• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.2
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• pp.85-94
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• 2016

Lane Designation is defined as reasonable road management to ensure the road safety and enhance road efficiency. While the lane designation system was abolished in 1999, it was redefined because of the increasing number of large vehicles in the passing lane, violent on driving and traffic accidents in 2000. The needs of improvement on operating the lane designation has been increasing more in recent due to the low ratio of compliance with the system and difficulties to keep the right lane due to need of turning and demand of widening of designates lane for two-wheeled vehicles and truck. In this study, we presented the improvement plan through the question survey, simulation analysis, safety evaluation. It found a problem that the low-speed vehicle is to use the upper level roadway, difficulties of supervision, the imbalance in the lane use, imbalance traffic and does not match the international standards. This study suggested five different alternatives through the survey. micro simulation has used in order to examine each alternative by management effectiveness and feasibility. It analyzed the traffic speed, efficiency, traffic balance of alternatives. Also, safety evaluation conducted in terms of the range of field-of-view to ensure the easiness of field of view by various configurational difference between the vehicles. By the analysis results of such indicators, This study presents proposals for improvement in operating designated lane that low-speed-big-sized vehicles keep to the right lane, and high-speed-small sized vehicles keep to the left lane.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.202-217
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• 2021

Following the fatal accident of pedestrians caused by Autonomous Vehicle by Uber, the world's largest ride-hailing company, two people were killed in a self-driving car accident by Tesla in April. There is a need to ensure the safety of road users. Accordingly, in order to secure the safety of Autonomous Vehicle driving, it is necessary to evaluate Autonomous Vehicle driving technologies in various situations based on the road and traffic environment in which the Autonomous vehicle will actually drive. Therefore, this study used UC-win/Road ver.14.0 based on general driver's license test questions to present a virtual reality-based Autonomous Vehicles driving ability evaluation tool among various driving ability test method. Based on this, it was intended to test driving ability for unexpected situations in complex and diverse driving environments, and to confirm its practical applicability as an optimal tool for Autonomous vehicle ability test and evaluation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.14-33
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• 2019

MaaS, which provides all-in-one service for comfort mobility for customers, has been popularly researched and developed mainly in Europe. Even in Korea, the research on MaaS is also carried out which is fit to the mobility environment and needs of Korea. Korea Land & Housing Corporation(LH) also carries out research on MaaS called 'Integrated Mobility' for the residents living in LH public apartments. This study categorised the around 800 public apartments into 12 groups based on 3 key characteristics, then analysed the preference on shared-transport services by the residents of representing apartments for the each group using C&RT method. The results show that the preference on shared-transport services such as car-sharing and ride-sharing (carpool) were higher in the apartments which are located in medium or small cities or lived by the more handicapped resident groups in mobility such as the elderly. The study suggests the method to analysis the relationship between residents' preference on shared transport services and bounded ertain areas such as apartments or districts and then to introduce shared transport service fit to target areas.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.173-184
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• 2018

As the portable traffic information collection system using probe vehicles spreads, it is becoming possible to collect road hazard information such as portholes, falling objects, and road surface freezing using in-vehicle sensors in addition to existing traffic information. In this study, we developed a integration and decision algorithm that integrates time and space in real time when multiple probe vehicles detect events such as road hazard information based on GPS coordinates. The core function of the algorithm is to determine whether the road hazard information generated at a specific point is the same point from the result of detecting multiple GPS probes with different GPS coordinates, Generating the data, (3) continuously determining whether the generated event data is valid, and (4) ending the event when the road hazard situation ends. For this purpose, the road risk information collected by the probe vehicle was processed in real time to achieve the conditional probability, and the validity of the event was verified by continuously updating the road risk information collected by the probe vehicle. It is considered that the developed hybrid processing algorithm can be applied to probe-based traffic information collection and event information processing such as C-ITS and autonomous driving car in the future.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.96-106
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• 2020

To solve the limitation of traffic monitoring that occur from embedded sensor such as loop and piezo sensors, the thermal imaging camera was installed on the roadside. As the length of Class 1(passenger car) is getting longer, it is becoming difficult to classify from Class 3(2-axle truck) by using an embedded sensor. The collected images were labeled to generate training data. A total of 17,536 vehicle images (640x480 pixels) training data were produced. CNN (Convolutional Neural Network) was used to achieve vehicle classification based on thermal image. Based on the limited data volume and quality, a classification accuracy of 97.7% was achieved. It shows the possibility of traffic monitoring system based on AI. If more learning data is collected in the future, 12-class classification will be possible. Also, AI-based traffic monitoring will be able to classify not only 12-class, but also new various class such as eco-friendly vehicles, vehicle in violation, motorcycles, etc. Which can be used as statistical data for national policy, research, and industry.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.44-61
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• 2022

With the increase of truck traffic, roads are becoming more congested and the risk of accidents is also increasing. Since the fatality rate of traffic accidents caused by trucks is about 2 to 3 times higher than that of passenger cars and buses, it is urgent to prepare policies for truck traffic safety. While most of the previous studies focused on factor analysis that contributes to traffic accidents, this study presented traffic safety policies (4 major-criteria and 12 sub-criteria) for trucks through driver interviews and previous studies. Then, the priority of the policies was evaluated by using Fuzzy-AHP. As a result, the improvement of truck drivers' working environment was evaluated as the most important criteria, and followed by the improvement of road traffic conditions. In detail, there is an urgent need to improve the freight car fare system, ensure sufficient rest for drivers, and strengthen the crackdown of illegal parking and stopping along roads. This study is expected to be usefully utilized in preparing traffic flow safety policies in preparation for the continuous increase of truck traffic.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.44-53
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• 2023

Recently, serious parking problems are occurring due to the difficulty of securing sufficient parking space, and it may lead to other traffic or social problems. In order to solve the parking problem in areas and districts beyond a certain range, a study on-roads and off-street parking lots reflecting regional characteristics is necessary. Therefore, this study establishing a parking demand calculation model for use as a basic study in establishing on-road and off-road characteristics. In order to conduct the study, Dong-fu, Daegu Metropolitan City was divided into dongs, and parking facilities and parking demand were investigated. The survey time was divided into daytime and nighttime on weekdays, and the types of vehicles were divided into three types: passenger car, small trucks and buses, large trucks and buses. As explanatory variables for calculating parking demand, the total floor area of buildings for each of six purposes was used, including detached houses, apartment houses, neighborhood living facilities, cultural and assembly facilities, business facilities, and sales facilities. As a result of the correlation analysis, among the six explanatory variables, the total area of neighborhood living facilities showed a significant correlation with on- and off-street parking demand. A regression analysis model was constructed using the total area of neighborhood living facilities as an explanatory variable, and statistically significant results were obtained.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.195-211
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• 2023

For the commercialization For the commercialization of autonomous vehicles (AV), the operational design domain (ODD) of automated driving systems (ADS) is to be clearly defined. A common language and consistent format must be prepared so that AV-related stakeholders can understand ODD at the same level. Therefore, overseas countries are presenting a standardized ODD framework and developing scenarios that can evaluate ADS-specific functions based on ODD. However, ODD includes conditions reflecting the characteristics of each country, such as road environment, weather environment, and traffic environment. Thus, it is necessary to clearly understand the meaning of the items defined overseas and to harmonize them to reflect the specific domestic conditions. Therefore, in this study, domestic optimization of the ODD classification system was performed by analyzing the domestic driving environment based on international standards. The driving environment of currently operating self-driving car test districts (Sangam, Seoul, and Gwangju) was investigated using the developed domestic ODD items. Then, based on the results obtained, the ranges of the ODDs in each test district were determined and compared.