Intelligent Traffic Control System Research Articles

A survey on reinforcement learning-based control for signalized intersections with connected automated vehicles

ABSTRACT Recent advancements in connected automated vehicles (CAVs) and reinforcement learning (RL) hold significant promise for enhancing intelligent traffic control systems. This paper conducts a systematic review of studies on RL-based urban traffic control at signalised intersections, highlighting the significant impact of CAVs on traffic control performance improvement. We first review the fundamental concepts of RL algorithms, establishing a foundational understanding for subsequent RL-based traffic control methods. We then review recent progress in RL-based traffic signal control using CV/CAV trajectory data, RL-based CAV trajectory planning, and the cooperative control of both traffic signals and CAVs at signalised intersections. Our aim is to provide researchers with a comprehensive roadmap for future research in RL-based traffic control at signalised intersections.

Taking Shenzhen and Shanghai as Examples to Analyze the Construction and Development of Smart Port Cities

In recent years, the enthusiasm for discussing building smart cities has been boosted greatly worldwide, and the residents of major super cities seem to be eager to understand what a smart city is. Unfortunately, due to the International Business Machines Corporations (IBM) recent introduction of the concept of smart cities, many people have misunderstood the concept of smart cities, and even mistakenly classify urban digitization as the scope of smart cities. The core element of a smart city is to use various Smart City Facilities (geographic information systems, urban security systems, intelligent traffic control systems, environmental monitoring systems, residents feedback systems) to bring the city onto an intelligent, automated, efficient, and livable road. This article aims to compare two significant Chinese port cities, Shenzhen and Shanghai, in their efforts to establish smart cities. It delves into the challenges they encountered during this process, analyzes the benefits accrued, and offers valuable reminding for cities aspiring to embark on their own smart city journey in the future.

Intelligent Traffic Light Control System

Abstract: The "Intelligent traffic light control system " project introduces an innovative approach to traffic management, aiming to expedite emergency vehicle passage. Utilizing Zigbee and Radio-Frequency Identification (RFID) technology, this system assigns a unique RFID tag to each vehicle, enabling precise tracking and control at traffic junctions. The scenarios involving emergency vehicle clearance, the system's responsiveness shines. When an emergency vehicle clearance approaches, the system rapidly receives the signal from zigbee transmitter associated with it and promptly triggers a green signal at the traffic junction. This prioritization ensures that emergency vehicles can navigate the traffic with minimal delays, potentially saving lives. Another remarkable feature of the system is stolen vehicle detection capability. In the event that a vehicle with a specific RFID tag is reported as stolen, the system is programmed to take action. It sends immediate SMS notifications to relevant authorities, such as law enforcement or the vehicle owner. This proactive response enhances the chances of recovering stolen vehicles and contributes to overall security. The hardware components of this system include Arduino Uno. Arduino Uno serves as the central control unit, processing RFID data and executing signal adjustments, while the GSM module facilitates real-time communication, enabling SMS alerts and notifications. The " Intelligent traffic light control system " project exemplifies how RFID technology and zigbee technology can revolutionize traffic management. By incorporating data analysis and automation, this system offers a multifaceted solution to common traffic-related challenges.

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

An Intelligent Transport System (ITS) is a wide-ranging framework that influences advanced technologies to improve efficiency of transportation networks. An ITS integrated with an IoT enhances the capabilities of transportation networks. Owing to quick development of vehicles, traffic congestion during road network is major problems. Therefore, intelligent traffic control system is required to create smart transportation through optimization of vehicle route, and so on. In this paper, Cox Regressive Fuzzified Outlier Robust Incremental Extreme Learning Machine (CRFORIELM) is developed for smart ITS. The IoT technology in IT’S is used to collect traffic data, vehicle information, Parking slot occupancy data. After data collection process, proposed CRFORIELM technique includes two major processing steps namely parking slot availability detection and traffic aware route optimization. The extreme learning machine includes three types of layers such as input, three hidden layers as well as output layer. Initial hidden layer predicts the parking slot availability using Cox regression is performed. With the parking lot availability predictions, integrate this information into a route optimization algorithm. This algorithm considers the real-time traffic conditions as well as parking availability for detecting optimal route by applying fuzzy triangular membership function in the second hidden layer. Finally, nearest route path is effectively determined to minimize travel time in intelligent transport system. Experimental analysis indicates that the CRFORIELM technique achieved 2%, 5%, 10% improvement in accuracy, sensitivity, specificity and reduced error rates, route detection time by 39%, and 36%, compared to existing methods.

A Fuzzy Based Intelligent Traffic Light Control (ITLC) Method: An Implementation in Ankara City

The escalating global population and increased vehicle usage have worsened traffic congestion in metropolitan areas, a significant urban challenge. Addressing this, adaptive traffic light control methods, especially at intersections, are being developed to improve traffic flow and reduce waiting times. This study significantly contributes to this field by implementing Fuzzy Logic in intelligent traffic light systems, focusing on Ankara's Polatlı Refik Cesur intersection. Using the SUMO simulation platform and Python programming, it analyzed waiting times and queue lengths. The initial phase used queue length for each intersection arm as an input. Fuzzy logic rules then determined the output, prioritizing street or phase order for optimal flow. The study further proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control plan. ANFIS merges neural network capabilities with fuzzy logic, using waiting time and queue length as inputs to regulate the green light duration. Compared to existing traffic systems, this model showed a substantial improvement. It achieved a 36.5% reduction in waiting times, underlining the efficiency of the Fuzzy Logic-based method. This approach not only enhances traffic management but also contributes significantly to the literature on intelligent traffic light control systems. By addressing key urban traffic issues, the study paves the way for future advancements in traffic management technologies. The findings highlight the potential of combining advanced computational methods, like ANFIS, with traditional traffic control techniques to optimize urban traffic flow, offering a blueprint for similar challenges in other metropolitan areas.

Lightweight Vehicle Detection Based on Improved YOLOv5s.

A vehicle detection algorithm is an indispensable component of intelligent traffic management and control systems, influencing the efficiency and functionality of the system. In this paper, we propose a lightweight improvement method for the YOLOv5 algorithm based on integrated perceptual attention, with few parameters and high detection accuracy. First, we propose a lightweight module IPA with a Transformer encoder based on integrated perceptual attention, which leads to a reduction in the number of parameters while capturing global dependencies for richer contextual information. Second, we propose a lightweight and efficient multiscale spatial channel reconstruction (MSCCR) module that does not increase parameter and computational complexity and facilitates representative feature learning. Finally, we incorporate the IPA module and the MSCCR module into the YOLOv5s backbone network to reduce model parameters and improve accuracy. The test results show that, compared with the original model, the model parameters decrease by about 9%, the average accuracy (mAP@50) increases by 3.1%, and the FLOPS does not increase.

A platoon ordering algorithm on reservation based intersection management systems

Traffic intersections act as pivotal junctions where various streams of traffic intersect and intersecting movements occur. When traffic volumes are high, intersections that are efficiently designed and equipped with intelligent traffic control systems can optimize the movement of vehicles. Accordingly, the time spent by vehicles at intersections increases, and these vehicles cause the vehicles behind them to slow down. This study aims to enhance the efficacy of reservation-based intersection control for platoons comprising vehicles arriving at intersections and turning in various directions. To assess the impact of different platooning algorithms on both the average speed of vehicles within the platoons and the total time spent at intersections, simulations were conducted using the SUMO environment. These simulations observed variations in the average speed values of vehicles and the duration spent by vehicles at intersections. The simulation results showed that the proposed reservation-based platoon ordering algorithm significantly contributed to increasing the average speed and reducing waiting time.

Multi-Agent Attention Double Actor-Critic Framework for Intelligent Traffic Light Control in Urban Scenarios With Hybrid Traffic

In real-world urban environments, hybrid and disorder traffic brings new challenges for the intelligent traffic light control system (ITLCS). Apart from coordinating traffic flows around intersections, the ITLCS is responsive to ensuring high priority vehicles pass through intersections quickly. To this end, we formulate the multiple intersections' decision-making problem as a Semi-Markov game and propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-agent attention double actor-critic (MAADAC)</i> framework to solve this game, integrating the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">options framework</i> with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">graph attention networks (GATs)</i> . Specifically, the options framework empowers agents to learn to make a long sequence of satisfactory decisions, such as keeping a reasonable phase for a short period to ensure high priority vehicles pass through intersections quickly. Besides, we adopt GATs to capture graph-structure mutual influences among agents. We set up a simulator based on real-world city road networks and conduct extensive experiments to evaluate the performance of MAADAC. The experimental results show that MAADAC can reduce high priority vehicles' waiting time in the interval of 18.16%-38.14% versus the density of vehicles in real-world urban scenarios over several state-of-the-art approaches. Also, our framework can guarantee the passing efficiency of high priority vehicles under various traffic conditions with the change in the proportion of high priority vehicles.

Design and Implementation of Arduino-based Intelligent Emergency Ambulance and Traffic Management System

The goal of the project is to construct a density-based traffic signal management system that can automatically modify the timing of the signal based on the volume of traffic at the crossing. Traffic signal regulation is becoming a major issue that causes congestion and has a major role in the delay of emergency vehicles because of the rise in automobiles on the road these days. The majority of urban areas in the world have serious traffic problems, thus it's time to switch from more human or fixed modes to automated systems with decision-making skills. The fixed-based traffic signalling system of today may become ineffective if only one lane is used at a time. In this work, Arduino-based intelligent traffic control system framework helps to reduce this issue.

AI-based YOLO V4 Intelligent Traffic Light Control System

With the growing number of city vehicles, traffic management is becoming one of the most persistent challenges. Traffic bottlenecks cause more significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, the effective result of wait times for the Commuters at the traffic signal point is not reduced. The proposed methodology employs Artificial Intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in lower vehicle waiting times.

A Study on Intelligent Traffic Monitoring System

Urban traffic has become increasingly congested in recent years due to the prevalence of personal vehicles. As a result, one of the major issues in major cities around the world is traffic. Congestion, accidents that take a lot of time and result in property damage, and environmental pollution are some of the traffic- related issues. In this research paper, an automated and intelligent traffic control system that makes use of computer vision and image processing techniques is designed and put into operation. The algorithm counts the number of vehicles on each route and provides an optimal waiting period based on the number of vehicles loaded onto each road. A dynamically managed traffic system can take the place of the traditional fixed-time, present traffic system using this completely automated approach. Low-cost image processing and traffic load balancing are the main topics of this study.

Enhance traffic flow prediction with Real-Time Vehicle Data Integration

&lt;p&gt;This study examines how sophisticated traffic control systems affect traffic flow. These cutting-edge solutions use real-time traffic data to increase road networks’ intelligence. These technologies enable the creation of a smoother and more efficient traffic flow by enhancing traffic signal timings and automatically rerouting cars towards less crowded routes. Notably, these innovations significantly lower air pollution, greenhouse gas emissions, and fuel consumption while also minimizing the financial and time expenses related to traffic congestion. Our unique Real-Time Vehicle Data Integration (RTVDI) algorithm is being used to portray the potential of intelligent traffic control systems. These technologies have the potential to revolutionize traffic management procedures by using real-time data and complex processes. They have the potential to improve commuter safety, increase road efficiency, and improve traffic flow.&lt;/p&gt;

An Effective Imputation Method Using Data Enrichment for Missing Data of Loop Detectors in Intelligent Traffic Control Systems

In intelligent traffic control systems, the features extracted by loop detectors are insufficient to accurately impute missing data. Most of the existing imputation methods use only these extracted features, which leads to the construction of data models that cannot fulfill the required accuracy. This deficiency is the main motivation to propose an enrichment imputation method for loop detectors namely EIM-LD, in which the imputation accuracy is increased for different missing patterns and ratios by introducing a data enrichment technique using statistical multi-class labeling. It first enriches the clean data by adding a statistical multi-class label, including C1…Cn classes. Then, the class of samples in the missed-volume data is labeled using the best data model constructed from the labeled clean data by five different classifiers. Experts of the traffic control department in Isfahan city determined classes of the statistical multi-class label for n = 5 (class labels), and we also developed subclass labels (n = 20) since the number of samples in the subclass labels was sufficient. Next, the enriched data are divided into n datasets, each of them is imputed independently using various imputation methods, and their results are finally merged. To evaluate the impact of using the proposed method, the original data, including missing volumes, are first imputed without our enrichment method. Then, the proposed method’s accuracy is evaluated by considering two class labels and subclass labels. The experimental and statistical results prove that the proposed EIM-LD method can enrich the real data collected by loop detectors, by which the comparative imputation methods construct a more accurate data model. In addition, using subclass labels further enhances the imputation method’s accuracy.

Deep Learning-Enhanced Hybrid Fruit Fly Optimization for Intelligent Traffic Control in Smart Urban Communities

The rapid urbanization accompanying the evolution into “smart” communities presents numerous challenges, not least of which is the significant increase in road vehicles. This proliferation exacerbates congestion and accident rates, posing major barriers to the successful implementation of innovative technologies such as wireless sensor networks (WSNs), surveillance cameras, and the Internet of Things (IoT). Accurate traffic flow prediction, a crucial component of these technological initiatives, requires a reliable and efficient methodology.This research explores the implementation of an intelligent traffic control system that employs a Transferable Texture Convolutional Neural Network (TTCNN). The design of this system eschews the traditional pooling layer, instead incorporating three convolutional layers and a single energy layer (EL). This configuration facilitates the provision of real-time traffic updates, which can enhance the utility and efficiency of the smart city infrastructure.A model inspired by the hybrid fruit fly (HFFO) optimizes the system's hyperparameters. The application of HFFO to the TTCNN showcases the potential for improved accuracy in traffic flow prediction. Simulation results suggest that the HFFO provides superior organizational boundaries for the TTCNN, enhancing the overall accuracy of the model's predictions. The hybrid forecasting method discussed herein demonstrates its potential to outperform other established techniques.This investigation sheds light on the potential benefits of applying deep learning algorithms and hybrid models in the context of traffic flow prediction and control, contributing to the ongoing development of smart urban communities.

ROAD TO ECO-ROUTING: A MODEL OF REGULATORY APPROACH FOR THE EU

Global warming is of key importance among the many negative manifestations of human impact on the environment. The main cause of climate change and the observed global warming is the emission of greenhouse gases (GHG) into the atmosphere, while the largest share of emissions is generated by transportation sector. It is declared that by 2050 GHG emissions in the transportation sector will be reduced by 90 per cent. In the part relating to road transport, a key role is to be played by digitization, transport automation, the use of intelligent traffic control systems and the abandonment of combustion engines in favour of electric ones. This study aims to present possible legal solutions aimed at introducing eco-routing solutions for vehicle traffic in the EU and obstacles to its widespread implementation and is aimed to begin discussion about this matter and the further studies in this area.

Iot Based Intelligent Traffic Control System with Automatic Fine System and Pollution Detection

Iot Based Intelligent Traffic Control System with Automatic Fine System and Pollution Detection

A Research Survey on Real-Time Intelligent Traffic System

Nowadays, it is crucial to continuously monitor and provide real-time analysis to reduce traffic-related accidents and practices such as vehicle overloading on the roads daily. As a result, we reviewed the literature's numerous methods and techniques for vehicle detection, recognition, identification, speed estimation, and license plate recognition. In this analysis, we examined 42 articles published in the last ten years, from 2012 to 2022. Based on our research, we found that the Deep CNN is the optimum method for vehicle categorization. The motivation of this review is that none of the aforementioned models are combined into a single model, so we present a comprehensive list of all these models that may be helpful to anyone conducting the study in this area. Therefore, after reviewing the chosen research publications, we propose 20+ datasets that might be used in the field for more research. We also discovered 15+ different ML models used to detect and identify vehicles. Finally, we observed that combining machine learning and AI (Artificial Intelligence) to create intelligent traffic control systems is a promising research area.

English

Autonomous vehicles in public transport can perform a wide range of real-world tasks such as: moving in factories, in public transport, in search and rescue, etc., thus requiring varying degrees of auto-navigation in response to changes in the environment. This paper presents the predictive model (MPC) for the active suspension system, the vehicle's damping system, combined with Deep Q-network (DQN) algorithm reinforcement learning method applied to control self-driving cars in public transport. Intelligent traffic control system to control movement to avoid fixed obstacles or movable obstacles in the event of external interference, taking into account the comfort of the occupants of the vehicle; taking into account the load, the conveying systems; for the purpose of application in the field of intelligent transportation. The research results, built on Matlab Simulink software, show that autonomous vehicles can safely complete intelligent navigation tasks in an unknown environment and become a real system intelligent with the ability to self-study and adapt well to many different environments and various nonlinear factors.

IMPROVED TRAFFIC CONTROL SYSTEM THROUGH DETECTION AND CLASSIFICATION OF VEHICLES USING CAMERA BASED CROWD SENSING WITH DEEP LEARNING APPROACH.

Abstract - Intelligent traffic light control system can be considered as one of the main aspects of a smart city. There is significant increase in the number of People that own vehicles and this leads to traffic congestion in urban cities of developing countries. Traffic congestion is one of the highly challenging problems people face in their daily lives which affect their working productivity, causes accident, unnecessary delays and possibly loss of lives. The control of traffics based on the densities and classes of the vehicles using deep learning approach proved to be a better approach in handling traffic jams according to the current state of the studies. However, the current state of the studies did not have special consideration for the class of special vehicles (VIPs) which need special consideration due to their importance and tight schedules. Also, it did not consider allocating time based on the classification. Thus, a model to improve the detection and classification of vehicles on side of intersection as well as special consideration for VIPs was proposed. The proposed model was built using Faster R-CNN based on Tensorflow machine learning framework on a Google Colab environment. The model was able to detect and classify vehicles as “special vehicles (i.e. VIP), Emergency (EV) and Others” classes of vehicles, a time allocation simulator was proposed based on the specified classes with special timing for VIPs. Results show that using the proposed method, 94.43% accuracy was achieved.

СТРАТЕГИЯ СНИЖЕНИЯ КОЛИЧЕСТВА ДОРОЖНО-ТРАНСПОРТНЫХ ПРОИСШЕСТВИЙ ПУТЕМ ВНЕДРЕНИЯ ИНТЕЛЛЕКТУАЛЬНЫХ ТРАНСПОРТНЫХ СИСТЕМ

This article discusses options for using subsystems of intelligent traffic control systems and reducing the level of accidents. It is necessary to create all possible conditions to ensure the structural and functional homogeneity of traffic flows within the framework of road safety. The purpose of this article is to review, describe and describe options for using ITS to control the level of collisions and reduce the number of fatal initial loads in road traffic accidents. In addition, these systems can also help improve traffic efficiency, which is the goal of traffic management.