Adaptive Traffic Control System Research Articles

Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review

The exponential increase in vehicles, quick urbanization, and rising demand for transportation are straining the world’s road infrastructure today. To have a sustainable transportation system with dynamic traffic volume, an Adaptive Traffic Signal Control system (ATSC) should be contemplated to reduce urban traffic congestion and, thus, help reduce the carbon footprints/emissions of greenhouse gases. With dynamic cleave, the ATSC system can adapt the signal timing settings in real-time according to seasonal and short-term variations in traffic demand, enhancing the effectiveness of traffic operations on urban road networks. This paper provides a comprehensive study on the insights, technical lineaments, and status of various research work in ATSC. In this paper, the ATSC is categorized based on several road intersections (RIs), viz., single-intersection (SI) and multiple-intersection (MI) techniques, viz., Fuzzy Logic (FL), Metaheuristic (MH), Dynamic Programming (DP), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and hybrids used for developing Traffic Signal Control (TSC) systems. The findings from this review demonstrate that modern ATSC systems designed using various techniques offer substantial improvements in managing the dynamic density of the traffic flow. There is still a lot of scope to research by increasing the number of RIs while designing the ATSC system to suit real-life applications.

Machine learning solutions for adaptive traffic signal control: A review of image-based approaches

This paper gives an overview and performance evaluation of various machine learning models implemented in management of urban traffic congestions, more specifically, adaptive traffic signal control systems. It considers a review of deep learning algorithms including R- CNN, Fast R-CNN, Faster R-CNN, SSD, YOLO v4, and YOLOv8, with regard to their efficiencies for vehicle detection and traffic prediction under varying scenarios. Certain traffic conditions, camera placements, and environmental factors—related performance for each of the models are discussed. The major performance in most of the scenarios was depicted by the YOLO v4. However, at the same time, YOLOv8 has shown potential to do much better than YOLO v4 on image processing and the resultant accuracy. It also proposes a new algorithm for traffic light timing, whose efficacy is tested using the SUMO simulation platform. While results have shown improvements in urban traffic management, a review underlines that such is in deep need of extensive real-world testing. Future directions should include views from varied angles and weather conditions, and the detection of emergency vehicles, probably with specialized datasets.

Optimizing Urban Mobility: A Comprehensive Study on Intelligent Traffic Signal Control Systems

This paper explores the application of Adaptive Traffic Signal Control (ATSC) systems integrated with Information and Communication Technology (ICT) and the Internet of Things (IoT) to improve urban traffic management. ATSC systems utilize real-time traffic data from various sensors and cameras to adjust signal timings dynamically, aiming to reduce congestion and enhance flow efficiency. The study highlights how these systems process and react to live traffic conditions, discussing the implementation of machine learning models and optimization algorithms that refine signal timing strategies based on traffic density and flow patterns. Challenges like data accuracy, system integration, cybersecurity, and environmental impacts on sensor performance are examined, alongside the potential for improving urban mobility and reducing environmental effects through smarter traffic management solutions. The paper concludes by emphasizing the importance of continuous advancements in technology and infrastructure to support the scalability and effectiveness of ATSC systems in modern cities.

Systematic approach to a government‐led technology roadmap for future‐ready adaptive traffic signal control systems

AbstractThe economic impact of inefficient traffic control systems is significant owing to prolonged commute durations, and increased energy consumption. Traffic signal control systems (TSCSs) significantly influence traffic flows at intersections. Therefore, adaptive TSCSs (ATSCSs) that can adjust to traffic conditions in real‐time have been proposed as more efficient alternatives. However, the expensive implementation of these systems highlights the need for judicious investments in appropriate technologies and infrastructure. Therefore, a comprehensive technology roadmap should be built that guides the future development of traffic control and the infusion of technologies to address traffic needs. Additionally, as ATSCSs are developed and managed by local governments, the perspective of a government‐led technology roadmap is required to guide the roadmap development and implementation. Although studies have explored technology roadmaps across numerous sectors, the viewpoint of roadmap development guided by government entities is frequently neglected despite the role of these entities in shaping technological policies, underwriting research and development initiatives, and driving nationwide innovation strategies. In this study, a comprehensive framework is proposed for developing technology roadmaps tailored for systems and technologies led by governmental entities. This framework has been adapted from the Advanced Technology Roadmap Architecture (ATRA) and brought original adjustments thereby addressing the research gap. The study also presents strategic recommendations for the ATSCS implementation in South Korea, integrating systems engineering principles for a holistic approach to technological advancements. The framework can be replicated to serve as a guide for governments seeking to implement effective and efficient technology roadmaps for public infrastructure systems.

Dual-Objective Reinforcement Learning-Based Adaptive Traffic Signal Control for Decarbonization and Efficiency Optimization

To improve traffic efficiency, adaptive traffic signal control (ATSC) systems have been widely developed. However, few studies have proactively optimized the air environmental issues in the development of ATSC. To fill this research gap, this study proposes an optimized ATSC algorithm to take into consideration both traffic efficiency and decarbonization. The proposed algorithm is developed based on the deep reinforcement learning (DRL) framework with dual goals (DRL-DG) for traffic control system optimization. A novel network structure combining Convolutional Neural Networks and Long Short-Term Memory Networks is designed to map the intersection traffic state to a Q-value, accelerating the learning process. The reward mechanism involves a multi-objective optimization function, employing the entropy weight method to balance the weights among dual goals. Based on a representative intersection in Changsha, Hunan Province, China, a simulated intersection scenario is constructed to train and test the proposed algorithm. The result shows that the ATSC system optimized by the proposed DRL-DG results in a reduction of more than 71% in vehicle waiting time and 46% in carbon emissions compared to traditional traffic signal control systems. It converges faster and achieves a balanced dual-objective optimization compared to the prevailing DRL-based ATSC.

CycLight: Learning traffic signal cooperation with a cycle-level strategy

This study introduces CycLight, a novel cycle-level deep reinforcement learning (RL) approach for network-level adaptive traffic signal control (NATSC) systems. Traditional RL-based step-by-step traffic signal controllers, while precise and responsive, demand high computational resources for frequent data exchanges and can increase traffic accident risks due to disordered phase switching. Different from the step-by-step control strategy aforementioned, the proposed CycLight adopts a cycle-level strategy, optimizing cycle length and splits simultaneously using Parameterized Deep Q-Networks (PDQN) algorithm. This cycle-level approach effectively reduces the computational burden associated with frequent data communication, meanwhile enhancing the practicality and safety of real-world applications. Moreover, a decentralized framework is formulated for multi-agent cooperation, while attention mechanism is integrated to accurately assess the impact of the surroundings on the current intersection. CycLight is tested in a large synthetic traffic grid using the microscopic traffic simulation tool, Simulation of Urban MObility (SUMO). Experimental results not only demonstrate the superiority of CycLight over other state-of-the-art approaches but also showcase its robustness against information transmission delays.

Dynamic traffic signal control for heterogeneous traffic conditions using Max Pressure and Reinforcement Learning

Optimization of green signal timing for each phase at a signalized intersection in an urban area is critical for efficacious traffic management and congestion mitigation. Many algorithms are developed, yet very few are for cities in developing nations where traffic is characterized by its heterogeneous nature. While some of the recent studies have explored different variants of Max Pressure (MP) and Reinforcement Learning (RL) for optimizing phase timing, the focus is limited to homogeneous traffic conditions. In developing nations, such as India, control systems, like fixed and actuated, are still predominantly used in practice. Composite Signal Control Strategy (CoSiCoSt) is also employed at a few intersections. However, there is a notable absence of advanced models addressing heterogeneous traffic behavior, which have a great potential to reduce delays and queue lengths. The present study proposes a hybrid algorithm for an adaptive traffic control system for real-world heterogeneous traffic conditions. The proposed algorithm integrates Max Pressure with Reinforcement Learning. The former dynamically determines phase order by performing pressure calculations for each phase. The latter optimizes the phase timings for each phase to minimize delays and queue lengths using the proximal policy optimization algorithm. In contrast to the past RL models, in which the phase timing is determined for all phases at once, in the proposed algorithm, the phase timings are determined after the execution of every phase. To assess the impact, classified traffic volume is extracted from surveillance videos of an intersection in Ludhiana, Punjab and simulated using Simulation of Urban Mobility (SUMO). Traffic volume data is collected over three distinct time periods of the day. The results of the proposed algorithm are compared with benchmark algorithms, such as Actuated, CoSiCoSt, and acyclic & cyclic Max Pressure, Reinforcement Learning-based algorithms. To assess the performance, queue length, delay, and queue dissipation time are considered as key performance indicators. Of actuated and CoSiCoSt, the latter performs better, and thus, the performance of the proposed hybrid algorithm is compared with CoSiCoSt. The proposed algorithm effectively reduces total delay and queue dissipation time in the range of 77.07%–87.66% and 53.95%–62.07%, respectively. Similarly, with respect to the best-performing RL model, the drop in delay and queue dissipation time range from 55.63 to 77.12% and 22.13 to 43.7%, respectively, which is significant at 99% confidence level. The proposed algorithm is deployed on a wireless hardware architecture to confirm the feasibility of real-world implementation. The findings highlight the algorithm’s potential as an efficient solution for queues and delays at signalized intersections, where mixed traffic conditions prevail.

Enhancing traffic control systems with live video analytics: Issues, challenges, opportunities, and recent problems

With the rapid urbanization and increasing number of vehicles on the roads, it has become imperative to develop innovative solutions that can monitor and manage traffic congestion automatically. Traffic congestion harms the economy, environment, and overall quality of life. To address these challenges, smart traffic management systems employ cutting-edge technologies such as live video analytics and sensor-based adaptive traffic control systems. These systems can predict traffic patterns, locate congestion hotspots, and uncover abnormalities contributing to road accidents in real time. However, adopting these technologies for traffic control systems raises important concerns such as robustness and sustainability across different traffic junctions, data integration from multiple sources, and computational feasibility for real time computation. Therefore, this paper aims to present an overview of the potential benefits and challenges in adapting the latest technologies including the Internet of things and machine learning for sustainable traffic management. Additionally, a case study of a smart city is presented to evaluate an adaptive traffic control system based on live camera feed analytics by leveraging computer vision techniques. The adaptive traffic control system is accurate in vehicle detection and counting. This system is very useful for smart cities where traffic signals need to be automated according to the density of vehicles.

Fusing crowdsourced data to an adaptive wireless traffic signal control system architecture

In this study, a novel wireless adaptive traffic signal control System architecture is proposed, in which crowdsourced data is integrated to detect the traffic state on each approach of an intersection. Under the wireless architecture, the green signal timing is calculated using a travel time-based Max Pressure control algorithm, which is performed on a single-board computer (SBC, RPi 4). The resulting green timings are sent from SBC to the master controller (ESP32) over serial UART and then to the slave (ESP32) wirelessly using ESP-NOW; slave ESP32 is placed on each approach of the intersection. It integrates a fail-safe mechanism to avoid any vehicular conflicts. The proposed system architecture also embeds a crowdsourced data-based pre-emption of intersections for efficient movement of emergency vehicles. The efficacy of the proposed system is compared using a simulation tool with real-world data. With respect to actuated and Max Pressure based on traffic flow, the proposed algorithm reduces the total delay by 54.8% and 22.96%, respectively. Further, the total queue dissipation time decreases by 8.32% and 5.45%, respectively. It outperforms the actuated signal control as well as Max Pressure based on traffic flows. The capability of transforming existing traffic signal controllers into wireless adaptive traffic signal control systems makes it a very affordable and scalable solution.

An Adaptive Traffic Light Control System Based On Artificial Intelligence

An Adaptive Traffic Light Control System Based On Artificial Intelligence

Elevating adaptive traffic signal control in semi‐autonomous traffic dynamics by using connected and automated vehicles as probes

AbstractIn this work, the connected vehicle's messages are used to create an enhanced adaptive traffic signal control (ATSC) system for improved traffic flow. Few existing studies use connected and automated vehicles (CAVs) to develop traffic signal control algorithms under hybrid connected and autonomous conditions. The proposed approach focuses on a four‐phase traffic intersection with both CAVs and human‐driven vehicles (HVs). CAVs share real‐time state information, and a model called Roads Dynamic Segmentation estimates queuing procedures and vehicle fleet numbers on dynamic road sections. This information is used in the Store and Forward Model (SFM) to predict intersection queuing length. The ATSC system, based on model predictive control (MPC), aims to minimize intersection queue length while considering traffic constraints (undersaturated, saturated, and oversaturated) and avoiding free‐flow problems due to queue overflow. To reduce computational complexity, a linear‐quadratic‐regulator (LQR) is used. Real‐world vehicle trajectories and the SUMO tool are used for experimental purposes. Results show that the proposed method reduces average delay by 38.50% and 33.42% compared to fixed timing and traditional MPC in cases of oversaturated traffic flow with 100% CAV penetration. Even with a penetration rate of only 20%, average delay decreases by 13.65% and 6.50%, respectively. This study showcases not only the potential benefits of CAV in enhancing traffic, but also enables the optimal utilization of green duration in signalized intersection control systems. This helps prevent traffic congestion and ensures the efficient and smooth movement of traffic flow.

Adaptive Traffic Control Using Cooperative Communication Through Visible Light

The study aims to create a Visible-Light Communication (VLC) system for secure vehicle management at intersections. This involves enabling communication between vehicles and infrastructure (V2V, V2I, and I2V) using headlights, streetlights, and traffic signals. Mobile optical receivers gather data, determine their location, and read transmitted information through joint transmission. An intersection manager coordinates traffic and communicates with vehicles through embedded Driver Agents. The system utilizes a "mesh/cellular" hybrid network configuration and encodes data into light signals emitted by transmitters. Optical sensors and filtering properties enable reception and decoding. The study demonstrates bidirectional communication, employing queue/request/response mechanisms and relative pose concepts for safe vehicle passage. A deep reinforcement learning model controls traffic light cycles, validated via simulation in a Simulation of Urban Mobility simulator. Results show that this adaptive traffic control system effectively collects detailed vehicle data and ensures secure communication within the short-range mesh network.

Performance Evaluation of Reinforcement Learning-Based Adaptive Signal Control Deployment in Urban Network

The adaptive traffic control system (ATCS) has shown great potential to address traffic congestion, fuel consumption, and air pollution issues because of its strong adaption to traffic fluctuation. With the popularity of artificial intelligence, Reinforcement Learning (RL) has been applied to ATCS and achieved excellent performance. In RL-based ATCS, the controller collects traffic state information and learns a better signal control policy by minimizing travel delay. For ATCS of an area-wide network, most existing studies assumed that all intersections are installed with ATCS. However, fully ATCS networks are difficult to achieve in the real world due to the high upgrade cost. Therefore, a practical ATCS deployment strategy in a network is needed for agencies with limited budgets. This study proposes a concept of penetration rate, or the percentage of intersections with ATCS in an area, for intelligent intersections. The weighted PageRank algorithm determines a reasonable deployment plan based on road connectivity and link volume. The relationship between penetration rate and traffic performance is investigated in the SUMO simulation. It reveals that the average stopped and control delays gradually decreased with the penetration rate increase. When the penetration rate reaches 75%, the ATSC performance is about 90% of the optimum. However, inequity and inefficiency exist in non-intelligent intersections regarding average queueing time and length at the penetration rate below 40%. Consequently, the suitable range of penetration rate should be between 40% and 75% to balance the costs and benefits of ATCS deployment with budget constraints.

АНАЛИЗ АДАПТИВНЫХ СИСТЕМ УПРАВЛЕНИЯ ДОРОЖНЫМ ДВИЖЕНИЕМ

This paper describes state-of-the-art adaptive control systems both in Split Cycle Offset Optimization (SCOOT), Optimization Policies for Adaptive Control (OPAC), Real-Time Hierarchical Optimized Distributed and Effective System (RHODES), Adaptive Control System-Lite (ACS-Lite), and worldwide. Urban road conditions and traffic signal timing to accommodate traffic volume changes and minimize stops and delays are controlled by adaptive traffic control systems on roadways. Based on the research objectives and comparing the positive aspects of past experiences will help traffic engineers and decision makers to select the most effective adaptive control system.

Adversarial Attacks on Deep Reinforcement Learning-based Traffic Signal Control Systems with Colluding Vehicles

The rapid advancements of Internet of Things (IoT) and Artificial Intelligence (AI) have catalyzed the development of adaptive traffic control systems (ATCS) for smart cities. In particular, deep reinforcement learning (DRL) models produce state-of-the-art performance and have great potential for practical applications. In the existing DRL-based ATCS, the controlled signals collect traffic state information from nearby vehicles, and then optimal actions (e.g., switching phases) can be determined based on the collected information. The DRL models fully “trust” that vehicles are sending the true information to the traffic signals, making the ATCS vulnerable to adversarial attacks with falsified information. In view of this, this article first time formulates a novel task in which a group of vehicles can cooperatively send falsified information to “cheat” DRL-based ATCS in order to save their total travel time. To solve the proposed task, we develop CollusionVeh , a generic and effective vehicle-colluding framework composed of a road situation encoder, a vehicle interpreter, and a communication mechanism. We employ our framework to attack established DRL-based ATCS and demonstrate that the total travel time for the colluding vehicles can be significantly reduced with a reasonable number of learning episodes, and the colluding effect will decrease if the number of colluding vehicles increases. Additionally, insights and suggestions for the real-world deployment of DRL-based ATCS are provided. The research outcomes could help improve the reliability and robustness of the ATCS and better protect the smart mobility systems.

A Controlling Traffic Light system using Fuzzy logic

Automatic control systems are a new advanced system uses computers and hardware controllers to act as a driving system. Increasing in cars numbers while we have the same infrastructure caused a large traffic problem so we need automatic adaptive controllers to control the open and close tasks of traffic light system. Fuzzy Logic is a conversation from classical logic with values of 0 for false and 1 for true to a range of values between 0 and 1.
 This study aims to create an adaptive traffic control system using fuzzy logic and image processing, we have a camera on each traffic light to calculate the number of cars there and then we feed the number of cars to a fuzzy logic system to decide which traffic light must become on

Adaptive green split optimization for traffic control with low penetration rate trajectory data

Adaptive traffic signal control systems often rely on expensive physical detection infrastructure. However, with the advent of widespread trajectory data, it is now possible to implement adaptive control entirely avoiding such costs. We present two simple adaptive control policies which only require sample delay and number of stops, with the goal to mitigate the presence of oversaturation. The simplicity stems from the necessity of controlling under any trajectory penetration rate. The two policies differ on the possibilities of the control infrastructure to be implemented. The first one minimizes oversaturation by deviating from a reference pre-timed signal plan. This signal plan can be an existing one or an estimated one from aggregating trajectory data. The second policy creates first a set of green split plans to be then selected by a control logic. This second policy is intended to be used in SCATS-like systems where signal plans are limited to a pre-defined discrete set. We propose a plan selection logics or alternatively, the original plan selection policy can be used as well. Both policies are tested in the field, achieving a significant reduction in delay, oversaturation and spillover ratios. Lastly, we test an application of this policy as an enhancement of SCATS systems in the presence of malfunctioning physical detectors.

Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction

Forecasting the forthcoming intersection movement-based traffic volume enables adaptive traffic control systems to dynamically respond to the fluctuation of traffic demands. In this paper, a deep-learning based Signal-control Refined Dynamic Traffic Graph (ScR-DTG) Model is proposed for advancing the network-level movement-based traffic volume prediction task. The proposed model attempts to further improve the-state-of-art and practice algorithms in traffic prediction for arterial network adaptive signal control utilizing tradition traffic flow theory boosted deep-learning methodology. For precisely inferencing the movement-based demand at cycle-to-cycle level, the proposed model incorporates spatial graph convolution inferencing layer and temporal inferencing layer to explore both the intricate spatial temporal dependencies, respectively. A signal control refining module is contrived to deduce the controlled movement saturation flow and introduce the essential control inferences, which is of great significance but frequently neglected in the previous researches. Additionally, according to the real-time movement specified travel time, this paper creatively constructs an adjacent graph with dynamic order for more accurately capturing the ever-changing spatial relevancies. Field experiments with multiple signal schemes were conducted in the downtown area of Zhangzhou (China). The promising results demonstrated the state-of-the-art accuracy than other high-performance volume prediction algorithms. Implementing the proposed model enables to obtain accurate movement-based volume predictions, which would assist the traffic management agencies in adjusting signal timing adaptively and further improve the efficiency of signal intersection.

Data-Driven Decision Support Platform for Selection of Intersections for Adaptive Traffic Signal Control

Adaptive traffic control systems (ATCSs) represent one of the most advanced traffic signal control strategies currently deployed in urban areas worldwide. One of the most important questions in the deployment of such a system is related to the determination of location (i.e., intersection/s, corridor) where ATCS should be deployed. Several past studies addressed this problem either for the specific “corridor-level” analysis or by using inadequate approaches that prevent agencies from observing how existing assets and their operational characteristics affect agency-wide deployment of ATCS. This study proposes a data-driven dashboard that uses the operational attributes of existing assets (usually readily available to many agencies nowadays) to rank assets/corridors in the network based on their “appropriateness” for the installation of ATCS systems. The core components of the proposed tool are a robust scoring engine that incorporates the signal parameters important for ATCS deployment, and a spatial module that aggregates the computed scores on a desired spatial level. These core components are encapsulated in a web-based map tool deployed on the cloud. The proposed tool has been deployed in a case study to support the installation of 1,100 ATCS signals in the road network of Miami-Dade County, Florida. The proposed tool was useful for producing an initial solution in the decision-making process on where to install ATCS. The developed tool is robust enough to be applied to other networks.

Adaptive Traffic Control System

Abstract: Whenever we hear the word traffic, we all think of people blaring horns and waiting endlessly, stress is always silent but prominent in the word traffic. We all face traffic daily and have experienced the frustration and stress that traffic induces. Our project focuses on reducing the traffic waiting time significantly and hence reducing air and noise pollution. Less waiting time at traffic means less wastage of fuel leading to energy conservation in the times of a global warming