• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.962-974
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• 2010

Recently, the convergence of information technology with bio-technology, nano-technology or other technologies has been creating a new paradigm. In a field of transportation, the intelligent transport systems which is a convergence of intelligent technologies and transportation systems have been studied. The Variable Speed Limit(VSL), is one of ITS technologies, is thought to improve safety and efficiency of transportation while controlling speed limit based on road conditions. Legacy studies have considered only one station for VSL algorithm. However, it is not appropriate for an urban freeway installed with many stations. In this paper, new algorithm is proposed to not only enhance effectiveness of VSL based on cooperation of stations but also reflect road conditions within 30 seconds. The proposed algorithm consists of 4 steps: the first is a "searching bottleneck station" step, the second is a "calculating a size of congestion" step, the third is a "calculating the number of controlled stations" step, the final is a "calculating VSL" step. This algorithm guarantees improved safety and minimum additional travel time. The travel time should be considered because drivers would against the VSL algorithm when the proposed algorithm occurs additional travel time. In our experiments, microscopic traffic simulator VISSIM is selected to perform a modeling work. The results show that proposed algorithm provides the improved safety and minimum increase of travel time.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.121-131
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• 2014

Traffic speed is an important measure in transportation. It can be employed for various purposes, including traffic congestion detection, travel time estimation, and road design. Consequently, accurate speed prediction is essential in the development of intelligent transportation systems. In this paper, we present an analysis and speed prediction of a certain road section in Busan, South Korea. In previous works, only historical data of the target link are used for prediction. Here, we extract features from real traffic data by considering the neighboring links. After obtaining the candidate features, linear regression, model tree, and k-nearest neighbor (k-NN) are employed for both feature selection and speed prediction. The experiment results show that k-NN outperforms model tree and linear regression for the given dataset. Compared to the other predictors, k-NN significantly reduces the error measures that we use, including mean absolute percentage error (MAPE) and root mean square error (RMSE).

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.4
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• pp.132-152
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• 2017

Network performance measure has been more and more important in transportation sector because traffic congestion has been steadily increasing in urban area. VKT is defined a sum of traveled distances of whole vehicles on the road network and one of the most important measure of effectiveness (MOE) for network performance measure. This paper aims to propose a methodology for estimating VKT and to apply it to calculate VKT in 6 major cities in Korea. We calculate VKT in 6 major cities by estimating traffic volumes on the uncollected road sections using regression kriging. It is expected that the proposed methodology can be applied various cities.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1312-1318
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• 2006

One of the common services of telematics is the car navigation that finds the shortest path from source to target. Until now, some routing algorithms of the car navigation do not consider the real-time traffic information and use the static shortest path algorithm. In this paper, we prosed the method to predict the traffic flow in the future. This prediction combines two methods. The former is an accumulated speed pattern, which means the analysis results for all past speeds of each road by classfying the same day and the same time inteval. The latter is the Kalman filter. We predicted the traffic flows of each segment by combining the two methods. By experiment, we showed our algorithm gave better precise predicition than only using accumulated speed pattern that is used commonly. The result can be applied to the car navigation to support a dynamic shortest path. In addition, it can give users the travel information to avoid the traffic congestion areas.

• Journal of Korean Society of Transportation
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• v.35 no.3
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• pp.220-233
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• 2017

Traffic volumes are fundamental data widely used in various traffic analysis, such as origin-and-destination establishment, total traveled kilometer distance calculation, congestion evaluation, and so on. The low number of links collecting the traffic-volume data in a large urban highway network has weakened the quality of the analyses in practice. This study proposes a method to estimate the traffic volume data on a highway link where no collection device is available by introducing a spatial statistic technique with (1) the traffic-volume data from TOPIS, and National Transport Information Center in the Ministry of Land, Infrastructure, and (2) the navigation data from private navigation. Two different component models were prepared for the interrupted and the uninterrupted flows respectively, due to their different traffic-flow characteristics: the piecewise constant function and the regression kriging. The comparison of the traffic volumes estimated by the proposed method against the ones counted in the field showed that the level of error includes 6.26% in MAPE and 5,410 in RMSE, and thus the prediction error is 20.3% in MAPE.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.12-20
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• 2008

Recent wireless communication technologies are envisioned as an innovative alternative to solve transportation problems. On ad hoc networks, as a wireless communication technology, nodes can communicate data without any infrastructure. In particular, vehicular ad hoc networks (VANETs), a specific ad hoc network applied to vehicles, enable vehicles equipped with a communication device to form decentralized traffic information systems in which vehicles share traffic information they experienced. This study investigated traffic information dissemination in a VANET-based traffic information system. For this study, an integrated transportation and communications simulation framework was developed, and experiments were conducted with real highway networks and traffic demands. The results showed that it took 3 minutes in the low traffic density situations (10 vehicle/lane.km) and 43 seconds in the high traffic density condition (40 vehicle/lane.km) to deliver traffic information of 5km away with 10% market penetration rate. In uncongested traffic conditions, information seems to be disseminated via equipped vehicles in the opposite direction. In congested traffic conditions, the sufficient availability of equipped vehicles traveling in the same direction reduces the chance to use vehicles in the opposing direction even though it is still possible.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4D
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• pp.351-360
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• 2010

In 2010, the number of registered vehicles reached almost at 17.48 millions in Korea. This dramatic increase of vehicles influenced to increase the number of traffic accidents which is one of the serious social problems and also to soar the personal and economic losses in Korea. Through this research, an enhanced intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network will be developed in order to obtain the important data for developing the countermeasures of traffic accidents and eventually to reduce the traffic accidents in Korea. Firstly, this research has investigated the influencing factors of road geometric features on the traffic volume of each approaching for the intersections where traffic accidents and congestions frequently take place and, a linear regression model of traffic accidents and traffic conflicts were developed by examining the relationship between traffic accidents and traffic conflicts through the statistical significance tests. Secondly, this research also developed an intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network through applying the intersection traffic volume, the road geometric features and the specific variables of traffic conflicts. Lastly, this research found out that the developed model is better than the existed forecasting models in terms of the reliability and accuracy by comparing the actual number of traffic accidents and the predicted number of accidents from the developed model. In conclusion, it is expect that the cost/effectiveness of any traffic safety improvement projects can be maximized if this developed intersection hazard prediction model by combining Genetic Algorithm and Artificial Neural Network use practically at field in the future.

• Journal of Korea Multimedia Society
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• v.14 no.7
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• pp.933-939
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• 2011

Recent heavy rains have caused natural disasters such as flooding and landslides nationwide. Because of flooding occurrence in most of the roads, traffic congestion and isolation caused many loss especially at rush hour. Constant monitoring and analysis of past disaster history data are needed to prevent disasters on areas prone to floods and disaster risk areas. If we managed to obtain traffic volume, speed, phase around intersection using disaster history data when disasters occurred, we can analyse traffic congestion, change of disaster scale and rainfall. In this study, We select a target district to develop by using a route from Dae-nam intersection in Busan Namgu Daeyoeon-dong, over Gwangan large bridge up until Haeundae Olympic intersection, We developed a system which searches disaster history information, traffic volume using disaster history data based on user selection of the road.

• Journal of KIISE:Computing Practices and Letters
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• v.5 no.6
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• pp.748-757
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• 1999

본 논문에서는 차량 영역의 추출을 이용한 효율적인 교통 혼잡도 측정 시스템을 설계하고 구현한다. 차량 영역 정보의 추출은 첫째 영역 분할, 둘째 작은 영역의 제거와 영역의 직사각형화, 셋째 영역의 병합 및 삭제의 단계로 나눌 수 있다. 영역 분할 단계에서는 획득한 도로 영상을 영역 기반 영역 분할에 의해 영역으로 분할한다. 그 다음 영역 분할 후의 영역 정보 중 차량 영역을 추출하는데 영향을 미치지 않는 작은 영역들을 제거하고, 남은 영역들을 직사각형화한다. 마지막으로 차선 별로 남은 영역들을 병합, 삭제함으로써 각 차선마다 차량 영역 정보를 추출할 수 있다. 이러한 방법은 배경 영상과 같은 부가적인 정보를 사용하지 않고 도로 자체 영상만으로 교통 혼잡도를 측정할 수 있으며, 그림자의 영향이 없을 경우 적용할 수 있는 기법이다.Abstract In this paper, we designed and implemented an efficient road congestion analysis system using regional information. To extract vehicle regions from a road image, the system process the image in five steps: segmentation, small region elimination, region rectangularization, region merging and region deletion. First, we segment road image by a threshold value. Then, we eliminate useless small regions to extract vehicle region, and perform region rectangularization. Finally, we extract vehicle region of each lane of the road by region merging and deletion. This method has the advantage of measuring road congestion without additional information such as background images. But this method must be applied to road images without shadow.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2008.04a
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• pp.408-409
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• 2008