台灣道路交通事故死亡風險長期高於多數已開發國家，顯示現行交通安全政策與執法策略仍有改善空間。既有交通心理與行為研究指出，駕駛人之違規決策並非完全獨立，而可能受到近期行駛經驗、周遭交通環境與執法可見度之影響，使交通違規風險在道路網路中呈現一定程度之時間延續與空間外溢特性。然而，鮮少有研究將此類現象形式化為一最佳化問題。為回應此研究缺口，本研究受影響力擴散模型之概念啟發，建立一套交通違規風險演化架構，並將多種執法手段之干預效果納入分析。

方法上，本文提出一個二階段隨機混合整數規劃模型。第一階段決定執法資源之配置與巡邏決策；第二階段則於多重隨機情境下評估違規影響所造成之期望成本，並以樣本平均近似法（Sample Average Approximation, SAA）處理。模型同時納入自然下降與執法效期等機制，以反映違規風險與執法效果之動態特性。考量中大型路網之計算負擔，本文另提出縮減子圖策略、路徑集模型與啟發式法，以提升求解效率。

數值實驗以桃園市歷史交通違規與事故資料建構不同規模路網。結果顯示，本研究模型可呈現違規風險演化與執法策略差異；相較於基準啟發式法，路徑集模型於中大型網路下可提供較佳解品質與計算效率。敏感度分析指出，節點間外溢效果為影響總成本之重要因素，科技執法較適合作為傳統警力之輔助；初始違規分布亦會影響執法工具與完全壓制所需人車配置，其中分散型風險較需要廣域覆蓋，分群型風險則可依熱區擴展調整巡邏資源。延伸情境結果進一步顯示，模型可處理後續之外生違規注入，具備風險情境調整之彈性。

綜合而言，本研究建立一套兼具風險演化邏輯、不確定性處理與執法資源配置決策之分析架構，可作為交通執法規劃與資源配置之決策支援基礎。未來可進一步結合更細緻之資料，進行參數校準與外部驗證，以提升模型之實務應用價值。

This thesis examines traffic violations from the perspective of risk evolution and connects traffic violation risk with policing resource allocation. Rather than treating violations as fully independent events, this study considers that drivers’ violation decisions may be influenced by recent driving experiences, surrounding traffic conditions, and enforcement visibility. As a result, traffic violation risk may exhibit spatial spillover and temporal persistence over a road network. To capture this phenomenon, the thesis draws on diffusion-based modeling concepts and develops an analytical framework for traffic violation risk evolution under enforcement interventions.

Methodologically, the study formulates a two-stage stochastic mixed-integer programming model. The first stage determines enforcement allocation and patrol decisions, while the second stage evaluates scenario-dependent hazardous, violation, and accident outcomes. The objective is to minimize the expected total cost associated with these states. To improve tractability, the thesis also develops a route-based reformulation, reduced-subgraph construction, candidate patrol-route generation procedures, and a staged greedy heuristic.

Computational experiments based on traffic violation and accident data from Taoyuan City, Taiwan, show that the proposed framework can effectively represent traffic violation risk evolution and provide useful decision support for enforcement planning under limited resources.

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