8 research outputs found
νλ‘λΈ μ°¨λ μλ£λ₯Ό μ΄μ©ν λμκ΅ν΅ λ€νΈμν¬μ μλ μΆμ μνν μ κ²½λ§ λͺ¨ν
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Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :곡과λν 건μ€ν경곡νλΆ,2020. 2. κ³ μΉμ.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.λμκ΅ν΅λ₯λ 볡μ‘μ±μ λ΄μ¬νκ³ μλ€. μ΄ λ³΅μ‘μ±μΌλ‘ μΈν΄, μΌλ°μ μΌλ‘ μ§μκ° κ°μ λλ‘ λ€νΈμν¬μ μλλ₯Ό μΆμ νλ λͺ¨νλ€μ μ¬μ©ν κ²½μ° μ¬λ¬κ°μ§ νκ³μ μ΄ λ°μνκ² λλ€. λ³Έ μ°κ΅¬λ λμκ΅ν΅λ₯ μμ λ§ν¬μμ νλ‘λΈ μ°¨λ λ°©μμΌλ‘ μμ§λ μλμλ£μ νΉμ±μ λΆμνκ³ , κΈ°μ‘΄ λͺ¨νμ νκ³μ μ μ μνκ³ , μ΄λ¬ν νκ³μ μ λν ν΄λ²μΌλ‘μ λ³νλ μνν μ κ²½λ§ λͺ¨νμ κ°λ°νμλ€. λͺ¨ν κ°λ°μ μμ΄, κΈ°μ‘΄ λͺ¨νμ νκ³μ μ 보μνκΈ° μν΄, λ³Έ μ°κ΅¬μμλ λμκ΅ν΅λ₯μ λ¨μλ₯μ νΉμ§μ μ£Όλͺ©νμλ€. μλ£ λΆμμ ν΅ν΄, λ³Έ μ°κ΅¬μμλ λ¨μλ₯μμ λνλλ νμμΌλ‘μ μ°¨λκ΅°μ λΆλ¦¬μ λμ λΉλμ μ μ΄μν λ°μμ νμΈνμλ€. ν΄λΉ νμλ€μ μ΄μ©νμ¬, λ³Έ μ°κ΅¬μμλ κ° μ°¨λκ΅°μ νΉμ§μ μ΄μ©ν μ΄μ©ν 2λ¨κ³ λͺ¨νκ³Ό, κ΅ν΅ μνλ₯Ό λΆλ¦¬νμ¬ μ μ©νλ μ νμ λλ‘μμ λ°©μμ μ μνμλ€. μΆκ°μ μΌλ‘, μλ£μ μμ§μ μμ΄ λΉλ°νλ κ²°μΈ‘ λ°μ΄ν°λ₯Ό ν¨κ³Όμ μΌλ‘ λ€λ£¨κΈ° μν λ₯λμ λ체 λ°©μμ κ°λ°νμλ€. κ°λ° λͺ¨νμ νκ· μ μΌλ‘ λμ μ νλλ₯Ό λ³΄μΌ λΏ μλλΌ, κΈ°μ‘΄ λͺ¨νλ€μ νκ³μ μΈ νΉμ μν©μ λν μ νλλ₯Ό μ κ³ νκ³ μΆμ κ°κ³Ό μΆμ λμκ°μ μκ΄κ΄κ³λ₯Ό λμ΄λ©°, μλ£μ μ£ΌκΈ°μ±μ μ μ νκ² νμ΅ν μ μμλ€.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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Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : 건μ€ν경곡νλΆ, 2011.2. κ³ μΉμ.Maste