Smart Traffic Control System Research Articles

Exploring Explainable Artificial Intelligence Techniques for Interpretable Neural Networks in Traffic Sign Recognition Systems

Traffic Sign Recognition (TSR) plays a vital role in intelligent transportation systems (ITS) to improve road safety and optimize traffic management. While existing TSR models perform well in challenging scenarios, their lack of transparency and interpretability hinders reliability, trustworthiness, validation, and bias identification. To address this issue, we propose a Convolutional Neural Network (CNN)-based model for TSR and evaluate its performance on three benchmark datasets: German Traffic Sign Recognition Benchmark (GTSRB), Indian Traffic Sign Dataset (ITSD), and Belgian Traffic Sign Dataset (BTSD). The proposed model achieves an accuracy of 98.85% on GTSRB, 94.73% on ITSD, and 92.69% on BTSD, outperforming several state-of-the-art frameworks, such as VGG19, VGG16, ResNet50V2, MobileNetV2, DenseNet121, DenseNet201, NASNetMobile, and EfficientNet, while also providing faster training and response times. We further enhance our model by incorporating explainable AI (XAI) techniques, specifically, Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM), providing clear insights of the proposed model decision-making process. This integration allows the extension of our TSR model to various engineering domains, including autonomous vehicles, advanced driver assistance systems (ADAS), and smart traffic control systems. The practical implementation of our model ensures real-time, accurate recognition of traffic signs, thus optimizing traffic flow and minimizing accident risks.

Vehicle Counting System and Smart Traffic Light Control System

Improving traffic flow efficiency has become more important due to the growing number of vehicles in urban areas, and this requires optimizing traffic control systems. In order to reduce traffic, a smart traffic control system using infrared sharp sensors to count vehicles at intersections is proposed in this paper. The system uses sensors to measure distances to a fixed barrier, accurately counting vehicles while ignoring pedestrians. It was originally designed for four-way intersections but is adaptable to various junction types. Each lane’s unique count is controlled by a separate Arduino Nano Board, and the data is saved in microcontrollers. Following the aggregation and processing of these counts, an Arduino Mega Board modifies the traffic light timings in response to actual vehicle flow.

Advantage prioritization of digital carbon footprint awareness in optimized urban mobility using fuzzy Aczel Alsina based decision making

City governments prioritize mobility in urban planning and policy. Greater mobility in a city leads to happier citizens. Although enhanced urban mobility is helpful, it comes with costs, notably in terms of climate change. Transportation systems that enable urban mobility often emit greenhouse gases. Cities must prioritize digital carbon footprint awareness. Cities may reduce the environmental impact of urban mobility while keeping its benefits by close monitoring and reducing the carbon footprint of digital technologies like transportation applications, ride-sharing platforms, and smart traffic control systems. The aim is to advantage prioritize three alternatives, namely doing nothing, upgrading and optimizing data centers and networks, and using renewable energy sources for data centers and networks to minimize the digital carbon footprint using the proposed decision making tool. This study consists of two stages. In the first stage, fuzzy Aczel-Alsina functions (fuzzy Aczel-Alsina weighted assessment — ALWAS method) based Ordinal Priority Approach (OPA) is proposed to find the weights of criteria. Secondly, fuzzy ALWAS Combined Compromise Solution (CoCoSo) model is improved to evaluate and choose the best alternative among the three alternatives. The improved ALWAS-CoCoSo model enables flexible nonlinear processing of uncertain information and simulation of different risk levels. Besides, we proposed the improved fuzzy OPA algorithm for processing uncertain and incomplete information. The case study is provided to the decision-makers to advantage prioritize the alternatives based on twelve criteria organized into four aspects, including digital carbon footprint, externalities, technical capability, and economics. The ranking results reveal that A3=2.445 is the best among the three alternative, while A1=1.705 is the worst alternative. The results show that the best way to reduce the digital carbon footprint is to use renewable energy sources to power data centers and networks (A3).

Smart Traffic Control System by Using FPGA and RFID

Abstract: Traffic problems are not only including the traffic congestion because of large vehicle tightness and causes the difficulty for emergency vehicles to reach their destination, usually red light traffic violation causes accidents in the roads. Existing proposed systems for smart traffic monitoring of vehicles are usually depending on vehicle density traffic system by using Arduino. This causes difficulty for emergency vehicles. To solve the congestion of traffic problems at intersection roads, it is necessary to implement a smart and real time traffic system. These systems are implemented and limited to at least one or two focus areas. To overcome these problems, Smart Traffic Control System using FPGA (Field Programmable Gate Array) IR sensors and RFID (Radio frequency Identifier) is introduced to sophisticated traffic moves and provide the lane for the ambulances. The RFID uses in this system is determines the emergency vehicles from the long distance and this information passes and controlled by the FPGA and it control the main traffic control lights. Here these functionalities and simulations are achieved by using Verilog programming language and this is implemented on Xilinx 14.7 Software. The controlling and switching of the traffic lights states (Red, Yellow and Green) is found on the FSM (Finite State Machine). This system can be implemented by using Spartan 6 family. This implementation shows the results of identifying the emergency vehicles and Green signal is on till that emergency vehicles are passed.

Implementation on IoT Based Traffic Management System for Emergency Vehicles

Abstract: In many Indian cities along with those in other nations, congestion caused by traffic is a serious issue. Traffic congestion is caused by signal failure, ineffective law enforcement, and poor traffic administration. The economy, the environment, and general quality of life are all negatively impacted by traffic congestion. Therefore, it is imperative that the traffic congestion issue be managed efficiently. The proposed system's goal is to suggest a smart traffic control and management system that makes use of the Internet of Things, a decentralised strategy and algorithms to handle all traffic circumstances more precisely. The shortcomings of the existing traffic control systems will be fixed by the proposed system. To reduce traffic congestion, a forecast of upcoming traffic density will be made using an algorithm. The proposed method enables an emergency vehicle to travel directly to its location by turning all red lights on its route into green ones, thereby reducing traffic congestion. The system manages traffic signals and reduces wait times during emergencies. This makes it an endeavour that can save a life.

Smart Traffic Light Control System with Automatic Vehicle Speed Breaker

The aim of the project is to design a smart traffic light control system interfaced with a barrier gate and an LDR based traffic light control system. When the signal timing changes a delay is provided with the help of a microcontroller, pop up barrier before the zebra crossing will open or close to allow vehicles to pass. When the signal is red the interfaced barrier gate closes and a buzzer notifies the closing of gate, thereby blocking the traffic but when the signal is green the same barrier opens and allows a proper flow of vehicles to avoid traffic congestion. We provide a time delay for all signals to change and buzzer action by using ESP32 microcontroller. ESP32 microcontroller is used for signal timing change as well as street light control according to LDR. Smart Street light which operates with the help of sensors Microcontrollers are programmed in such a way to adjust their timing and phasing to meet changing traffic conditions. The circuit, besides being reliable and compact, is also cost effective. This project presents the way of conservation of energy by reducing electricity wastage as well as to reduce the man power. The project uses light emitting diodes (LED) that do not consume an enormous amount of electricity. LDR is used as a sensor for the functioning of street lights effectively.

Cyber-Physical System for Smart Traffic Light Control.

In recent years, researchers have proposed smart traffic light control systems to improve traffic flow at intersections, but there is less focus on reducing vehicle and pedestrian delays simultaneously. This research proposes a cyber-physical system for smart traffic light control utilizing traffic detection cameras, machine learning algorithms, and a ladder logic program. The proposed method employs a dynamic traffic interval technique that categorizes traffic into low, medium, high, and very high volumes. It adjusts traffic light intervals based on real-time traffic data, including pedestrian and vehicle information. Machine learning algorithms, including convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM), are demonstrated to predict traffic conditions and traffic light timings. To validate the proposed method, the Simulation of Urban Mobility (SUMO) platform was used to simulate the real-world intersection working. The simulation result indicates the dynamic traffic interval technique is more efficient and showcases a 12% to 27% reduction in the waiting time of vehicles and a 9% to 23% reduction in the waiting time of pedestrians at an intersection when compared to the fixed time and semi-dynamic traffic light control methods.

RFID BASED TRAFFIC CONTROL SYSTEM FOR EMERGENCY VEHICLE

In developing nations like India, the development of the population and the number of cars has increased traffic congestion and the number of traffic accidents. However, by identifying crowded locations and enhancing traffic flow, a smart traffic control system that makes use of RFID sensors has the ability to alleviate these problems. By giving emergency vehicles precedence, this approach also makes it possible for them to get to their destination quickly. Drivers can also gain from the real-time data offered by RFID sensors, which can help decrease idling and save time. This data can be used to recommend other routes to drivers so they can avoid crowded regions and cut down on the amount of time they spend stuck in traffic. In addition to enhancing traffic flow, this also aids in lowering fuel consumption and emissions, creating a cleaner and greener environment. To manage the growing traffic flow and lower traffic accidents, emerging nations like India can use a smart traffic control system using RFID sensors. Overall, an RFID-based smart traffic control system has the potential to significantly improve the environment, the economics, and the welfare of road users in emerging countries like India.

Density based smart traffic control system using canny edge detection algorithm along with object detection

It is urgently necessary to combine current advancements to work on the cutting edge inrush hour jam the executives, as urban congestion is one of the world’s biggest concerns. Existing methodologies, for example, traffic police and traffic lights are neither fulfilling nor viable. Consequently, a traffic management system that utilizes sophisticated edge detection and digital image processing to measure vehicle density in real time is developed in this setting. Computerizedimage processing should be used to detect edges. To extract significant traffic data from CCTV images, the edge recognition method is required. The astute edge finder outperforms other processes in terms of accuracy, entropy, PSNR (peak signal to noise ratio), MSE (mean square error), and execution time. There are a number of possible edge recognition calculations. In terms of reaction time, vehicle the board, mechanization, dependability, and overall productivity, this framework performs significantly better than previous models. Utilizing a few model images of various traffic scenarios, appropriate schematics are also provided for a comprehensive approach that includes image collection, edge distinguishing evidence, and green sign classification. Also recommended is a system with object identification and priority for ambulances stuck in traffic.

A Smart Traffic Control System Based on Pixel-Labeling and SORT Tracker

A Smart Traffic Control System Based on Pixel-Labeling and SORT Tracker

SMART TRAFFIC CONTROL SYSTEM USING WIRELESS COMMUNICATION

In this project smart traffic control system using wireless communication is implemented. By using Arduino microcontroller entire system is controlled. When an emergency vehicle approaches the crossing, the RFID Reader reads the information and this information is passed to the poles of nearest junction. When the sensing device nearest to the traffic light finally receives the signal from its preceding pole, the traffic light changes to green. In the same way when green light is on then buzzer also give indication. This facilitates the smooth and quick passage of the emergency vehicle.

Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network

Traffic congestion is a worsening problem owing to an increase in traffic volume. Traffic congestion increases the driving time and wastes fuel, generating large amounts of fumes and accelerating environmental pollution. Therefore, traffic congestion is an important problem that needs to be addressed. Smart transportation systems manage various traffic problems by utilizing the infrastructure and networks available in smart cities. The traffic signal control system used in smart transportation analyzes and controls traffic flow in real time. Thus, traffic congestion can be effectively alleviated. We conducted preliminary experiments to analyze the effects of throughput, queue length, and waiting time on the system performance according to the signal allocation techniques. Based on the results of the preliminary experiment, the standard deviation of the queue length is interpreted as an important factor in an order allocation technique. A smart traffic signal control system using a deep Q-network, which is a type of reinforcement learning, is proposed. The proposed algorithm determines the optimal order of a green signal. The goal of the proposed algorithm is to maximize the throughput and efficiently distribute the signals by considering the throughput and standard deviation of the queue length as reward parameters.

The City of Zagreb Lower Town Urban mobility development program

Common development issues in historic city centers are dense urban blocks (with buildings that do not follow modern construction standards), narrow traffic corridors, and low mobility of pedestrians and cyclists. Modern solutions, like smart traffic control systems, parking restrictions, the introduction of speed limitations and dynamic yellow lanes, increasing control of existing yellow lanes, and the establishment of multimodal solutions, are introduced to eliminate traffic problems in these areas. When it comes to the abovementioned development issues, the historic Lower Town area in Croatia’s capital Zagreb, with residential and commercial buildings and infrastructure dating from the beginning of the 20th century, is no exception. Following a very strong earthquake that hit in March 2020, severely damaging the structures in Zagreb’s historical center, a program for the complete reconstruction of this area is being developed, which will contain the Urban mobility development program. This paper presents the process and the results of Zagreb City’s Lower Town urban transport reprogramming performed by the University of Zagreb, Faculty of Civil Engineering. The main premise of the Zagreb historic center reconstruction program is that the quality of life in the area will be improved through the regeneration of neglected and unorganized city blocks, as well as the planning and construction of new public spaces. The aspiration is the transformation of Lower Town into the "15-minute city". The prerequisite for this is (1) improved mobility and (2) reshaped transportation system, with the emphasis on cost-effective technical solutions and the integration of zero-emission modes of transport, and green mobility. At the same time, it will be necessary to preserve buildings and infrastructure that are part of the identity of the City and to prevent the transformation of the reconstruction process into a process of gentrification.

Design and Implementation of an Unreal Engine 4-Based Smart Traffic Control System for Smart City Applications

Traffic congestion is a serious problem nowadays, especially in Dhaka city. With the increasing population and automation, it has become one of the most critical issues in our country. There can be a lot of causes of congestion in traffic, such as insufficient capacity, large red signal delay, unrestrained demand, etc, which causes extra time delay, extra fuel consump-tion, a speed reduction of vehicle, and financial loss. The traffic control system is one of the most important factors affecting traffic flow. Poor traffic management around these hotspots could result in prolonged traffic jams. Small critical locations that are frequent hotspots for congestion are a common byproduct of poorly constructed road networks in many developing countries. In this research, we first offer a straightforward automated image processing method for analyzing CCTV camera image feeds to determine the level of traffic congestion. Our system’s design seeks to use real-time photos from the cams at traffic intersections to calculate traffic density using image processing, and to adjust the traffic signal on the data obtained based on the current traffic congestion on the road. We suggest tailoring our system to erratic traffic feeds with poor visual quality. Using live Surveillance camera feeds from multiple traffic signals in Dhaka city, we demonstrate evidence of this bottleneck breakdown tendency persisting over prolonged time frames across multiple locations. To partially address this problem, we offer a local adapting algorithm that coordinates signal timing behavior in a restricted area and can locally minimize congestion collapse by maintaining time-variant traffic surges. Using simulation-based research on basic network topologies, we show how our local decongestion protocol may boost the capacity of the road and avoid traffic collapse in limited scenarios.

Design and implement a smart traffic light controlled by internet of things

The rise of the population produces an increase in the number of vehicles on the road, which creates heavy traffic in the roads and that causes many issues for the citizens and traffic cops an extra two emergency instances so it’s necessary with developing technology to solve this problem. in this research, we used the Arduino UNO microcontroller board to build a new smart traffic light controller (STLC). Signal lights produce traffic congestion, and the system makes every attempt to alleviate it. In this paper, we designed a smart traffic control system by using Arduino to solve the problem of congestion at the intersection of the Dor al Moalemen in Wasit city , working to prevent traffic jam and reduce time, Using Arduino mega, ultrasonic sensor, and a camera esp32, the suggested technique analyses and manages everyday traffic at a three-line intersection. Furthermore, the suggested system achieves three-line intersection sync and implements a balance between the number of vehicles on each side and the green light. when Traffic violation the camera will capture the car number and send it to the database by using telegram.

Implementing smart traffic light control system using FPGAs: retrofit proposal

Implementing smart traffic light control system using FPGAs: retrofit proposal

Priority Based Smart Traffic Control System For Emergency Vehicle

Major Problem That Is Being Faced By Everyone Day To Day Is TrafficCongestion. Traffic Congestion May Occur Due To Many Reasons Few Being PoorInfrastructure, Uneven Distribution Of Development. In Developing Countries, The TrafficLights Use Fixed Time Concepts Where The Duration Of Lights Are Independent Of TheTraffic Density. This Yields Poor Traffic Control And Severe Traffic Congestions. NowadaysThe Traffic Control System Is Quite Inefficient. There Are Many Situations Where TheTraffic In Different Lanes Varies Due To The Timer-Based Signalling The Less Dense LaneAlso Need To Wait For The Set Time. The Main Aim Of This Work Is To First Detect ThePresence Of Any Emergency Vehicle Along With Estimating The Number Of Vehicles InEach Lane. Based On A Smart Traffic Control System, Priority Is Given To The Lane WithEmergency And Restoring The Mode To Normal And Then Prioritizing The Lane WithHigher Density.

Development of a Smart Traffic Light Control System Using IoT Technology

The development of a smart traffic light control system using IoT technology has become an essential topic in urban traffic management. This research paper focuses on the development of an IoT-based real-time traffic monitoring system for city governance. The system integrates traffic data from various sources, including cameras, sensors, and other data sources, to provide real-time traffic updates to city officials and drivers. The proposed system utilizes IoT technology to collect data from various traffic sources and processes it to determine the optimal time for traffic lights to switch between green, yellow, and red. The system uses a combination of machine learning algorithms and traffic models to predict traffic flow patterns and optimize traffic light timings. Additionally, the system can identify potential traffic accidents and send alerts to nearby emergency services. The proposed system is evaluated using simulation techniques and has shown a significant improvement in reducing traffic congestion and improving traffic flow in a simulated urban environment. Furthermore, the system provides real-time data analytics to city officials, allowing them to make informed decisions on traffic management and city planning. Overall, the development of a smart traffic light control system using IoT technology has significant potential in improving traffic management in urban areas. The proposed system can reduce traffic congestion, improve traffic flow, and enhance overall traffic safety. Therefore, it is essential to continue research and development in this area to implement the proposed system on a larger scale.

Smart traffic light control system using image processing

Traffic congestion has become a serious issue due to the growing number of vehicles in Malaysia. Traffic light control system is widely used to control the flow of road junction. Currently, most of the traffic light system used pre-time and count down timers to control traffic flow. Due to the fixed-time setting, often the system unable to handle unexpected heavy traffic flows and cause traffic jam. Thus, there is a need of adaptive traffic signals which are able to do real time monitoring to control traffic light signal based on traffic density. This study proposed an adaptive traffic light control system that uses image processing and image matching technique in controlling the traffic in an effective manner by taking images of each lane at a junction. The density of traffic in the images at each junction are compared. Results show that more time are allocated for the vehicles on the densest road to pass compared to other less dense road. Edge operation detector is used to detect the density of traffic at each lane. In this study, a comparison study was carried out by applying five different edge detectors namely Roberts, Sobel, Log, Canny and active contour. Among these detectors, Canny edge operation detector has found to be the best as it could extract actual edges with average time of 0.453 second.

Influence of following vehicle’s tailway and classification on subject driver’s response to the circular yellow indication

Traffic crashes at signalized intersections are frequently linked to driver behavior at the onset of the circular yellow (CY) indication. To better understand behavioral factors that influence a driver’s decision to stop or go at an intersection, this study analyzed the behavior of the driver of a subject vehicle at the onset of the CY indication. Driver performance data from 53 participants were collected in the Oregon State University Driving Simulator, simulating scenarios of driving through high-speed intersections under various conditions. Data included interactions where the driver stopped at the stop line (n = 644) or proceeded through the intersection (n = 628) in response to a CY indication. Data were analyzed as panel data while considering 12 indicator variables related to the driver’s stop/go decision. These indicator variables included time to stop line (TTSL), tailway time, following vehicle type, vehicle speed at the onset of the CY indication, and demographics (age, gender, driving experience, level of education, personal vehicle type, number of times driving per week, number of miles driving last year, participation in previous simulation studies. A random-parameter binary logit model was used to determine contributing factors for driver decision making at the onset of CY indication while accounting for unobserved heterogeneity. Four indicator variables were significantly related to the driver’s stop/go decision, but three factors varied across observations. Findings showed that a driver’s stop/go decision in response to a CY indication was associated with the time to the stop line (TTSL), tailway time to the following vehicle, subject vehicle speed at the onset of the CY indication, and driver’s age (20–36 years), but was not significantly associated with classification of the following vehicle. Also, the findings indicated that a shorter tailway increased a subject driver’s red-light running frequency. These findings provide insights into variables that affect driver decisions in a vehicle-following situation at the onset of the CY indication. This information can help make better decisions in smart traffic control systems such as to extend/decrease the green interval slightly to avoid decisions that are more difficult.