Macroscopic Structural Summarization of Road Networks for Mobile Traffic Information Services

Asahara, Akinori; Shimada, Shigeru; Maruyama, Kishiko

doi:10.1109/mdm.2006.103

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Subsequently, several similar methods have been proposed with regard to road generalization, wherein the relative importance of the road is estimated and the order of the road is determined on the basis of a combination of other road attributes and the length and connectivity relationship. Bjrke proposed an algorithm [8] for deselecting roads on a map on the basis of information theory. In this method, a similarity function is calculated from an arbitrary point in the road network, which is called the information point.…”

Section: Related Workmentioning

confidence: 99%

On-Demand Generalization of Road Networks Based on Facility Search Results

Yamamoto

Murase

Takahashi

2019

IEICE Trans. Inf. & Syst.

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Road generalization is a method for thinning out road networks to allow easy viewing according to the size of the map. Most conventional road generalization methods mainly focus on the length of a stroke, which is a chain of links with good continuity based on the principle of perceptual grouping applied to network data such as roads and rivers. However, in the case of facility search in a web map service, for example, a "restaurant guide map," a road generalization mechanism can be more effective if it depends not only on the stroke length but also on the facility search results. Accordingly, in this study, we implement an on-demand road generalization method that adapts to both the facility search results and the stroke length. Moreover, a sufficiently fast response speed is achieved for practical use in web map services. In particular, this study proposes a fat-stroke model that links facility information to individual strokes and implements a road generalization method that uses this model to improve the response time. In addition, we develop a prototype based on the proposed system. The system evaluation results are based on three indicators, namely, response time of the road generalization system, connectivity between strokes, and connectivity between stroke and facilities. Our experimental results suggest that the proposed method can yield improved response times by a factor of 100 or more while affording higher connectivity.

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Section: Related Workmentioning

confidence: 99%

On-Demand Generalization of Road Networks Based on Facility Search Results

Yamamoto

Murase

Takahashi

2019

IEICE Trans. Inf. & Syst.

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“…Estimation of the amount of traffic on the roads at any given point of time is the first step in mitigating traffic congestion [2]. A common method is to place sensors on the road and count the number of times they are actuated by the passing wheels of a vehicle.…”

Section: Introductionmentioning

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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