隨著人口成長與都市建設發展，約束規劃與資訊預測在城市中可謂無所不在；其中作為影響生活品質的一大重要因素，衛星都市間抑或是市中心交通運輸的重要性不言而喻。然而現今雖已有大量針對城市交通運輸的資訊預測抑或是路線規劃等研究，卻少有研究著重於「基於推估數據的路線規劃」框架中的協同關係。在此類型框架中，首先會基於異質性城市資料進行初步數據推估；並將推估所得的結果作為輸入資訊抑或是成本函數以進行路線規畫。雖然定義陳述貌似單純，但當面臨城市空間中的多準則最佳化時，具備此框架性質的問題將會成為非決定性多項式完整問題。換言之，由於約束的複雜性、以及由異質性城市特徵與源自但不僅限於類神經網路等數據推估模型所得之啟發式函數致使最佳化目標產生的不確定性，該原始問題可以被視為非單調多準則最佳化問題的變形。因此，本篇論文著重於「基於異質性城市資料推估數據之多準則競合路線規劃」框架中四個不同但彼此間高度相關的研究問題，包含根據異質性城市資料與基於路線特徵提取所進行的人潮推估、基於非單調資料與最佳化目標的集中競合線性路線規劃、基於非單調資料與最佳化目標的集中競合環狀路線規劃、以及基於有限可及資料的分散競爭式固定資源搜尋路線規劃。與此同時，上述四種研究問題可分別對應至真實世界中不同的城市交通運輸應用，依序為基於路線特徵提取的載客量預測、基於現有大眾運輸系統的額外線性路線規劃、基於現有大眾運輸系統的額外環狀路線規劃、以及分散式的停車格搜尋。對此，本篇論文提出了包含基於路線影響區(Route-Affecting Region)的特徵提取與特徵工程方法、與基於路線影響區的載客量推估模型配合以進行非單調線性路線規劃的偏向延展演算法(BiasSpan)、與基於路線影響區的載客量推估模型及針對銷售員旅行問題的成熟演算法配合以進行非單調環狀路線規劃的顫抖手多重啟發演算法(Trembling Hand)、以及結合時空間需求預測以進行停車格巡航路線規劃的操舵轉移演算法(Conntrans)。相關實驗的資料取自於臺南、芝加哥、及舊金山等城市的真實資料，其中城市具備各自相異且獨特的城市特徵；而多面向實驗結果則展示了上述方法(路線影響區、偏向延展演算法、顫抖手多重啟發演算法、操舵轉移演算法)的有效性及適應性。總結而言，本篇論文展示了交通運輸中基於異質性城市資料推估數據之多準則競合路線規劃所具備的效力及多樣性。

Constrained planning and informatics forecasting is ubiquitous in urban space with the rapid growth of population and urban infrastructure. Meanwhile, acting as one of the key factors that affect the quality of life, transportation between satellite cities or inside the city center turns out to be a crucial issue. Accordingly, a plethora of frameworks and algorithms have been proposed in solving either inference or route planning problem. However, few literatures focus on the synergistic relationship in the inference-then-planning architecture where inference problem is first solved with input features retrieved from urban space and the route planning section is conducted on the basis of information or cost functions derived from the inference model. In spite of its deceptively simple statement, the inference-then-planning problem ends up being an NP-Complete problem when optimizing multiple criteria in urban space. In other words, the original inference-then-planning problem could be viewed as a variation of non-monotonic multi-criteria (multi-objective) optimization problem due to the complexity in constraints and the uncertainty in objectives, which are originated from the heterogeneous urban features and heuristics developed from especially the neural-network-based inference module. Thereafter, this thesis focuses on four various yet closely dependent research problems belong to the inference-then-planning framework for multi-criteria competitive and cooperative route planning built on heterogeneous urban data, including the passenger flow inference with heterogeneous urban data and route-based feature extraction, the centralized cooperative linear route planning with non-monotonicity in data and objectives, the centralized cooperative circular route planning with non-monotonicity in data and objectives, and the distributed competitive searching for stationary resources with limited data accessibility. Furthermore, these targeted research issues are modeled into passenger flow inference with route-affecting region, linear route planning for existing mass transit, circular route planning for existing mass transit, and distributed competitive searching for parking slot in represent of their real-world applications respectively. Corresponding approaches along with algorithms including the Route-Affecting Region (RAR) for urban feature extraction and engineering, BiasSpan synergized with RAR-based passenger flow inference for non-monotonic linear route planning, Trembling Hand Metaheuristic (TH) synergized with RAR-based passenger flow inference and TSP solver for circular route planning, and Conntrans with spatio-temporal demand estimation for parking slot cruising. Comprehensive evaluations based on multiple real-world data with distinct urban characteristics in Tainan, Chicago, and San Francisco are conducted to examine the effectiveness and adaptability of the proposed methods. As a consequence, the efficacy along with the variety of synergizing inference results derived from heterogeneous urban data in the route planning process for transportation is demonstrated.

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