Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing

Boulmakoul, Azedine; Karim, Lamia; Mandar, Meriem; Idri, Abdelfettah; Daissaoui, Abdellah

doi:10.1155/2015/578601

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…e confounding network-based dictionarybased fusion method is better than other translation fusion methods in specific tasks [31]. Among these three different translation fusion strategies, the sentence-level and lexicallevel fusion methods are the two more popular translation fusion methods.…”

Section: Fusion Of Machine Translationsmentioning

confidence: 99%

Networked Artificial Intelligence English Translation System Based on an Intelligent Knowledge Base and Translation Method Thereof

Ren

2021

Mobile Information Systems

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Language translation is often conducted in work and study. Traditional language translation is based on lexical structure analysis. However, natural language is not so standardized, which causes this translation method to have fundamental defects, no matter how much the algorithm is improved. The translation results and human translation will be very different. This paper mainly studies the networked artificial intelligence. The English translation system and translation methods are based on a smart knowledge base. Bringing an example of English-Chinese translation to suggest translations according to the intelligent knowledge base explains in detail the principle of intelligent knowledge-based translation and the advantage of this translation method compared with the traditional translation method based on lexical structure analysis. In the experiment of this paper, when the variance is 2/N, 30/N, 100/N, and 2N, it is the experimental data for an in-depth study. When the variance is 2/N, 30/N, and 100/N, the result is the same as that when the variance is 0.5; the result when the variance is 2N also conforms to the trend in the tables, which is close to the effect of the smoothing algorithm, which verifies the effectiveness of the system in this paper.

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Section: Fusion Of Machine Translationsmentioning

confidence: 99%

Networked Artificial Intelligence English Translation System Based on an Intelligent Knowledge Base and Translation Method Thereof

Ren

2021

Mobile Information Systems

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“…To detect the traffic intensity, there are 3 critical approach in the modelling of the traffic flows in roads namely the spatial modelling [6]- [9], time prediction modelling [10]- [12] and non-parametric modelling. Spatial modelling refers to using the physical space on real time to estimate the motion of the vehicles based on the physical parameters such as road distance, car length, road curvature and other key elements.…”

Section: Figure 1 Traffic Congestion In Kuala Lumpur Malaysiamentioning

confidence: 99%

Measuring the Road Traffic Intensity using Neural Network with Computer Vision

Hamdan¹,

Khalifah²,

Gunawan³

2018

IJEECS

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Traffic congestion plagues all driver around the world. To solve this problem computer vision can be used as a tool to develop alternative routes and eliminate traffic congestions. In the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT) this solution will have a greater impact on current systems. In this paper, the Macroscopic Urban Traffic model is used using computer vision as its source and traffic intensity monitoring system is implemented. The input of this program is extracted from a traffic surveillance camera and another program running a neural network classification which can classify and distinguish the vehicle type is on the road. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated.

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“…To detect the traffic intensity, there are 3 critical approach in the modelling of the traffic flows in roads namely the spatial modelling [7], [8] [9] [10], time prediction modelling [11] [12] [13] and nonparametric modelling. Spatial modelling refers to using the physical space on real time to estimate the motion of the vehicles based on the physical parameters such as road distance, car length, road curvature and other key elements.…”

Section: Related Workmentioning

confidence: 99%

Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

Gani

Khalifa

Gunawan

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. Object detection and tracking is a field of research that has many applications in the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT). In this paper, a traffic intensity monitoring system is implemented based on the Macroscopic Urban Traffic model is proposed using computer vision as its source. The input of this program is extracted from a traffic surveillance camera which has another program running a neural network classification which can identify and differentiate the vehicle type is implanted. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated. relevant articles in literature searches, great care should be taken in constructing both. Lastly the limitation and the future work is concluded.

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Towards Scalable Distributed Framework for Urban Congestion Traffic Patterns Warehousing

Cited by 9 publications

References 24 publications

Networked Artificial Intelligence English Translation System Based on an Intelligent Knowledge Base and Translation Method Thereof

Networked Artificial Intelligence English Translation System Based on an Intelligent Knowledge Base and Translation Method Thereof

Measuring the Road Traffic Intensity using Neural Network with Computer Vision

Traffic intensity monitoring using multiple object detection with traffic surveillance cameras

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