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Journal articles on the topic 'Adaptive Traffic Signal Timer'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

P, Joyson Silva, Vignesh R, Sukumar Binu, and Kumar Nithish. "Adaptive Traffic Signal Timer for A Signal in Chennai Metropolitan City Using Python and OpenCV." Indian Journal of Science and Technology 16, no. 13 (2023): 1007–13. https://doi.org/10.17485/IJST/v16i13.2131.

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Abstract <strong>Background:</strong> The current traffic control system in India’s metropolises is ineffective because of the randomization of traffic density patterns throughout the day. For a predetermined period of time, the traffic signal timers switch traffic’s direction. Vehicles must therefore wait for a long time even when there is little traffic. <strong>Objectives:</strong> To continuously adjust the traffic signal timer based on the varying real-time traffic density and to significantly lessen traffic congestion. <strong>Methods:</strong> An ada

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Silva, P. Joyson, R. Vignesh, Binu Sukumar, and Nithish Kumar. "Adaptive Traffic Signal Timer for A Signal in Chennai Metropolitan City Using Python and OpenCV." Indian Journal Of Science And Technology 16, no. 13 (2023): 1007–13. http://dx.doi.org/10.17485/ijst/v16i13.2131.

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Sowmya, B. "Adaptive Traffic Management System using CNN (YOLO)." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3726–32. http://dx.doi.org/10.22214/ijraset.2021.35768.

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The huge number of vehicles on the roadways is making congestion a significant problem. The line longitudinal vehicle waiting to be processed at the crossroads increases quickly, and the traditionally used traffic signals are not able to program it properly. Manual traffic monitoring may be an onerous job since a number of cameras are deployed over the network in traffic management centers. The proactive decision-making of human operators, which would decrease the effect of events and recurring road congestion, might contribute to the easing of the strain of automation.The traffic control fram

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G, Dr Kanjana. "Adaptive Traffic Management System Using Reinforcement Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1387–92. https://doi.org/10.22214/ijraset.2025.67570.

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Traffic congestion is a significant issue in urban areas, leading to increased travel delays, fuel consumption, and environmental pollution. Traditional traffic management systems, which use fixed-timer signals, rule-based controls, manual intervention by traffic police, and electronic sensor-based methods, often struggle to adapt to dynamic traffic conditions. To address these challenges an Adaptive Traffic Management System (ATMS) using Reinforcement Learning (RL) is proposed to optimize signal timings and improve traffic flow. The system dynamically adjusts traffic signal timings based on r

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Matei, Lucian, Ilie Dumitru, Laurențiu Racilă, and Matei Vînatoru. "Adaptive Traffic Signal Control on a National Road Intersection." Applied Mechanics and Materials 822 (January 2016): 455–60. http://dx.doi.org/10.4028/www.scientific.net/amm.822.455.

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Adaptive traffic signal control is the process by which the timing of a traffic signal is continuously adjusted based on the changing arrival patterns of vehicles at an intersection, usually with the goal of optimizing a given measure of effectiveness. In this paper a traffic signal program is developed in which the characteristics of a traffic signal cycle are optimized at the conclusion of every phase based on the arrival times of vehicles to an intersection, using stopped delay as the measure of effectiveness. The methodology which leads to the signal plan is shown to provide improvement in

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Shelby, Steven G., Darcy M. Bullock, and Douglas Gettman. "Resonant Cycles in Traffic Signal Control." Transportation Research Record: Journal of the Transportation Research Board 1925, no. 1 (2005): 215–26. http://dx.doi.org/10.1177/0361198105192500122.

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The potential benefits of using resonant cycle times in traffic signal control on an arterial are investigated. Resonant cycles are cycle lengths that result in good arterial progression over a range of traffic flows. The notion of resonant cycle times contrasts with the prevalent adaptive control practice of setting the arterial cycle length in proportion to flow levels at the most congested intersection on the arterial. This research was motivated by the development of appropriate adaptive algorithms for closed-loop system control in the FHWA ACS-Lite project. Simulation experiments with TRA

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M, Prabu. "Traffic Vision: AI-Powered Traffic Monitoring System and Signal Optimization." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2708–15. https://doi.org/10.22214/ijraset.2025.68829.

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This paper is focused on Traffic Vision, an integrated AI-powered traffic monitoring and management system built on computer vision, machine learning, and adaptive control strategies to optimize urban traffic flows. The major functions of the system include real-time processing of video feeds for vehicle, pedestrian, emergency vehicle, and accident detection and tracking, as well as traffic density and flow parameters. A new adaptive traffic signal control algorithm employs this information to dynamically adapt traffic light timings according to current conditions. Experimental results show a

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Mishra, S., D. Bhattacharya, A. Gupta, and V. R. Singh. "ADAPTIVE TRAFFIC LIGHT CYCLE TIME CONTROLLER USING MICROCONTROLLERS AND CROWDSOURCE DATA OF GOOGLE APIs FOR DEVELOPING COUNTRIES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W7 (September 20, 2018): 83–90. http://dx.doi.org/10.5194/isprs-annals-iv-4-w7-83-2018.

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<p><strong>Abstract.</strong> Controlling of traffic signals optimally helps in avoiding traffic jams as vehicle volume density changes on temporally short and spatially small scales. Nowadays, due to embedded system development with the rising standards of computational technology, condense electronics boards as well as software packages, system can be developed for controlling cycle time in real time. At present, the traffic control systems in India lack intelligence and act as an open-loop control system, with no feedback or sensing network, due to the high costs involved.

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Pranevičius, Henrikas, and Tadas Kraujalis. "KNOWLEDGE BASED TRAFFIC SIGNAL CONTROL MODEL FOR SIGNALIZED INTERSECTION." TRANSPORT 27, no. 3 (2012): 263–67. http://dx.doi.org/10.3846/16484142.2012.719545.

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Intelligent transportation systems have received increasing attention in academy and industry. Being able to handle uncertainties and complexity, expert systems are applied in vast areas of real life including intelligent transportation systems. This paper presents a traffic signal control method based on expert knowledge for an isolated signalized intersection. The proposed method has the adaptive signal timing ability to adjust its signal timing in response to changing traffic conditions. Based on the traffic conditions, the system determines to extend or terminate the current green signal g

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Mathiane, Malose John, Chunling Tu, Pius Adewale, and Mukatshung Nawej. "A Vehicle Density Estimation Traffic Light Control System Using a Two-Dimensional Convolution Neural Network." Vehicles 5, no. 4 (2023): 1844–62. http://dx.doi.org/10.3390/vehicles5040099.

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One of the world’s challenges is the amount of traffic on the roads. Waiting for the green light is a major cause of traffic congestion. Low throughput rates and eventual congestion come from many traffic signals that are hard coded, irrespective of the volume of the amount of traffic. Instead of depending on predefined time intervals, it is essential to build a traffic signal control system that can react to changing vehicle densities. Emergency vehicles, like ambulances, must be given priority at the intersection so as not to spend more time at the traffic light. Computer vision techniques c

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Mohanadevi, Mrs A., M. Poornika, and R. Keerthana. "AI - DRIVEN ADAPTIVE TRAFFIC SIGNAL OPTIMIZATION SYSTEM WITH EMERGENCY VEHICLE." International Journal of Engineering Applied Sciences and Technology 9, no. 09 (2025): 38–42. https://doi.org/10.33564/ijeast.2025.v09i09.006.

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The AI Enhanced Adaptive Traffic Flow Management System is able to adjust traffic signal timings based on the real-time traffic situation. The system uses computer vision and deep learning technologies, specifically YOLO (You Only Look Once) vehicle detection, in live streaming videos from traffic cameras to determine traffic volume. In order to improve traffic control effectiveness and reduce delays, the system adjusts the green signal time based on the density of cars in the monitored area. Standard traffic control systems use fixed timers for traffic lights, so the flow of traffic is not as

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Shukla, Prashant, Parth Sharma,, Ojas Mayur,, Kunal Garg, and Dr Narendra. "Density Based Traffic Management System." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–9. https://doi.org/10.55041/isjem02557.

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Urban traffic congestion has emerged as a critical challenge in modern cities, with static traffic signal systems exacerbating inefficiencies in traffic flow management. Conventional systems rely on fixed timers that do not adapt to real-time traffic density, leading to prolonged idling, increased fuel consumption, and elevated greenhouse gas emissions. For instance, the INRIX Global Traffic Scorecard 2022 reported that the average U.S. driver lost 51 hours annually due to congestion, costing the economy over $81 billion in wasted time and fuel. In developing nations like India, where heteroge

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Galvão, Gonçalo, Manuel Augusto Vieira, Manuela Vieira, Paula Louro, and Mário Véstias. "Enhancing Urban Traffic Management with Visible Light Communication and Reinforcement Learning." EPJ Web of Conferences 305 (2024): 00030. http://dx.doi.org/10.1051/epjconf/202430500030.

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This paper introduces Visible Light Communication (VLC) to enhance traffic signal efficiency and vehicle trajectory management at urban intersections. A multi-intersection traffic control system is proposed, integrating VLC localization services with learning-based traffic signal control. VLC facilitates communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. By leveraging vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) interactions, joint transmission and data collection are achieved via mobile optic

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Phand, Aniket. "REAL-TIME TRAFFIC LIGHT OPTIMIZATION USING AI AND IOT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31427.

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In modern cities, the escalating number of vehicles has led to significant traffic issues, affecting road capacity and service quality. Traditional traffic control systems, reliant on fixed signal timers, struggle to adapt to changing traffic dynamics, exacerbating congestion. This paper proposes an innovative approach to traffic management utilizing advanced Computer Vision technology, specifically the YOLOv7 object detection algorithm. By analysing live CCTV footage at intersections, the system dynamically assesses traffic density, identifies vehicle types, and adjusts signal timings in real

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Smith, Stephen, Gregory Barlow, Xiao-Feng Xie, and Zachary Rubinstein. "Smart Urban Signal Networks: Initial Application of the SURTRAC Adaptive Traffic Signal Control System." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 434–42. http://dx.doi.org/10.1609/icaps.v23i1.13594.

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In this paper, we describe a pilot implementation and field test of a recently developed approach to real-time adaptive traffic signal control. The pilot system, called SURTRAC (Scalable Urban Traffic Control), follows the perspective of recent work in multi-agent planning and implements a decentralized, schedule-driven approach to traffic signal control. Under this approach, each intersection independently (and asynchronously) computes a schedule that optimizes the flow of currently approaching traffic through that intersection, and uses this schedule to decide when to switch green phases. Th

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Fahrunnisa, Zulfa, Rahmadwati Rahmadwati, and Raden Arief Setyawan. "Adaptive Traffic Light Signal Control Using Fuzzy Logic Based on Real-Time Vehicle Detection from Video Surveillance." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 10, no. 2 (2024): 235–51. https://doi.org/10.26555/jiteki.v10i2.28712.

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Intersections often become the focal points of congestion due to poor traffic signal management, reduced productivity, increased travel duration, gas emissions, and fuel consumption. Existing traffic light systems maintained constant signal duration regardless of traffic situations, resulting in green signals for lanes with no vehicle queues that increased waiting times in other lanes. Therefore, a real-time traffic signal optimization system using Fuzzy Logic control, utilizing vehicle queue and flow rate real-time data from video surveillance, is needed. This research used recorded video fro

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Korade, Dr Nilesh B., Dr Mahendra B. Salunke, Dr Amol A. Bhosle, et al. "Real-Time Traffic Light Optimization Using Yolov9 and Length-Based Metrics." International Journal of Electrical and Electronics Research 13, no. 2 (2025): 287–95. https://doi.org/10.37391/ijeer.130212.

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The Indian traffic control system faces lots of difficulties due to the increasing volume of vehicles, ineffective systems for traffic administration during peak hours, and the frequent need for manual intervention due to the inadequate performance of traffic signals in managing heavy traffic flow. Traditional traffic lights in India have defined timings for each lane, which frequently cause longer traffic jams in lanes with more traffic. This study presents an intelligent traffic control system that incorporates the YOLOv9 model for real-time traffic length prediction and intelligently alloca

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Zhao, Rui, Haofeng Hu, Yun Li, Yuze Fan, Fei Gao, and Zhenhai Gao. "Sequence Decision Transformer for Adaptive Traffic Signal Control." Sensors 24, no. 19 (2024): 6202. http://dx.doi.org/10.3390/s24196202.

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Urban traffic congestion poses significant economic and environmental challenges worldwide. To mitigate these issues, Adaptive Traffic Signal Control (ATSC) has emerged as a promising solution. Recent advancements in deep reinforcement learning (DRL) have further enhanced ATSC’s capabilities. This paper introduces a novel DRL-based ATSC approach named the Sequence Decision Transformer (SDT), employing DRL enhanced with attention mechanisms and leveraging the robust capabilities of sequence decision models, akin to those used in advanced natural language processing, adapted here to tackle the c

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Parinith, R. Iyer, Raman Iyer Shrutheesh, Ramesh Raghavendran, MR Anala, and N. Subramanya K. "Adaptive real time traffic prediction using deep neural networks." International Journal of Artificial Intelligence (IJ-AI) 8, no. 2 (2019): 107–19. https://doi.org/10.11591/ijai.v8.i2.pp107-119.

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The ever-increasing sale of vehicles and the steady increase in population density in metropolitan cities have raised many growing concerns, most importantly commute time, air and noise pollution levels. Traffic congestion can be alleviated by opting adaptive traffic light systems, instead of fixedtime traffic signals. In this paper, a system is proposed which can detect, classify and count vehicles passing through any traffic junction using a single camera (as opposed to multi-sensor approaches). The detection and classification are done using SSD Neural Network object detection algorithm. Th

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Mamta Chauhan. "Vehicle Identification for Performance Evaluation of LTE-Based Traffic Management against AI and Conventional Techniques." Journal of Information Systems Engineering and Management 10, no. 18s (2025): 650–63. https://doi.org/10.52783/jisem.v10i18s.2978.

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These most industrialized countries now focus on traffic management because traffic numbers have risen together with global traffic expansion. The technological progression resulted in a modern traffic management solution which allows counting vehicles as well as monitoring their speeds for enhanced transportation planning systems. The established system has lowered the incidence of accidents stemming from deteriorating traffic conditions. Manual road traffic surveys have been ongoing for many years since automation installations proved difficult to implement. Wireless sensor networks stand as

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Jiang, Peng Peng, Andreas Poschinger, and Tong Yan Qi. "MOTION - A Developing Urban Adaptive Traffic Signal Control System." Advanced Materials Research 779-780 (September 2013): 788–91. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.788.

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This paper mainly focuses on the recent development of MOTION system. MOTION system, as a developing urban adaptive traffic signal control system, is now more and more worldwide recognized. In this paper, first the original idea of MOTION is descript. Calculation procedures of MOTION algorithm are followed. Then methods of optimization, including optimization of green split, optimization of cycle time, optimization of offset times are introduced. Platoon Model, as a vital concept in optimization of offset times, are explained in details. Multi functional levels in MOTION system including the t

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Laddha, Ayush. "Intelligent Traffic Management System with Adaptive Signal Control and Emergency Vehicle Prioritization." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 1392–97. http://dx.doi.org/10.22214/ijraset.2023.56720.

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Abstract: Efficient traffic management is essential for ensuring smooth flow and minimizing congestion on roadways. In this project, we propose an Arduino-based solution that utilizes IR sensors for real-time traffic monitoring and signal timing adjustment, coupled with a Bluetooth module to prioritize emergency vehicles during critical situations. The IR sensors are deployed at intersections to monitor vehicle presence, enabling real-time traffic monitoring. The Arduino Uno microcontroller processes the sensor data and dynamically adjusts signal timings to ensure smooth traffic flow. Addition

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Anandan Dhanaraj. "Adaptive Signal Control and Routing using Real-Time Data Processing." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2219–29. https://doi.org/10.32628/cseit25112801.

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This article examines the implementation and impact of adaptive signal control systems enhanced by real-time data processing to solve urban traffic congestion. The article explores how these intelligent traffic management systems dynamically adjust signal timing based on actual traffic conditions rather than predetermined schedules, resulting in significant improvements in urban mobility. The article details the system architecture, incorporating strategic sensor placement, real-time analytics frameworks, and sophisticated control algorithms that enable responsive traffic management. Through a

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Zou, Yuan-Yang, Xue-Guo Xu, and Gui-Hua Lin. "Adaptive multi-objective traffic signal control using NLRMNSGA-II algorithm." Canadian Journal of Civil Engineering 45, no. 11 (2018): 973–85. http://dx.doi.org/10.1139/cjce-2017-0354.

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In this paper, we consider an adaptive system for controlling green times at junction. For this adaptive system, we present a multi-objective optimization model, which is much easier to solve than some existing models. Furthermore, to solve the new model, we suggest an algorithm, called NLRMNSGA-II, which is based on the nonlinear least regression and a modified non-dominated sorting genetic algorithm. Our numerical experiments indicate that the NLRMNSGA-II is an efficient algorithm for the considered adaptive system.

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Sathish Rao. "Traffic Signal Timing and Real Time Optimization." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2082–89. https://doi.org/10.47392/irjaeh.2025.0304.

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The optimization of traffic signal timing has evolved from simple fixed schedules to dynamic, real-time adaptive systems empowered by artificial intelligence (AI) and machine learning (ML). As urban traffic congestion intensifies and environmental sustainability becomes critical, intelligent traffic control systems are poised to play a central role in modern smart cities. This review synthesizes recent advancements in real-time traffic signal optimization, highlighting methodologies such as deep reinforcement learning, evolutionary algorithms, and multi-agent systems. While experimental result

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Agrawal, Aditi, and Rajeev Paulus. "Improving traffic and emergency vehicle clearence at congested intersections using fuzzy inference engine." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3176. http://dx.doi.org/10.11591/ijece.v11i4.pp3176-3185.

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Traffic signals play an important role in controlling and coordinating the traffic movement in cities especially in urban areas. As the traffic is exponentially increasing in cities and the pre-timed traffic light control is insufficient in effective timing of the traffic lights, it leads to poor traffic clearance and ultimately to heavy traffic congestion at intersections. Even the Emergency vehicles like Ambulance and Fire brigade are struck at such intersections and experience a prolonged waiting time. An adaptive and intelligent approach in design of traffic light signals is desirable and

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Aditi, Agrawal, and Paulus Rajeev. "Improving traffic and emergency vehicle clearance at congested intersections using fuzzy inference engine." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3176–85. https://doi.org/10.11591/ijece.v11i4.pp3176-3185.

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Traffic signals play an important role in controlling and coordinating the traffic movement in cities especially in urban areas. As the traffic is exponentially increasing in cities and the pre-timed traffic light control is insufficient in effective timing of the traffic lights, it leads to poor traffic clearance and ultimately to heavy traffic congestion at intersections. Even the Emergency vehicles like Ambulance and Fire brigade are struck at such intersections and experience a prolonged waiting time. An adaptive and intelligent approach in design of traffic light signals is desirable and

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E Pavan Kumar, Harshitha C, A Bhavishya, Gattu Pranava Kumar Reddy, and Mr. Saida. "Lane-Wise Traffic Intelligence Using Deep Vision Systems for Signal Optimization." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2234–40. https://doi.org/10.47392/irjaeh.2025.0328.

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Urban traffic congestion remains a critical challenge affecting commute times, fuel efficiency, and air quality. This project presents a data-driven approach to traffic flow optimization by dynamically adjusting traffic signal timings based on real-time vehicle density across multiple lanes. Utilizing computer vision techniques, such as YOLO-based vehicle detection, the system captures live video feeds from intersections to estimate vehicle count per lane. The signal timings are then optimized to prioritize lanes with higher traffic density, thereby reducing overall waiting times and improving

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Singh, Amarpreet, Sandeep Singh, and Alok Aggarwal. "ADAPTIVE TRAFFIC SYSTEM CONTROLLERS IN TRAFFIC ENGINEERING : A SURVEY." Suranaree Journal of Science and Technology 30, no. 3 (2023): 010224. http://dx.doi.org/10.55766/sujst-2023-03-e03030.

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In today’s era, traffic congestion is the widest spread problem observed all over the world, arising as consequence of exponential rise in vehicle count at the traffic intersections. This growth has largely affected the people as they are experiencing enhanced delay in travelling time and increased fuel consumption which led to wastage of billions of dollars. The current road infrastructure design and traffic signal controlling using a cycle of fixed time phase of green/red/yellow lights are not adequate to tackle the rising demands of traffic in an optimum way. These traditional traffic signa

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Buniel, Gideon G., and Carlo P. Tantoy. "Implementation of Surigao Real-Time Adaptive Traffic Signal Algorithm (RATSA) for Traffic Management in Barangay Luna, Surigao City, Philippines." International Journal of Research and Scientific Innovation IX, no. VIII (2024): 284–300. http://dx.doi.org/10.51244/ijrsi.2024.1108024.

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This research presents the implementation and evaluation of an adaptive traffic light system prototype in Barangay Luna, Surigao City, Philippines. The system utilizes Arduino Mega boards and ultrasonic sensors to detect vehicle presence in three lanes, dynamically adjusting traffic light sequences to optimize traffic flow. Data was collected over multiple trials, assessing various scenarios of vehicle detection. The results demonstrated that the adaptive system significantly reduced wait times and improved traffic efficiency compared to conventional fixed-time systems. Key findings highlighte

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Patil, Vijay. "AI-Based Adaptive Traffic Management." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 945–47. https://doi.org/10.22214/ijraset.2024.65908.

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Traffic congestion is one of the biggest and most important issues facing modern city dwellers, necessitating effective and efficient solutions. AI-powered traffic light optimization has grown in popularity. The available literature indicates that intelligent approaches outperform traditional management techniques. The amount of cars on each intersection road determines the signal timings, which are adjusted to reduce traffic at the intersection. The recommended topology may significantly reduce traffic jams and wait times at the intersection. A lot of benefits can arise from an efficient traf

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Buniel, Gideon G., and Carlo P. Tantoy. "SURATSA: Implementation of Surigao Real-Time Adaptive Traffic Signal Algorithm (RATSA) for Traffic Management in Barangay Luna, Surigao City, Philippines." International Journal of Research and Scientific Innovation XI, no. VIII (2024): 342–57. http://dx.doi.org/10.51244/ijrsi.2024.1108029.

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This research presents the implementation and evaluation of an adaptive traffic light system prototype in Barangay Luna, Surigao City, Philippines. The system utilizes Arduino Mega boards and ultrasonic sensors to detect vehicle presence in three lanes, dynamically adjusting traffic light sequences to optimize traffic flow. Data was collected over multiple trials, assessing various scenarios of vehicle detection. The results demonstrated that the adaptive system significantly reduced wait times and improved traffic efficiency compared to conventional fixed-time systems. Key findings highlighte

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Vlasceanu, Ioana-Miruna, Vasilica-Cerasela-Doinita Ceapa, Ioan Stefan Sacala, et al. "Comparative Evaluation of Fuzzy Logic and Q-Learning for Adaptive Urban Traffic Signal Control." Electronics 14, no. 14 (2025): 2759. https://doi.org/10.3390/electronics14142759.

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In recent years, the number of vehicles in cities has visibly increased, leading to continuous modifications in general mobility. Pollution levels and congestion cases are reaching higher numbers as well, pointing to a need for better optimization solutions. Several existing control systems still rely on fixed timings for traffic lights, lacking an adaptive approach that can adjust the timers depending on real-time conditions. This study aims to provide a design for such a tool, by implementing two different approaches: Fuzzy Logic Optimization and an Adaptive Traffic Management strategy. The

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Almaliki, Malik, Amna Bamaqa, Mahmoud Badawy, Tamer Ahmed Farrag, Hossam Magdy Balaha, and Mostafa A. Elhosseini. "Adaptive Traffic Light Management for Mobility and Accessibility in Smart Cities." Sustainability 17, no. 14 (2025): 6462. https://doi.org/10.3390/su17146462.

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Urban road traffic congestion poses significant challenges to sustainable mobility in smart cities. Traditional traffic light systems, reliant on static or semi-fixed timers, fail to adapt to dynamic traffic conditions, exacerbating congestion and limiting inclusivity. To address these limitations, this paper proposes H-ATLM (a hybrid adaptive traffic lights management), a system utilizing the deep deterministic policy gradient (DDPG) reinforcement learning algorithm to optimize traffic light timings dynamically based on real-time data. The system integrates advanced sensing technologies, such

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Idris, Amidora, Muhammad Syakirin Ihsanuddin Rosman, and Muhammad Abdy. "Prediction and Traffic Light Control Performance using Fuzzy Logic at Intersections of Road." Semarak International Journal of Fundamental and Applied Mathematics 4, no. 1 (2024): 85–97. https://doi.org/10.37934/sijfam.4.1.8597.

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The rapid expansion in the number of vehicles in Malaysia has resulted in a high traffic density on the country's roadways, which has become a significant challenge for Malaysia's road traffic planners. In its current configuration, the traffic light system in Malaysia makes use of a fixed timer system, which is comprised of timing signal controllers and adaptive signal controllers. It is calculated based on the statistics of the peak hour and the time is kept consistent even during the non-peak hours. Number of how many cars are on the road or how wide the road is, this one standard green tim

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Duan, Lin, and Hongxing Zhao. "An Adaptive Signal Control Model for Intersection Based on Deep Reinforcement Learning Considering Carbon Emissions." Electronics 14, no. 8 (2025): 1664. https://doi.org/10.3390/electronics14081664.

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To address the needs of enhancing adaptive control and reducing emissions at intersections within intelligent traffic signal systems, this study innovatively proposes a deep reinforcement learning signal control model tailored for mixed traffic flows. Addressing shortcomings in existing models that overlook mixed traffic scenarios, neglect optimization of CO2 emissions, and overly rely on high-performance algorithms, our model utilizes vehicle queue length, average speed, numbers of gasoline and electric vehicles, and signal phases as state information. It employs a fixed-phase strategy to dec

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Vieira, Manuel Augusto, Gonçalo Galvão, Manuela Vieira, Paula Louro, Mário Vestias, and Pedro Vieira. "Enhancing Urban Intersection Efficiency: Visible Light Communication and Learning-Based Control for Traffic Signal Optimization and Vehicle Management." Symmetry 16, no. 2 (2024): 240. http://dx.doi.org/10.3390/sym16020240.

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This paper introduces a novel approach, Visible Light Communication (VLC), to optimize urban intersections by integrating VLC localization services with learning-based traffic signal control. The system enhances communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. Through Vehicle-to-Vehicle (V2V) and Infrastructure-to-Vehicle (I2V) interactions, joint data transmission and collection occur via mobile optical receivers. The goal is to reduce waiting times for pedestrians and vehicles, enhancing overall traffic s

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Panchal, Mihir, and Pankaj Prajapati. "FPA-DQN: A Fairness- and Pressure-Aware Dueling Deep Q-Network for Adaptive Traffic Signal Control Using UAV-Based Trajectory Data." Indian Journal Of Science And Technology 18, no. 28 (2025): 2257–72. https://doi.org/10.17485/ijst/v18i28.735.

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Objectives: This research introduces FPA-DQN, a Fairness- and Pressure-Aware Dueling Deep Q-Network designed to optimize adaptive traffic signal control at real-world intersections. The framework aims to minimize average vehicle waiting times and queue disparities while addressing congestion and detection limitations using UAV-derived trajectory data. Method: Aerial video footage was collected via UAV and processed using DataFromSky to extract lane-level traffic trajectories. The data was simulated in SUMO for training and evaluation. The FPA-DQN model employs a Dueling DQN structure with Prio

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Szoke, Laszlo, Szilárd Aradi, and Tamás Bécsi. "Traffic Signal Control with Successor Feature-Based Deep Reinforcement Learning Agent." Electronics 12, no. 6 (2023): 1442. http://dx.doi.org/10.3390/electronics12061442.

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In this paper, we study the problem of traffic signal control in general intersections by applying a recent reinforcement learning technique. Nowadays, traffic congestion and road usage are increasing significantly as more and more vehicles enter the same infrastructures. New solutions are needed to minimize travel times or maximize the network capacity (throughput). Recent studies embrace machine learning approaches that have the power to aid and optimize the increasing demands. However, most reinforcement learning algorithms fail to be adaptive regarding goal functions. To this end, we provi

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Khitrov, Ihor, and Viktoriia Nykonchuk. "IMPROVING THE EFFICIENCY OF TRAFFIC LIGHT CONTROL AT ROAD INTERSECTIONS." Avtoshliakhovyk Ukrayiny, no. 1 (278)’ 2024 (March 31, 2024): 60–67. http://dx.doi.org/10.33868/0365-8392-2024-1-278-60-67.

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From the functional point of view, the intersection is the most complex element of the road network. It is here that the traffic flows in different directions cross, and various maneuvers take place. This indicates that the intersection is a place with an increased concentration of conflict situations and an increased risk of traffic accidents. At most high-flow intersections, traffic is controlled by traffic lights, and their inefficient operation can lead to unnecessarily long wait times and overall increase in traffic delays. The research analyzed a regulated intersection at the crossing of

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Cao, Sen, Yaping Sun, Xingchen Zhang, and Mengyang Yang. "Intelligent connected adaptive signal control considering pedestrians based on the EXP-DDQN algorithm." PLOS One 20, no. 6 (2025): e0322945. https://doi.org/10.1371/journal.pone.0322945.

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With the increasing integration of Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HDVs) in urban traffic systems, along with highly variable pedestrian crossing demands, traffic management faces unprecedented challenges. This study introduces an improved adaptive signal control approach using an enhanced dual-layer deep Q-network (EXP-DDQN), specifically tailored for intelligent connected environments. The proposed model incorporates a comprehensive state representation that integrates CAV-HDV car-following dynamics and pedestrian flow variability. Additionally, it features

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Purnamasari, Epih, and Mohammad Fajar Nurwildani. "Efficient Graph-Based Algorithm for Real-Time Traffic Flow Optimization in Smart Cities." ALCOM: Journal of Algorithm and Computing 1, no. 1 (2025): 45–53. https://doi.org/10.63846/a7m8wy45.

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With the rapid increase in vehicle density, urban traffic congestion has become a significant challenge, leading to inefficiencies in transportation systems and increased fuel consumption. Existing traffic management methods, such as fixed signal timing and heuristic-based optimizations, struggle to adapt to real-time traffic fluctuations. While recent studies have explored graph-based models and reinforcement learning for traffic optimization, they often fail to capture complex spatiotemporal dependencies dynamically. To address this gap, we propose a novel graph-based algorithm that integrat

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Liu, Jiayu. "Enhancing Urban Traffic Management: A Comprehensive Study of Adaptive Control Traffic Light Systems." Highlights in Science, Engineering and Technology 111 (August 19, 2024): 473–79. https://doi.org/10.54097/m7eqkp82.

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With the rapid development of urbanization, traffic congestion has become an increasingly serious issue, posing significant challenges to urban mobility and environmental sustainability. To improve traffic efficiency and alleviate transportation strain, this paper explores the adaptive control traffic light system, a promising solution leveraging advanced technologies. The paper begins by introducing the basic principles and technical features of adaptive traffic light control systems, which adjust signal timings based on real-time traffic conditions. These systems utilize a range of technolog

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RAJA, Dr V. SAI SHANMUGA, Dr G. GUNASEKARAN, and CHINCHU NAIR. "OPTIMAL TRAFFIC CONTROL SYSTEM FOR TRAFFIC CONGESTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem29177.

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A smart city's traffic management system is regarded as one of its primary components. Traffic jams are a common sight on the roadways in metropolitan areas due to the rapid increase in population and urban mobility. In order to address road traffic management issues and assist authorities with appropriate planning, an intelligent traffic management system utilizing the Yolo algorithm and Open CV approach is proposed in this project. A workable model for counting automobiles in traffic was developed using image processing as the basis. image processing methods classified and tallied moving veh

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V, Lokesh. "Density Based Traffic Management using Arduino Sensor." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2665–70. https://doi.org/10.22214/ijraset.2025.68788.

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This work focuses on developing a density-based traffic management system using Arduino and infrared (IR) sensors to optimize traffic signal timings based on real-time vehicle density. By detecting and analyzing traffic flow at intersections, the system dynamically adjusts green light durations, reducing congestion, minimizing wait times, and improving fuel efficiency. The proposed approach enhances urban traffic management by offering a cost-effective, scalable, and adaptive solution suitable for smart city applications. Initial testing demonstrates significant improvements in traffic flow, h

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Sura, Sahil, and Himmi Gupta. "Deep Learning Algorithm for Optimization of Wait Time at a Traffic Signal Controlled Intersection for Smart Traffic Management." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 373–85. http://dx.doi.org/10.22214/ijraset.2024.57950.

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Abstract: The urban landscape is constantly shifting and adapting as a result of factors such as population increase, immigration, and changes in the economy, politics, and culture. Concerns like growing road obstruction, transit delays, pollution levels, consumption of gasoline, etc. have resulted from higher traffic volumes in metropolitan areas. Life expectancy, road safety, environmental quality, and commute times are all impacted by traffic congestion. At junctions, traffic lights play a crucial role in regulating vehicular flow. The traditional pre-timed traffic systems have been ineffec

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Bhosale Patil, Harshavardhan. "Design and Implementation of Smart Traffic Control System Based on Traffic Density." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03657.

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Abstract- With the rapid growth of urban populations and the increasing number of vehicles on the road, traffic congestion has become a significant problem in metropolitan areas. Conventional traffic control systems, which rely on pre-set signal timers, often fail to address real-time traffic conditions effectively, leading to inefficient traffic flow, increased fuel consumption, and environmental degradation. The paper “Smart Traffic Control System Based on Traffic Density Using YOLO” introduces a novel approach to address this issue by integrating artificial intelligence and computer vision

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Bahmanyar, Reza, Jens Hellekes, Manuel Mühlhaus, Veronika Gstaiger, and Franz Kurz. "Traffic Pattern Analysis at Urban Intersections through Vehicle Detection in Aerial Imagery." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-G-2025 (July 10, 2025): 151–58. https://doi.org/10.5194/isprs-annals-x-g-2025-151-2025.

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Abstract. This paper explores the application of aerial image sequences to analyze vehicle flows at urban intersections, with the goal of generating data that can inform adaptive traffic signal timing and improve urban mobility in complex, interconnected road networks. Using deep learning techniques, we detect vehicles in aerial images taken at different times of the day and week at a given urban intersection. This approach allows us to infer vehicle density, identify queuing patterns, and analyze traffic light cycles. Such analysis can assess the robustness of signal timing under different tr

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Isukapati, Isaac K., Hana Rudová, Gregory J. Barlow, and Stephen F. Smith. "Analysis of Trends in Data on Transit Bus Dwell Times." Transportation Research Record: Journal of the Transportation Research Board 2619, no. 1 (2017): 64–74. http://dx.doi.org/10.3141/2619-07.

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Transit vehicles create special challenges for urban traffic signal control. Signal timing plans are typically designed for the flow of passenger vehicles, but transit vehicles—with frequent stops and uncertain dwell times—may have different flow patterns that fail to match those plans. Transit vehicles stopping on urban streets can also restrict or block other traffic on the road. This situation results in increased overall wait times and delays throughout the system for transit vehicles and other traffic. Transit signal priority (TSP) systems are often used to mitigate some of these issues,

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Sohani, Arnav, Ishan Gaikwad, Omkar Lonkar, Samiksha Roy, and Vaibhav Sawalkar. "Smart Traffic Light Control System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–6. https://doi.org/10.55041/ijsrem39220.

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Abstract—Traffic congestion is a significant issue in urban areas, leading to increased travel time, fuel consumption, and environmental pollution. This project introduces an adaptive Traffic Light Control System that dynamically prioritizes traf- fic direction based on real-time vehicle counts obtained from cameras. The system ensures efficient traffic flow by allocating green light duration proportionally to the vehicle density in each direction while maintaining fairness through a minimum green time of 3 seconds for all directions. The implementation includes a clockwise rotation of the gre

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