| 研究生: |
林昀丘 Yun-Ciou Lin |
|---|---|
| 論文名稱: |
車輛移動模型與智慧型路由方法之研究--以新屋交流道周邊道路為例 Study of Mobility Model and Intelligent Routing Method for Advanced Vehicles – Taking Xinwu Interchange as an Example |
| 指導教授: |
陳彥文
Yen-Wen Chen |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
資訊電機學院 - 通訊工程學系 Department of Communication Engineering |
| 畢業學年度: | 98 |
| 語文別: | 中文 |
| 論文頁數: | 48 |
| 中文關鍵詞: | 智慧型運輸系統 、智慧型路由 、車輛移動模型 、交通號誌控制 |
| 外文關鍵詞: | Traffic-Signal Control, Mobility model, Intelligent Routing, ITS |
| 相關次數: | 點閱:7 下載:0 |
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ITS 中便利性服務一直都是熱門討論的研究議題,本篇論文提出運用於交通號誌之動態控制管理方法及具有便利性的車輛『智慧型路由』方法,來達到有效率的整體性車輛路由規劃。並探討常被使用的車輛移動模型之真實性(Realism of models)。
本論文首先探討車輛移動模型之真實性,而跟車模型(Car-following model)由於有引用駕駛行為模式,並且是根據道路限速及與前車之安全車距來調整車速,因此較能真實的模擬現實環境之車流。此外在於動態交通號誌設計上,引用Webster equation來決定交通號誌週期,並根據各相位之車流來決定綠燈通行時間。最後,提出一智慧型路由方法,是根據預先排程的車輛數目來預測抵達目的路段之速度,並依各路段之行駛時間以Dijkstra最短路徑演算法來決定路由。
模擬是以中壢地區新屋交流道周邊道路為拓樸,並套用實際速限及交通號誌時間以求能真實模擬現實環境。最後模擬結果與最短路徑演算法作比較,以呈現兩者效能之差異。
Comfortable application has been one of the popular research topics in ITS recently. In this thesis, we propose a dynamic Traffic-Signal Control method and a vehicle-based intelligent routing method to increase the efficiency of the vehicle transportation. And the proposed scheme is explored in a real road topology and vehicle mobility model to investigate the routing performance.
The car-following model, include the driver''s behavior, is based on the speed limits and the safety distance headway to adjust the speed. Therefore, it can better simulate the real traffic flow. Then, we use Webster equation to design the traffic signal cycle, and decide the time intervals of traffic light according to the flow condition. Finally, an intelligent routing method is based on the pre-scheduling number of vehicles to predict the speed when arrive at the target segment. And then, use the predicted traveling time on each segment to determine the routing path according to the Dijkstra’s algorithm. The simulation results show the performance of proposed method and the shortest path algorithm especially in the heavy traffic load.
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