Adaptive &amp; Coordinated Traffic Signal System

Dampage, Eng. S.U.; Munasingha, T. D.; Gunathilake, W. D. K.; Weerasundara, A.G.; Udugahapattuwa, D. P. D.

doi:10.1109/gucon48875.2020.9231222

Cited by 6 publications

(1 citation statement)

References 2 publications

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“…Bouktif et al [9] customized a Parameterized Deep Q-Network (P-DQN) architecture, and the evaluation results using Simulation of Urban Mobility (SUMO) showed that it surpassed other benchmarks, achieving a reduction of the travel time by 5.78%. In the work of Dampage et al [159], YOLOv3-tiny was retained and combined with OpenCV, and the traffic density was measured, which drives the signaling schemes using a trained DQN. For a multi-intersection scenario, it achieved an increase of the average speed by 18% compared with a static traffic light system.…”

Section: • Hybrid Deep Q-networkmentioning

confidence: 99%

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

Reza

Oliveira

Machado

et al. 2021

Sensors

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With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.

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Section: • Hybrid Deep Q-networkmentioning

confidence: 99%