• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.257-269
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• 2024

Purpose: The purpose of this study is to explain the pivotal role of the travel forecasting process in urban transportation planning. This study emphasizes the use of travel forecasting models to anticipate future traffic. Method: This study examines the methodology used in urban travel demand modeling within transportation planning, specifically focusing on the Urban Transportation Modeling System (UTMS). UTMS is designed to predict various aspects of urban transportation, including quantities, temporal patterns, origin-destination pairs, modal preferences, and optimal routes in metropolitan areas. By analyzing UTMS and its operational framework, this research aims to enhance an understanding of contemporary urban travel demand modeling practices and their implications for transportation planning and urban mobility management. Result: The result of this study provides a nuanced understanding of travel dynamics, emphasizing the influence of variables such as average income, household size, and vehicle ownership on travel patterns. Furthermore, the attraction model highlights specific areas of significance, elucidating the role of retail locations, non-retail areas, and other locales in shaping the observed dynamics of transportation. Conclusion: The study methodically addressed urban travel dynamics in a four-ward area, employing a comprehensive modeling approach involving trip generation, attraction, distribution, modal split, and assignment. The findings, such as the prevalence of motorbikes as the primary mode of transportation and the impact of adjusted traffic patterns on reduced travel times, offer valuable insights for urban planners and policymakers in optimizing transportation networks. These insights can inform strategic decisions to enhance efficiency and sustainability in urban mobility planning.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.179-187
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• 1999

Purpose : This paper introduces a computationally inexpensive context-dependent classification of multi-echo MRI with Bayes compound decision model. In order to produce accurate region segmentation especially in homogeneous area and along boundaries of the regions, we propose a classification method that uses contextual information of local enighborhood system in the image. Material and Methods : The performance of the context free classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at he local neighborhood level. In order to improve the classification accuracy, we use the contextual information which resolves ambiguities in the class assignment of a pattern based on the labels of the neighboring patterns in classifying the image. Since the data immediately surrounding a given pixel is intimately associated with this given pixel., then if the true nature of the surrounding pixel is known this can be used to extract the true nature of the given pixel. The proposed context-dependent compound decision model uses the compound Bayes decision rule with the contextual information. As for the contextual information in the model, the directional transition probabilities estimated from the local neighborhood system are used for the interaction parameters. Results : The context-dependent classification paradigm with compound Bayesian model for multi-echo MR images is developed. Compared to context free classification which does not consider contextual information, context-dependent classifier show improved classification results especially in homogeneous and along boundaries of regions since contextual information is used during the classification. Conclusion : We introduce a new paradigm to classify multi-echo MRI using clustering analysis and Bayesian compound decision model to improve the classification results.

• Journal of Korean Society of Transportation
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• v.17 no.5
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• pp.99-110
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• 1999

This study deals with the adaptability questions of O-D table estimation models. Its objectives are two-fold; (1) to estimate the characteristics of various O-D table estimation models(i.e. linear regression models. entropy models and statistic models) and (2) to find the model which estimates the O-D table with the best accuracy under the various data conditions. In Pursuing the above, this study gives the particular attentions to the test of the models, using the Sioux Falls network and equilibrium assignment method of MINUTP. The major findings are the followings. Firstly. it finds that the statistic models have the most goodness of fat among all models, if the required data are all Prepared. But it Presents that statistic models are the most sensitive against the underspecification and inconsistency problems of link data. Secondly, It shows that the linear regression models have the worst goodness of fat among all models. But the linear regression models are the most insensitive to the underspecification and inconsistency problems. Thirdly, THE/1 model of entropy model is sensitive against the underspecification and incon-sistency problems, but THE/2 model is insensitive. Finally, other informations like total volume, zonal Production and attraction volumes in 0-D table, help models to gain the better goodness of fit. Especially, in the statistic models. both the zonal production and attraction volume data are helpful to estimate the link volumes. It can be expected that the results dive some implications not only to the selection of optimal model under the various given data, but also to the development or modification of model.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.15-22
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• 2009

There exist some limits when we forecast urban railway demand by traditional 4 step model. The first reason is that the model based on socioeconomic data by an administrative unit, 'Dong', yields a 'Dong' unit trip matrix. But a 'Dong' often has two or more stations. The second reason is that urban railway demand by station would be affected rather by station access area on foot than by a 'Dong' unit. So the model based on 'Dong' characteristic data have some inaccuracies in itself. Owing to the limits of the model based on 'Dong' unit data, there exits some difficulty in forecasting urban railway demand by station. So this paper studied two alternatives. The first is to forecast the demand by using the data of station access area on foot rather than 'Dong' unit data. This needs too much time and effort to collect data and analyse them, while the accuracy of the model didn't improve a lot. The second is to adjust the location of 'Dong' centroid and the length of centroid connector link. By this way we can reflect the characteristics of station access area on foot under traditional 4 step model. Comparing the expected demand to the observed data for each station, the result looks like very similar.

• Journal of Korean Society of Transportation
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• v.23 no.5 s.83
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• pp.157-167
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• 2005

Seoul metropolitan public transport reform results in the introduction of the semi-public operation and distance-based fare policies. With implementation of these policies, public transport revenue allocation has been (will be) evolved very complicated because the existing revenue allocation issues have not only been clearly solved, which is generated by the combined relationship among Korea Railroad Corporation (KRC). Seoul Metropolitan Subway Corporation (SMSC). Seoul Metropolitan Rapid Transit Corporation (SMRTC), and Incheon Rapid Transit Corporation (IRTC), but also the revenue allocation problem between bus and urban railroad-related organizations need to be considered in this combined framework. On top of that. based on the future plans such as the private sector's railroad construction plan(s), the light rail transit construction plans of several local governments and the join of remained bus lines of Seoul metropolitan areas, it is understood that the revenue allocation among public transport operating organization will become one of main issues of operation organization as well as local and central governments. As a basic approach for revenue allocation of public transport operation organizations, the purpose of this paper is to propose an integrated model applicable to estimate degree of service contribution in passenger carriage in the combined public transport network. With a hypothesis that the complete electronic card system is deployed, this paper supposes every passenger's loading and alighting stations is recordable. Thereby, this paper limits research scope as to Seoul metropolitan railroad area since used route(s) between origin and destination stations can not be traceded because transfer stations each passenger path through is not recorded. Each model proposed in the paper is as follows: 1. a generalized cost reflecting passenger's transfer behavior; 2.a K path model for determining similar routes between O-D; 3.an assignment model for loading O-D trips onto the detected similar routes using Logit Model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• Journal of Navigation and Port Research
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• v.38 no.4
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• pp.357-366
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• 2014

On December of 2003, IMO's $23^{rd}$ Assembly discussed 'Guidelines on places of refuge for ships in need of assistance' At the discussion, Res. A.949(23) has been selected to appoint recommended place of refuge for countries signatory to the IMO Convention. IMO defines "Places of Refuge" as a places where a ship in need of assistance can take action to enable it to stabilize its condition and reduce the hazards to navigation, and to protect human life and the environment. Appointing and managing a Place of refuge can be a delicate problem because of its close connection to each country's coastal and environmental protection policies. However, in case of marine accident, the appointment or management of the place of refuge has a potential to avoid further damage and reduce to the minimum any environmental and estate losses. Currently a number of foreign countries, designated and operated a place of refuge. But, place of refuge selected method criteria were different by country and also does not have any standardized designating place of refuge model. Therefor, this study suggested the model of assigned places of refuge according to objective indication in order to assign reasonable and efficient places of refuge in domestic waters in the future by investigating and analyzing imported facts in considering the assignment of places of refuge in foreign countries and describing these imported data into quantitative value. In designating the model place of refuge, the final place of refuge location was presented by evaluating the probability of marine accidents, analyzing the location, and evaluating the supporting establishment.

• Journal of the Korea Society for Simulation
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• v.27 no.2
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• pp.35-48
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• 2018

The study of air defense against North Korean tactical ballistic missiles (TBM) should consider the rapidly changing battlefield environment. The study for target re-designation for intercept missiles enables effective operation of friendly defensive assets as well as responses to dynamic battlefield. The researches that have been conducted so far do not represent real-time dynamic battlefield situation because the hit probability for the TBM, which plays an important role in the decision making process, is fixed. Therefore, this study proposes a target re-designation algorithm that makes decision based on hit probability which considers real-time field environment. The proposed method contains a trajectory prediction model that predicts the expected trajectory of the TBM from the current position and velocity information by using random forest and moving window. The predicted hit probability can be calculated through the trajectory prediction model and the simulator of the intercept missile, and the calculated hit probability becomes the decision criterion of the target re-designation algorithm for the missile. In the experiment, the validity of the methodology used in the TBM trajectory prediction model was verified and the superiority of using the hit probability through the proposed model in the target re-designation decision making process was validated.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.161-172
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• 2005

This study is to develop Bus Transit Route Analysis and Evaluation Model that can product the quantitative performance measures for Bus Transit Route Network Design. So far, in Korea, there are no so many models that evaluate a variety of other performance measures or service quality that are of concern to both the transit users and operator because of lower-level bus database system and transit route network analysis algorithm's limit. The BTRAEM in this research differ from the previous approach in that the BTRAEM employs a multiple path transit trip assignment model that explicitly considers the transfer and different travel time after boarding. And we develop input-output data structure and quantitative performance measure for the BTRAEM. In the numerical experimental applying BTRAEM to Mandl transit network, We got the meaningful results on performance measure of bus transit route network. In the future, we expect BTRAEM to give a good solution in real transit network.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2534-2546
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• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.