• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.546-548
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• 2019

In recent days, vehicle safety technologies for supporting safe vehicle driving attract public attention. This paper proposes multi-log platform based vehicle safety system (MLPVSS) that analyzes multi-log data (i.e., log-data on human, object, and place) and supports vehicle safety. The MLPVSS gathers sensor data and image data on the human, object, and place, and then generates multi-log data that are context-aware data on the human, object, and place. The MLPVSS can detect, predict, and response vehicle dangers. The MLPVSS can contribute to reduce car accidents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3858-3874
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• 2021

As an essential part of the urban transportation system, precise perception of the traffic flow parameters at the traffic signal intersection ensures traffic safety and fully improves the intersection's capacity. Traditional detection methods of road traffic flow parameter can be divided into the micro and the macro. The microscopic detection methods include geomagnetic induction coil technology, aerial detection technology based on the unmanned aerial vehicles (UAV) and camera video detection technology based on the fixed scene. The macroscopic detection methods include floating car data analysis technology. All the above methods have their advantages and disadvantages. Recently, indoor location methods based on wireless signals have attracted wide attention due to their applicability and low cost. This paper extends the wireless signal indoor location method to the outdoor intersection scene for traffic flow parameter estimation. In this paper, the detection scene is constructed at the intersection based on the received signal strength indication (RSSI) ranging technology extracted from the wireless signal. We extracted the RSSI data from the wireless signals sent to the road side unit (RSU) by the vehicle nodes, calibrated the RSSI ranging model, and finally obtained the traffic flow parameters of the intersection entrance road. We measured the average speed of traffic flow through multiple simulation experiments, the trajectory of traffic flow, and the spatiotemporal map at a single intersection inlet. Finally, we obtained the queue length of the inlet lane at the intersection. The simulation results of the experiment show that the RSSI ranging positioning method based on wireless signals can accurately estimate the traffic flow parameters at the intersection, which also provides a foundation for accurately estimating the traffic flow state in the future era of the Internet of Vehicles.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.25-31
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• 2021

Moving toward an aged society, traffic accidents involving elderly drivers have also attracted broader public attention. A rapid increase of senior involvement in crashes calls for developing appropriate crash-severity prediction models specific to senior drivers. In that regard, this study leverages machine learning (ML) algorithms so as to predict the severity of vehicle-pedestrian collisions induced by elderly drivers. Specifically, four ML algorithms (i.e., Logistic model, K-nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM)) have been developed and compared. Our results show that Logistic model and SVM have outperformed their rivals in terms of the overall prediction accuracy, while precision measure exhibits in favor of RF. We also clarify that driver education and technology development would be effective countermeasures against severity risks of senior driver-induced collisions. These allow us to support informed decision making for policymakers to enhance public safety.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.118-133
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• 2020

Research interest in the Mobility-as-a-Service (MaaS) concept for enhancing users' mobility experience is increasing. In particular, dynamic pricing techniques based on reinforcement learning have emerged since adjusting prices based on the demand is expected to help mobility services, such as taxi and car-sharing services, to gain more profit. This paper provides a simulation framework that considers more practical factors, such as demand density per location, preferred prices, the distance between users and drivers, and distance to the destination that critically affect the probability of matching between the users and the mobility service providers (e.g., drivers). The aforementioned new practical features are reflected on a data structure referred to as the Matching Matrix. Using an efficient algorithm of computing the probability of matching between the users and drivers and given a set of precisely identified high-demand locations using HDBSCAN, this study developed a better reward function that can gear the reinforcement learning process towards finding more realistic dynamic pricing policies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1D
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• pp.71-77
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• 2010

This study presented the operation method through of productivity on eight analysis work items (TBM boring, cutter check and exchange, TBM maintenance, succeeding facilities, reinforcement in tunnel, operation alternation, a tram car) which have developed equipment at WRITH with TBM a waterway tunnel works. It was inquired lose time with analyzed result by work items and removed lose time. It was analyzed TBM boring length, TBM boring length percentage and TBM boring length time. This study analyzed TBM operation utility factor of a foreign work with TBM operation boring length percentage, a monthly average boring length, pure boring length percentage etc. and assumed a monthly average boring length and a monthly average boring length of rise forecast. Based on analyzed Data, TBM boring has been forecasted propriety pure boring length at compressive strength $675{\sim}1662kgf/cm^2$.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.31 no.6_1
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• pp.483-491
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• 2013

By a development of car navigation systems and mobile or positioning technology, it increases interest in location based services, especially pedestrian navigation systems. Updating of digital maps is important because digital maps are mass data and required to short updating cycle. In this paper, we proposed change detection for different network data-sets based on areal feature matching. Prior to change detection, we defined type of updating between different network data-sets. Next, we transformed road lines into areal features(block) that are surrounded by them and calculated a shape similarity between blocks in different data-sets. Blocks that a shape similarity is more than 0.6 are selected candidate block pairs. Secondly, we detected changed-block pairs by bipartite graph clustering or properties of a concave polygon according to types of updating, and calculated Fr$\acute{e}$chet distance between segments within the block or forming it. At this time, road segments of KAIS map that Fr$\acute{e}$chet distance is more than 50 are extracted as updating road features. As a result of accuracy evaluation, a value of detection rate appears high at 0.965. We could thus identify that a proposed method is able to apply to change detection between different network data-sets.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.5
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• pp.558-568
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• 2011

Hazardous volatile organic compounds (HVOCs) have been increasingly getting concern in urban air chemistry due to photochemical smog as well as its toxicity or potential hazards. In this study, we investigated their concentrations and the properties in tunnel, urban roadside and residential area. As a result, among 36HVOCs measured in this study, BTEX (benzene, toluene, ethylbenzene, xylene) and dichlorodifluoromethane, 1,2,4-trimethylbenzene, trichlorofluoromethane were detected above the concentration of $1{\mu}g/m^3$ in every sampling site and the most abundant compound was toluene. The other compounds were detected at trace level or below the detection limit. In addition, we found that three CFCs (chlorofluorocarbons), such as CFC-12, CFC-11, CFC-113, were persistently detected because of the emission in the past. Toluene to benzene ratio (T/B) at tunnel and roadside were calculated to be 4.3~5.3 and at residential area 15.4, suggesting that the residential area had several emission sources other than car exhaust. The ratio of X/E (m,p-xylene to ethylbenzene) ratio was calculated to be 1.8~2.1 at tunnel, 1.7 at roadside and 1.2 at residential area, which means this ratio reflected well the relative photochemical reactivity between these compounds. Good correlation between m,p-xylene and ethylbenzene ($r^2$ > 0.85) were shown in every study sites. This indicated that correlation between $C_2$-alkylbenzenes were not severely affected by 3-way catalytic converter. In this study, it was demonstrated that the concentration of benzene was very low, compared with national air quality standard (annual average of $5{\mu}g/m^3$). Its concentration were $2.52{\mu}g/m^3$ in roadside and $1.34{\mu}g/m^3$ in residential area. We thought this was the result of persistent policy implementation including the reduction of benzene content in gasoline enforced on January 1, 2009.

• Korean Journal of Biological Psychiatry
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• v.24 no.2
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• pp.59-67
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• 2017

Shift workers experience a disruption in the circadian sleep-wake rhythm, which brings upon adverse health effects such as fatigue, insomnia and decreased sleep quality. Moreover, shift work has deleterious effects on both work productivity and safety. In this review, we present a brief overview of the current literature on the consequences of shift work, especially focusing on attention-associated cognitive decline and related behavioral changes. We searched two electronic databases, PubMed and RISS, using key search terms related to cognitive domains, deleterious effects, and shift work. Twenty studies were eligible for the final review. The consequences of shift work can be classified into the following three categories extracted from the literature review : 1) work accidents ; 2) commuting accidents such as car accidents that occur on the way to and from work ; and 3) attendance management at work (i.e., absenteeism, tardiness, and unscheduled early departure). These cognitive and behavioral consequences of shift work were also found to be associated with sleep disorders in shift workers. Thus, improvements in the shift work system are necessary in order to enhance workers' health conditions, work productivity, and safety.

• Science of Emotion and Sensibility
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• v.18 no.3
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• pp.35-48
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• 2015

It is frequently found in daily life that people who experience good luck as lottery winners try to improve their background (e.g., home, car) but it has not been empirically validated why they do that. Present research attempts to explore the prediction that people who experience good luck expand the scope of attention to background and those who undergo bad luck shrink the scope of attention to adjacent objects. Findings from Experiment 1a indicate that participants who experienced good luck (won the rock-paper-scissors game) paid more attention to background and performed worse in the "find the hidden picture" (below FHP) task while those who underwent bad luck (lost the rock-paper-scissors game) paid more attention to objects, leading to better performance in the FHP task. It is also found in Experiment 1a that, if people washed their hands after experiencing good or bad luck, the opposite result occurred. Experiment 1b confirmed that the rock-paper-scissor game manipulated good and bad luck successfully and did not influence self-control. Experiment 2 shows that people who strongly believe in good luck performed poorly in FHP task while those who do not believe in good luck performed well in FHP task. Overall, three experiments support the proposed research hypotheses. Implications of the study findings for cognitive psychology and related fields including consumer and sports psychology are discussed.

• The Journal of Information Systems
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• v.27 no.1
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• pp.153-169
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• 2018

Purpose Since human brains catch images faster than texts or numbers, infographics has been widely used in business in the form of "information dashboard" to enhance the efficiency of decision-making. Groupware, however, has neglected the adoption and use of infographics, in particular, in the idea generation process. Given that an overall performance of groupware-based idea generation is no better than that of the (paper-and-pencil-based) Nominal Group Technique, Jung et al. (2010) adopted the notion of infographics in the form of performance feedback to solve the productivity paradox. With the consistent results, which demonstrate beneficial effects of infographics on performance enhancement, an interesting observation that groups with the bar chart treatment performed better than groups with the dot chart treatment was made. The main purpose of this study was to find if there were a performance consistency between the outcomes from the previous study and the outcomes from the current study. Design/methodology/approach In experiment 1, we employed the same system used in the previous study (i.e., Jung et al., 2010). As individuals' contributions accumulated, the mechanism visually displayed individuals' performances two-dimensionally in the form of a bar chart or a dot chart. Then, we compared the performance outcomes from this study to the outcomes from previous study (i.e., Jung et al., 2010). In experiment 2, we modified the performance graph to test the effect of "playfulness" on performance by converting dots to car images. Then, we compared the performance outcome from experiment 2 to the outcomes from experiment 1. Findings Just like our interesting (and unexpected) finding in Jung et al.'s study (2010), the outcome confirmed a consistent superior performance of a bar chart. This implies that a bar chart is a better choice when stimulating performance with a visual aid in the context of groupware-based idea generation. Although a bar chart was criticized in a way that errors of length-area judgments are 40 ~ 250% greater than those of positional judgments along a common scale, such illusion turned out to be facilitating upward performance comparison better. Regarding Experiment 2, the outcome showed that the revised-dot graph is as good as the bar graph in terms of quantity and quality score of ideas. We attribute the performance enhancement of the resized-dot to the interaction between the motivational characteristic and the situational characteristic of playfulness because individuals in the revised-dot graph treatment performed better than individuals in the dot graph treatment. Given the order of performance (Bar >= Revised Dot > Dot) that the revised-dot treatment performed the same as (or lower than) the bar treatment, an additional research is warranted to reach to a consistent outcome.