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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/22/7705 |
| _version_ | 1797508416996900864 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:03:45Z |
| format | Article |
| id | doaj.art-0af2940407e4434abd0b13b9b8af084a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:03:45Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0af2940407e4434abd0b13b9b8af084a2023-11-23T01:28:38ZengMDPI AGSensors1424-82202021-11-012122770510.3390/s21227705Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control SystemSelim Reza0Hugo S. Oliveira1José J. M. Machado2João Manuel R. S. Tavares3Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, PortugalFaculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, PortugalDepartamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, PortugalDepartamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, PortugalWith 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.https://www.mdpi.com/1424-8220/21/22/7705intelligent traffic managementtraffic forecastingtraffic signal controlimage processingdeep learningmachine vision |
| spellingShingle | 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 Sensors intelligent traffic management traffic forecasting traffic signal control image processing deep learning machine vision |
| title | 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_short | 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 |
| topic | intelligent traffic management traffic forecasting traffic signal control image processing deep learning machine vision |
| url | https://www.mdpi.com/1424-8220/21/22/7705 |
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