• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.4
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• pp.10-18
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• 2012

This paper develops a decentralized advanced traveler information system (ATIS) under the connected vehicle environment, recently regarded as one of most promising tools in Intelligent Transportation Systems (ITS). The performance of the proposed ATIS is reinforced by introducing autonomous automatic incident detection (AAID) function. The proposed ATIS is implemented and tested using an off-the-shelf microscopic simulation model (VISSIM) on a simple traffic network under idealized communication conditions. A key attribute of this experiment is the inclusion of a non-recurrent traffic state (i.e., traffic incident). Simulation results indicate that the ATIS using V2V communication is efficient in saving drivers' travel time and AAID plays an important role in improving the effectiveness of the system.

• Journal of Advanced Navigation Technology
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• v.12 no.1
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• pp.46-53
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• 2008

The ITS(Intelligent Transport System) enables us to provide solutions on traffic problems, while maximizing safety and efficiency of road and transportation systems, by combining technologies from information and communication, electrical engineering, electronics, mechanics, control and instrumentation with transportation systems. The issues that an integration system for massive traffic data sources must face are due to several factors such as the variety and amount of data available, the representational heterogeneity of the data in the different sources, and the autonomy and differing capabilities of the sources. In this paper, we describe how to extract and load of the heterogeneous massive traffic data from the operational databases, such as FTMS and ARTIS using commercial tools. Also, we experiment on traffic data warehouses with integrated quality management techniques for providing high quality data.

• 한국ITS학회지
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• v.4 no.1
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• pp.17-24
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• 2007

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.4
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• pp.14-25
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• 2018

In recent years, it has become technically easier to explain factors related with traffic accidents in the Big Data era. Therefore, it is necessary to apply the latest analysis techniques to analyze the traffic accident data and to seek for new findings. The purpose of this study is to compare the predictive performance of the negative binomial regression model and the deep learning method developed in this study to predict the frequency of traffic accidents in expressways. As a result, the MOEs of the deep learning model are somewhat superior to those of the negative binomial regression model in terms of prediction performance. However, using a deep learning model could increase the predictive reliability. However, it is easy to add other independent variables when using deep learning, and it can be expected to increase the predictive reliability even if the model structure is changed.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.98-112
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• 2018

Intentional aggressive driving (IAD) is a very dangerous driving behavior that threatens to attack the adjacent vehicles. Most existing studies have focused on the independent driving characteristics of attack drivers. However, the identification of interactions between the offender and the victim is necessary for the traffic safety analysis. This study established multi-agent driving simulation environments to systematically analyze vehicle interactions in terms of traffic safety. Time-to-collision (TTC) was adopted to quantify vehicle interactions in terms of traffic safety. In addition, a exponential decay function was further applied to compare the overall pattern of change in crash potentials when IAD events occurred. The outcome of this study would be useful in developing policy-making activities to enhance traffic safety by reducing dangerous driving events including intentional aggressive driving.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.2
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• pp.58-72
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• 2018

Ganghwa has met a new development period in land use and infrastructure based on the 4th National Development Planning, however the public transportation system is not systematically operated yet. This paper analyzes the bus trip pattern in Ganghwa using transportation card data during a week. The result indicates that average 7,100 people use buses a day and the most frequent use occurred in Friday. Clear peak-hours between 7 and 8 A.M. and between 4 and 5 P.M. were appeared due to commuting and school trips. According the result of regression analysis, population and the number of hospitals and schools area showed positive relationships with but trips reflecting regional characteristics. The research contributes to providing basic data for constructing an efficient public transportation system in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.1
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• pp.50-63
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• 2017

While transit is inconvenient, it is also inevitable for the efficient public transportation. Reducing the number of transfers as much as possible is most important in providing the convenience of public transportation and facilitating the public transportation. As for the public transportation card data, 61,986 items on weekdays and 69,100 items on weekends were collected. Pattern analysis and traffic influence factors were analyzed using traffic data card. Trip chain results revealed that people have more transit transfers for shopping and leasure than commuting purposes on weekends and that commuting distance and time increase by 10 km and 9.9 minutes, respectively. Besides, results of the structural equation model showed that factor 1(total travel time, total travel distance), factor 2(number of people getting on and off), factor 3(transit time), and factor 4(number of bus connections, number of operations) were found to have significant effects on the number of transfers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.11-30
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• 2019

Smartcards are used as the basic data for utilizing the various transportation policies and evaluations, etc. and provided the transportation basic statistics index. However, the main problem of the smartcard data is that the most of users do not take the alighting tag at the stop, so there is a limit to the scope of use for the total O-D trip data because incomplete O-D traffic data of transportation card users. In this study, a decision tree of learning model is estimated for the alighting stop of smartcard users. The model estimation accuracy in range less than 2 stops interval was 89.7% on average. By eliminating the incompleteness alighting stop of smartcard data through this model, it is expected to be used as the basic data for various transportation analyses and evaluations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.3
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• pp.1-14
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• 2017

Recently, the automobile industry is rapidly changing due to autonomous vehicles based on advanced ICT technology. As a result, studies related to autonomous vehicles have also been actively conducted. However, most studies are focusing on the autonomous driving technology so that the prediction of changes in road traffic safety and associated legal system due to the introduction of autonomous vehicles are lacking. The purpose of this study is to suggest improvement methods and priorities of road traffic safety projects according to the introduction of autonomous vehicles. As a result of the AHP analysis using the results of the questionnaire surveyed for autonomous driving car experts, it was analyzed that revision of the traffic safety inspection law and development of education system for autonomous driving motor drivers and operators should be given top priority.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.264-279
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• 2021

Most automated vehicle evaluation scenarios are developed based on the typical driving situations that automated vehicles will face. However, various situations occur during actual driving, and sometimes complex judgments are required. This study is to define a situation that requires complex judgment for safer driving of an automated vehicle as a dilemma situation, and to suggest a driving strategy necessary to secure driving safety in each situation. To this end, we defined dilemma situations based on the automated vehicle ethics guidelines, the criteria for recognition of error rate in automobile accidents, and suggestions from the automated vehicle developers. In addition, in the defined dilemma situations, the factors affecting movement for establishing driving strategies were explored, and the priorities of factors affecting driving according to the Road Traffic Act and driving strategies were derived accordingly.