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

Ride, record, repeat: tracking of cycling data as communication on three levels and how each meet a corresponding basic psychological need

2017 Fall.Includes bibliographical references.Self-tracking of health related data has grown more popular in the last decade. It is helpful to view this behavior as communication on three levels: communicating with the device, communicating with the self, and communicating with others. One theory of motivation, Self-determination Theory claims that motivation is internalized and therefore more effective to the degree to which the basic psychological needs of autonomy, competence, and relatedness are met. In this qualitative study, 18 cyclists (9 male and 9 females) were interviewed regarding their own self-tracking of their rides on training apps like Strava and Training Peaks. The cyclists in this thesis provided some correlation between uploading their data to a device and the satisfaction of the need for autonomy. When viewing and responding to data visualizations of their rides, they were able to meet the need for competence. And they found that by using the social aspects of the apps they could satisfy their need for relatedness

System of data recollection, analysis and transfer by NFC for transport systems

This paper shows the development and test of a prototype that collects and analyzes data of cycling trips, such as position, distance, speed, and travel time. This device was made to provide an extra security layer to the data transfer in embedded systems. The raw information that is produced by the project described in this paper is highly valued, so there is a need to record and transfer data in a secure way between electronic devices. The solution proposed in this project was based on the Near Field Communication (NFC) protocol to make the communication stage from an Electronic Control Unit to NFC tags, and then, the information stored in the tags can be read from smartphones that have an NFC module inside them. The data was generated from a Global Position System (GPS) module and antenna using the Haversine formula to calculate the distance between two geographical points, this considering the earth as a sphere to minimize the error produced taking the earth as a plane surface. A functional device that includes extra security of the data transfer in cycling trips, through NFC technology was successfully developed, however, future implementations can be extrapolated to other areas, such as the transportation sector

Evaluating Impacts of Shared E-scooters from the Lens of Sustainable Transportation

As the popularity of shared micromobility is increasing worldwide, city governments are struggling to regulate and manage these innovative travel technologies that have several benefits, including increasing accessibility, reducing emissions, and providing affordable travel options. This dissertation evaluates the impacts of shared micromobility from the perspective of sustainable transportation to provide recommendations to decision-makers, planners, and engineers for improving these emerging travel technologies. The dissertation focuses on four core aspects of shared micromobility as follows: 1) Safety: I evaluated police crash reports of motor vehicle involving e-scooter and bicycle crashes using the most recent PBCAT crash typology to provide a comprehensive picture of demographics of riders crashing and crash characteristics, as well as mechanism of crash and crash risk, 2) Economics: I estimated the demand elasticity of e-scooters deployed, segmented by weekday type, land use, category of service providers based on fleet size using negative binomial fixed effect regression model and K-means clustering, 3) Expanding micromobility to emerging economies: Using dynamic stated preference pivoting survey and panel data mixed logit model, I assessed the intentions to adopt shared micromobility in mid-sized cities of developing countries, where these innovative technology could be the first wave of decarbonizing transportation sector, and 4) Micromobility data application: I identified five usage-clusters of shared e-scooter trips using combination of Principal Component Analysis (PCA) and K-means clustering to propose a novel framework for using micromobility data to inform data-driven decision on broader policy goals. Based on the key findings of the research, I provide five recommendations as follows: 1) decision-makers should be proactive in incorporating new travel technologies like shared micromobility, 2) city governments should leverage shared micromobility usage and operation data to empower the decision-making process, 3) each shared micromobility vehicles should be approached uniquely for improving road safety, 4) city governments should consider regulating the number of service providers and their fleet sizes, and 5) decision-makers should prioritize expanding shared micromobility in emerging economies as one of the first efforts to the decarbonizing transportation sector

Use of Emerging Technology as Part of the Experiential Learning Process in Ultradistance Cycling: A Phenomenological Study

Technology is well entrenched as part of our everyday lives and formal learning settings. The role technology plays as part of informal learning of sports and physical activities has not been explored as thoroughly. This study examined the use of technology by ultradistance cyclists as part of their experiential learning process. Data collection was through semi-structured interviews of 10 cyclists who routinely utilized technology in preparing for and participating in ultradistance events. Emerging themes were organized utilizing NVIVO software. While identified themes were similar to the phases of the Kolb (2014) experiential learning model, there was also a strong temporal component. Technology usage themes prior to an event included Abstract Conceptualization, Route Planning, and Training. Technology usage themes during an event included Active Experimentation, Concrete Experience, and Coping with Equipment, Mental, or Physical Challenges. A technology usage theme after an event included Reflective Observations. Participants also expressed preferences in technology characteristics; themes included Record and Display information, Easy to Use, Syncing Between Devices, and Reliability. Kolb and Kolb (2005) identified a number of features that enhanced informal experiential learning spaces in higher education. Technology could replicate these features to enhance the experiential learning process in ultradistance cycling

Exploring informants’ perspectives on the role of crowdsourced active travel data

Peer reviewedPostprin

Spatial Patterns of Micromobility Ridership: A Multi-City Analysis

Lightweight, electricity-powered vehicles such as electric bicycles and scooters, known as micromobility, are expanding rapidly in urban areas worldwide. Micromobility holds a promising potential in improving transportation by a number of means, including filling public transportation gaps and reducing dependence upon the private car and thereby internal combustion engine emissions. As a result of the proliferation of micromobility sharing schemes around the world, ridership trajectories can be obtained. Through Ridereport (https://www.ridereport.com), datasets have been acquired from several cities (Santa Monica, California, USA; San Francisco, California, USA; Portland, Oregon, USA; Austin, Texas, USA; Auckland, New Zealand; and Melbourne, Victoria, Australia), this work analyzes the spatial patterns of micromobility which can be obtained from these datasets using the open-sourced exploratory spatial data analysis software, GeoDa. Across all cities, micromobility ridership exhibits positive spatial dependence. Meaning places with high micromobility ridership tend to cluster spatially. This spatial dependence has been explored further using Local Indication of Spatial Association (LISA) coupled with aerial imagery for qualitative assessment. Five themes related to high micromobility ridership spatial clusters were able to be detected (i.e., major thoroughfares, bridges, trails and open spaces, transits, and Hubs). The study contributes methodologically to the field of geographic information systems (GIS) and operationally to the field of transportation

Electric Bike Product Conception and Styling According to Design Trends

The following case study portrays the several steps required to conceive a product from scratch. The first step involves an in-depth analysis of today’s electric bicycle market in order to obtain data and information relating to the levels of innovation and comfort required by customers. Then, we evaluate the implementation of a useful method to understand the level of innovation that the product must have to be competitive on the market. The second part studies the architecture of the product, considering the different components already sold on the market which will become part of the project. The third part concerns a comparison between different stylistic trends that the vehicle may have (in order to outline the best one). The fourth part concerns the CAD realization of the virtual model complete with all its parts, including a structural verification study of the frame. The last part studies the presentation of the product to the customer, exploring different effective ways to communicate what the strengths of the new product will be (also allowing them to customize it before its realization). The plan for the realization of the new product, starting from the concept to arrive at the final presentation to the customer, follows the methods proposed by applying a series of steps to develop a generic new product in an efficient, sensible, and methodical manner. Therefore, we will refer to quality function deployment (QFD), benchmarking (BM), design for X, until reaching the final prototyping and testing phases

Public Bikesharing in North America During a Period of Rapid Expansion: Understanding Business Models, Industry Trends & User Impacts, MTI Report 12-29

Public bikesharing—the shared use of a bicycle fleet—is an innovative transportation strategy that has recently emerged in major cities around the world, including North America. Information technology (IT)-based bikesharing systems typically position bicycles throughout an urban environment, among a network of docking stations, for immediate access. Trips can be one-way, round-trip, or both, depending on the operator. Bikesharing can serve as a first-and-last mile connector to other modes, as well as for both short and long distance destinations. In 2012, 22 IT-based public bikesharing systems were operating in the United States, with a total of 884,442 users and 7,549 bicycles. Four IT-based programs in Canada had a total of 197,419 users and 6,115 bicycles. Two IT-based programs in Mexico had a total of 71,611 users and 3,680 bicycles. (Membership numbers reflect the total number of short- and long-term users.) This study evaluates public bikesharing in North America, reviewing the change in travel behavior exhibited by members of different programs in the context of their business models and operational environment. This Phase II research builds on data collected during our Phase I research conducted in 2012. During the 2012 research (Phase I), researchers conducted 14 expert interviews with industry experts and public officials in the United States and Canada, as well as 19 interviews with the manager and/or key staff of IT-based bikesharing organizations. For more information on the Phase I research, please see the Shaheen et al., 2012 report Public Bikesharing in North America: Early Operator and User Understanding. For this Phase II study, an additional 23 interviews were conducted with IT-based bikesharing organizations in the United States, Canada, and Mexico in Spring 2013. Notable developments during this period include the ongoing expansion of public bikesharing in North America, including the recent launches of multiple large bikesharing programs in the United States (i.e., Citi Bike in New York City, Divvy in Chicago, and Bay Area Bike Share in the San Francisco Bay Area). In addition to expert interviews, the authors conducted two kinds of surveys with bikesharing users. One was the online member survey. This survey was sent to all people for whom the operator had an email address.The population of this survey was mainly annual members of the bikesharing system, and the members took the survey via a URL link sent to them from the operator. The second survey was an on-street survey. This survey was designed for anyone, including casual users (i.e., those who are not members of the system and use it on a short-term basis), to take “on-street” via a smartphone. The member survey was deployed in five cities: Montreal, Toronto, Salt Lake City, Minneapolis-Saint Paul, and Mexico City. The on-street survey was implemented in three cities: Boston, Salt Lake City, and San Antonio

Analysis of the use and perception of shared mobility: A case study in Western Australia

The sharing economy has acquired a lot of media attention in recent years, and it has had a significant impact on the transport sector. This paper investigates the existing impact and potential of various forms of shared mobility, concentrating on the case study of Wanneroo, Western Australia. We adopted bibliometric analysis and visualization tools based on nearly 700 papers collected from the Scopus database to identify research clusters on shared mobility. Based on the clusters identified, we undertook a further content analysis to clarify the factors affecting the potential of different shared mobility modes. A specially designed questionnaire was applied for Wanneroo’s residents to explore their use of shared mobility, their future behaviour intentions, and their perspectives on the advantages and challenges of adoption. The empirical findings indicate that the majority of respondents who had used shared mobility options in the last 12 months belong to the low-mean-age group. The younger age group of participants also showed positive views on shared mobility and would consider using it in the future. Household size in terms of number of children did not make any impact on shared mobility options. Preference for shared mobility services is not related to income level. Bike sharing was less commonly used than the other forms of shared mobility