• 한국도로학회논문집
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• 제15권3호
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• pp.93-101
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• 2013

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

• 한국측량학회지
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• 제37권2호
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• pp.91-97
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• 2019

In automatic map generalization, the formalization of cartographic principles is important. This study proposes and evaluates the selection method for road network generalization that analyzes existing maps using reverse engineering and formalizes the selection rules for the road network. Existing maps with a 1:5,000 scale and a 1:25,000 scale are compared, and the criteria for selection of the road network data and the relative importance of each network object are determined and analyzed using $T{\ddot{o}}pfer^{\prime}s$ Radical Law as well as the logistic regression model. The selection model derived from the analysis result is applied to the test data, and road network data for the 1:25,000 scale map are generated from the digital topographic map on a 1:5,000 scale. The selected road network is compared with the existing road network data on the 1:25,000 scale for a qualitative and quantitative evaluation. The result indicates that more than 80% of road objects are matched to existing data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.861-880
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• 2023

Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.

• 한국콘텐츠학회논문지
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• 제7권7호
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• pp.1-10
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• 2007

본 논문에서는 도로망에서 이동하는 객체들의 현재 위치를 효율적으로 갱신하는 색인 기법을 제안한다. 기존의 도로망 모델은 이웃한 도로 세그먼트의 연결 정보를 포함하지 않기 때문에 객체가 이웃한 도로 세그먼트로 이동할 때 갱신 성능을 저하시킨다. 본 논문에서는 이러한 문제점을 해결하기 위해 교차점 기반 도로망 모델과 새로운 색인 구조를 제안한다. 제안하는 교차점 기반 도로망 모델은 도로를 분할할 때 교차점을 포함하도록 분할하여 연결 정보가 유지되도록 한다. 성능 평가를 통해 제안하는 색인 기법이 기존 색인 구조에 비해 이동 객체의 갱신 성능이 3배 향상됨을 보인다.

• 농촌계획
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• 제11권1호
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• pp.1-8
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• 2005

The purpose of this study is to develop a simulation model of rural road path for infrastructure of green-tourism and public service in rural areas. This study makes an objective function for moving cost minimization considering car travel time according to road characteristics, which can route the optimal shortest road paths between the center places and all rear villages, based on GIS coverages of road-village network for connecting between center places and rural villages as input data of the model. In order to verify the model algorithm, a homogeneous hexagonal network, assuming distribution of villages with same population density and equal distance between neighborhood villages on a level plane area, was tested to simulate the optimal paths between the selected center nodes and the other rear nodes, so that the test showed reasonable shortest paths and road intensity defined in this study. The model was also applied to the actual rural area, Ucheon-myun, which is located on Hoengsung-gun, Kangwon-do, with 72 rural villages, a center village (Uhang, 1st center place) in the area, a county conte. (Hoengsung-eup, 2nd center place), and a city (Wonju, 3rd center place), as upper settlement system. The three kinds of conte. place, Uhang, Hoengsung-eup, and Wonju, were considered as center places of three scenarios to simulate the optimal shortest paths between the centers and rural villages, respectively. The simulation results on the road-village network with road information about pavement and width of road show that several spans having high intensity of road are more important that the others, while some road spans have low intensity of road.

• 한국인공지능학회지
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• 제11권4호
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• pp.1-8
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• 2023

In this paper, we propose a black ice detection platform framework using Convolutional Neural Networks (CNNs). To overcome black ice problem, we introduce a real-time based early warning platform using CNN-based architecture, and furthermore, in order to enhance the accuracy of black ice detection, we apply a multi-scale dilation convolution feature fusion (MsDC-FF) technique. Then, we establish a specialized experimental platform by using a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Experimental results of a real-time black ice detection platform show the better performance of our proposed network model compared to conventional image segmentation models. Our proposed platform have achieved real-time segmentation of road black ice areas by deploying a road black ice area segmentation network on the edge device Jetson Nano devices. This approach in parallel using multi-scale dilated convolutions with different dilation rates had faster segmentation speeds due to its smaller model parameters. The proposed MsCD-FF Net(2) model had the fastest segmentation speed at 5.53 frame per second (FPS). Thereby encouraging safe driving for motorists and providing decision support for road surface management in the road traffic monitoring department.

• 한국인공지능학회지
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• 제11권3호
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• pp.17-22
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• 2023

In this paper, we propose a black ice detection system using Convolutional Neural Networks (CNNs). Black ice poses a serious threat to road safety, particularly during winter conditions. To overcome this problem, we introduce a CNN-based architecture for real-time black ice detection with an encoder-decoder network, specifically designed for real-time black ice detection using thermal images. To train the network, we establish a specialized experimental platform to capture thermal images of various black ice formations on diverse road surfaces, including cement and asphalt. This enables us to curate a comprehensive dataset of thermal road black ice images for a training and evaluation purpose. Additionally, in order to enhance the accuracy of black ice detection, we propose a multi-scale dilation convolution feature fusion (MsDC-FF) technique. This proposed technique dynamically adjusts the dilation ratios based on the input image's resolution, improving the network's ability to capture fine-grained details. Experimental results demonstrate the superior performance of our proposed network model compared to conventional image segmentation models. Our model achieved an mIoU of 95.93%, while LinkNet achieved an mIoU of 95.39%. Therefore, it is concluded that the proposed model in this paper could offer a promising solution for real-time black ice detection, thereby enhancing road safety during winter conditions.

• Structural Monitoring and Maintenance
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• 제8권4호
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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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• 제20권4호
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• pp.29-36
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• 2012

최근 도시와 도시간의 물류량 증가로 인하여 교통혼잡비용이 증가하고, 기후변화협약에 따른 이산화탄소 감축이 의무화됨에 따라 저탄소 화물운송체계 개념이 소개되었다. 연료소비량 및 탄소배출량을 고려한 화물운송계획을 수립하기 위해서는 현실의 도로 기하정보를 표현하는 3차원 도로망도가 필수적이다. 본 연구는 화물운송의 주요대상인 도시와 도시간의 간선도로를 중심으로 지형 및 도로구조물을 고려하기 위하여 기존 2차원 교통주제도와 수치표고모델을 이용하여 도로의 실제 기하정보를 반영하는 3차원 도로망도 모델을 제안한다. 제안 모델은 실험 도로구간(평택항-의왕IC)을 대상으로 구축하고 GPS/INS 측량을 통해 구축한 3차원 도로망도가 도로의 기하정보를 잘 표현함을 검증하였다(RMSE=0.87m). 또한, 연료소모량 시뮬레이션을 통해 기존의 2차원 도로망도에 비해 제안모델이 현실도로의 연료소모량을 효과적으로 반영함을 알 수 있었다. 본 연구를 통해 복잡한 도로의 3차원 기하정보를 반영하여 에너지 및 환경문제를 효과적으로 고려할 수 있는 Green-ITS기반의 화물 경로계획 및 네비게이션 시스템 개발이 가능할 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3369-3385
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• 2019

There are various research challenges in vehicular ad hoc networks (VANETs) that need to be focused until an extensive deployment of it becomes conceivable. Design and development of a scalable routing algorithm for VANETs is one of the critical issue due to frequent path disruptions caused by the vehicle's mobility. This study aims to provide a novel road-aware routing protocol for vehicular networks named as Two-level hierarchical Hybrid Road-Aware (THERA) routing for vehicular ad hoc networks. The proposed protocol is designed explicitly for inter-vehicle communication. In THERA, roads are distributed into non-overlapping road segments to reduce the routing overhead. Unlike other protocols, discovery process does not flood the network with packet broadcasts. Instead, THERA uses the concept of Gateway Vehicles (GV) for the discovery process. In addition, a route between source and destination is flexible to changing topology, as THERA only requires road segment ID and destination ID for the communication. Furthermore, Road-Aware routing reduces the traffic congestion, bypasses the single point of failure, and facilitates the network management. Finally yet importantly, this paper also proposes a probabilistical model to estimate a path duration for each road segment using the highway mobility model. The flexibility of the proposed protocol is evaluated by performing extensive simulations in NS3. We have used SUMO simulator to generate real time vehicular traffic on the roads of Gangnam, South Korea. Comparative analysis of the results confirm that routing overhead for maintaining the network topology is smaller than few previously proposed routing algorithms.