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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 游張松 | |
| dc.contributor.author | Yun-Sheng Liu | en |
| dc.contributor.author | 劉昀昇 | zh_TW |
| dc.date.accessioned | 2021-06-13T15:17:20Z | - |
| dc.date.available | 2009-07-30 | |
| dc.date.copyright | 2008-07-30 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-07-23 | |
| dc.identifier.citation | [1] A. D. Febbraro, D. Giglio, N. Sacco. Urban Traffic Control Structure Based on Hybrid Petri Nets. 2004. IEEE Transactions on Intelligent Transporatation Systems, 5(4). pp. 224-237.
[2] B. J. Driessen, K. S. Kwok. 1998. A Multi-Objective Dynamic Programming Approach to Constrained Discrete-Time Optimal Control. Proceedings of the American Control Conference. pp. 2077-2083. [3] C. S. Shih, W. S. Liu. 2002. State-Dependent Deadline Scheduling. Proceedings of the 23rd IEEE Real-time Systems Symposium. [4] E. W. Dijkstra. 1959. A note on two problems in connexion with graphs. Numerische Mathematik, 1, S. pp. 269–271. [5] G. Perakis, G. Roels. 2006. An Analytical Model for Traffic Delays and the Dynamic User Equilibrium Problem. Oper. Res. 54(6). pp. 1151-1171. [6] G. Rétvári, J. J. Bíró. 2007. On Shortest Path Representation. IEEE/ACM Transactions on Networking. 15(6). pp. 1293-1306. [7] G. W. Evans. 1984. An Overview of Techniques for Solving Multiobjective Mathematical Programs. Management Science. 30(11). pp. 1268-1282. [8] H. E. Lin. 2005. A Review of Travel-time Prediction in Transport and Logistics. Proceedings of the Eastern Asia Society for Transportation Studies, Vol. 5, pp. 1433 – 1448. [9] J. Anderson and M. Bell. 1998. Travel time estimation in urban road networks. Proceedings of IEEE Conference. pp. 924-929. [10] J. V. Luciani, C. Y. Chen. 1994. An Analytical Model for Partially Blocking Finite-Buffered Switching Networks. IEEE/ACM Transactions on Networking. 5(2). pp. 533-540. [11] L. R. Foulds. 1983. The Heuristic Problem-Solving Appr. The Journal of the Operational Research Society. 34(10). pp. 927-934. [12] M. A. Wiering, E. D. de Jong. 2007. Computing Optimal Stationary Policies for Multi-Objective Markov Decision Processes. Proceedings of the 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning. pp. 158-165. [13] M. S'anchez, J. Cano, D. Kim. 2006. Predicting Traffic lights to Improve Urban Traffic Fuel Consumption. 2006 6th International Conference on ITS Telecommunications Proceedings. pp. 331-336. [14] P. L. Findlay, K. A. H. Kobbacy, D. J. Goodman. 1989. Optimization of the Daily Production Rates for an Offshore Oilfield. The Journal of the Operational Research Society. 40(12). pp. 1079-1088. [15] P. Y. Li, R. Horowitz, L. Alvarez, J. Frankel and A. M. Robertson. 1995. Traffic Flow Stabilization. Proceedings of American Control Conference. pp. 144-149. [16] S. Chen, M. Song, S. Sahni. 2008. Two Techniques for Fast Computation of Constrained Shortest Paths. IEEE/ACM Transactions on Networking. 16(1). pp. 105-115. [17] V. Chankong, Y. Y. Haimes, D. M. Gemperline. 1981. A Multiobjective Dynamic Programming Method for Capacity Expansion. 26(5). pp. 1195-1207. [18] Y. S. Huang, T. H. Chung, T. H. Lin. 1995. A New Modeling Methodology of Urban Traffic Lights Based on Timed Coloured Petri Nets. IEEE International Conference on Systems, Man, and Cybernetics. pp. 85-90. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/36967 | - |
| dc.description.abstract | 本研究提出狀況相依速度調節最佳化問題，是為一個體企圖藉由調整本身行動之速度以達到交通旅行最佳效用，例如最短旅行時間、最平順之駕駛過程…等。一個狀況相依速度調節最佳化問題包含三種特徵，狀況相依之非線性關係、非封閉之編碼區間、多目標規劃。如此的特徵，使得傳統之模型存在著高度的運算複雜度，是為高階次方成長。因此，本論文提出一種幾何方式的呈現模式，得以精準地詮釋狀況相依速度調節最佳化問題之內在行為，同時，並發現到具有群集形式的解集合空間。根據對於狀況相依速度調節最佳化問題的發現，本研究提出一種最佳化之模型與有效的演算法「快速搜尋演算法(FSA)」，得以針對狀況相依速度調節最佳化問題取得最佳解。最後，本研究更以實際之交通資料進行實驗，得出可行之最佳解作為實際案例。本研究之貢獻在於成功地解析狀況相依速度調節最佳化問題，可直接應用於既有之衛星導航系統，更可延伸至其他具有狀況相依性之應用領域。 | zh_TW |
| dc.description.abstract | This thesis proposed a state-dependent velocity-scheduling (SDVS) problem which tries to optimize travel utilities, such as traffic-time, driving smoothness…etc, by altering the entity’s traveling velocity. The SDVS problem consists of three major physical characteristics: 1) A State-dependent problem, 2) Open-state configuration, 3) Multi-objective optimization. Such characteristics make formal model of SDVS problem to be computational complex, with a high-ordered complexity. Therefore, in this thesis, we propose geometrical representation for the SDVS problem. By adopting the geometrical representation, we discover the inner-state and inter-state behavior of SDVS problem. Based on the geometrical representation, we identify the solution cluster of SDVS problem. Hence, we propose an optimization model and an efficient algorithm (Fast-searching Algorithm, FSA) to solve SDVS problem. Finally, we use a real world data as experiment and get the optimized result as a real world case. The contribution of this thesis is successfully resolve the ambiguity of the SDVS problem as initiative, which can be adopted into GPS guiding system and extended to other state-dependent applications. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T15:17:20Z (GMT). No. of bitstreams: 1 ntu-97-R95741076-1.pdf: 20251092 bytes, checksum: 0c543793b0343ae23c7b689968be2723 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | Contents
Chapter 1: Introduction 1 1.1 Introduction to state-dependent travel time optimization 1 1.2 Research background and related works 2 1.3 Research motivation 4 1.4 The organization of thesis 5 Chapter 2: SDVS Problem 6 2.1 Physical Characteristic of SDVS Problem 6 2.2 Formal model in solving SDVS Problem 8 Chapter 3: Methodology of Solving SDVS Problem 11 3.1 Geometric Representation 11 3.2 Modeling the SDVS Problem 12 3.3 Enumeration of the model 14 3.4 Solution Space Clusters 17 3.5 Mathematical Characteristics 18 3.6 Probing Mechanism 28 3.7 Fast-searching Algorithm 31 3.8 Result of the Methodology 33 Chapter 4: Conclusion and Future works 36 Reference 38 Figures Figure 2-1: Formal model of SDVS Problem 9 Figure 2-2: Optimization Model of Formal Model 10 Figure 3-1: Geometrical Representation of SDVS Problem 11 Figure 3-2: Revised Optimization Model of SDVS Problem 14 Figure 3-3: Flowchart of Range-Search Algorithm 15 Figure 3-4: Spanning Tree Representation of SDVS Problem 17 Figure 3-5: Enumeration Result with Cluster of SDVS Problem 18 Figure 3-6: Movement Model of SDVS Problem 19 Figure 3-7: Law of motion of SDVS Problem 21 Figure 3-8: Optimization Model in Geometrical Representation of SDVS Problem 30 Figure 3-9: Flowchart of Fast-search Algorithm 32 Figure 3-10: Worst-case Performance of Fast-search Algorithm 33 Figure 3-11: Result of SDVS Problem with Geometrical Representation and FSA 34 Figure 3-12: Network Representation of Fast-search Algorithm’s Result 35 Tables Table 2-1: Physical Characteristics of SDVS Problem 6 Table 3-1: Summary of Assumptions 12 Table 3-2: Optimal Solution of Experiment 34 | |
| dc.language.iso | en | |
| dc.title | 一種狀況相依之交通時間最佳化研究 | zh_TW |
| dc.title | A State-dependent Traffic-time Optimization | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 朱惠中,張舜德 | |
| dc.subject.keyword | 狀況相依速度調節最佳化問題,運輸管理,物流,最佳化,演算方法, | zh_TW |
| dc.subject.keyword | SDVS Problem,Transportation,Logistics,Optimization,Algorithm, | en |
| dc.relation.page | 38 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2008-07-25 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 商學研究所 | zh_TW |
| 顯示於系所單位： | 商學研究所 | |
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