政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/159056

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/159056

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政大典藏 > College of Social Science > Department of Public Administration > Theses > Item 140.119/159056

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/159056

Title:	人機協作下的交通執法：台北市科技執法成效與自動化決策風險探討 Human-Machine Collaboration in Traffic Law Enforcement: Evaluating the Effectiveness of Technological Enforcement and the Risks of Automated Decision-Making in Taipei
Authors:	李奕萱 Lee, Yi-Hsuan
Contributors:	朱斌妤 Chu, Pin-Yu 李奕萱 Lee, Yi-Hsuan
Keywords:	科技執法政策成效政策順服委託代理人理論自動化決策 Technological Enforcement Policy Effectiveness Policy Compliance Principal-Agent Theory Automated Decision-Making
Date:	2025
Issue Date:	2025-09-01 14:53:57 (UTC+8)
Abstract:	本研究旨在探討台北市科技執法成效與自動化決策風險，採混合研究方法，蒐集分析客觀交通資料以及政策利害關係人（民眾與執法警察）主觀看法，共分為四個面向：一、以敘述統計和成對樣本中位數差異檢定分析科技執法前後的交通事故變化；二、透過問卷探討駕駛人對政策之順服態度與行為改變；三、訪談警察了解科技執法對其執勤工作的影響；四、結合委託代理理論，探討警察對AI自動化治理的看法與潛在風險。研究範圍為大安區及文山區，並以取締項目設置最多的「闖紅燈、不停讓行人、不依規定轉彎、不依標誌標線號誌指示行駛」作為本研究探討之取締項目。研究發現，在科技執法設備啟用前後的交通事故變化上，雖部分路段觀察到事故數短期下降，但整體而言，無論是一年或三個月的比較，統計結果皆未達顯著水準。事故變化可能受多重因素影響，如路口設計、駕駛行為等，顯示科技執法在短期內對事故的抑制效果有限，需配合其他交通治理手段方能發揮整體效益。在駕駛人政策順服問卷調查中，共回收117份問卷，分析民眾對科技執法政策的熟悉度、價值觀、個人效益與政策順服之間的關係。研究發現，價值觀與個人效益對政策順服具有顯著正向影響，顯示當民眾認同政策理念並感受到實質利益時，順服意願較高。相反地，政策內容熟悉度則與政策順服無顯著相關。結果強調政策推動應著重於溝通政策價值及效益，而非僅傳遞內容資訊，以促進民眾自發配合。在深度訪談科技執法對於警察工作影響方面，研究發現科技執法雖由AI自動偵測違規，但最終舉發仍需警察審核。科技執法能減輕警力負擔、提升效率，降低執法風險，並提供客觀且標準化證據，減少爭議。最後，以委託代理人理論角度訪談警察在使用AI科技執法系統時面臨的挑戰，研究發現多數警察反映演算法的透明度不足，系統運作邏輯不公開，造成執法人員在執法過程中存在資訊不對稱，難以完全理解AI的判斷依據；其次，由於資訊不對稱及系統特性，警察需保持基本判斷力並擁有干預權，避免過度依賴AI導致誤判；第三，AI在面對特殊情境如光線變化或人車複雜交會時，容易產生誤判，增加執法糾紛的風險，因此人工介入與系統持續優化不可或缺；最後，目前大數據資料建置及整合不足，加上不同廠商技術水準不一，導致系統準確度與執法標準不一致，將影響整體的執法效能與信任度。在政策建議方面，科技執法設備對事故數影響有限，建議結合安全教育與交通設計共同預防。AI系統仍有誤判與透明度問題，需持續優化系統並加強人工審核，建立雙重把關保障公正。此外，應建立正式回饋機制，促進警察與廠商溝通，並公開執法成效提升信任。而宣導上應強調實際效益，增加民眾配合度。其他配套方面，應彈性處理特殊情況，推動法規標準化及強化個資保護，提升數位執法合法性與接受度。 This study aims to explore the effectiveness of technological law enforcement and the risks of automated decision-making in Taipei City. A mixed-methods approach was adopted, combining objective traffic data analysis with the subjective perspectives of key policy stakeholders, including the public and frontline police officers. The research is structured around four dimensions: (1) Descriptive Statistics and Wilcoxon Signed-Rank Test were used to analyze traffic accident changes before and after the implementation of automated enforcement; (2) a survey was conducted to examine drivers’ attitudes toward policy compliance and behavioral changes; (3) interviews were conducted with police officers to understand the impact of automated enforcement on their daily duties; and (4) the Principal-Agent Theory was applied to analyze officers' views on AI-driven governance and its potential risks. The study focused on Daan and Wenshan Districts, targeting the most frequently enforced violations: running red lights, failing to yield to pedestrians, improper turns, and disobeying signs or signals. Findings indicate that while certain intersections showed a short-term decline in accidents after implementation, the overall statistical results—whether compared over three months or one year—did not reach a significant level. Accident trends may be influenced by multiple factors such as intersection design and driver behavior, suggesting that the short-term impact of technological enforcement on accident reduction is limited. Additional traffic governance strategies are necessary to achieve comprehensive effectiveness. In the driver compliance survey, a total of 117 valid responses were collected, analyzing the relationship between policy familiarity, values, perceived personal benefits, and policy compliance. Results showed that personal values and perceived benefits significantly and positively influenced compliance. This implies that when the public agrees with the policy rationale and perceives tangible benefits, they are more willing to comply. In contrast, familiarity with policy content was not significantly related to compliance, highlighting the importance of communicating policy values and benefits rather than merely conveying informational content. From the in-depth interviews with police officers, the study found that although AI systems automatically detect violations, final citations still require officer verification. Technological enforcement helps reduce manpower burdens, improves efficiency, lowers enforcement risks, and provides objective, standardized evidence to reduce disputes. From the Principal-Agent Theory perspective, several challenges were identified. First, officers expressed concerns about the lack of algorithm transparency and the system’s undisclosed logic, resulting in information asymmetry and difficulty understanding the AI's basis for decisions. Second, due to such asymmetry and system limitations, officers must retain discretionary judgment and intervention rights to prevent overreliance on AI, which could lead to misjudgments. Third, AI systems are prone to errors in complex scenarios, such as variable lighting or heavy pedestrian-vehicle interaction, increasing the risk of enforcement disputes—underscoring the need for continuous human oversight and system refinement. Lastly, the current insufficiency in big data infrastructure and inconsistent technological standards across vendors contribute to variable system accuracy and enforcement standards, potentially affecting the overall effectiveness and public trust in enforcement systems. In terms of policy recommendations, given the limited impact of technological enforcement on accident reduction, it is advised to integrate it with safety education and improved traffic design. Since AI systems still face issues of misjudgment and opacity, continuous system optimization and strengthened manual review mechanisms are necessary to ensure fairness and accountability. A formal feedback mechanism should also be established to facilitate communication between police and vendors and to publicly share enforcement outcomes to build trust. Public outreach should emphasize the practical benefits of enforcement to enhance compliance. Moreover, flexible handling of special cases, standardized legal regulations, and strengthened personal data protection are essential to improve the legitimacy and public acceptance of digital enforcement.
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Description:	碩士國立政治大學公共行政學系 112256023
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0112256023
Data Type:	thesis
Appears in Collections:	[Department of Public Administration] Theses

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