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

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...

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Autores principales:	Selim Reza, Hugo S. Oliveira, José J. M. Machado, João Manuel R. S. Tavares
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	intelligent traffic management traffic forecasting traffic signal control image processing deep learning machine vision Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/0af2940407e4434abd0b13b9b8af084a
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spelling	oai:doaj.org-article:0af2940407e4434abd0b13b9b8af084a2021-11-25T18:58:45ZUrban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System10.3390/s212277051424-8220https://doaj.org/article/0af2940407e4434abd0b13b9b8af084a2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7705https://doaj.org/toc/1424-8220With 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.Selim RezaHugo S. OliveiraJosé J. M. MachadoJoão Manuel R. S. TavaresMDPI AGarticleintelligent traffic managementtraffic forecastingtraffic signal controlimage processingdeep learningmachine visionChemical technologyTP1-1185ENSensors, Vol 21, Iss 7705, p 7705 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	intelligent traffic management traffic forecasting traffic signal control image processing deep learning machine vision Chemical technology TP1-1185
spellingShingle	intelligent traffic management traffic forecasting traffic signal control image processing deep learning machine vision Chemical technology TP1-1185 Selim Reza Hugo S. Oliveira José J. M. Machado João Manuel R. S. Tavares Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System
description	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.
format	article
author	Selim Reza Hugo S. Oliveira José J. M. Machado João Manuel R. S. Tavares
author_facet	Selim Reza Hugo S. Oliveira José J. M. Machado João Manuel R. S. Tavares
author_sort	Selim Reza
title	Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System
title_short	Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System
title_full	Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System
title_fullStr	Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System
title_full_unstemmed	Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System
title_sort	urban safety: an image-processing and deep-learning-based intelligent traffic management and control system
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0af2940407e4434abd0b13b9b8af084a
work_keys_str_mv	AT selimreza urbansafetyanimageprocessinganddeeplearningbasedintelligenttrafficmanagementandcontrolsystem AT hugosoliveira urbansafetyanimageprocessinganddeeplearningbasedintelligenttrafficmanagementandcontrolsystem AT josejmmachado urbansafetyanimageprocessinganddeeplearningbasedintelligenttrafficmanagementandcontrolsystem AT joaomanuelrstavares urbansafetyanimageprocessinganddeeplearningbasedintelligenttrafficmanagementandcontrolsystem
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Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System

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