• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of Distribution Science
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• v.14 no.5
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• pp.81-89
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• 2016

Purpose - Prior researches have empirically focused on the effect of management consulting quality and consultant capability on entrepreneurial firms' performance. This study, however, focused on investigating the moderating role of absorptive capacity and resource capability between management consulting and entrepreneurial firms' performance. So, this study investigated the relationship among consulting quality, consultant capability, absorptive capacity, resource capability, and entrepreneurial firms' performance from the resource based view (RBV). Especially, this study focused on the mediating role of absorptive and resource capability in relational structure of entrepreneurial firms' dimensions. Research design, data, and methodology - In this study, research hypotheses and model are established by the prior researches from the fields of strategic management and entrepreneurial behavior. Concretely, H1~H4 are the relationship between consulting (consulting quality, consultant capability) and innovation (absorptive capacity, resource capability); H5 is the relationship between absorptive capacity and resource capability; and H6~H7 are the relationship between innovation (absorptive capacity, resource capability) and management performance. The data was collected 207 copies from entrepreneurial firms in South Korea. These firms were established in January 2014 and maintained by November 2015 in high-tech industry. The questionnaire was consisted of five dimensions; consulting quality, consultant capability, absorptive capacity, resource capability, and management performance. Each dimension measured multi items on a 5-point Likert scale. The hypotheses and research model are analyzed using structural equation modeling (SEM) with AMOS 22. Results - The results of this study are as follows. 1) Consulting quality significantly influenced on the absorptive capacity of entrepreneurial firms. 2) But, consultant capability did not influence on the absorptive capacity of entrepreneurial firms. 3) Consulting quality and consultant capability significantly influenced on the resource capability of entrepreneurial firms. 4) Absorptive capacity significantly influenced on the resource capability of entrepreneurial firms; 5) Absorptive capacity did not significantly influence on the management performance of entrepreneurial firms. 6) Resource capability, however, significantly influenced on the management performance of entrepreneurial firms. By these results, absorptive capacity of entrepreneurial firms had a mediating role partly among consulting quality, consultant capability, and management capability. The resource capability of entrepreneurial firms had a mediating role among consulting quality, consultant capability, and management capability, perfectly. Conclusions - According to this study, the high level of consulting quality and consultant capability may enforce the resource capability of entrepreneurial firms. It means, practically, that external knowledge is a driver for innovation, and then the innovation effects on the management performance of entrepreneurial firms. So, at the initial stage, the management consulting programs are very important to entrepreneurial firms and should be conceived as an essential element. This study may contribute to the advancement of academic in field of new start business, small business, or venture business based on resources, especially the role of absorptive capacity and resource capability between consulting programs and management performance. However, this study has some limitations. They are the measurement of consulting quality's items, cross-sectional research, and the limitation of concept and industry.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.70-81
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• 2020

Point-by-point data, which is abundantly collected by vehicles with embedded GPS (Global Positioning System), generate useful information. These data facilitate decisions by transportation jurisdictions, and private vendors can monitor and investigate micro-scale driver behavior, traffic flow, and roadway movements. The information is applied to develop app-based route guidance and business models. Of these, speed data play a vital role in developing key parameters and applying agent-based information and services. Nevertheless, link speed values require different levels of physical storage and fidelity, depending on both collecting and reporting intervals. Given these circumstances, this study aimed to establish an appropriate collection interval to efficiently utilize Space Mean Speed information by vehicles with embedded GPS. We conducted a comparison of Probe-vehicle data and Image-based vehicle data to understand PE(Percentage Error). According to the study results, the PE of the Probe-vehicle data showed a 95% confidence level within an 8-second interval, which was chosen as the appropriate collection interval for Probe-vehicle data. It is our hope that the developed guidelines facilitate C-ITS, and autonomous driving service providers will use more reliable Space Mean Speed data to develop better related C-ITS and autonomous driving services.

• Journal of Korean Society of Transportation
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• v.26 no.4
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• pp.173-184
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• 2008

Development of an accident estimation model based on accident data can be made after accident occurrences. However, the taking of historical accident data is not easy, and there have been differences between real accident data and police-reported accident data. Also, another difficult shortcoming is that historical traffic accident data better consider driver behavior or intersection characteristics. A new method needs to be developed that can predict accident occurrences for traffic safety improvement in black spots. Traffic conflict decision techniques can acquire and analyze data in time and space, requiring less data collection through investigation. However, there are shortcomings: as existing traffic conflict techniques do not operate automatically, the analyst's opinion could easily affect the study results. Also, existing methods do not consider the severity of traffic conflicts. In this study, the authors presented traffic conflict decision criteria which consider conflict severity, including opposing left turn traffic conflict and cross traffic conflict decision criteria. In order to test these criteria, the authors acquired three signalized intersection images (two intersections in Sungnam city and one intersection in Paju) and analyzed the acquired images using image processing techniques based on individual vehicle tracking technology. Within the analyzed images, level 1 conflicts occurred 343 times over three intersections. Some of these traffic conflicts resulted in level 3 conflict situations. Level 3 traffic conflicts occurred 25 times. From the study results, the authors found that traffic conflict decision techniques can be an alternative to evaluate traffic safety in black spots.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.13 no.4
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• pp.63-72
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• 2014

With the rapid development in the economy and other fields as well, the standard of living in South Korea has been improved, and consequently, the demand of automobiles has quickly increased. It leads to various traffic issues such as traffic congestion, traffic accident, and parking problem. In particular, this illegal parking caused by the increase in the number of automobiles has been considered one of the main reasons to bring about traffic congestion as intensifying any dispute between neighbors in relation to a parking space, which has been also coming to the fore as a social issue. Therefore, this study looked into Daejeon Metropolitan City, the city that is understood to have the highest automobile sharing rate in South Korea but with relatively few cases of illegal parking crackdowns. In order to investigate the theoretical problems of the illegal parking, this study conducted a decision-making tree model-based Exhaustive CHAID analysis to figure out not only what makes drivers park illegally when they try to park vehicles but also those factors that would tempt the drivers into the illegal parking. The study, then, comes up with solutions to the problem. According to the analysis, in terms of the influential factors that encourage the drivers to park at some illegal areas, it was learned that these factors, the distance, a driver's experience of getting caught, the occupation and the use time in order, have an effect on the drivers' deciding to park illegally. After working on the prediction model, four nodes were finally extracted. Given the analysis result, as a solution to the illegal parking, it is necessary to establish public parking lots additionally and first secure the parking space for the vehicles used for living and working, and to activate the campaign for enhancing illegal parking crackdown and encouraging civic consciousness.

• Korean Journal of Culture and Social Issue
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• v.14 no.3
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• pp.21-40
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• 2008

This study was intended to validate and develop the driving stress scale. In a preliminary investigation, literature studies on the stress and open questionnaire were administered and examined in four regions in Korea. As a result, 121 items driving stress questionnaire were developed. In the study, this driving stress questionnaire was examined to 450 drivers located seven regions in Korea. The factors analysis revealed 5 meaningful factors[(Progress Obstacle: PO), (Traffic Circumstance: TC), (Accident & Regulation: AR), (Regulation Observance: RO), (Time Pressure: TP)] with 38 items. When internal consistency for each 5 factor was calculated, all sub-scale revealed a satisfactory level of Cronbach's α. Also, correlations with Driving Behaviour Inventory-General Driver Stress(DBI-GEN) and risk driving behaviors(speed driving, drunken driving, offence accident, defence accident) supported consistently validity of the Driving Stress Scale(DSS). Finally the result were discussed and implications are suggested for future studies.