• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.12
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• pp.1373-1380
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• 2014

In order to calculate accurate traffic and traffic speed, qualified and sufficient GPS data should be provided. However, it is difficult to provide accurate traffic information using restricted GPS data from probe vehicles because of communication costs. This paper developed a algorithm that recovers links omitted by restricted GPS data with topology information, and calculate traffic speed with original links and recovered links. T traffic information service of city with a new algorithm can provide more accurate traffic and traffic speed than the original system.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.10a
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• pp.177-181
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• 2008

본 논문은 제주 택시 텔레매틱스 시스템의 운영과정에서 축적되고 있는 각 택시들의 이동이력 데이터를 기반으로 관심구간의 통행속도에 관련된 필드들을 효율적으로 추출하는 기법을 설계하고 구현한다. 구현된 인터페이스는 도로네트워크 상에서 관심구간의 양끝점을 입력받아 $A^*$ 알고리즘을 수행하여 경로상에 포함된 각 링크를 결정한 후 해당 링크 아이디를 포함하는 질의문의 스켈리튼을 생성한다. 이 질의문을 수정하여 관심구간의 속도 레코드수, 속도 평균, 승객탑승시의 속도, 요일별 시간대별 평균 속도 등 다양한 정보를 체계적으로 검색할 수 있다. 제주시 연삼로 구간에 대한 시험적 검색 결과는 승객이 탑승한 경우 전체 경우 보다 $30{\sim}50%$ 정도의 보고수, $2{\sim}4$ kmh 빠른 통행 속도 등을 보이고 있으며 시간대별 통계는 요일별 통행속도 패턴의 변화를 정량화하고 있다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.31-44
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• 2004

The study of Estimation model for the short-term travel time prediction. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Before providing a dynamic shortest path finding, the prediction model should be verified. To verify the prediction model, three models such as Kalman filtering, Stochastic Process, ARIMA. The ARIMA model should adjust optimal parameters according to the traffic conditions. It requires a frequent adjustment process of finding optimal parameters. As a result of these characteristics, It is difficult to use the ARIMA model as a prediction. Kalman Filtering model has a distinguished prediction capability. It is due to the modification of travel time predictive errors in the gaining matrix. As a result of these characteristics, the Kalman Filtering model is likely to have a non-accumulative errors in prediction. Stochastic Process model uses the historical patterns of travel time conditions on links. It if favorably comparable with the other models in the sense of the recurrent travel time condition prediction. As a result, for the travel time estimation, Kalman filtering model is the better estimation model for the short-term estimation, stochastic process is the better for the long-term estimation.

• Journal of Korean Society of Transportation
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• v.23 no.6 s.84
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• pp.91-102
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• 2005

When it comes to the process of information storage, people are likely to organize individual information into the forms of groups rather than independent attributes, and put them together in their brains. Likewise, in case of finding the shortest path, this study suggests that a Hierarchical Road Network(HRN) model should be selected to browse the most desirable route, since the HRN model takes the process mentioned above into account. Moreover, most of drivers make a decision to select a route from origin to destination by road hierarchy. It says that the drivers feel difference between the link travel tine which was measured by driving and the theoretical link travel time. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Stochastic Process model uses the historical patterns of travel time conditions on links. The HRN model has compared favorably with the conventional shortest path finding model in tern of calculated speeds. Even more, the result of the shortest path using the HRN model has more similar to the survey results which was conducted to the taxi drivers. Taxi drivers have a strong knowledge of road conditions on the road networks and they are more likely to select a shortest path according to the real common sense.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.199-209
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• 2008

This paper is described the concepts and limitations for the traditional directional design hour volume estimation. The main objective of this paper is to establish an estimation method of probabilistic directional design hour volume in order to improve the limitation for the traditional approach method. To express the traffic congestion of specific road segment, this paper proposed the link travel time as the probability that the road capacity can accommodate a certain traffic demand at desired service level. Also, the link travel time threshold was derived from chance-constrained stochastic model. Such successive probabilistic process could determine optimal ranked design hour volume and directional design hour volume. Therefore, the probabilistic directional design hour volume can consider the traffic congestion and economic aspect in road planning and design stage. It is hoped that this study will provide a better understanding of various issues involved in the short term prediction of directional design hourly volume on different types of roads.

• Journal of Korean Society of Transportation
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• v.21 no.1
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• pp.91-102
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• 2003

There are two goals in this paper. The one is development of existing CA(Cellular Automata) model to explain more realistic deceleration process to stop. The other is the application of the updated CA model to forecasting simulation to predict short term link travel time that takes a key rule in finding the shortest path of route guidance system of ITS. Car following theory of CA models don't makes not response to leading vehicle's velocity but gap or distance between leading vehicles and following vehicles. So a following vehicle running at free flow speed must meet steeply sudden deceleration to avoid back collision within unrealistic braking distance. To tackle above unrealistic deceleration rule, “Slow-to-stop” rule is integrated into NaSch model. For application to interrupted traffic flow, this paper applies “Slow-to-stop” rule to both normal traffic light and random traffic light. And vehicle packet method is used to simulate a large-scale network on the desktop. Generally, time series data analysis methods such as neural network, ARIMA, and Kalman filtering are used for short term link travel time prediction that is crucial to find an optimal dynamic shortest path. But those methods have time-lag problems and are hard to capture traffic flow mechanism such as spill over and spill back etc. To address above problems. the CA model built in this study is used for forecasting simulation to predict short term link travel time in Kangnam district network And it's turned out that short term prediction simulation method generates novel results, taking a crack of time lag problems and considering interrupted traffic flow mechanism.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.115-124
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• 2010

VDF(volume-delay function) is one of the most important factor to improve the reliability of traffic demand estimation because it is for estimation of link travel time based on the traffic volume variation. Because VDF of link except for freeway is applied as the parameter of BPR(bureau of public road) of U.S., it causes to deteriorate the accuracy of traffic demand estimation. The purpose of this paper is to establish new parameter of VDF based on the real-surveyed traffic data in order to improve the problem of the existing VDF. We suggest the reclassification of road hierarchy, the approach of traffic survey, the estimating method of VDF parameter, and the improvements of new VDF application. The new VDF allows us to estimate more realistic traffic situation in parts of demand, travel time and path between origin-destination.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.2
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• pp.34-44
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• 2007

Many urban cities deployed ITS technologies to improve the efficiency of traffic operation and management including a real-time franc control system (i.e., COSMOS). The system adopted loop detector system to collect traffic information such as volume, occupancy time, degree of saturation, and queue length. This paper investigated the applicability of detector information within COSMOS to represent the congestion level of the links. Initially, link travel times obtained from the field study were related with each of detector information. Results showed that queue length was highly correlated with link travel time, and direct link travel time estimation using the spot speed data produced high estimation error rates. From this analysis, a procedure was proposed to estimate congestion level of the links using both degree of saturation and queue length information.

• Journal of Korean Society of Transportation
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• v.23 no.8 s.86
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• pp.171-179
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• 2005

This paper presents the algorithm using fuzzy inference that preestimates each link speed changed by different kinds of road situations. The elements we are considered are time zone, rainfall probability information and lane control information. This paper is consists of three parts. First of all we set up the fuzzy variables, and preestimate link speed changed by various road situations. For this process, we build the membership functions for each fuzzy variable and establish the fuzzy inference relations to find how fuzzy variables influence on link speed. Second, using backtracking method, we search the shortest route influenced by link speed changed by fuzzy inference. Third, we apply this algorithm to hypothetical network and find the shortest path. As a result, it is shown that this algorithm choose appropriate roundabout path according to the changing road situations.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.348-359
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• 2017

Recently, various public transportation activation policies are being implemented in order to mitigate traffic congestion in metropolitan areas. Especially in the metropolitan area, the bus information system has been introduced to provide information on the current location of the bus and the estimated arrival time. However, it is difficult to predict the travel time due to repetitive traffic congestion in buses passing through complex urban areas due to repetitive traffic congestion and bus bunching. The previous bus travel time study has difficulties in providing information on route travel time of bus users and information on long-term travel time due to short-term travel time prediction based on the data-driven method. In this study, the path based long-term bus travel time prediction methodology is studied. For this purpose, the training data is composed of 2015 bus travel information and the 2016 data are composed of verification data. We analyze bus travel information and factors affecting bus travel time were classified into departure time, day of week, and weather factors. These factors were used into clusters with similar patterns using self organizing map. Based on the derived clusters, the reference table for bus travel time by day and departure time for sunny and rainy days were constructed. The accuracy of bus travel time derived from this study was verified using the verification data. It is expected that the prediction algorithm of this paper could overcome the limitation of the existing intuitive and empirical approach, and it is possible to improve bus user satisfaction and to establish flexible public transportation policy by improving prediction accuracy.