• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.697-705
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• 2021

Currently, roadway operation agencies provide interior zones of tunnels with emergency information including crash, fire, and vehicles' stop, through state-of-the-art technologies such as variable message signs and radio-based broadcast systems. However, when coping with an emergency in tunnel interior zones, such information could be too late for drivers to access. A radio-based emergency alert system at the access zones of freeway tunnels, on the other hand,could be a good alternative for solving this problem. Therefore, the objective of this study is to assess user acceptability of such an alternative system. To carry out this study, an online survey was conducted on 762 drivers, and the survey results were analyzed using a structural equation modeling to identify factors affecting acceptability of the proposed system. As a result, driver characteristics such as age group, driving frequency, and driving career, utilization of conventional traffic information, and usefulness of conventional traffic information have a positive impact on acceptability. It is expected that the findings of the study will be a basis to effectively address and deploy a new emergency alert system at the access zones of freeway tunnels.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.

• Journal of Korean Society of Transportation
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• v.29 no.5
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• pp.121-138
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• 2011

More affordable and available cutting-edge technologies (e.g., wireless vehicle communication) are regarded as a possible alternative to the fixed infrastructure-based traffic information system requiring the expensive infrastructure investments and mostly implemented in the uninterrupted freeway network with limited spatial system expansion. This paper develops an advanced decentralized traveler information System (ATIS) using vehicle-to-vehicle (V2V) communication system whose performance (drivers' travel time savings) are enhanced by three complementary functions (autonomous automatic incident detection algorithm, reliable sample size function, and driver behavior model) and evaluates it in the typical $6{\times}6$ urban grid network with non-recurrent traffic state (traffic incident) with the varying key parameters (traffic flow, communication radio range, and penetration ratio), employing the off-the-shelf microscopic simulation model (VISSIM) under the ideal vehicle communication environment. Simulation outputs indicate that as the three key parameters are increased more participating vehicles are involved for traffic data propagation in the less communication groups at the faster data dissemination speed. Also, participating vehicles saved their travel time by dynamically updating the up-to-date traffic states and searching for the new route. Focusing on the travel time difference of (instant) re-routing vehicles, lower traffic flow cases saved more time than higher traffic flow ones. This is because a relatively small number of vehicles in 300vph case re-route during the most system-efficient time period (the early time of the traffic incident) but more vehicles in 514vph case re-route during less system-efficient time period, even after the incident is resolved. Also, normally re-routings on the network-entering links saved more travel time than any other places inside the network except the case where the direct effect of traffic incident triggers vehicle re-routings during the effective incident time period and the location and direction of the incident link determines the spatial distribution of re-routing vehicles.

• Journal of Hospice and Palliative Care
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• v.21 no.1
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• pp.23-32
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• 2018

Purpose: Little is known regarding the extent to which dying patients with chronic obstructive pulmonary disease (COPD) receive life-sustaining procedures and palliative care in U.S. hospitals. We examine hospital cost trends and the impact of palliative care utilization on the use of life-sustaining procedures in this population. Methods: Retrospective nationwide cohort analysis was performed using National Inpatient Sample (NIS) data from 2005 and 2014. We examined the receipt of both palliative care and intensive medical procedures, defined as systemic procedures, pulmonary procedures, or surgeries using the International Classification of Diseases, 9th revision (ICD-9-CM). Results: We used compound annual growth rates (CAGR) to determine temporal trends and multilevel multivariate regressions to identify factors associated with hospital cost. Among 77,394,755 hospitalizations, 79,314 patients were examined. The CAGR of hospital cost was 5.83% (P<0.001). The CAGRs of systemic procedures and palliative care were 5.98% and 19.89% respectively (each P<0.001). Systemic procedures, pulmonary procedures, and surgeries were associated with increased hospital cost by 59.04%, 72.00%, 55.26%, respectively (each P<0.001). Palliative care was associated with decreased hospital cost by 28.71% (P<0.001). Conclusion: The volume of systemic procedures is the biggest driver of cost increase although there is a cost-saving effect from greater palliative care utilization.