Browsing by Author "Thahir, Adam Rizvi"

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Grafik Teorisi Tabanlı Trafik Işığı Yöntemi
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2022) Thahir, Adam Rizvi; Güngör, Vehbi Çağrı; Coşkun, Mustafa; 01. Abdullah Gül University
Traffic congestion and delays caused in traffic light intersections can adversely affect countries in terms of money, time, and air pollution. With the advancement of computational power as well as artificial intelligent algorithms, researchers seek novel and optimized solutions to the traffic congestion problem. Most modern traffic light systems use manually designed traffic phase plans at intersections, and although this has proven to be relatively sufficient for today's traffic management systems, implementing a smarter traffic phase selection system is deemed to be more effective. Traditional approaches rely heavily on traffic history (static information), whereas Reinforcement Learning (RL) algorithms, which offer an 'adoptable'/dynamic traffic management system, are gaining increased research interest. Despite the usefulness of these RL based deep learning techniques, they inherently suffer from training time to apply them in real-world traffic management systems. This study aims to alleviate the training time problem of deep learning-based techniques, The research brings forth a novel graph-based approach that is able to use known occupancies of roads to predict which other roads in a given network would become congested in the future. Based on the predictions obtained, we are able to dynamically set traffic light times in all intersections within a connected network, starting from roads with known occupancies, and moving along connected roads that are anticipated to be congested. Predications are done using edge-based semi-supervised graph algorithms. Conducted simulations show that our approach can yield comparable average wait time to that of deep-learning based approach in minutes, compared to the much longer training time required by the deep-learning models. Keywords: Deep Learning, Reinforcement Learning, Traffic Flow, Congestion
Citation - WoS: 1
Citation - Scopus: 1
Intelligent Traffic Light Systems Using Edge Flow Predictions
(Elsevier, 2024) Thahir, Adam Rizvi; Coskun, Mustafa; Kilic, Sultan Kubra; Gungor, Vehbi Cagri; 01. Abdullah Gül University
In this paper, we propose a novel graph-based semi-supervised learning approach for traffic light management in multiple intersections. Specifically, the basic premise behind our paper is that if we know some of the occupied roads and predict which roads will be congested, we can dynamically change traffic lights at the intersections that are connected to the roads anticipated to be congested. Comparative performance evaluations show that the proposed approach can produce comparable average vehicle waiting time and reduce the training/learning time of learning adequate traffic light configurations for all intersections within a few seconds, while a deep learning-based approach can be trained in a few days for learning similar light configurations.
Citation - Scopus: 1
Traffic Light Management Systems Using Reinforcement Learning
(Institute of Electrical and Electronics Engineers Inc., 2022) Can, Sultan Kubra; Thahir, Adam Rizvi; Cos¸kun, Mustafa; Güngör, Vehbi Çağrı; 01. Abdullah Gül University
While reducing traffic congestion and decrease the number of traffic accidents in the intersections, most of the traffic light management approaches cannot adapt well to fast changing traffic dynamics and growing demands of the intersections with modern world developments. To overcome this problem, adaptive traffic controllers are developed, and detectors and sensors are added to systems to enable adoption and dynamism. Recently, reinforcement learning has shown its capability to learn the dynamics of complex environments, such as urban traffic. Although it was studied in single junction systems, one of the problems was the lack of consistency with how the real world system works. Most of the systems assume that the environment is fully observable or actions would be freely executed using simulators. This study aims to merge usefulness of reinforcement learning methods with real-world traffic constraints. Comparative performance evaluations show that the reinforcement learning algorithm (Advantage Actor-Critic (A2C)) converges well while staying stable under changing traffic dynamics. © 2022 Elsevier B.V., All rights reserved.