• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.3
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• pp.38-50
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• 2023

Recent advances in artificial intelligence (AI) technology have enabled the integration of AI technology into image sensors, such as Closed-Circuit Television (CCTV), to detect specific traffic incidents. However, most incident detection methods have been carried out using fixed equipment. Therefore, there have been limitations to incident detection for all roadways. Nevertheless, the development of mobile image collection and analysis technology, such as image sensors and edge-computing, is spreading. The purpose of this study is to estimate the reducing effect of the incident detection time according to the introduction level of mobile image collection and analysis equipment (or connected vehicles). To carry out this purpose, we utilized data on the number of incidents collected by the Suwon branch of the Gyeongbu expressway in 2021. The analysis results showed that if the market penetration rate (MPR) of connected vehicles is 4% or higher for two-lane expressway and 3% or higher for three-lane expressways, the incident detection time was less than one minute. Furthermore, if the MPR is 0.4% or higher for two-lane expressways and 0.2% or higher for three-lane expressways, the incident detection time decreased compared to the average incident detection time announced by the Korea Expressway Corporation for both two-lane and three-lane expressways.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.2
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• pp.14-26
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• 2009

This study presented a novel method to estimate travel times under incident conditions. Predictive travel time information was defined and evaluated with the proposed method. The proposed method utilized individual vehicle speeds obtained from global positioning systems (GPS) and inter-vehicle communications(IVC) for more reliable real-time travel times. Individual vehicle trajectory data were extracted from microscopic traffic simulations using AIMSUN. Market penetration rates (MPR) and IVC ranges were explored with the accuracy of travel times. Relationship among travel time accuracy, IVC ranges, and MPR were further identified using regression analyses. The outcomes of this study would be useful to derive functional requirements associated with traffic information systems under forthcoming ubiquitous transportation environment

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.110-123
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• 2019

Traffic data is the most basic element necessary for transportation planning and traffic system operation. Recently, a method of estimating traffic flow characteristics using distance to a leading vehicle measured by an ADAS camera has been attempted. This study investigated the feasibility of the ADAS vehicle reflecting the distance error of image-based vehicle identification technology as a means to estimate the traffic flow through the normalized root mean square error (NRMSE) based on the number of lanes, traffic demand, penetration rate of probe vehicle, and time-space estimation area by employing the microscopic simulation model, VISSIM. As a result, the estimate of low density traffic flow (i.e., LOS A, LOS B) is unreliable due to the limitation of the maximum identification distance of ADAS camera. Although the reliability of the estimates can be improved if multiple lanes, high traffic demands, and high penetration rates are implemented, artificially raising the penetration rates is unrealistic. Their reliability can be improved by extending the time dimension of the estimation area as well, but the most influential one is the driving behavior of the ADAS vehicle. In conclusion, although it is not possible to accurately estimate the traffic flow with the ADAS camera, its applicability will be expanded by improving its performance and functions.

• Journal of Korean Society of Transportation
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• v.35 no.4
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• pp.332-347
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• 2017

Analysis of the interaction between the automated vehicles and manual vehicles is very important in analyzing the performance of automated cooperative driving environments. In particular, the automated vehicle platooning can affect the driving behavior of adjacent manual vehicles. The purpose of this study is to analyze the lane change behavior of the manual vehicles in automated vehicle platonning environment and to conduct the experiment and questionnaire surveys in three stages. In the first stage, a video questionnaire survey was conducted, and responsive behaviors of manual vehicles were investigated. In second stage, the driving simulator experiments were conducted to investigate the lane change behaviors of in automated vehicle platonning environments. To analyze the lane change behavior of the manual vehicles, lane change durations and acceleration noise, which are indicators of traffic flow stability, were used. The driving behavior of manual vehicles were compared across different market penetration rates (MPR) of automated vehicles and human factors. Lastly, NASA-TLX (NASA Task Load Index) was used to evaluate the workload of the manual vehicle drivers. As a result of the analysis, it was identified that manual vehicle drivers had psychological burdens while driving in automated vehicle platonning environments. Lane change durations were longer when the MPR of the automated vehicles increased, and acceleration noise were increased in the case of 30-40 years old or female drivers. The results from this study can be used as a fundamental for more realistic traffic simulations reflecting the interaction between the automated vehicles and manual vehicles. It is also expected to effectively support the establishment of valuable transportation management strategy in automated vehicle environments.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.77-91
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• 2023

Recently, there has been a worldwide increase in research and development on automated vehicles for commercialization. It is expected that the use of level 3 autonomous vehicles on continuous-flow roads will be introduced and will increase. Consequently, various studies have been conducted to investigate the impact of mixed traffic flow with automated vehicles based on the market penetration rate (MPR). However, these studies have been conducted independently, and the results have shown different trends. Therefore, this study attempted a quantitative analysis of the impact of automated vehicles on mixed traffic flow on uninterrupted roads through a meta-analysis. The results showed that the effect size estimated from an MPR of 75% or higher was statistically significant.

• Journal of Korean Society of Transportation
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• v.36 no.1
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• pp.38-50
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• 2018

Emerging automated driving environments will lead to a mixed traffic flow depending on the interaction between automated vehicles (AVs) and manually driven vehicles (MVs) because the market penetration rate (MPR) of AVs will gradually increase over time. Understanding the characteristics of mixed traffic conditions, and developing a method to control both AV and MV maneuverings smoothly is a backbone of the traffic management in the era of automated driving. To facilitate smooth vehicle interactions, the maneuvering of AVs should be properly determined by various traffic and road conditions, which motivates this study. This study investigated whether the aggressiveness of AV maneuvering, defined as automated driving aggressiveness (ADA), affect the performance of mixed traffic flow. VISSIM microscopic simulation experiments were conducted to derive proper ADAs for satisfying both the traffic safety and the operational efficiency. Traffic conflict rates and average travel speeds were used as indicators for the performance of safety and operations. While conducting simulations, level of service(LOS) and market penetration rate(MPR) of AVs were also taken into considerations. Results implies that an effective guideline to manage the ADA under various traffic and road conditions needs to be developed from the perspective of traffic operations to optimize traffic performances.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.29-44
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• 2009

A variety of research efforts, using advanced wireless communication technologies, have been made to develop more reliable traffic information system. This study presents a novel decentralized traffic information system based on vehicle infrastructure integration (VII). A major objective of this study was also to devise a methodology for determining appropriate spacing of roadside equipment (RSE) to fully exploit the benefits of the proposed VII-based traffic information system. Evaluation of travel time estimation accuracy was conducted with various RSE spacings and the market penetration rates of equipped vehicle. A microscopic traffic simulator, VISSIM, was used to obtain individual vehicle travel information for the evaluation. In addition, the ANOVA tests were conducted to draw statistically significant results of simulation analyses in determining the RSE spacing. It is expected that the proposed methodology will be a valuable precursor to implementing capability-enhanced next generation traffic information systems under the forthcoming ubiquitous transportation environment.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.251-264
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• 2008

One of the crucial elements to fully facilitate the various benefits of intelligent transportation systems (ITS) is to obtain more reliable traffic monitoring in real time. To date, point and section-based traffic measurements have been available through existing surveillance technologies, such as loops and automatic vehicle identification (AVI) systems. However, seamless and more reliable traffic data are required for more effective traffic information provision and operations. Technology advancements including vehicle tracking and wireless communication enable the acceleration of the availability of individual vehicle travel information. This study presents a UBIquitous PRObe vehicle Surveillance System (UBIPROSS) using vehicle-to-vehicle (V2V) wireless communications. Seamless vehicle travel information, including origin-destination information, speed, travel times, and other data, can be obtained by the proposed UBIPROSS. A set of parameters associated with functional requirements of the UBIPROSS, which include the market penetration rate (MPR) of equipped vehicles, V2V communication range, and travel time update interval, are investigated by a Monte Carlo simulation- (MCS) based evaluation framework. In addition, this paper describes prototypical implementation. Field test results and identified technical issues are also discussed. It is expected that the proposed system would be an invaluable precursor to develop a next-generation traffic surveillance system.