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研究生： 劉昭堂
Liu, Chao-Tang
論文名稱： 以ARX模型預測公共自行車系統之運量—比較人口因子及旅次因子之差異性
Using Autoregressive with Exogenous Variable Models for the Ridership Predictions of Bicycle Sharing Systems: Comparison between Population- and Trip-based Data
指導教授： 胡守任
Hu, Shou-Ren
共同指導教授: 魏健宏
Wei, Chien-Hung
學位類別： 博士
Doctor
系所名稱： 管理學院 - 交通管理科學系
Department of Transportation and Communication Management Science
論文出版年： 2022
畢業學年度： 110
語文別： 英文
論文頁數： 156
中文關鍵詞： 公共自行車 、社會經濟特性 、旅次特性 、外生輸入自我迴歸模式
外文關鍵詞： Bicycle-sharing system, Socioeconomic characteristic, Trip attribute, Autoregressive with exogenous variable model
ORCID： 0000-0002-3889-1975
相關次數： 點閱：105  下載：14 
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    • 公共自行車系統的發展與都市地區的實質特徵具高度的相關性，因都會區中的每個區域具有不同社會經濟與人口組成特性，這些特質對於公共自行車使用量及系統規模的增減有不同的影響，因此如何有效評估特定區域內公共自行車系統的營運績效，對於都市地區整體運輸系統的發展相當重要。彙整過往的研究發現，多數研究均以區域內「人口數」作為衡量公共自行車系統績效的重要因子之一，惟本研究認為「旅次」資料更適合用於預測公共自行車系統的使用量，因為在都市運輸規劃中「旅次」數量，方代表區域內人口的潛在活動強度，對於預測公共自行車系統的使用量及績效，更具代表性；另本研究亦從公共自行車歷年使用量及系統規模擴展資料，用以評估區域內之租賃站，於未來應增加或減少，以有效控制經營成本。

    本研究彙整2009年至2017年高雄市行政區的相關資料(包含：社經資料、人口統計及旅次起訖資料)及高雄公共自行車系統(CityBike)的運量、租賃站數及車站容量等資料，並以租借站區域(站級資料)及行政區(地區資料)為單位進行資料分類，使用時間序列分析法並考量外生變量下，進行模型建構。研究結果顯示，「旅次」資料相對於人口數對於公共自行車的使用量之預測較為準確，且可判別租借站借還運量與不同旅次目的起訖量(例如：家-工作旅次或家-學校旅次等)的關聯性。另有關系統擴展議題，本研究從「CityBike系統單一租賃站1平方公里內租賃站容量」該因子之實證研究結果發現，並非所有的區域會隨著CityBike系統站數的擴增，而增加使用量，部份區域呈現負向的成長。本研究相關成果，可以提供公共自行車系統的營運者，準確評估既有系統的使用量，並在有限的預算之下，以整體系統最大化效益進行設備投資與增減租賃站之評估，期能實現公共自行車系統永續經營之目標。

    The successful development of bicycle-sharing systems (BSS) has been influenced by the socioeconomic characteristics and geographical attributes of the metropolitan areas they are set up in. Trip generation and attraction volumes, which represent the true flow of resident activity within a specific area, may influence BSS ridership, particularly for people using a BSS for first- or last-mile services. However, most studies have thus far used population and other socioeconomic data to investigate BSS ridership and have not considered trip attributes. Although population-related attributes may influence BSS ridership, they cannot account for the spatial distributions of vehicular or passenger trips between specific origins and destinations. In addition, this study also explored BSS ridership data and system scale over the years to evaluate whether BSS rental stations in a specific area should be increased or decreased for cost control.

    In contrast with previous studies, this study collected nine years of BSS ridership data, number of rental stations, station capacity and related socioeconomic characteristics regarding the CityBike system in Kaohsiung City, Taiwan, including users’ trip attributes. The autoregressive with an exogenous variable (ARX) model was used to analyze the factors influencing the ridership of the CityBike system. The results indicated that trip attributes are more relevant than population data in predicting BSS ridership and determining correlations between renting and returning in the CityBike system and the original destination depending on the trip’s purpose (such as journeys from home to work or school).

    Concerning the system expansion issue, by investigating the effect of the “capacity of CityBike station in 1 km buffer” variable, this study found that some districts in urban areas are oversupplied with stations during specific periods, thereby decreasing the system’s overall efficiency. Accurate predictions of BSS ridership over time can enable the allocation of limited resources to establish new stations or improve infrastructure for future sustainable BSS development.

    摘要 I ABSTRACT II 致謝 III TABLE OF CONTENTS IV LIST OF TABLES VII LIST OF FIGURES VIII Chapter 1 INTRODUCTION 1 1.1 Motivation 1 1.2 Research Scope 2 1.3 Objective 4 1.4 Research Methodology 5 1.5 Research Content with Research Flow Chart 6 Chapter 2 LITERATURE REVIEW 9 2.1 BSS History and Development 9 2.2 Key Influencing Factors on BSSs Usage 16 2.2.1 Demographic and Socioeconomic Variables 17 2.2.2 Spatial Variables 18 2.2.3 Temporal Variables 23 2.2.4 Other Influencing Variables 25 2.3 Modeling Approach 26 2.4 Comments on the Reviewed Literature 29 Chapter 3 MODEL FORMULATION 39 3.1 Problem Statements and Assumptions 39 3.2 Model Design 40 3.3 Model Structure and Formulation 42 3.3.1 Description of the Developed Models 42 3.3.2 Dependent Variables Hierarchical Division 45 3.3.3 Notation of Development Models 49 3.3.4 Hierarchical Regression Model (HRM model, M1 model) 50 3.3.5 Autoregressive Model (AR Model) and Autoregressive with Exogenous Variable Model (ARX Model, M2 model) 53 Chapter 4 EMPIRICAL STUDY AND RESULTS 63 4.1 Background Information 63 4.2 Data Collection 72 4.3 Results and Discussion 76 4.3.1 Model Calibration 76 4.3.2 Results of Hierarchical Level in M1/M2 82 4.3.3 Results of the First-Level Variables in M1/M2 84 4.3.4 Result of the Second Level Variables in M1/M2 86 4.3.5 Result of the Third-Level Variables in M1/M2 93 4.3.6 Results of CityBike Ridership Prediction in M1/M2 94 4.3.7 Policy Implication 105 Chapter 5 CONCLUSIONS AND FUTURE WORK 108 5.1 Conclusions 108 5.2 Limitations of the Research 109 5.3 Future Work 109 REFERENCES 111 APPENDICES 118

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