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    κ³΅κ³΅μžμ „κ±° ν™œμš© νŒ¨ν„΄ 뢄석을 μœ„ν•œ μ‹œκ°μ  뢄석 도ꡬ λ””μžμΈ

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    ν•™μœ„λ…Όλ¬Έ(박사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ 전기·컴퓨터곡학뢀, 2021.8. κΉ€μ„±μ€€.With the development of sensors, various transportation related data such as activities and movements of citizens are being accumulated. Accordingly, urban planning researchers have made many attempts to obtain meaningful insights through data-driven analysis. For studying domain problems, we closely collaborated with urban planning researchers. Their main concern was to identify the route choice behaviors of public bicycle riders, which is called route choice modeling (RCM). In the process of our collaboration, we identified the two limitations in their RCM analysis process. First, there was no visual interface that can effectively support the whole RCM process. In their process, data exploration and modeling steps were not systematically interlocked and were quite fragmented, which impedes the cognitive flow of the researchers. Second, there was no means to understand various origin-destination (OD) movement behaviors between different public bicycle riders. For this reason, domain researchers could not take bicycle riders’ characteristics into account in conducting their study. In this dissertation, we present two analysis approaches to address the issues mentioned above. In the first study, we present RCMVis, a visual analytics system to support interactive RCManalysis. The system supports three interactive analysis stages: exploration, modeling, and reasoning. In the exploration stage,we help analysts interactively explore trip data from multiple OD pairs and choose a subset of data they want to focus on. In the modeling stage, we integrate a k-medoids clustering method and a path-size logit model into our system to enable analysts to model route choice behaviors from trips with support for feature selection, hyperparameter tuning, and model comparison. Finally, in the reasoning stage, we help analysts rationalize and refine the model by selectively inspecting the trips that strongly support the modeling result. The domain experts discovered unexpected insights from numerous modeling results, allowing them to explore the hyperparameter space more effectively to gain better results. In the second study, we suggest a method to discover various OD movement behaviors of different bicycle riders by exploring the latent feature space. To extract latent features of riders,we train Sequence-to-Sequence (Seq2Seq) model on the riders’ trip records. After extracting the latent features, we represent these features in two-dimensional space using the dimensionalityreduction technique. As a result, we found various OD movement behaviors by exploring the spatio-temporal characteristics using our carefully designed visualizations and interactions. In addition, we identified that how the OD movement behaviors can affect the route choice behaviors of riders. We believe that the two suggested analysis approaches will help solve many problems in the urban planning domain.졜근 GPS와 같은 μ„Όμ„œλ“€μ˜ λ°œλ‹¬λ‘œ 인해 κ΅ν†΅μˆ˜λ‹¨κ³Ό κ΄€λ ¨λœ λ„μ‹œ μ‹œλ―Όλ“€μ˜ λ‹€μ–‘ν•œ ν™œλ™κ³Ό μ›€μ§μž„ λ“±μ˜ 데이터듀이 μΆ•μ λ˜κ³  μžˆλ‹€. 그에 따라 λ„μ‹œ κ³„νš μ—°κ΅¬μžλ“€μ€ μœ μš©ν•œ 톡찰을 μ–»κΈ° μœ„ν•œ λ‹€μ–‘ν•œ 데이터 기반 뢄석듀을 μ‹œλ„ν•˜κ³  μžˆλ‹€. λ„μ‹œ κ³„νš λΆ„μ•Όμ˜ 연ꡬλ₯Ό μœ„ν•΄ μš°λ¦¬λŠ” λ„μ‹œ κ³„νš μ—°κ΅¬μžλ“€κ³Όμ˜ κΈ΄λ°€ν•œ ν˜‘μ—…μ„ μ§„ν–‰ν•˜μ˜€λ‹€. κ·Έλ“€μ˜ 주된 μ—°κ΅¬λŠ” 경둜 선택 λͺ¨λΈλ§μ΄λΌκ³  λΆˆλ¦¬λŠ” κ³΅κ³΅μžμ „κ±° μ΄μš©μžλ“€μ˜ 경둜 선택 ν–‰μœ„λ₯Ό μ•Œμ•„λ‚΄κΈ° μœ„ν•œ μ—°κ΅¬μ˜€λ‹€. ν˜‘μ—…μ˜ κ³Όμ •μ—μ„œ μš°λ¦¬λŠ” κ·Έλ“€μ˜ 경둜 선택 λͺ¨λΈλ§μ˜ 과정이 μ§€λ‹Œ ν•œκ³„λ₯Ό λ°œκ²¬ν•˜κ²Œ λ˜μ—ˆλ‹€. 첫째둜, 경둜 선택 λͺ¨λΈλ§μ˜ μ „ 과정을 효과적으둜 μ§€μ›ν•˜λŠ” μ‹œκ°ν™” 및 μΈν„°νŽ˜μ΄μŠ€κ°€ λΆ€μž¬ν•˜μ˜€λ‹€. 특히 κ·Έλ“€μ˜ 연ꡬ κ³Όμ •μ—μ„œλŠ” 데이터 μ‹œκ°ν™”μ™€ λͺ¨λΈλ§μ΄ μ²΄κ³„μ μœΌλ‘œ λ§žλ¬Όλ €μžˆμ§€ μ•Šκ³  νŒŒνŽΈν™”λ˜μ–΄ μžˆμ–΄μ„œ 연ꡬλ₯Ό μœ„ν•œ 인지적 흐름이 λ°©ν•΄λ₯Ό λ°›μ•˜λ‹€. λ‘˜μ§Έλ‘œ, μ„œλ‘œ λ‹€λ₯Έ κ³΅κ³΅μžμ „κ±° μ‚¬μš©μžλ“€μ˜ μΆœλ°œμ§€-λͺ©μ μ§€ (OD; origin-destination) μ›€μ§μž„ ν–‰νƒœλ₯Ό νŒŒμ•…ν•  수 μžˆλŠ” μˆ˜λ‹¨μ΄ λΆ€μž¬ν•˜μ˜€λ‹€. 이 λ•Œλ¬Έμ— μ—°κ΅¬μžλ“€μ€ 경둜 선택 λͺ¨λΈλ§ λ“± μ—¬λŸ¬ μ—°κ΅¬μ—μ„œ μžμ „κ±° μ΄μš©μžλ“€μ˜ μ„œλ‘œ λ‹€λ₯Έ νŠΉμ„±μ„ λ°˜μ˜ν•˜μ§€ λͺ»ν•˜λŠ” λ¬Έμ œκ°€ μžˆμ—ˆλ‹€. λ³Έ λ…Όλ¬Έμ—μ„œλŠ” μœ„μ—μ„œ μ–ΈκΈ‰ν•œ 두 가지 문제λ₯Ό ν•΄κ²°ν•˜κΈ° μœ„ν•œ 뢄석 λ°©μ•ˆμ„ μ œμ•ˆν•œλ‹€. 첫째둜, μ‚¬μš©μž μƒν˜Έμž‘μš©μ„ ν†΅ν•œ 경둜 선택 λͺ¨λΈλ§μ΄ κ°€λŠ₯ν•œ μ‹œκ°μ  뢄석 도ꡬ인 RCMVisλ₯Ό μ œμ•ˆν•œλ‹€. 이 μ‹œμŠ€ν…œμ€ 탐색, λͺ¨λΈλ§, ν•΄μ„μ˜ μ„Έ 과정을 μ§€μ›ν•œλ‹€. 탐색 κ³Όμ •μ—μ„œλŠ” 뢄석가듀이 λ‹€μ–‘ν•œ OD 데이터λ₯Ό νƒμƒ‰ν•˜κ³  λͺ¨λΈλ§ ν•  데이터λ₯Ό κ²°μ •ν•˜λ„λ‘ ν•œλ‹€. λͺ¨λΈλ§ κ³Όμ •μ—μ„œλŠ” k-λ©”λ„μ΄λ“œ (k-medoids) ꡰ집화 방법과 경둜-크기 λ‘œμ§“ (PSL; path-size logit) λͺ¨λΈμ„ μ±„νƒν•˜μ—¬ 주어진 데이터에 λŒ€ν•΄ 경둜 선택 λͺ¨λΈλ§μ„ ν•  수 있게 ν•˜μ˜€λ‹€. μ΄λ•Œ νŠΉμ§• 선택과 ν•˜μ΄νΌνŒŒλΌλ―Έν„° 선택을 톡해 ν•œ λ²ˆμ— λ‹€μ–‘ν•œ 결과듀을 ν™•μΈν•˜κ³  비ꡐ할 수 있게 ν•˜μ˜€λ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ 해석 κ³Όμ •μ—μ„œλŠ” μ„ νƒλœ λͺ¨ν˜•μ— λŒ€ν•΄ 데이터 μˆ˜μ€€μ˜ 해석을 ν•  수 있게 ν•œλ‹€. 이 μ‹œμŠ€ν…œμ„ 톡해 뢄석가듀은 기쑴에 μ–»κΈ° μ–΄λ €μ› λ˜ λ‹€μ–‘ν•œ 톡찰듀을 얻을 수 μžˆμŒμ„ ν™•μΈν•˜μ˜€λ‹€. 두 번째 μ—°κ΅¬λ‘œ, μš°λ¦¬λŠ” 잠재 νŠΉμ§• 곡간 탐색을 기반으둜 μ„œλ‘œ λ‹€λ₯Έ μžμ „κ±° μ΄μš©μžλ“€μ˜ λ‹€μ–‘ν•œ OD μ›€μ§μž„ ν–‰νƒœλ₯Ό νŒŒμ•…ν•  수 μžˆλŠ” 방법을 μ œμ‹œν•˜μ˜€λ‹€. μžμ „κ±° μ΄μš©μžλ“€μ˜ 톡행듀을 μ‹œν€€μŠ€ (sequence) λ°μ΄ν„°λ‘œ λ‚˜νƒ€λ‚Ό 수 μžˆμŒμ— μ°©μ•ˆν•˜μ—¬ κ·Έλ“€μ˜ 톡행 기둝을 μ‹œν€€μŠ€ 투 μ‹œν€€μŠ€ (Seq2Seq) λͺ¨ν˜•μ„ μ΄μš©ν•˜μ—¬ ν•™μŠ΅μ‹œμΌ°λ‹€. κ·Έ ν›„, ν•™μŠ΅λœ λͺ¨ν˜•μ„ 톡해 얻은 잠재적 νŠΉμ§•λ“€μ„ μ°¨μ›μΆ•μ†Œλ₯Ό 톡해 2차원 곡간상에 λ‚˜νƒ€λ‚΄μ–΄ κ·Έ 뢄포λ₯Ό ν™•μΈν•˜μ˜€λ‹€. μš°λ¦¬λŠ” 잠재 νŠΉμ§• 곡간과 OD μ›€μ§μž„ ν–‰νƒœλ₯Ό 탐색할 수 μžˆλŠ” μ‹œκ°ν™”λ₯Ό λ””μžμΈν•˜μ˜€κ³ , 그것듀을 μ΄μš©ν•΄ λ‹€μ–‘ν•œ μ‹œκ³΅κ°„μ  νŠΉμ§•λ“€μ„ νŒŒμ•…ν•  수 μžˆμ—ˆλ‹€. λ˜ν•œ μ„œλ‘œ λ‹€λ₯Έ μ›€μ§μž„ ν–‰νƒœλ₯Ό κ°–λŠ” μ΄μš©μžλ“€μ˜ 경둜 선택 ν–‰νƒœλŠ” μ–΄λ–»κ²Œ λ‹€λ₯Έμ§€μ— λŒ€ν•œ 뢄석도 μ§„ν–‰ν•˜μ˜€λ‹€. μš°λ¦¬λŠ” μ œμ‹œλœ 두 방법이 λ„μ‹œ κ³„νš μ—°κ΅¬μžλ“€μ΄ 문제λ₯Ό 해결함에 μžˆμ–΄ 도움이 될 것이라고 λ―ΏλŠ”λ‹€.CHAPTER 1. Introduction 1 1.1 Background and Motivation 1 1.2 Thesis Statement and Research Questions 5 1.2.1 Designing RCMVis: A Visual Analytics System for Route Choice Modeling 5 1.2.2 Discovering OD Movement Behaviors of Different Bicycle Riders Using Latent Feature Exploration 6 1.3 Dissertation Outline 8 CHAPTER 2. Related Work 9 2.1 Route Choice Modeling 9 2.2 Analysis of Movement Behaviors 11 2.3 Visual Analytics of Public Bicycle Sharing System 12 2.4 OD Visualization 13 2.5 Trajectory Visual Analytics 14 CHAPTER 3. RCMVis: A Visual Analytics System for Route Choice Modeling 17 3.1 Background 19 3.1.1 Domain Situation Analysis 19 3.1.2 Data Preprocessing and Abstraction 21 3.1.3 Task Analysis and Abstraction 25 3.2 Route Choice Model 27 3.2.1 Choice Set Generation 27 3.2.2 Model Estimation 29 3.2.3 Goodness of Fit 31 3.2.4 Estimation Contribution Score 32 3.3 The RCMVis Design 32 3.3.1 Exploration Stage 33 3.3.2 Modeling Stage 44 3.3.3 Reasoning Stage 50 3.4 System Implementation 53 3.5 Evaluation 53 3.5.1 Case Study 53 3.5.2 Domain Expert Interview 66 3.6 Discussion 67 3.7 Summary 70 CHAPTER 4. Discovering OD Movement Behaviors of Different Bicycle Riders Using Latent Feature Exploration 71 4.1 Learning Latent Feature Representations 72 4.1.1 Data Description 73 4.1.2 Feature Engineering 76 4.1.3 Model Selection and Implementation 78 4.2 Visualization 80 4.2.1 Rider View 82 4.2.2 OD Filter View 85 4.2.3 Temporal Matrix 86 4.2.4 Spatial Map 87 4.2.5 Station View 88 4.3 Implementation 91 4.4 Results 91 4.4.1 Major Patterns 92 4.4.2 Minor Patterns 100 4.4.3 Outliers 101 4.4.4 Route Choice Modeling 101 4.5 Discussion 103 4.6 Summary 104 CHAPTER 5. Conclusion 106 APPENDIX A. Data Preprocessing in RCMVis 122 A.1 Introduction 122 A.2 Road Network 122 A.3 Route Attribute 123 A.3.1 Route Distance 124 A.3.2 Number of Intersections 124 A.3.3 Number of Traffic Lights 125 A.3.4 Road Type Ratios 126 A.3.5 Bike Lane Ratio 126 A.3.6 Slopes 127 A.3.7 Path Size 128λ°•
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