffic density. Furthermore, in an emergency, they can still overcome and accelerate the speed of passing vehicles by manually intervening with the traffic light through a smartphone application connected wirelessly to the traffic light. The intelligent traffic light is made with Arduino Bluetooth Control (ABC), Bluetooth HC-05, Arduino Mega 2560, and infrared (IR) sensor E18-D80NK. The results show that the green light setting is based on the input from the IR sensor both near sensor (NS) and far sensor (FS) work well. When NS detects a vehicle with a green light, it increases Y seconds from the default X seconds. If FS detects a vehicle with a green light, it increases by Z seconds. Settings with the ABC application can randomly turn on the green light in four lanes according to the will of the smartphone operator.

Real-world sensor dataset for city inbound-outbound critical intersection analysis

This paper reports the release of dataset that describes the critical city inbound-outbound intersection in the central business district’s Sathorn area of Bangkok, Thailand. The available traffic volume and occupancy are captured by sensors located on the Sathorn area’s links. Induction loop coil sensors and thermal and CCTV cameras have been installed at the approaching links of the critical Sathorn-Surasak intersection. Traffic volume data have been collected from the CCTV cameras every 5 s from 2016 to 2019, and occupancy and volume data have been collected from the loop coil sensors every 5 s from May to September 2016 during a social experiment that was part of Sathorn Model project. Occupancy and volume data have also been collected from the thermal cameras every 5 s from May to June 2016. The dataset provides temporal and spatial coverage of Sathorn Road’s primary urban areas, including weekdays, weekends, and public holidays. This dataset can be resources for research on traffic state estimation, traffic light control optimization, and the analysis of critical intersections.

IoT BASED ENERGY EFFICIENCY and FAULT DETECTION SMART STREET LIGHT USING GSM

-Automation plays an increasingly very important role in the world economy and in daily life. Automatic systems are being preferred over any kind of manual system. We can also call it an “SMART STREET LIGHT SYSTEM”. Intelligent light sensing refers to public street lighting and traffic control that adapts to movement by pedestrians, cyclists and cars. Intelligent street lighting and traffic control, also referred to as adaptive street lighting, dims when no activity is detected, but brightens when movement is detected. This type of lighting is different from traditional, stationery and illumination, or dimmable street lighting that dims at predetermined times. The proposed work shows automatic control of streetlights and traffic light control as a result of which power is saved and can control traffic cognition to some extent. In the scope of industrialization, automation is a step beyond mechanization. Whereas mechanization provided human operators with machinery to assist the users with muscular requirements of work, automation greatly decreases the need for human sensory and mental requirements as well. Basically, street lighting is one of the important parts. Therefore, the street lamps are relatively simple but with the development of urbanization, the number of streets increases rapidly with high traffic density. Key Words: Arduino UNO, GSM, Power supply, LED, IR Sensor, LDR Sensor.

STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control

The development of intelligent traffic light control systems is essential for smart transportation management. While some efforts have been made to optimize the use of individual traffic lights in an isolated way, related studies have largely ignored the fact that the use of multi-intersection traffic lights is spatially influenced, as well as the temporal dependency of historical traffic status for current traffic light control. To that end, in this article, we propose a novel Spatio-Temporal Multi-Agent Reinforcement Learning (STMARL) framework for effectively capturing the spatio-temporal dependency of multiple related traffic lights and control these traffic lights in a coordinating way. Specifically, we first construct the traffic light adjacency graph based on the spatial structure among traffic lights. Then, historical traffic records will be integrated with current traffic status via Recurrent Neural Network structure. Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method. Finally, the experimental results on both synthetic and real-world data have demonstrated the effectiveness of our STMARL framework, which also provides an insightful understanding of the influence mechanism among multi-intersection traffic lights.

Intelligent Traffic Light Control by Exploring Strategies in an Optimised Space of Deep Q-Learning

Intelligent traffic light control is one of the modern approaches to solve traffic congestion, where reinforcement learning is a widely used method. Conventionally, reinforcement learning is used to determine whether to change the current phase (or choose a traffic phase) after each small interval. One major drawback of these approaches is that it makes the current traffic light phase duration uncertain before the current phase terminates. Directly determining the duration of the traffic light phase can effectively avoid this shortcoming. An adaptive traffic light timing system is proposed in this paper which can directly control the phase duration. In the proposed system, the Q-learning algorithm is employed and the action space is defined as all possible phase durations. In addition, the reward function is redefined to guide the agent to balance more traffic metrics, and the state is redefined to reduce the state space. Finally, the proposed system is evaluated by equal, unequal, and complex traffic scenarios. Results show that the proposed system has a better performance compared with other methods in controlling traffic lights, even on complex traffic scenarios.

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.

Integrated tripartite modules for intelligent traffic light system

&lt;span&gt;The traffic in urban areas is primarily controlled by traffic lights, contributing to the excessive, if not properly installed, long waiting times for vehicles. The condition is compounded by the increasing number of road accidents involving pedestrians in cities across the world. Thus, this work presents an integrated tripartite module for an intelligent traffic light system. This system has enough ingredients for success that can solve the above challenges. The proposed system has three modules: the intelligent visual monitoring module, intelligent traffic light control module, and the intelligent recommendation module for emergency vehicles. The monitor module is a visual module capable of identifying the conditions of traffic in the streets. The intelligent traffic light control module configures many intersections in a city to improve the flow of vehicles. Finally, the intelligent recommendation module for emergency vehicles offers an optimal path for emergency vehicles. The evaluation of the proposed system has been carried out in Al-Sader city/Bagdad/Iraq. The intelligent recommendation module for the emergency vehicles module shows that the optimization rate average for the optimal path was in range 67.13% to 92%, where the intelligent traffic light control module shows that the optimization ratio was in range 86% to 91.8%.&lt;/span&gt;

СРАВНИТЕЛЬНАЯ ОЦЕНКА ЭФФЕКТИВНОСТИ АЛГОРИТМОВ МАШИННОГО ОБУЧЕНИЯ С ПОДКРЕПЛЕНИЕМ И ОПТИМИЗАЦИИ ПРИ УПРАВЛЕНИИ ТРАНСПОРТНЫМИ ПРОЦЕССАМИ В СРЕДЕ ANYLOGIC

The task of increasing the throughput of sections of the transport network of the metropolis with the existing infrastructure is solved by means of automated traffic control systems, the purpose of which is to form control actions on the objects of the transport system in real time. An adequate response to load changes in the transport network is implemented by controlling traffic light objects using built-in adaptive algorithms, among which artificial intelligence technologies, in particular, neural networks, are increasingly being used. The noted approaches compete with the widely used optimization-based adaptation method (phase duration, displacement, etc.). The proposed article examines the issue of comparing the efficiency of an optimization algorithm with reinforcement machine learning algorithms by the criterion of the time spent by vehicles in the intersection system in the Anylogic simulation environment. This study will help determine a more efficient solution to the problem of setting the duration of traffic light control phases. It was shown that in the reinforcement learning algorithm, the ability to adapt to input intensities within the schedule is higher compared to the optimization algorithm. However, the reinforcement algorithm is more sensitive to the type of schedule than the optimization algorithm, which outperformed the latter by about 20% at the optimal point for the weekday schedule. The advantage of the reinforcement algorithm was more pronounced on the 2nd schedule, which features a tendency to increase traffic intensity, namely: 61% compared to the optimization and 70% compared to the base setting. It turned out to be practically insensitive to the “detuning” of the input data relative to the optimal policy when changing the intensity levels within this schedule. Thus, it was shown that the results of the regulation of the traffic process at the studied real intersection, obtained by modeling using reinforcement learning, are superior to the optimization approach, but are sensitive to the given intensity schedule.

DESAIN SISTEM KONTROL PENANGGULANGAN KEMACETAN LALU LINTAS ADAPTIF BERBASIS PLC

Traffic lights play an important role at every intersection in minimizing congestion, especially at intersections three and four. Today, the most common setting for traffic lights involves setting a fixed time. This pattern applies throughout the day, although the density of vehicles at each crossing is different. As a result, traffic jams on one side of the intersection are often unavoidable. Based on these problems, in this study an adaptive traffic light control system based on Programmable Logic Controller was built. In this test, the amber and green lights are set to about 3 and 5 seconds, respectively. During crowded situations, the duration of the green light is reduced to 10 seconds. It has been determined through testing that when the traffic light sensor detects a traffic jam, the green light will be on for 10 seconds, while under normal conditions it will be on for 5 seconds.

Traffic Incident Detection Based on mmWave Radar and Improvement Using Fusion with Camera

Improving traffic efficiency and safety is the goal of all countries due to the increasingly congested road environment worldwide. The progress of intelligence has promoted the development of the transportation industry. As the first step to intelligence, perception technology is an important part to realize intelligent transportation. Accurate and efficient traffic management systems, such as the automatic control of traffic lights at urban intersections or highway emergency disposal, need the support of advanced environmental sensing technology. In the application of traffic perception, millimeter wave radar and camera are two important sensors. Radar has been widely used in traffic incident perception due to its all-weather working capability; however, there are problems such as inability to detect stationary targets and poor target classification performance. Camera has the advantages of accurate target angle information measurement and rich details, but there are problems of inaccurate ranging and speed measurement and performance degradation in harsh weather conditions. Considering the complementary characteristics of the two sensors in information, an improved incident detection method based on radar-camera fusion is proposed. This method combines the advantages of millimeter wave radar and camera and improves the robustness of the traffic incident detection system. The detection performance is verified in the real experiment. The results show that the detection accuracy of the proposed fusion system is better than that of a single millimeter wave radar in all scenarios, and the accuracy is improved by more than 50% in some cases.

Biased Pressure: Cyclic Reinforcement Learning Model for Intelligent Traffic Signal Control.

Existing inefficient traffic signal plans are causing traffic congestions in many urban areas. In recent years, many deep reinforcement learning (RL) methods have been proposed to control traffic signals in real-time by interacting with the environment. However, most of existing state-of-the-art RL methods use complex state definition and reward functions and/or neglect the real-world constraints such as cyclic phase order and minimum/maximum duration for each traffic phase. These issues make existing methods infeasible to implement for real-world applications. In this paper, we propose an RL-based multi-intersection traffic light control model with a simple yet effective combination of state, reward, and action definitions. The proposed model uses a novel pressure method called Biased Pressure (BP). We use a state-of-the-art advantage actor-critic learning mechanism in our model. Due to the decentralized nature of our state, reward, and action definitions, we achieve a scalable model. The performance of the proposed method is compared with related methods using both synthetic and real-world datasets. Experimental results show that our method outperforms the existing cyclic phase control methods with a significant margin in terms of throughput and average travel time. Moreover, we conduct ablation studies to justify the superiority of the BP method over the existing pressure methods.

Self-perceived disease control in childhood eczema

Background Atopic eczema (AE) is a common relapsing inflammatory skin disease in children which is often associated with chronicity and poor quality of life. Unlike atopic asthma, control of AE is seldom assessed in therapeutics. Aim To investigate the utility of a Traffic Light Control (TLC) system as a measurement/assessment of self-perceived eczema control. Methods This is a prospectively study of all Chinese children (aged 6–18 years old) with eczema attending the pediatric dermatology clinic of a tertiary hospital from January to June 2020. Eczema control, eczema severity, quality of life and biophysical skin condition of consecutive patients at the pediatric dermatology clinic of a teaching hospital were evaluated with the validated Chinese versions of Depressive, Anxiety, Stress Scales (DASS-21), Patient Oriented Eczema Measure (POEM), transepidermal water loss (TEWL), and stratum corneum skin hydration (SH), respectively. With a visual TLC analogy, patients were asked if their eczema is under control (green light), worsening (yellow) or in flare-up (red light). Results Among AE patients (n = 36), self-perceived TLC as green (under control), amber (worsening) and red (flare up) reflected acute and chronic severity (SCORAD, NESS, POEM) and quality of life (CDLQI) (p < .0001), but not SH, TEWL or Depression, anxiety and stress. Conclusions Eczema control can be semi-quantified with a child-friendly TLC self-assessment system. AE patients reporting worse eczema control have worse acute and chronic eczema severity, more impairment of quality of life; but not the psychologic symptoms of depression, anxiety and stress or skin hydration or transepidermal water loss. TLC can be linked to an eczema action plan to guide patient management.

Traffic Behavior Recognition from Traffic Videos under Occlusion Condition: A Kalman Filter Approach

Real-time traffic data at intersections is significant for development of adaptive traffic light control systems. Sensors such as infrared radiation and GPS are not capable of providing detailed traffic information. Compared with these sensors, surveillance cameras have the potential to provide real scenes for traffic analysis. In this research, a You Only Look Once (YOLO)-based algorithm is employed to detect and track vehicles from traffic videos, and a predefined road mask is used to determine traffic flow and turning events in different roads. A Kalman filter is used to estimate and predict vehicle speed and location under the condition of background occlusion. The result shows that the proposed algorithm can identify traffic flow and turning events at a root mean square error (RMSE) of 10. The result shows that a Kalman filter with an intersection of union (IOU)-based tracker performs well at the condition of background occlusion. Also, the proposed algorithm can detect and track vehicles at different optical conditions. Bad weather and night-time will influence the detecting and tracking process in areas far from traffic cameras. The traffic flow extracted from traffic videos contains road information, so it can not only help with single intersection control, but also provides information for a road network. The temporal characteristic of observed traffic flow gives the potential to predict traffic flow based on detected traffic flow, which will make the traffic light control more efficient.

Deep Reinforcement Learning-Based Traffic Light Scheduling Framework for SDN-Enabled Smart Transportation System

This work proposes a traffic-light scheduling framework using the deep reinforcement learning technique to balance the traffic flow and to prevent congestion in the dense regions of the city via a software-defined control interface. A software-defined control enabled architecture is proposed to monitor the traffic conditions and it generates the traffic light control signal (Red/Yellow/Green) accordingly. For an intelligent traffic light control signal, a Deep Reinforcement Learning (DRL) model is proposed which takes vehicular dynamics as inputs from the real-time traffic environment such as heterogeneous vehicles count, speed, traffic density etc. To determine the congestion, a threshold policy is proposed and deployed on control server which generates the congestion prevention signal. A DRL agent operates in the coordination of congestion prevention signal and generates an effective traffic light control signal. The proposed model is evaluated through a realistic simulation on Indian city OpenStreetMap by using a well-known open-source simulator (SUMO). The comparative results show that the proposed solution improves several performance metrics such as average waiting time, throughput, average queue length, and average speed in the interval of 28.34% – 66.62%, 24.76% – 66.60%, 30.89% – 69.80%, and 16.62% – 43.67% respectively over other states of the art approaches.