공공자전거 활용 패턴 분석을 위한 시각적 분석 도구 디자인

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·컴퓨터공학부, 2021.8. 김성준.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.최근 GPS와 같은 센서들의 발달로 인해 교통수단과 관련된 도시 시민들의 다양한 활동과 움직임 등의 데이터들이 축적되고 있다. 그에 따라 도시 계획 연구자들은 유용한 통찰을 얻기 위한 다양한 데이터 기반 분석들을 시도하고 있다. 도시 계획 분야의 연구를 위해 우리는 도시 계획 연구자들과의 긴밀한 협업을 진행하였다. 그들의 주된 연구는 경로 선택 모델링이라고 불리는 공공자전거 이용자들의 경로 선택 행위를 알아내기 위한 연구였다. 협업의 과정에서 우리는 그들의 경로 선택 모델링의 과정이 지닌 한계를 발견하게 되었다. 첫째로, 경로 선택 모델링의 전 과정을 효과적으로 지원하는 시각화 및 인터페이스가 부재하였다. 특히 그들의 연구 과정에서는 데이터 시각화와 모델링이 체계적으로 맞물려있지 않고 파편화되어 있어서 연구를 위한 인지적 흐름이 방해를 받았다. 둘째로, 서로 다른 공공자전거 사용자들의 출발지-목적지 (OD; origin-destination) 움직임 행태를 파악할 수 있는 수단이 부재하였다. 이 때문에 연구자들은 경로 선택 모델링 등 여러 연구에서 자전거 이용자들의 서로 다른 특성을 반영하지 못하는 문제가 있었다. 본 논문에서는 위에서 언급한 두 가지 문제를 해결하기 위한 분석 방안을 제안한다. 첫째로, 사용자 상호작용을 통한 경로 선택 모델링이 가능한 시각적 분석 도구인 RCMVis를 제안한다. 이 시스템은 탐색, 모델링, 해석의 세 과정을 지원한다. 탐색 과정에서는 분석가들이 다양한 OD 데이터를 탐색하고 모델링 할 데이터를 결정하도록 한다. 모델링 과정에서는 k-메도이드 (k-medoids) 군집화 방법과 경로-크기 로짓 (PSL; path-size logit) 모델을 채택하여 주어진 데이터에 대해 경로 선택 모델링을 할 수 있게 하였다. 이때 특징 선택과 하이퍼파라미터 선택을 통해 한 번에 다양한 결과들을 확인하고 비교할 수 있게 하였다. 마지막으로 해석 과정에서는 선택된 모형에 대해 데이터 수준의 해석을 할 수 있게 한다. 이 시스템을 통해 분석가들은 기존에 얻기 어려웠던 다양한 통찰들을 얻을 수 있음을 확인하였다. 두 번째 연구로, 우리는 잠재 특징 공간 탐색을 기반으로 서로 다른 자전거 이용자들의 다양한 OD 움직임 행태를 파악할 수 있는 방법을 제시하였다. 자전거 이용자들의 통행들을 시퀀스 (sequence) 데이터로 나타낼 수 있음에 착안하여 그들의 통행 기록을 시퀀스 투 시퀀스 (Seq2Seq) 모형을 이용하여 학습시켰다. 그 후, 학습된 모형을 통해 얻은 잠재적 특징들을 차원축소를 통해 2차원 공간상에 나타내어 그 분포를 확인하였다. 우리는 잠재 특징 공간과 OD 움직임 행태를 탐색할 수 있는 시각화를 디자인하였고, 그것들을 이용해 다양한 시공간적 특징들을 파악할 수 있었다. 또한 서로 다른 움직임 행태를 갖는 이용자들의 경로 선택 행태는 어떻게 다른지에 대한 분석도 진행하였다. 우리는 제시된 두 방법이 도시 계획 연구자들이 문제를 해결함에 있어 도움이 될 것이라고 믿는다.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 128박

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