隨機旅行時間下小客車共乘配對模式暨求解演算法之研究

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姓名

張聲傑(Sheng-chieh Chang) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

隨機旅行時間下小客車共乘配對模式暨求解演算法之研究
(A Carpool Matching Model and Solution Algorithms under Stochastic Travel Times)

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摘要(中)

近年來台灣地區交通量成長迅速，個人小客車擁有比例與使用頻率亦日益增加，透過共乘制度除了可提高小客車之乘載率，紓解都會區交通雍塞與停車問題，並且可降低因全球油價上漲造成旅行成本增加所帶來的衝擊，以及達到節能減碳等優點。然而，目前實務上小客車的共乘配對上，多採用人工經驗進行排程規劃，不僅費時且缺乏系統分析，使得共乘的績效降低。過去小客車共乘的研究多以平均旅行時間為依據，進行共乘配對與排程，此作法未考量實際旅行時間的隨機性。在實際共乘進行時若隨機旅行時間造成之擾動過大，將使原規劃的配對與排程結果失去最佳性。因此，考量隨機性旅行時間之影響，建構一隨機性小客車共乘配對模式，期能提供一有效的規劃輔助工具，以幫助決策者有效地進行規劃。
本研究利用時空網路流動技巧建立一此隨機模式，模式中包含供車群車流、供車群人流與不供車群人流網路，以定式共乘群在時空中的流動與配對。本研究進一步修改隨機模式之旅行時間為平均旅行時間，建立一確定性模式。此兩模式可定式為特殊之整數多重貨物網路流動問題，屬NP-hard問題。為面臨實務之大型問題難以在有限時間內僅用數學規劃軟體求解，本研究發展一啟發式演算法以有效地求解問題。此外，本研究亦發展一模擬評估方法，以評估兩模式於實際共乘進行時之績效。最後為評估本研究中模式與演算法之實用績效，以實際狀況資料及合理假設產生測試例，進行本研究之範例測試並針對不同參數進行敏感度分析，結果顯示本模式與演算法在實用上可有效的運用，並進一步提出結論與建議。

摘要(英)

Respecting traffic volume has significantly grown and private cars become more popular than before in Taiwan. Therefore, carpool that enhances the car occupancy rate can not only relieve traffic congestion, but also reduce the travel cost which comes from the price of global petroleum going up, even save energy. As regards in Taiwan, carpool matching is manually performed by planning personnels with experience in current practice, without a systematic analysis. Such a manual approach is considered to be inefficient and ineffective. In other words, stochastic disturbances arising from variations in car travel times in actual operations are neglected. In the worst scenario, where car travel times fluctuate wildly during operations, the planned schedule could be disturbed enough to lose its optimality. Therefore, focusing on many-to-many origin-destination (OD), we constructed a stochastic carpool matching model that considers the influence of stochastic travel times. The matching model is expected to be an effective tool for the planner to solve carpool members matching.
We employed network flow techniques to construct the stochastic carpool matching model, including multiple CVG (a carpool member group who can provide a vehicle) vehicle-flow networks, CVG passenger-flow networks and multiple CNG (a carpool member group who cannot provide any vehicle) passenger-flow networks to formulate the flows of CVGs and CNGs in the dimensions of time and space. Then, we modified the stochastic travel times in the stochastic carpool matching model as an average travel time to develop a deterministic scheduling model. The two models are formulated as special integer multiple commodity network flow problems, which are characterized as NP-hard. Since the problem sizes are expected to be huge in real practice, the models are difficult to be solved in a reasonable time. Therefore, we also developed a heuristic algorithm for efficiently solving matching problems. In addition, to evaluate the stochastic and deterministic carpool matching models, we also developed a simulation-based evaluation method. The performance of the solution method in practice is evaluated by carrying out a case study using real data and suitable assumptions, and then sensitive analysis is performed for different parameters. The test results show the model to be good and that the solution method could be useful in practice.

關鍵字(中)

★ 啟發解法
★ 多重貨物網路流動問題
★ 時空網路
★ 隨機性旅行時間
★ 小客車共乘

關鍵字(英)

★ Heuristics
★ Multiple commodity network flow problem
★ Time-space network
★ Stochastic travel times
★ Carpool

論文目次

摘要i
ABSTRACTii
誌謝iii
目錄iv
圖目錄vii
表目錄viii
第一章緒論1
1.1研究背景與動機1
1.2研究目的與範圍2
1.3研究方法與流程2
第二章文獻回顧4
2.1 共乘定義與分類4
2.2 共乘歷史與現況6
2.3 小客車共乘相關文獻7
2.4 撥召與共乘相關文獻8
2.5 時空網路相關文獻11
2.6 隨機擾動之相關理論與文獻12
2.6.1 隨機性問題相關理論13
2.6.2 隨機擾動相關文獻15
2.7 大型含額外限制之整數網路流動問題啟發式演算法18
2.8 文獻評析20
第三章模式構建22
3.1 隨機性小客車共乘配對模式24
3.1.1 模式基本假設或已知資訊24
3.1.2 隨機性模式之時空網路26
3.1.2.1 供車群之車流時空網路26
3.1.2.2 供車群之人流時空網路29
3.1.2.3 不供車群之人流時空網路30
3.1.2.4 非預期懲罰成本設計32
3.1.3 模式符號說明 40
3.1.4 數學定式42
3.2 確定性小客車共乘配對模式44
3.2.1 確定性模式之時空網路44
3.2.2 確定性模式之數學定式46
3.3 模擬評估方法46
3.4 模式應用47
3.5 小結48
第四章求解演算法設計49
4.1 目標值上限49
4.2 目標值下限51
4.3 車輛與乘客配對演算法51
4.4 小結52
第五章範例測試53
5.1 輸入資料53
5.1.1 車輛規劃資料53
5.1.2 共乘旅次資料55
5.2 模式發展56
5.2.1 問題規模56
5.2.2 模式輸入資料57
5.3 電腦演算環境及設定58
5.3.1 電腦演算環境58
5.3.2 相關參數設定58
5.3.3 模式輸出資料59
5.4 測試結果與分析60
5.4.1 隨機狀況數目60
5.4.2 隨機性小客車共乘配對測試結果61
5.4.3 不同模式間之分析比較63
5.5 敏感度分析65
5.5.1 懲罰成本之敏感度分析66
5.5.1.1 非預期懲罰成本66
5.5.1.2 乘客未服務懲罰成本68
5.5.2 後續時段受影響乘客人數之敏感度分析69
5.6 情境分析70
5.6.1 系統服務共乘人數分析70
5.6.2 非預期懲罰成本之折減率與累進係數情境分析71
5.7 小結74
第六章結論與建議75
6.1 結論75
6.2 建議76
6.3 貢獻76
參考文獻77
附錄85
附錄一 CPLEX CALLABLE LIBRARY CODE85
附錄二隨機狀況模擬次數測試結果86
附錄三非預期懲罰成本之敏感度分析87
附錄四未服務懲罰成本值之敏感度分析91

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指導教授

顏上堯(Shang-Yao Yan)

審核日期

2010-7-22

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