臺灣博碩士論文加值系統

電動機車共享系統（electric scooter sharing system, ESSS）是對環境友善的替代性交通運輸方案，也能夠解決自行車分享系統在騎乘距離及坡度上的限制。在消費者需求及使用資訊有限的情況下，本研究透過分析測試系統之使用記錄，設定關鍵需求參數（顧客到達率、旅行距離、活動時間）與供應參數（電動機車與充電設備數量），並在考量系統之服務水準、建置成本、及使用的便利性等面向下，以模擬最佳化方法求解不同潛在需求下的最佳電動機車數量，同時透過敏感度分析找出影響最佳電動機車部署數量的關鍵因子。研究結果可提供相關系統的管理者在建置及管理系統上的洞見。

Electric Scooter Sharing Systems (ESSS) are environmental friendly transportation alternatives that offer extra benefits to existing bicycle share customers. This study investigates the optimal initial vehicle deployment for a fully automated ESSS implemented in a city with the highest density of universities in Taiwan. With limited empirical demand and usage information, we simulated the critical demand parameters (trip rates, trip lengths and trip durations) and supply parameters (number of e-scooter and charging dock) and then used the method of optimization by simulation to determine the optimal number of e-scooter under different potential demand situations. Sensitivity analyses were also conducted to identify factors that highly affect the service level or the optimal fleet size. Results obtained from this study not only provide some operational guidelines for the full field deployment of the system, but also offer useful insights to managers who are interested in employing similar electric scooter sharing systems.

摘要 iii
Abstract iv
Acknowledgements v
Table of Content vi
List of Figure viii
List of Table ix
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Problem Statements and Objectives 3
1.3 Thesis Organization 4
Chapter 2 Literature Review 5
2.1 Vehicle Sharing System 5
2.2 Bike Sharing System 6
2.3 Car Sharing System 8
2.4 Fleet Size Simulation Approach 9
Chapter 3 Research Methodology 11
3.1 Electric Scooter Sharing System (ESSS) 11
3.1.1 ESSS Description 11
3.1.2 Components of ESSS 12
3.1.3 Operational Concepts of Electric Scooter Sharing System 13
3.1.4 Electric Scooter Sharing System Mechanism 14
3.2 Problem Statement 15
3.3 Solution Approach 15
3.3.1 Input Data Collection 17
3.3.2 Building Simulation Model 17
3.3.3 Verification and Validation Model 17
3.3.4 Experiments and Analysis 18
3.3.5 Conclusions and Recommendations 18
3.4 Simulation-based Optimization 18
Chapter 4 Simulation and Analysis 20
4.1 Input Analysis 20
4.1.1 Arrival Rate 20
4.1.2 Activity Duration 28
4.2 Model Construction 29
4.2.1 Trip Generation 29
4.2.2 Processing Event 30
4.2.3 Cost Structure 32
4.2.4 Model implemented using Arena 33
4.3 Experiments 34
4.4 Verification and Validation 38
4.5 Sensitivity Analysis 38
Chapter 5 Conclusions 44
Reference 46
Appendix 50

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