Fuzzy deep learning based urban traffic incident detection

Hatri, Chaimae El; Boumhidi, Jaouad

doi:10.1109/isacv.2017.8054903

Cited by 47 publications

(10 citation statements)

References 9 publications

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“…In the second stage by finding the research problem raised [43], the problem is that there is something basic of research on which to base the research, with the problem it will be able to find out what to find out in the problemsolving process [44].…”

Section: B Find a Problemmentioning

confidence: 99%

Review of Vehicle Surveillance Using Iot in the Smart Transportation Concept

Dewi¹

2021

IJEM

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The background of this research raises the problem of the development of smart transportation in terms of monitoring and enforcement of traffic on the highway, with the proposed system that will help many parties such as the police and the government. The system that is running is to develop a system using CCTV that is placed on every corner of the capital city to replace the police in carrying out road surveillance and law enforcement against lawbreakers, especially in the traffic sector. The method used in this research is by using literature review of many previous research journals, by reading many journals will be able to add knowledge and can deepen ongoing research. The problem raised in this research is finding solutions to problems in the transportation sector, especially smart transportation, using smart transportation will be able to connect all systems that have been made. This research produces a system proposal that can be used in further research and can be applied in terms of the development of smart cities, especially smart transportation.

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Section: B Find a Problemmentioning

confidence: 99%

Review of Vehicle Surveillance Using Iot in the Smart Transportation Concept

Dewi¹

2021

IJEM

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“…The application of learning has used online learning, general learning and religious learning [40], with the use of technology with online media learning can be anywhere and anytime, learning and teaching can be done online or offline [41].…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Greedy Algorithm on Traffic Violation Enforcement

Dewi¹,

Putra²

2021

IJEME

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The background of this research is how the application of the algorithm in traffic control systems, with the Greedy algorithm, the system will decide what action and what punishment will be given to traffic offenders on the highway, with the Greedy algorithm, the decisions taken will be based on data and facts. based on existing laws, so decisions made based on law and the human side cannot influence the decisions to be taken by the system based on the application of the Greedy algorithm. The research method used in this study uses literature reviews by reading many previous research journals, it will be able to add knowledge and deepen the research we are doing this time, with the literature review method, we will be able to find new problems and can be used as new research because The literature review is very helpful for our research this time. The system that is being used is using CCTV and can determine what decisions and punishments will be given to traffic offenders, through evidence based on images taken by cameras placed at red lights or corners of the capital's highway, the system this has been active effectively but with the implementation of the algorithm will increase. This research will produce a system proposal and be able to find out whether the application of the Greedy algorithm is correct and can help the current system by implementing the algorithm, so the existing system is more perfect. The main contribution of this research is that the use of the Greedy algorithm can help control the traffic system to enforce the law.

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“…For traffic prediction, deep learning is used with a combination of different techniques such as big data, in-memory computing with different configurations and stacked autoencoders to represent traffic features. [154][155][156][157][158] Basically, traffic prediction can be further classified into three categories: speed prediction, traffic flow prediction, and traffic accident risk prediction. 159 Although machine learning and deep learning are mostly used to detect objects and their behaviors, its uses should be broader to include data to make informed decisions, exchanging data among vehicles, pedestrians, motorcyclists, cyclists among others, to contribute to accident reduction and improve vehicular traffic.…”

Section: Activity Recognitionmentioning

confidence: 99%

Deep learning support for intelligent transportation systems

Ibáñez

Contreras-Castillo

Zeadally

2020

Trans Emerging Tel Tech

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Intelligent Transportation Systems (ITS) help improve the ever-increasing vehicular flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation of massive amounts of data generated by all the digital devices connected to the transportation network enables the creation of datasets to perform an in-depth analysis of the data using deep learning methods. Such methods can help predict traffic performance, automated traffic light management, lane detection, and identifying objects near vehicles to increase the safety and efficiency of ITS. We discuss some of the challenges that need to be solved to achieve seamless integration between ITS and deep learning methods to address issues such as (1) improving traffic flow/transportation logistics, (2) predicting best routes for the transportation of goods, (3) optimal fuel consumption, (4) intelligent environmental conditions perception, (5) traffic speed management, and accident prevention.

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Fuzzy deep learning based urban traffic incident detection

Cited by 47 publications

References 9 publications

Review of Vehicle Surveillance Using Iot in the Smart Transportation Concept

Review of Vehicle Surveillance Using Iot in the Smart Transportation Concept

Application of Greedy Algorithm on Traffic Violation Enforcement

Deep learning support for intelligent transportation systems

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