• Transportation Technology and Policy
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• v.11 no.3
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• pp.59-63
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• 2014

• Journal of the Korea Convergence Society
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• v.8 no.3
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• pp.41-48
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• 2017

The TAGO and TOPIS systems have limitations in providing not only information collection problems but also customized information that meets the requirements of users in providing information. In addition, there is a lack of integrated link information for various means. In addition, there is a limitation in using public transportation because all the users can not conveniently use public transportation because of lack of user - customized information and traffic - related information. In this paper, in this paper, we analyze the current status of domestic and international public transport information systems and services, Through this, we expect to be able to provide customized information tailored to the requirements of users by providing integrated linking information for various means.

• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.51-62
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• 2022

AI-based speed prediction studies have been conducted quite actively. However, while the importance of explainable AI is emerging, the study of interpreting and reasoning the AI-based speed predictions has not been carried out much. Therefore, in this paper, 'Explainable Deep Graph Neural Network (GNN)' is devised to analyze the speed prediction and assess the nearby road influence for reasoning the critical contributions to a given road situation. The model's output was explained by comparing the differences in output before and after masking the input values of the GNN model. Using TOPIS traffic speed data, we applied our GNN models for the major congested roads in Seoul. We verified our approach through a traffic flow simulation by adjusting the most influential nearby roads' speed and observing the congestion's relief on the road of interest accordingly. This is meaningful in that our approach can be applied to the transportation network and traffic flow can be improved by controlling specific nearby roads based on the inference results.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.1-11
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• 2008

As bus is realized by economical, environmental transportation that is available mass transport than car, various policy for improvement of services is achieved. As innovative public transportation systems like BMS (Bus Management System) have established, it is possible to manage bus service efficiently. However, the present bus service management system mainly focuses on enhancing service reliability represented by schedule adherence index. This study discusses the necessity of a special management for bus bunching phenomena at stops, and develops two kinds of bus bunching indices based on the Number of Berth and the Average Bus Arrival Rate. The bus bunching indices were measured by utilizing the bus operational information from BMS at the Seoul TOPIS(Transportation & Information Service). In order to evaluate the sensitivity of the Indices, the indices were applied to two different bus groups: buses on exclusive bus median lane, and regular (shared) lanes. As analysis result, is bunching as is near in downtown and is bunching to peak time morning than the afternoon. Compared with the schedule adherence index, the suggested indices were proved as an efficient complementary indices in the evaluation of the bus operational performance. The results of index comparison between exclusive bus median lane and shared lanes can promote the expansion of exclusive bus median lane. Moreover, it can also be used as a reference in deciding bus station scale including the Number of Berth and the route adjustment plan.

• 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.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.