隨著經濟發展人們對交通運輸的需求也日益增加，政府乃積極推動各種交通建設。如今國內的市區道路已逐漸飽和，從原本的新建工程轉為養護工程，面對數量如此龐大的道路，維護管理就變成一個很重要的課題。而本研究之鋪面維護推論模式是基於影響鋪面的因子對IRI的變化量進行預測，經由準確的預測結果，作為日後道路管理單位排定路段更新計畫與經費安排的參考。
本研究透過案例學習發展建立鋪面維護推論模式，首先整理相關文獻，並利用影響圖和因子篩選表來彙整鋪面影響因子，再結合國際糙度指標(IRI)，確認輸入與輸出變數並建立案例資料庫，作為鋪面維護推論模式之基礎。接著應用 Symbiotic Organisms Search - Least Squares Support Machine(SOS-LSSVM)[1]為推論模式之核心，學習歷史案例，找出輸入變數與道路平坦度之間的映射關係，進而選擇維護路段並計算維修費用。藉此作為臺北市政府在管理市區道路時分配維修經費的依據。
本研究之鋪面維護推論模式，其預測結果為高精確、高相關且變異性小而穩定，經由結果比較後，證實可提供較其他人工智慧模式更佳的預測準確率，以協助管理單位即時管控道路情形。

With the development of the social economy, the people demands of transportation is growing day by day, to satisfy citizen's desire, government actively promoting lots of transport infrastructure. Now the internal urban road is approach to saturation and reach the conversion from construction to management, face so many road isnʼt an easy job, so that's why management is so important. In this research, the Evolutionary International Roughness Index Inference Model used the influence factors of pavement to predict the IRI change between two years. Through a precise prediction of IRI, the management unit can’t arrange road section update and funds distribution of city road.
This research established the Evolutionary International Roughness Index Inference Model by learning the historical cases. First, the related references was collected and then the influence diagram and the filter diagram were used to figure out the influence factors of pavement. The next step is to combine them
to International Roughness Index(IRI). Furthermore, the input and the output variables were identified to establish the database of historical cases which is the base of the model. Second, the Symbiotic Organisms Search - Least Squares Support Machine (SOS-LSSVM) was applied to the model as its main core in order to discover the relationship between the input (influence factors ) , and the output variables(Estimate the IRI changes between two years) which can be combine with last year IRI then you can get this year IRI. According to the result management unit can decide the priority of road maintance and calculate the expense.
The obtained results show that this research can provide a highly accurate and stable forecasting. By comparing the results with other Artificial Intelligence (AI) methods, it is confirmed that Evolutionary International Roughness Index Inference Model is useful way to control road situation.

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