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    ν”„λ‘œλΈŒ μ°¨λŸ‰ 자료λ₯Ό μ΄μš©ν•œ λ„μ‹œκ΅ν†΅ λ„€νŠΈμ›Œν¬μ˜ 속도 μΆ”μ • μˆœν™˜ν˜• 신경망 λͺ¨ν˜•

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    ν•™μœ„λ…Όλ¬Έ(박사)--μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› :κ³΅κ³ΌλŒ€ν•™ κ±΄μ„€ν™˜κ²½κ³΅ν•™λΆ€,2020. 2. 고승영.Urban traffic flows are characterized by complexity. Due to this complexity, limitations arise when using models that have commonly been using to estimate the speed of arterial road networks. This study analyzes the characteristics of the speed data collected by the probe vehicle method in links on the urban traffic flow, presents the limitations of existing models, and develops a modified recurrent neural network model as a solution to these limitations. In order to complement the limitations of existing models, this study focused on the interrupted flow characteristics of urban traffic. Through data analysis, we verified the separation of platoons and high-frequency transitions as phenomena in interrupted flow. Using these phenomena, this study presents a two-step model using the characteristics of each platoon and the selected dropout method that applies traffic conditions separately. In addition, we have developed an active imputation method to deal with frequent missing data in data collection effectively. The developed model not only showed high accuracy on average, but it also improved the accuracy of certain states, which is the limitation of the existing models, increased the correlation between the estimated value and the estimated target value, and properly learned the periodicity of the data.λ„μ‹œκ΅ν†΅λ₯˜λŠ” λ³΅μž‘μ„±μ„ λ‚΄μž¬ν•˜κ³  μžˆλ‹€. 이 λ³΅μž‘μ„±μœΌλ‘œ 인해, 일반적으둜 지역간 κ°„μ„  λ„λ‘œ λ„€νŠΈμ›Œν¬μ˜ 속도λ₯Ό μΆ”μ •ν•˜λ˜ λͺ¨ν˜•λ“€μ„ μ‚¬μš©ν•  경우 μ—¬λŸ¬κ°€μ§€ ν•œκ³„μ μ΄ λ°œμƒν•˜κ²Œ λœλ‹€. λ³Έ μ—°κ΅¬λŠ” λ„μ‹œκ΅ν†΅λ₯˜ μƒμ˜ λ§ν¬μ—μ„œ ν”„λ‘œλΈŒ μ°¨λŸ‰ λ°©μ‹μœΌλ‘œ μˆ˜μ§‘λœ μ†λ„μžλ£Œμ˜ νŠΉμ„±μ„ λΆ„μ„ν•˜κ³ , κΈ°μ‘΄ λͺ¨ν˜•μ˜ ν•œκ³„μ μ„ μ œμ‹œν•˜κ³ , μ΄λŸ¬ν•œ ν•œκ³„μ μ— λŒ€ν•œ ν•΄λ²•μœΌλ‘œμ„œ λ³€ν˜•λœ μˆœν™˜ν˜• 신경망 λͺ¨ν˜•μ„ κ°œλ°œν•˜μ˜€λ‹€. λͺ¨ν˜• κ°œλ°œμ— μžˆμ–΄, κΈ°μ‘΄ λͺ¨ν˜•μ˜ ν•œκ³„μ μ„ λ³΄μ™„ν•˜κΈ° μœ„ν•΄, λ³Έ μ—°κ΅¬μ—μ„œλŠ” λ„μ‹œκ΅ν†΅λ₯˜μ˜ 단속λ₯˜μ  νŠΉμ§•μ— μ£Όλͺ©ν•˜μ˜€λ‹€. 자료 뢄석을 톡해, λ³Έ μ—°κ΅¬μ—μ„œλŠ” 단속λ₯˜μ—μ„œ λ‚˜νƒ€λ‚˜λŠ” ν˜„μƒμœΌλ‘œμ„œ μ°¨λŸ‰κ΅°μ˜ 뢄리와 높은 λΉˆλ„μ˜ μ „μ΄μƒνƒœ λ°œμƒμ„ ν™•μΈν•˜μ˜€λ‹€. ν•΄λ‹Ή ν˜„μƒλ“€μ„ μ΄μš©ν•˜μ—¬, λ³Έ μ—°κ΅¬μ—μ„œλŠ” 각 μ°¨λŸ‰κ΅°μ˜ νŠΉμ§•μ„ μ΄μš©ν•œ μ΄μš©ν•œ 2단계 λͺ¨ν˜•κ³Ό, ꡐ톡 μƒνƒœλ₯Ό λΆ„λ¦¬ν•˜μ—¬ μ μš©ν•˜λŠ” 선택적 λ“œλ‘­μ•„μ›ƒ 방식을 μ œμ‹œν•˜μ˜€λ‹€. μΆ”κ°€μ μœΌλ‘œ, 자료의 μˆ˜μ§‘μ— μžˆμ–΄ λΉˆλ°œν•˜λŠ” κ²°μΈ‘ 데이터λ₯Ό 효과적으둜 닀루기 μœ„ν•œ λŠ₯동적 λŒ€μ²΄ 방식을 κ°œλ°œν•˜μ˜€λ‹€. 개발 λͺ¨ν˜•μ€ ν‰κ· μ μœΌλ‘œ 높은 정확도λ₯Ό 보일 뿐 μ•„λ‹ˆλΌ, κΈ°μ‘΄ λͺ¨ν˜•λ“€μ˜ ν•œκ³„μ μΈ νŠΉμ • 상황에 λŒ€ν•œ 정확도λ₯Ό μ œκ³ ν•˜κ³  μΆ”μ •κ°’κ³Ό μΆ”μ • λŒ€μƒκ°’μ˜ 상관관계λ₯Ό 높이며, 자료의 주기성을 μ μ ˆν•˜κ²Œ ν•™μŠ΅ν•  수 μžˆμ—ˆλ‹€.Chapter 1. Introduction 1 1.1. Study Background and Purpose 1 1.2. Research Scope and Procedure 8 Chapter 2. Literature Review 11 2.1. Data Estimation 11 2.2. Traffic State Handling 17 2.3. Originality of This Study 20 Chapter 3. Data Collection and Analysis 22 3.1. Terminology 22 3.2. Data Collection 23 3.3. Data Analysis 26 Chapter 4. Model Development 54 4.1. Basic Concept of the Model 54 4.2. Model Development 58 Chapter 5. Result and Findings 72 5.1. Estimation Accuracy of Developed Models 72 5.2. Correlation Analysis of Developed Model 77 5.3. Periodicity Analysis for Developed Models 81 5.4. Accuracy Analysis by Traffic State 86 5.5. Summary of the Result 92 Chapter 6. Conclusion 94 6.1. Summary 94 6.2. Limitation of the Study 95 6.3. Applications and Future Research 96 Appendix 98 Bibliography 119Docto

    Game theoretic approach on the problem of robust railway network design with capacity constraint

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    ν•™μœ„λ…Όλ¬Έ (석사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : κ±΄μ„€ν™˜κ²½κ³΅ν•™λΆ€, 2011.2. 고승영.Maste
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