• Journal of Korean Society of Transportation
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• v.28 no.4
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• pp.129-139
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• 2010

This study proposes a model for measuring the connectivity of nodes in road networks. The connectivity index between two nodes is characterized by the number of routes, degree of circuitousness, design speed, and route capacity between the nodes. The connectivity index of a node is then defined as the weighted average of the connectivity indexes between the node and other nodes under consideration. The weighting factor between two nodes is determined by the travel demand and distance between them. The application of the model to a toy network shows that it reasonably well quantifies the level of connectivity of nodes in the network. If flow of rail networks can be measured in the same scale as that of road networks and the capacity of rail links can be estimated, the model proposed in this paper could be applied to intermodal transportation networks as well.

• Structural Monitoring and Maintenance
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• v.8 no.4
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• pp.361-378
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• 2021

While the pavement rating system is being utilized for periodic road condition assessment in the Eastern Region municipality of Saudi Arabia, the condition assessment is costly, time-consuming, and not comprehensive as only few parts of the road are randomly selected for the assessment. Thus, this study is aimed at developing a condition assessment model for a specific sample of a residential road network in Dammam City based on an individual road and a road network. The model was developed using the Analytical Hierarchy Process (AHP) according to the defect types and their levels of severity. The defects were arranged according to four categories: structure, construction, environmental, and miscellaneous, which was adopted from sewer condition coding systems. The developed model was validated by municipality experts and was adjudged to be acceptable and more economical compared to results from the Eastern region municipality (Saudi Arabia) model. The outcome of this paper can assist with the allocation of the government's budget for maintenance and capital programs across all Saudi municipalities through maintaining road infrastructure assets at the required level of services.

• Spatial Information Research
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• v.15 no.2
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• pp.95-109
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• 2007

Recently people have been offered location based services on road networks. The navigation system, one of applications, serves to find the nearest gas station or guide divers to the shortest path based 2D map. However 3D map is more important media than 2D map to make sense friendly for the real. Although 3D map's data size is huge, portable devices' storage space is small. In this paper, we define continuous perspective queries to support 3D map to mobile user on road networks and propose this queries processing method.

• Spatial Information Research
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• v.21 no.4
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• pp.7-14
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• 2013

Recently, R&D(Research and Development) is processing actively on range search in the road network environments. However, the existing representative range search techniques have shortcomings in that the greater the number of POI's, the more increased storage space or the more increased search time due to inefficient search process. Accordingly, In this paper, we proposed a range search technique using QRMP(QR-tree using Middle Point) to solve the problems of conventional range search techniques. In addition, we made a formula to obtain the total size of the storage space for QRMP and proved the excellence of the range search technique proposed in this paper through the experiment using actual road networks and POI data.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.2 s.191
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• pp.47-57
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• 2007

The vibration environment essentially companied by vehicle operation on the road is determined by the shape of road surface, which is called profile. In general, the profile and severity of unpaved road is an important issue in the reliability of durability test for vehicles. In order to maintain severity of unpaved road, it is necessary to develop profilometer system. We developed profilometer system which is composed of data processing computer, power unit, air compressor and sensors. This paper focuses on the severity management of unpaved test courses using neural networks. This paper presents the maintenance range for cross-country course in CPG(Chang-won Proving Ground) and the evaluation of similarity degree between unpaved roads.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.656-657
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• 2008

Light Detection and Ranging (LiDAR) data has been used to detect the objects of earth surface from huge point clouds gotten from the laser scanning system equipped on airplane. According to the precision of 3~5 points per square meter, objects like buildings, cars and roads can be easily described and constructed. Many various areas, such as hydrological modeling and urban planning adopt this kind of significant data. Researchers have been engaging in finding accurate road networks from LiDAR data recent years. In this paper, A novel algorithm with regard to extracting road points from LiDAR data has been developed based on the continuity and structural characteristics of road networks.

• Journal of Construction Engineering and Project Management
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• v.5 no.1
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• pp.26-31
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• 2015

Success of the construction companies is based on the successful completion of projects within the agreed cost and time limits. Artificial neural networks (ANN) have recently attracted much attention because of their ability to solve the qualitative and quantitative problems faced in the construction industry. For the estimation of cost and duration different ANN models were developed. The database consists of data collected from completed projects. The same data is normalised and used as inputs and targets for developing ANN models. The models are trained, tested and validated using MATLAB R2013a Software. The results obtained are the ANN predicted outputs which are compared with the actual data, from which deviation is calculated. For this purpose, two successfully completed highway road projects are considered. The Nftool (Neural network fitting tool) and Nntool (Neural network/ Data Manager) approaches are used in this study. Using Nftool with trainlm as training function and Nntool with trainbr as the training function, both the Projects A and B have been carried out. Statistical analysis is carried out for the developed models. The application of neural networks when forming a preliminary estimate, would reduce the time and cost of data processing. It helps the contractor to take the decision much easier.

• Journal of the Korea Society of Computer and Information
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• v.24 no.10
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• pp.79-89
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• 2019

While most research focuses on the k-nearest neighbors (kNN) queries in the database community, an important type of proximity queries called k-farthest neighbors (kFN) queries has not received much attention. This paper addresses the problem of finding the k-farthest neighbors in road networks. Given a positive integer k, a query object q, and a set of data points P, a kFN query returns k data objects farthest from the query object q. Little attention has been paid to processing kFN queries in road networks. The challenge of processing kFN queries in road networks is reducing the number of network distance computations, which is the most prominent difference between a road network and a Euclidean space. In this study, we propose an efficient algorithm called FANS for k-FArthest Neighbor Search in road networks. We present a shared computation strategy to avoid redundant computation of the distances between a query object and data objects. We also present effective pruning techniques based on the maximum distance from a query object to data segments. Finally, we demonstrate the efficiency and scalability of our proposed solution with extensive experiments using real-world roadmaps.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.2
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• pp.289-297
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• 2009

In the paper, we propose the methodology to extract training dataset automatically for supervised classification of road networks. For the preprocessing, we co-register the airborne photos, LIDAR data and large-scale digital maps and then, create orthophotos and intensity images. By overlaying the large-scale digital maps onto generated images, we can extract the initial training dataset for the supervised classification of road networks. However, the initial training information is distorted because there are errors propagated from registration process and, also, there are generally various objects in the road networks such as asphalt, road marks, vegetation, cars and so on. As such, to generate the training information only for the road surface, we apply the Expectation Maximization technique and finally, extract the training dataset of the road surface. For the accuracy test, we compare the training dataset with manually extracted ones. Through the statistical tests, we can identify that the developed method is valid.

• Proceedings of the KOR-KST Conference
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• 2007.11a
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• pp.104-113
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• 2007