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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪英超 | zh_TW |
| dc.contributor.advisor | Ying-Chao Hung | en |
| dc.contributor.author | 李岳耘 | zh_TW |
| dc.contributor.author | Yue-Yun Li | en |
| dc.date.accessioned | 2026-02-26T16:55:34Z | - |
| dc.date.available | 2026-02-27 | - |
| dc.date.copyright | 2026-02-26 | - |
| dc.date.issued | 2026 | - |
| dc.date.submitted | 2026-01-22 | - |
| dc.identifier.citation | [1] Allsop, R. E. (1971). SIGSET: A computer program for calculating traffic signal settings. Traffic Engineering & Control, 13(2), 58–60.
[2] Andriani, D. E. (2025). Dynamic pricing strategy for perishable products with stochastic customer behavior. Master’s thesis, Institute of Industrial Engineering, National Taiwan University. [3] Basermann, A., Röhrig-Zöllner, M., & Illmer, J. (2015). Performance and productivity of parallel Python programming: A study with a CFD test case. Proceedings of the 5th Workshop on Python for High-Performance and Scientific Computing, 1–10. [4] Batty, M. (2018). Digital twins. Environment and Planning B: Urban Analytics and City Science, 45(5), 817-820. [5] Beckmann, M., McGuire, C. B., & Winsten, C. B. (1956). Studies in the Economics of Transportation. Yale University Press. [6] Ben-Ameur, W. (2004). Computing the initial temperature of simulated annealing. Computational Optimization and Applications, 29(3), 369–385. [7] Cai, X., Langtangen, H. P., & Moe, H. (2005). On the performance of the Python programming language for serial and parallel scientific computations. Scientific Programming, 13(1), 31-56. [8] Chen, J. M. (2025). A data-driven approach to capacity Planning for public bike sharing stations. Master’s thesis, Institute of Industrial Engineering, National Taiwan University. [9] Dameri, R. P., & Rosenthal-Sabroux, C. (2014). Smart City: How to Create Public and Economic Value with High Technology in Urban Space. Springer. [10] Eom, M., & Kim, B. I. (2020). The traffic signal control problem for intersections: a review. European Transport Research Review, 12(1), 50. [11] Feng, Y., Head, K. L., Khoshmagham, S., & Zamanipour, M. (2015). A real-time adaptive signal control in a connected vehicle environment. Transportation Research Part C: Emerging Technologies, 55, 460-473. [12] He, Q., Head, K. L., & Ding, J. (2011). Heuristic algorithm for priority traffic signal control. Transportation Research Record, 2259(1), 1-7. [13] Hung, Y. C., Michailidis, G., & Chuang, S. C. (2014). Estimation and monitoring of traffic intensities with application to control of stochastic systems. Applied Stochastic Models in Business and Industry, 30(2), 200-217. [14] Kokash, N. (2005). An introduction to heuristic algorithms. Department of Informatics and Telecommunications, 1, 1-7. [15] Le Sueur, E., & Heiser, G. (2010). Dynamic voltage and frequency scaling: The laws of diminishing returns. Proceedings of the 2010 International Conference on Power Aware Computing and Systems, 1–8. [16] Liashchynskyi, P., & Liashchynskyi, P. (2019). Grid search, random search, genetic algorithm: a big comparison for NAS. arXiv preprint, arXiv:1912.06059, 1–11. [17] Little, J. D. (1961). Approximate expected delays for several maneuvers by a driver in Poisson traffic. Operations Research, 9(1), 39-52. [18] Lo, H. K. (1999). A novel traffic signal control formulation. Transportation Research Part A: Policy and Practice, 33(6), 433-448. [19] Matias, R., Carvalho, A. M., Araujo, L. B., & Maciel, P. R. (2011). Comparison analysis of statistical control charts for quality monitoring of network traffic forecasts. Proceedings of the 2011 IEEE International Conference on Systems, Man, and Cybernetics, 404–409. [20] Miller, A. J. (1963). Settings for fixed-cycle traffic signals. Journal of the Operational Research Society, 14(4), 373-386. [21] Montgomery, D. C. (2020). Introduction to statistical quality control. John Wiley & Sons. [22] Münz, G., & Carle, G. (2008). Application of forecasting techniques and control charts for traffic anomaly detection. Proceedings of the 19th ITC Specialist Seminar on Network Usage and Traffic, 1–18. [23] Newell, G. F. (1960). Queues for a fixed-cycle traffic light. The Annals of Mathematical Statistics, 31(3), 589–597. [24] Nikolaev, A. G., & Jacobson, S. H. (2010). Simulated annealing. Handbook of Metaheuristics, 1–39. Springer. [25] Rardin, R. L., & Uzsoy, R. (2001). Experimental evaluation of heuristic optimization algorithms: A tutorial. Journal of Heuristics, 7(3), 261-304. [26] Regulations on installation of traffic signs, markings, and signals. https://law.moj.gov.tw/LawClass/LawAll.aspx?PCode=K0040014 [27] Robertson, D. I. (1969). TRANSYT: A traffic network study tool. RRL Report LR 253, 1–37. [28] Robertson, D. I. (1986). Research on the TRANSYT and SCOOT methods of signal coordination. ITE journal, 56(1), 36-40. [29] Ross, S. M. (2022). Simulation. Academic press. [30] Stevanovic, A., Stevanovic, J., & Kergaye, C. (2013). Optimization of traffic signal settings based on surrogate measures of safety. Transportation Research Part C: Emerging Technologies, 32, 159–178. [31] TomTom. (2024). TomTom traffic index: Ranking. https://www.tomtom.com/traffic-index/ranking/ [32] Webster, F. V. (1958). Traffic Signal Settings. Road Research Laboratory Technical Paper No. 39, 1–44. [33] Yu, C., Feng, Y., Liu, H. X., Ma, W., & Yang, X. (2018). Integrated optimization of traffic signals and vehicle trajectories at isolated urban intersections. Transportation Research Part B: Methodological, 112, 89–112. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101712 | - |
| dc.description.abstract | 交通號誌控制對號誌化路口的運作效率及智慧城市的建設至關重要。然而，在臺灣，許多路口仍依賴固定時制的號誌控制方法，此方法無法因應隨時間變化且具隨機性的交通需求，容易導致車隊過長、路口壅塞及車輛延滯等問題。本研究提出一種結合數位孿生模型的自適應交通號誌控制方法。系統透過影像監控設備蒐集即時車流資料，並配合統計管制方法，在偵測到交通流量出現顯著變化時，即以最新資料建立車流到達的隨機模型。接著，運用數位孿生模型模擬不同號誌時制方案，評估其平均車輛延滯時間，最終即時更新號誌為效能最佳的設定。
基於上述模型，本研究構建一個整數規劃問題，在實務考量的限制下以最小化平均車輛延滯為目標。透過持續監測，系統能在到達模式轉變時重新求解最佳設定，提供比固定時制更具反應能力的替代方案，進而提升路口運作效率與駕駛體驗。此概念是智慧城市發展的重要基礎，藉由大規模、資料驅動的交通管理，減少壅塞與污染排放，同時改善路網可靠性。 | zh_TW |
| dc.description.abstract | Traffic signal control is pivotal to the efficiency of signalized intersections and to smart-city operations. In Taiwan, however, many intersections still rely on fixed-time plans that cannot adapt to time-varying, stochastic demand, leading to long queues, excessive delays, and congestion. This study proposes a digital twin-enabled adaptive control strategy that updates signal settings whenever material changes in traffic flow are detected. The system employs camera-based vehicle detection to monitor real-time traffic conditions, constructing a digital twin simulation environment that mirrors actual intersection operations and integrating a stochastic arrival model to evaluate delays under candidate signal settings.
Based on these models, we formulate an integer-programming problem that minimizes average vehicle delay subject to operational constraints. Continuous monitoring enables online re-optimization as arrival patterns evolve, yielding a responsive alternative to fixed-time control and improving the efficiency of intersection operations and the driving experience. This capability is foundational to Smart City development, enabling scalable, data-driven traffic management that cuts congestion and emissions while improving network reliability. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-02-26T16:55:34Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2026-02-26T16:55:34Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 致謝 - i
摘要 - ii Abstract - iii Table of Contents - iv List of Figures - vi List of Tables - viii Table of Notations - ix Chapter 1 Introduction - 1 1.1 Research Background and Motivations - 1 1.2 Research Objective - 3 1.3 Main Contribution - 4 1.4 Thesis Structure - 4 Chapter 2 Literature Review - 6 2.1 Traffic Signal Control - 6 2.2 Stochastic Arrival Process - 9 2.3 Traffic Flow Monitoring - 10 2.4 Optimization Algorithm - 12 Chapter 3 Signalized Intersection Control System - 14 3.1 Signalized Intersection and Traffic Flow - 14 3.2 Vehicle Delay - 17 3.3 Real-Tiem Data Collection - 21 3.4 Adaptive EWMA-Based Traffic Monitoring - 23 3.5 Digital Twin Simulation Model - 27 3.6 The Optimization Problem – Minimize the Average Delay - 32 3.7 Heuristic Algorithm - 35 Chapter 4 Scenario Introduction - 41 4.1 Initial Parameter Setting - 41 4.2 Scenario Design - 43 4.2.1 Scenario 1 (Unbalanced Traffic Flow) - 43 4.2.2 Scenario 2 (Balanced Traffic Flow) - 44 4.3 Traffic Monitoring Setting - 45 4.4 Simulation Structure - 46 Chapter 5 Numerical Results - 48 5.1 Scenario 1 - 48 5.2 Scenario 2 - 55 5.3 Computational Cost - 62 5.4 Comparison with Fixed-Time Signal Control - 63 5.5 Sensitivity Analysis - 65 Chapter 6 Conclusion - 76 References - 79 Appendix: Algorithm - 83 1. Vehicle Arrivals Generation - 83 2. Real-Time Traffic Monitoring - 83 3. Estimating Vehicle Delay in One Direction - 85 | - |
| dc.language.iso | en | - |
| dc.subject | 適應性號誌控制 | - |
| dc.subject | 隨機建模 | - |
| dc.subject | 整數規劃 | - |
| dc.subject | 數位孿生 | - |
| dc.subject | 模擬 | - |
| dc.subject | Adaptive traffic signal control | - |
| dc.subject | stochastic arrivals | - |
| dc.subject | integer programming | - |
| dc.subject | digital twin | - |
| dc.subject | simulation | - |
| dc.title | 基於數位孿生之適應性號誌控制方法 | zh_TW |
| dc.title | Camera-based Digital Twin For Real-Time Adaptive Traffic Signal Control | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 114-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 黃奎隆;藍俊宏;喻奉天 | zh_TW |
| dc.contributor.oralexamcommittee | Kwei-Long Huang;Jakey Blue;Vicent Yu | en |
| dc.subject.keyword | 適應性號誌控制,隨機建模整數規劃數位孿生模擬 | zh_TW |
| dc.subject.keyword | Adaptive traffic signal control,stochastic arrivalsinteger programmingdigital twinsimulation | en |
| dc.relation.page | 88 | - |
| dc.identifier.doi | 10.6342/NTU202600234 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2026-01-22 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 工業工程學研究所 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 工業工程學研究所 | |
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|---|---|---|---|
| ntu-114-1.pdf 未授權公開取用 | 5.27 MB | Adobe PDF |
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