| 研究生: |
王領 Ling Wang |
|---|---|
| 論文名稱: |
初期通勤型自行車道規劃模式暨求解演算法之研究 The optimal model and Solution Algorithm for Commuter type Bicycle Lane Initial Network Planning |
| 指導教授: |
顏上堯
Shang-yao Yan |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 土木工程學系 Department of Civil Engineering |
| 畢業學年度: | 100 |
| 語文別: | 中文 |
| 論文頁數: | 88 |
| 中文關鍵詞: | 網路設計 、自行車道 、通勤 、拉氏鬆弛法 、多重貨物網路流動 |
| 外文關鍵詞: | commuters, Lagrangian relaxation., network design, bicycle lane, multiple commodity network flow problem |
| 相關次數: | 點閱:15 下載:0 |
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近年來,由於台灣都市的人口密集、交通機能多元且集中等所衍生出短途、多目的旅次型態。受世界性環保議題與樂活概念之影響,各國政府與民間團體開始積極地鼓勵民眾加入節能減碳的行列,除了使用大眾運輸工具外,亦提倡使用自行車等綠色運具,以改善因汽機車使用造成之廢棄排放量與污染問題。遺憾的是,各縣市政府雖大力推行自行車作為通學、通勤之運具,對於自行車道路網建置卻無一完善、安全且連續性之自行車道路網,以致於自行車之使用率不如預期。
本研究以初期通勤型自行車道規劃者之立場,考量實際營運時需求量固定,整合各區域之旅行時間、安全性等相關額外限制,發展一具方向性之自行車道網路模式,用以協助政府與決策者有效地規劃初期通勤型自行車道路網。在模式求解部分,為有效求解實務之大型問題,本研究針對此模式以C++ 程式語言結合CPLEX發展一拉氏鬆弛演算法進行求解。為評估模式及演算法之績效,本研究參考台北市實務資料,隨機產生若干不同模式之測試範例,進行模式及啟發式解法的測試與方案分析,測試結果良好,最後提出本研究之結論與建議。
Taiwan''s high population density and busy traffic in urban areas result in the needs of transportation for short-distances or with multi-purposes. To improve the problem of air pollution, the government recommends people choose energy-saving ways of transportations, such as bicycles and public transportations. However, for many bikers and commuters, the construction of safe, continual and connected urban bicycle lane networks are still not yet finished, leading to overestimated utilizations.
This research aims at helping the government with the commuters’ bicycle lane network planning in the first phase, and offering a model designed for people’s safety, integrating the connection to different places, and saving their time.To efficiently solve large-scale problems occurring in real world, we develop a solution algorithm for each model, based on Lagrangian relaxation methods. As for the matching framework and solution algorithm in practice, we perform a case based on the operating date from Taipei city department of transportation study. A computerized random generator is designed to generate different problem instances used for testing. The results show the model could be useful. Finally, conclusions and suggestions are given.
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