| 摘要: | 「交通量指派」為旅運需求預測最為核心之模組。假設每位理性用路人均選擇使用 起迄對間之最短路徑，則依照旅行時間/成本之種類不同，可以分別建構明確性、隨機性、 以及動態性之交通量指派模型，並以Frank-Wolfe，PARTAN，GP，或Dial 等演算法求 解。 過去十年，交通量指派有兩項重大議題被(重新)提出來討論，第一個議題是有關用 路人之行為假設，即為了更加符合實際產生之路網流量型態，除了之前之Wardrop 原則 之外，是否仍需增加其他之用路人之行為假設？第二個議題是有關交通量指派演算法之 運算效率，有鑑於傳統交通量指派演算法表現不佳，亟需發展快速精準(且可產生路徑 流量唯一解)之交通量指派演算法。為了詳細探討這兩個重要之議題，並將研究成果延 伸應用至更為複雜之極大熵雙限旅次分配/交通量指派問題，本計劃書提出三年之研究計 畫內容。 第一年之主要研究內容為：(1) 建構一般化之交通量指派模型，(2) 撰寫TAPAS (Traffic Assignment by Paired Alternative Segments)電腦程式，(3) 改良TAPAS 之搜尋PAS 之方法，(4) 修正TAPAS 之PAS 流量分配之方法，(5) 數例測試。 第二年之研究重點為熵基礎之交通量指派模型(entropy-based traffic assignment, EBTA)，其主要內容為：(1) 建構數學模型以及推導最佳化條件， (2) 發展一個可利用 “起點基礎一般化路段成本函數”(origin-based generalized link cost function)之交通量指 派演算法，(3) 探討如何偵測與刪除EBTA 模型之負成本迴圈問題。 第三年之主要研究內容為將前兩年之研究成果應用至更為複雜之極大熵雙限旅次 分配/交通量指派問題，其主要內容為：(1) 建構數學模型以及推導最佳化條件， (2) 發 展“延伸性”之交通量指派演算法(extended traffic assignment algorithm)，(3) 建立適用 “延伸性”之交通量指派演算法之超級路網架構，(4)測試所發展演算法之參數設定，(5) 數例測試。 ;Traffic assignment (TA) is the core element of travel demand forecasting, or more broadly, of transportation planning. Assuming drivers’ behavior of searching for shortest paths, and depending on the quality/type of travel information made available to travelers, a variety of traffic assignment models – such as deterministic (Beckman et al., 1956; Sheffi, 1985), stochastic (Dial, 1971; Sheffi and Powell, 1981, 1982), and dynamic (Ran and Boyce, 1996; Chen, 1999) – have been developed and, subsequently, solved using the so-called traditional traffic assignment solution algorithms including Frank-and-Wolfe, parallel tangent (PARTAN), gradient projection (GP) methods among others. In the past decade, two important TA relevant issues have been raised and indeed attracted a lot of attention from both researchers and practitioners. The first issue is concerned with the behavior assumptions on rational travelers. The second notable issue is about the unsatisfactory computational efficiency of the traditional TA solution algorithms. To well treat the above two important issues and make a natural extension to a more complicated and possibly more useful combined model application, i.e., entropy-based distribution/assignment combined model, this research proposal will be conducted in three years. In the first year, a generalized traffic assignment model formulation will be proposed (which includes most, if not all, of current traffic assignment models as special cases), the TAPAS (Traffic Assignment by Paired Alternative Segments) solution algorithm for the MEUE (maximum entropy user equilibrium) model will be coded into a computer program and the associated algorithmic drawbacks (appeared in the searching of paired alternative segments (PASs) and redistributing PAS flows among relevant origins) will be improved and demonstrated with numerical examples. In the second year, the EBTA (entropy-based traffic assignment) problem will be elaborately tackled in several aspects: (1) the associated equilibrium conditions will be derived and analyzed. (2) A workable solution algorithm that employs an “origin-based generalized link cost function” will be proposed. (Note: The big advantage of the “origin-based generalized link cost function” will become apparent later because the dimensional curse of path enumeration can be avoided.) (3) Furthermore, the detection and removal of negative cycles in an EBTA network will also be addressed. In the third year, the experience and lesson learned from the first two years will be carried over and applied to tackle a more complicated combined model called entropy-based doubly constrained distribution/assignment problem. The associated supernetwork representation techniques and the innovative concept of “extended traffic assignment” for the modified traffic assignment solution algorithms will be adopted to efficiently solve the doubly constrained distribution/assignment problem with large transportation networks. |
