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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90077完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴勇成 | zh_TW |
| dc.contributor.advisor | Yung-Cheng Lai | en |
| dc.contributor.author | Christopher Tacker-Mischenko | zh_TW |
| dc.contributor.author | Christopher Tacker-Mischenko | en |
| dc.date.accessioned | 2023-09-22T17:18:56Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-09-22 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-11 | - |
| dc.identifier.citation | Abdelhafiez, E. A., Salama, M. R., & Shalaby, M. A. (2017). Minimizing passenger travel time in URT system adopting skip-stop strategy. Journal of rail transport planning & management, 7(4), 277-290.
Asakura, Y., Iryo, T., Nakajima, Y., Kusakabe, T., Takagi, Y., & Kashiwadani, M. (2008). Behavioural analysis of railway passengers using smart card data. WIT Transactions on the Built Environment, 101, 599-608. Attoh-Okine, N. O., & Shen, L. D. (1995, July). Security issues of emerging smart cards fare collection application in mass transit. In Pacific Rim TransTech Conference. 1995 Vehicle Navigation and Information Systems Conference Proceedings. 6th International VNIS. A Ride into the Future (pp. 523-526). IEEE. Bagchi, M., & White, P. R. (2005). The potential of public transport smart card data. Transport Policy, 12(5), 464-474. Dial, R. B. (1971). A probabilistic multipath traffic assignment model which obviates path enumeration. Transportation research, 5(2), 83-111. Hong, S. P., Min, Y. H., Park, M. J., Kim, K. M., & Oh, S. M. (2016). Precise estimation of connections of metro passengers from Smart Card data. Transportation, 43, 749-769. Jia, L., Lei, D., Zhang, Y., Zeng, Q., & Wang, J. (2017). The model and algorithm of distributing cooperation profits among operators of urban rail transit under PPP pattern. Cluster Computing, 20, 3023-3036. Kim, K. M., Hong, S. P., Ko, S. J., & Kim, D. (2015). Does crowding affect the path choice of metro passengers?. Transportation Research Part A: Policy and Practice, 77, 292-304. Kusakabe, T., Iryo, T., & Asakura, Y. (2010). Estimation method for railway passengers’ train choice behavior with smart card transaction data. Transportation, 37, 731-749. Pelletier, M. P., Trépanier, M., & Morency, C. (2011). Smart card data use in public transit: A literature review. Transportation Research Part C: Emerging Technologies, 19(4), 557-568. Shin, H., Kim, D. K., Kho, S. Y., & Cho, S. H. (2021). Valuation of Metro Crowding Considering Heterogeneity of Route Choice Behaviors. Transportation Research Record, 2675(2), 162-173. Zhou, F., & Xu, R. H. (2012). Model of passenger flow assignment for urban rail transit based on entry and exit time constraints. Transportation Research Record, 2284(1), 57-61. (2015, May 15). 臺北捷運系統相鄰兩站間之行駛時間、停靠站時間. Data.gov.tw. Retrieved July 9, 2023, from https://data.gov.tw/dataset/128420?_x_tr_hist=true%5D. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90077 | - |
| dc.description.abstract | NONE | zh_TW |
| dc.description.abstract | Metro networks possess many stations which travelers can freely connect between while within them. Due to the exponential complexity of accounting for every possible path a passenger may take between any two stations, the concept of feasible paths helped narrow down path selection to routes which strictly advance the traveler toward their destination and away from their origin. Travelers are often assumed to follow these feasible paths as rational actors to minimize their travel time and fare costs, along with any other utility one route may possess over another. The utility of passenger comfort, considered during crowded metro situations, is still primarily perceived as a choice between two or more feasible paths. However, travelers in the Taipei Metro will go further and backtrack to their respective terminal station before heading toward their destination to achieve this comfort. While backtracking has been considered in networks where there might be a time advantage, such as backtracking to an express service station or backtracking to a transfer station with reduced transfer times, backtracking has yet to be considered strictly for the utility of traveler comfort at the expense of longer travel times and potentially higher fares. A backtracking algorithm is developed to capture this backtracking behavior from smart card transaction data and used to train a binary logit model in an attempt to find which variables most directly influence the traveler’s decision to backtrack. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T17:18:56Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-22T17:18:56Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | TABLE OF CONTENTS
ABSTRACT i LIST OF FIGURES iv LIST OF TABLES vi CHAPTER 1: INTRODUCTION 1 1.1: Motivation of Research 1 1.2: Defining Backtracking Behavior 2 1.3: Selected Study Region Within the Taipei Metro Network 4 1.4: Research Tasks 8 1.5: Thesis Organization 9 CHAPTER 2: LITERATURE REVIEW 11 2.1: Smart Card Transaction Data 11 2.2: Passenger Path Behavior 12 2.3: Backtracking Research 14 2.4: Crowding in Transportation Network 16 2.5: Summary of Findings 17 CHAPTER 3: METHODOLOGY 19 3.1: Process Overview 19 3.2: Assumptions of Traveler Behaviors 20 3.3: Available Smart Card Transaction Data 21 3.4: Generating Train Schedules from Timetable Collation 23 3.5: Initial Train Assignment and Validation Algorithm 26 3.6: Backtracking Algorithm 27 3.7: Sampled Data for Training Binary Logit Model 29 CHAPTER 4: CASE STUDY 31 4.1: Overview of Backtracking Algorithm Results 31 4.2: Backtracking Algorithm Results Based on Time of Day 32 4.3: Backtracking Algorithm Results Based on Station Location 35 4.4: Discussion of Variables in Binary Logit Model 37 4.5: Model Performance Versus Trained Data Set 43 4.6: Model Performance Versus Untrained Data Set 45 4.7: Model Performance Versus Other Data Sets 46 CHAPTER 5: CONCLUSION AND FUTURE RESEARCH 50 5.1: Conclusion 50 5.2: Limitations of Current Research 50 5.3: Research Contributions 53 5.4: Potential Avenues for Future Research 54 5.5: Credits and Acknowledgements 54 REFERENCES 55 APPENDIX A: BINARY LOGIT MODEL FROM VALIDATED SAMPLED DATA 57 APPENDIX B: STATION CHARACTERISTICS 59 | - |
| dc.language.iso | en | - |
| dc.subject | 二元Logit模型 | zh_TW |
| dc.subject | 乘客路徑行為 | zh_TW |
| dc.subject | 智能卡交易數據 | zh_TW |
| dc.subject | 城域網絡 | zh_TW |
| dc.subject | 回溯 | zh_TW |
| dc.subject | Binary Logit Model | en |
| dc.subject | Smart Card Transaction Data | en |
| dc.subject | Metro Network | en |
| dc.subject | Passenger Path Behavior | en |
| dc.subject | Backtracking | en |
| dc.title | 利用智慧卡票證資料探索臺北捷運乘客回返行為之研究 | zh_TW |
| dc.title | Exploring Passenger Backtracking Behavior in Taipei Metro Using Smart Card Transaction Data | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 許聿廷 | zh_TW |
| dc.contributor.coadvisor | Yu-Ting Hsu | en |
| dc.contributor.oralexamcommittee | 朱致遠; 蕭傑諭 | zh_TW |
| dc.contributor.oralexamcommittee | James Chu;Chieh-Yu Hsiao | en |
| dc.subject.keyword | 乘客路徑行為,智能卡交易數據,城域網絡,二元Logit模型,回溯, | zh_TW |
| dc.subject.keyword | Passenger Path Behavior,Smart Card Transaction Data,Metro Network,Binary Logit Model,Backtracking, | en |
| dc.relation.page | 59 | - |
| dc.identifier.doi | 10.6342/NTU202303649 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-08-12 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2028-08-08 | - |
| 顯示於系所單位: | 土木工程學系 | |
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