• Journal of Korean Society of Transportation
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• v.25 no.3
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• pp.145-154
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• 2007

Determining stop spacing is a very important process in transit system planning. This study is involved in an analytical approach to decide the transit stop spacing. Transit stop spacing should be longer as 1) user access speed, 2) user travel time, and 3) dwell time increase, and shorter as 1) passengers (boardings and alightings) and 2) headway increase. In this study, a methodology is proposed to determine transit stop spacing to minimize total cost (user cost plus operator cost) with irregular passenger distribution (boardings and alightings) Without considering in-vehicle passengers, the transit stop spacing should be shorter in the concentrated sections of the passenger distribution than in others to minimize total cost. Through the conceptual analysis, it is verified that the transit stop spacing could be longer as the in-vehicle passengers increase in certain sections. This study proposes a simple practical method to determine transit stop spacing and locations instead of a dynamic programming method which generally includes a complex and difficult calculation. If the space axis is changed to a time axis. the methodology of this study could be expanded to analyze a solution for the transit service (or headway) schedule problem.

• Journal of Korean Society of Transportation
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• v.17 no.1
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• pp.29-39
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• 1999

An earlier study of the effect of rain found that the capacity of freeway systems was reduced, but did not address the effects of rain on the nature of traffic flows. Indeed, the substantial variation due to the intensity of adverse weather conditions is entirely rational so that its effects must be considered in freeway facility design. However, all of the data in Highway Capacity Manual(HCM) have come from ideal conditions. The primary objective of this study is to investigate the effect of rain on urban freeway traffic flows in Seoul. To do so, the relations between three key traffic variables(flow rates, speed, occupancy), their threshold values between congested and uncontested traffic flow regimes, and speed distribution were investigated. The traffic data from Olympic Expressway in Seoul were obtained from Imagine Detection System (Autoscope) with 30 seconds and 1 minute time periods. The slope of the regression line relating flow to occupancy in the uncongested regime decreases when it is raining. In essence, this result indicates that the average service flow rate (it may be interpreted as a capacity of freeway) is reduced as weather conditions deteriorate. The reduction is in the range between 10 and 20%, which agrees with the range proposed by 1994 US HCM. It is noteworthy that the service flow rates of inner lanes are relatively higher than those of other lanes. The average speed is also reduced in rainy day, but the flow-speed relationship and the threshold values of speed and occupancy (these are called critical speed and critical occupancy) are not very sensitive to the weather conditions.

• Journal of Korean Society of Transportation
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• v.34 no.3
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• pp.222-233
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• 2016

Recently, mobile phone data was applied in travel demand modeling as a new source of dynamic population movement. This study is also aimed to estimate "occupancy population" during a given period of time within a given spatial region using mobile phone data. An occupancy population was defined as the number of people residing or moving within a given time and space. In case of Seoul Metropolitan area, we divided the area into a number of administrative districts as zones for analysis and estimated the occupancy population of each zone by mobile phone data collected by SK telecom Co., a wireless telecommunication provider in Korea. For the expansion of mobile phone data, a new concept of "communication probability" was introduced and applied in the estimation of occupancy population of each zone by the hour. We compared the estimated number with the daytime population and the daytime population index referred by the Statistics Korea. The results showed that a positive correlation existed between the estimated number and the statistical number by nationwide survey. It was concluded that mobile phone data could be more cost-effective sources than a conventional survey method to estimate the pattern of population movement by the hour or by the day.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Proceedings of the KOR-KST Conference
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• 1995.12a
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• pp.45-73
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• 1995

The current paper presents a system dynamics model which can generate the land use anq transportation system performance simultaneously is proposed. The model system consists of 7 submodels (population, migration of population, household, job growth-employment-land availability, housing development, travel demand, and traffic congestion level), and each of them is designed based on the causality functions and feedback loop structure between a large number of physical, socio-economic, and policy variables. The important advantages of the system dynamics model are as follows. First, the model can address the complex interactions between land use and transportation system performance dynamically. Therefore, it can be an effective tool for evaluating the time-by-time effect of a policy over time horizons. Secondly, the system dynamics model is not relied on the assumption of equilibrium state of urban systems as in conventional models since it determines the state of model components directly through dynamic system simulation. Thirdly, the system dynamics model is very flexible in reflecting new features, such as a policy, a new phenomenon which has not existed in the past, a special event, or a useful concept from other methodology, since it consists of a lots of separated equations. In Chapter I, II, and III, overall approach and structure of the model system are discussed with causal-loop diagrams and major equations. In Chapter V _, the performance of the developed model is applied to the analysis of the impact of highway capacity expansion on land use for the area of Montgomery County, MD. The year-by-year impacts of highway capacity expansion on congestion level and land use are analyzed with some possible scenarios for the highway capacity expansion. This is a first comprehensive attempt to use dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions. The model structure is not very elaborate mainly due to the problem of the availability of behavioral data, but the model performance results indicate that the proposed approach can be a promising one in dealing comprehensively with complicated urban land use/transportation system.

• International Journal of Highway Engineering
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• v.11 no.2
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• pp.45-54
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• 2009

Despite vehicle instability problems caused by gusts of wind on freeways located in mountain or seaside areas, current national highway design standards overlook their detrimental effects, and if higher design speed freeways being proposed now by the government are in operation, the strong effect of the gust of wind becomes a highway alignment design issue. This paper presents the vehicle movements and their resulting safety effects by checking vehicle sliding and overturn based on vehicle dynamic analysis for the case when a gust of wind blows to vehicles negotiating curves on higher speed freeways. In this analysis, vehicle types, curve radii, motorist responsive time to vehicle driving path changes, and vehicle speeds are systematically arranged to get vehicle sliding and overturn values in each different conditions. The results showed that there were little overturn possibilities when wind speed would stay in 50m/sec with higher than 600 meter curve radii. Interestingly it was also found in sliding checks that, although being safe at less than 15.0m/sec wind speed levels, there appeared the need of vehicle travel prohibitions when the wind speed could exceed 25.0m/sec level. The findings in this research is of information in future higher speed freeway designs, and particularly useful when designing freeways passing frequent gust wind areas.

• Journal of Korea Water Resources Association
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• v.40 no.4
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• pp.325-334
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• 2007

In this study, a long-term unsteady simulation model has been developed using rigid water column theory which is more accurate than Extended-period model and more efficient comparing with water-hammer simulation model. The developed model is applied to 24-hours unsteady simulation considering daily water-demand and water-hammer analysis caused by closing a valve. For the case of 24-hours daily simulation, the pressure of each node decreases as the water demand increase, and when the water demand decrease, the pressure increases. During the simulation, the amplitudes of flow and pressure variation are different in each node and the pattern of flow variation as well as water demand is quite different than that of KYPIPE2. Such discrepancy necessitates the development of unsteady flow analysis model in water distribution network system. When the model is applied to water-hammer analysis, the pressure and flow variation occurred simultaneously through the entire network system by neglecting the compressibility of water. Although water-hammer model shows the lag of travel time due to fluid elasticity, in the aspect of pressure and flow fluctuation, the trend of overall variation and quantity of the result are similar to that of water-hammer model. This model is expected for the analysis of gradual long-term unsteady flow variations providing computational accuracy and efficiency as well as identifying pollutant dispersion, pressure control, leakage reduction corresponding to flow-demand pattern, and management of long-term pipeline net work systems related with flowrate and pressure variation in pipeline network systems

• Spatial Information Research
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• v.21 no.5
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• pp.33-41
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• 2013

Recently, the number of disasters has been seriously increasing. The total damages by the natural or man-made disasters during the past years resulted in tremendous fatalities and recovery costs. It is necessary to have efficient emergency evacuation management which is concerned with identifying evacuation route, and the estimation of evacuation and clearance times. An emergency evacuation model is important in identifying critical locations, and developing various evacuation strategies. In that existing evacuation models have focused on route analysis for indoor evacuation, there are only a few models for areawide emergency evacuation analysis. Therefore, we developed a mesoscopic model by using Cube Avenue and performed evacuation simulation, targeting road network in City of Fargo, North Dakota. Consequently, a mesoscopic model developed in this study is used to carry out dynamic analysis using network and input variable of existing travel demand model. The results of this study show that the model is an appropriate tool for areawide emergency evacuation analysis to save time and cost. Henceforth, the results of this study can be applied to develop a disaster evacuation model which can be used for a variety of disaster simulation and evaluation based on scenarios in the local metropolitan area.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.9
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• pp.978-988
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• 2005

A three-dimensional dynamic model was applied to Lake Paldang, Han River in this study. The model was calibrated and verified using the data measured under different ambient conditions. The model results were in reasonable agreements with the field measurements in both calibration and verification. Utilizing the validated model, we analyzed the spatial and temporal distributions of temperature, current, residence time, and spreading pattern of incoming flows within the lake. Relatively low velocity and high temperature were computed at the surface layer in the southern region of the Sonae island. The longest residence time within the lake was predicted in the southern region of the Sonae island and the downstream region of the South Branch. This can be attributed to the fact that the back currents caused by the dam blocking occur mainly in these regions. Vertical thermal profiles indicated that the thermal stratifications would be occurred feebly in early summer and winter. During early spring and fall, it appeared that there would be no discernible differences at the vertical temperature profiles in the entire lake. The vertical overturns, however, do not occur during these periods due to an influence of high discharge flows from the dam. During midsummer monsoon season with high precipitation, the thermal stratification was disrupted by high incoming flow rates and discharges from the dam and very short residence time was resulted in the entire lake. In this circulation patterns, the plume of the Kyoungan stream with smallest flow rate and higher water temperature tends to travel downstream horizontally along the eastern shore of the south island and vertically at the top surface layer. The model results suggest that the Paldang lake should be a highly hydrodynamic water body with large spatial and temporal variations.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.