本研究考量混合車流污染排放濃度影響構建適應性號誌控制模式，以降低於敏感區域（例如醫院、學校）路側的污染排放及濃度。本研究以污染排放作為績效，並分析污染排放經大氣擴散後，對路側地區污染濃度之影響。本模式包含四子模式：混合車流格位傳遞模式、車輛排放推估模式、污染擴散模式，以及基因模糊邏輯控制樣式（GFLC）。最後與定時號制進行比較，結果顯示本模式比定時號制，降低0.3%～25.3%的濃度值。另外，考量污染濃度門檻限制，並研擬三種獨立路口號誌控制策略進行比較。當幹道綠燈愈長且週期愈短的號誌設定，可有效控制路側污染。應用於連續路段時，本研究以五路口路段為例，擬定三種號誌控制策略，結果以全連鎖策略最能有效降低路側污染濃度。

This paper proposes an adaptive signal control model which can simultaneously minimize the total traffic emissions and reduce the pollution concentration at road-side environmental sensitive areas (such as hospitals or schools) This paper employs traffic emission as the performance index, and analyze the impact of pollution concentration at roadside. The proposed model comprises four key sub-models: Mixed Traffic Cell-Transmission model (MCTM), traffic emission model, emission dispersion model, and Genetic Fuzzy Logic Controller (GFLC). We compare the performance of the proposed model and the pre-timed signal control system. The results show that the GFLC model can effectively curtail the concentration values of pre-timed signal control system by 0.3% ~ 25.3%. In addition, to meet the limitation of emission concentration level, three control strategies fur isolated intersections are then proposed and compared. The results show that as green time is longer or cycle length is shorter, the emission concentration of roadside sensitive area becomes less severe. For a series of consecutive intersections along an arterial (a case study on five intersections), three strategies for signal control are compared. The results show that the coordinated signal control for all intersections performs best. The applicability of the proposed model has been proven.