• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.493-498
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• 2017

Recently, traffic data is analyzed for efficiency of bus operation, D2D(: Door to Door) service, and self-driving of public transportation. However, various studies have been carried out to predict the delay time of public transportation, especially buses, but the research to date has been insufficient due to limitations of simple analysis and data acquisition. In this study, delay time estimation is performed by collecting and processing data such as day of the week, weather, and time of day based on bus operation information. The proposed method in this paper can be applied to autonomous public transport and public traffic control system by improving the accuracy by adding variables in the future.

• Annual Conference of KIPS
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• 2022.11a
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• pp.1071-1073
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• 2022

본 논문은 항만 완전 자동화를 위하여, 자율주행 트랙터와 스마트 신호등을 도입한 IoT 기반 스마트 항만 교통제어 시스템을 메타버스를 통해 제안한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.5
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• pp.222-233
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• 2024

In recent years, there has been a trend towards seeking an integrated solution to social problems (fine dust, carbon emissions, etc.) through technological advancements in the utilization of automotive big data, the diversification of traffic environment improvement policies, and technological innovations. This study compares the differences in bus travel patterns between various types of buses in time and space by using digital odometer data, and draws implications by analyzing fuel consumption and greenhouse gas GHG emissions. As a result of the study, the acceleration dispersion value for diesel buses was higher than for CNG buses. The units of GHG emissions buses on weekdays in the metropolitan area of Gyeonggi-do were about 16% higher than on weekends compared to non-metropolitan areas due to differences in driving patterns. The methodology and results of this study are expected to be utilized in various fields, such as setting standard bus driving modes for autonomous buses, improving the economic efficiency of DRT buses, and in developing patterns to drive buses more economically.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.200-215
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• 2023

With the rapid advance of autonomous driving technology, the related vehicle market is experiencing explosive growth, and it is anticipated that the era of fully autonomous vehicles will arrive in the near future. However, along with the development of autonomous driving technology, questions regarding its safety and reliability continue to be raised. Concerns among technology adopters are increasing due to media reports of accidents involving autonomous vehicles. To promote the improvement of the safety of autonomous vehicles, it is essential to analyze previous accident cases and identify their causes. Therefore, in this study, we aimed to analyze the factors influencing the severity of autonomous vehicle accidents using previous accident cases and related data. The data used for this research primarily comprised autonomous vehicle accident reports collected and distributed by the California Department of Motor Vehicles (CA DMV). Spatial information on accident locations and additional traffic data were also collected and utilized. Given that the primary data used in this study were accident reports, a Poisson regression analysis was conducted to model the expected number of accidents. The research results indicated that the severity of autonomous vehicle accidents increases in areas with low lighting, the presence of bicycle or bus-exclusive lanes, and a history of pedestrian and bicycle accidents. These findings are expected to serve as foundational data for the development of algorithms to enhance the safety of autonomous vehicles and promote the installation of related transportation infrastructure.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.349-360
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• 2024

The purpose of this study is to analyze the performance of the road network according to the penetration rate of autonomous vehicles (AV) of Level 4 or higher and the change in traffic volume. First, prior studies related to vehicle control variables of AV were reviewed, and future traffic demand in 2040, which is predicted to have a 50 % market share of AVs, was reflected in the simulation analysis. In addition, the change in traffic flow of continuous and intermittent flows was analyzed by increasing the AV market penetration rate and traffic volume of passenger cars, trucks, and buses by 25 % step by step from 0 to 100 %. As a result of the analysis, it was confirmed that the travel time increased as the traffic increased, and the pattern of decreasing the travel time due to the increase in the share of AVs, that is, the development of technology, can also be confirmed. Furthermore, it was also confirmed that the traffic speed showed a trend of increasing as the share of AVs increased. In this study, it was confirmed that the law of diminishing marginal rate of substitution (MRS) was satisfied by calculating the MRS according to the combination of traffic volume and speed while increasing the market penetration rate of AVs. Furthermore, it was confirmed that the convexity of the indifference curve was also satisfied in both intermittent and continuous traffic flow environments.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.17-34
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• 2022

This paper develops a model for dynamic station assignment to optimize the Demand Responsive Transit (DRT) operation. In the process of optimization, we use the bus travel time as a variable for DRT management. In addition, walking time, waiting time, and delay due to detour to take other passengers (detour time) are added as optimization variables and entered for each DRT passenger. Based on a network around Anaheim, California, reserved origins and destinations of passengers are assigned to each demand responsive bus, using K-means clustering. We create a model for selecting the dynamic station and bus route and use Non-dominated Sorting Genetic Algorithm-III to analyze seven scenarios composed combination of the variables. The result of the study concluded that if the DRT operation is optimized for the DRT management, then the bus travel time and waiting time should be considered in the optimization. Moreover, it was concluded that the bus travel time, walking time, and detour time are required for the passenger.

• Journal of Auto-vehicle Safety Association
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• v.12 no.2
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• pp.14-20
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• 2020

This paper presents a vehicle mass and road grade estimator for developing an automated bus. To consider the dynamic characteristics of a bus varying with the number of passengers, the longitudinal controller needs the estimation of the vehicle's mass and road grade in real-time and utilizes the information to adjust the control gains. Discrete Kalman filter is applied to estimate the time-varying road grade, and the recursive least squares algorithm is adopted to account for the constant mass estimation. After being implemented in MATLAB/Simulink, the estimators are evaluated with the dynamic model and experimental data of the target bus. The proposed estimators will be applied to complement the algorithm of the longitudinal controller and proceed with algorithm verification.

• Journal of Auto-vehicle Safety Association
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• v.11 no.3
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• pp.19-29
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• 2019

Recently, autonomous vehicles have been studied actively. Autonomous vehicles can detect objects around them using their on board sensors, estimate collision probability and maneuver to avoid colliding with objects. Many algorithms are suggested to prevent collision avoidance. However there are limitations of complex and diverse environments because algorithm uses only the information of attached environmental sensors and mainly depends on TTC (time-to-Collision) parameter. In this paper, autonomous driving algorithm using I2V communication-based cooperative sensing information is developed to cope with complex and diverse environments through sensor fusion of objects information from infrastructure camera and object information from equipped sensors. The cooperative sensing based autonomous driving algorithm is implemented in autonomous shuttle bus and the proposed algorithm proved to be able to improve the autonomous navigation technology effectively.

• Annual Conference of KIPS
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• 2022.05a
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• pp.379-380
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• 2022

KAIST 캠퍼스 기반의 실습환경 구축을 위하여 캠퍼스 전체를 스마트시티 테스트베드로 사용하며 CCTV 네트워크 기반 모니터링/관제 시스템 구축, 교통, 방범, 가로등, CCTV, 교내 버스 등 인프라 통합 관제 및 보안 실습실 구축하고 교내 자율주행 기술 연구진과 실습 협력 추진을 통한 캠퍼스 기반의 실전 스마트 환경을 토대로 다각도의 보안 공격/방어 실습을 진행하고 지자체 및 컨소시엄 기업들과 산학협력 프로젝트를 진행하기 위하여 구축한 내용을 설명한다.

• Journal of Auto-vehicle Safety Association
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• v.15 no.4
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• pp.16-22
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• 2023

This paper presents a proactive longitudinal motion planning algorithm for improving the safety of an automated bus. Since the field of view (FOV) of the autonomous vehicle was limited depending on onboard sensors' performance and surrounding environments, it was necessary to implement vehicle-to-vehicle (V2V) communication for overcoming the limitation. After a virtual V2V-equipped target was constructed considering information obtained from V2V communication, the reference motion of the ego vehicle was determined by considering the state of both the V2V-equipped target and the sensor-detected target. Model predictive control (MPC) was implemented to calculate the optimal motion considering the reference motion and the chance constraint, which was deduced from manual driving data. The improvement in driving safety was confirmed through vehicle tests along actual urban roads.