• Transportation Technology and Policy
• /
• v.7 no.1
• /
• pp.37-45
• /
• 2010

• KOREAN ASSOCIATION OF OCCUPATIONAL HEALTH NURSES
• /
• v.19 no.4
• /
• pp.63-63
• /
• 2012

• Journal of Korean Society for Atmospheric Environment
• /
• v.3 no.1
• /
• pp.55-64
• /
• 1987

Actual driving pattern of each motor vehicle type was measured and analyzed in Seoul area and vehicle emission rate was measured and traffic data were used to estimate vehicular emission factor and motor vehicle-related air pollutant emission. The analysis of contribution ratio of each vehicle type showed that LPG taxi's took 38.1% of total vehicular CO, gasoline passenger cars 37.5%, therefore, these cars are major sources of CO, gasoline passenger cars took 45.4% of total vehicular HC, motorcycles 25.3%, LPG taxi's 16.2%, so motorcycles can be said to play an important role in HC emission. For NOx, buses and trucks were thought to be major sources as buses took 36.8% and truck 26.4%. Diesel vehicles, on the other hand, took most $SO_2$ and particulate matter emission. Total emission from motor vehicles in Seoul was estimated to be 547 t/day of CO, 68t/day of HC, 163t/day of NOx, 18t/day of $SO_2$ and 19t/day of paticulate matter.

• Journal of Korean Society of Transportation
• /
• v.13 no.4
• /
• pp.47-59
• /
• 1995

The main purpose of this study is identifying the factors which affect the mode choice behavior of work trips. Disaggregate behavioral approach is used for the analysis . The data were collected using the questionnaire survey method in Taegu. Two models were developed in this study which are multinomial logit model(MODEL-1) for auto, taxi and bus and multinomial logit model (MODEL-2) for auto, taxi, bus and subway. The stated preference (SP) data were used for the analysis of the subway mode choice behavior. MODEL-1 provide reasonable results for the future application. A multinomial model (MODEL-2) developed using the stated preference(SP) data was tested for the use of future transportation mode. It is four that the those models provides reasonable results in terms of behavioral and statistical consideration.

• Journal of Korean Academy of Nursing
• /
• v.35 no.5
• /
• pp.904-913
• /
• 2005

Purpose: The purpose of this study was to investigate smoking cessation behavior of male taxi drivers in Korea on the basis of the Transtheoretical model(TTM), and to validate the usefulness of TTM. Method: Data were collected using a self-reported questionnaire including smoking history and major factors of TTM from 208 subjects who were current smokers or ex-smokers. Data were analyzed by descriptive statistics and ANOVA. Result: Most subjects ($85.1\%$) were current smokers. Stages of change were precontemplation ($44.7\%$) and contemplation ($27.4\%$). Subjects in precontemplation stages had the lowest mean score in processes of change and the highest mean scores in decisional balance(pros) and temptation(positive affective, habitual/craving). According to stages of change, there were statistically significant differences in processes of change, decisional balance, and temptation. Conclusion: This study supported the generalization of TTM. As this study showed that the subjects didn't have motivation in smoking cessation, applying tailored smoking cessation programs for taxi drivers is needed.

• Journal of Korean Society of Transportation
• /
• v.13 no.1
• /
• pp.83-94
• /
• 1995

Many taxis called "Mobum" in Seoul are empty and waiting along the curb during the day time. However, it is almost impossible for elderly and handicapped people, and women carrying babies to use them .It is because they can not serve passengers who request services by phone. If taxis, expecially "Mobum" taxis which offer high quality services, can be dispatched based on requests by phone, not only elderly and handicapped people but also auto commuters will easily call and use them. This paper shows a taxi dispatching system. The system minimizes the total empty vehicle hours under given number of empty vehicles and passengers and their locations. As a result, the system maximizes number of services and revenue under given number of taxis. The system adopts a well -known linear programming model and the model can fast and easily be solved by PC level linear programming packages. Also, practical solutions of the system's constraints such as size and travel time forecast are discussed.ecast are discussed.

• The Korean Journal of Zoology
• /
• v.31 no.2
• /
• pp.147-155
• /
• 1988

Six species of false spider mites were collected and ciassifled in South Korea. Among them, a taxonomic description was carried out on the following four species new to Korea: 1. Aegyptobia nothus Pritchard and Baker, 2. Pentomerismus taxi (Hailer), 3. P. oregonensis McGregor, 4. Brevipalpus lewisi McGregor. And also esterase and alkaline phosphatase patterns obtained by polyacrylamide slab gel electrophoresis were compared on some spades. Esterase rymogram showed difference among species in band number and mobility.

• ETRI Journal
• /
• v.43 no.6
• /
• pp.1058-1080
• /
• 2021

Crime is not a completely random event but rather shows a pattern in space and time. Capturing the dynamic nature of crime patterns is a challenging task. Crime prediction models that rely only on neighborhood influence and demographic features might not be able to capture the dynamics of crime patterns, as demographic data collection does not occur frequently and is static. This work proposes a novel approach for crime count and hotspot prediction to capture the dynamic nature of crime patterns using taxi data along with historical crime and demographic data. The proposed approach predicts crime events in spatial units and classifies each of them into a hotspot category based on the number of crime events. Four models are proposed, which consider different covariates to select a set of independent variables. The experimental results show that the proposed combined subset model (CSM), in which static and dynamic aspects of crime are combined by employing the taxi dataset, is more accurate than the other models presented in this study.

• International Journal of Computer Science & Network Security
• /
• v.21 no.6
• /
• pp.207-212
• /
• 2021

Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier.

• Proceedings of the KOR-KST Conference
• /
• 1999.10a
• /
• pp.243-248
• /
• 1999