由於用路人資訊日益受到重視且第二高速公路的通車以及高快速道路路網的形成，正確及有效的提供用路人行前旅行時間資訊，對於交通壅塞抒解及整體交通績效提升乃致於作為駕駛者選擇適當之路徑與出發之時間皆有著極大的正面影響。而利用即時之交通資料預測未來旅行時間，為先進旅行者資訊系統中不可或缺之交通資訊。 本研究運用微觀角度之車流模擬程式產生車輛偵測器與探測車資料，並自行構建一套使用探測車單一資料來源之浮動加總旅行時間預測模式，以及融合車輛偵測器與探測車資料，運用類神經網路構建雙資料來源之類神經資料融合旅行時間預測模式，進而探討不同流量型態、不同資料收集時距、不同探測車比例等相關參數之實驗組合，再者以真實資料對於模式輸出結果進行驗證。 經由反覆的校估與測試結果可以得知，本研究所構建之浮動加總旅行時間預測模式與類神經資料融合旅行時間預測模式，其預測效果良好，皆屬於『高精準預測』，於高速公路旅行時間相關資料提供方面，可作為交通相關單位參考之用途。 Due to the traveler information emphasized, the inauguration of the second freeway and the completion of expressway toward network, the offering of travel time information precisely and efficiently to traveler will be helpful in reducing traffic congestion, increasing entire performance of transportation and selecting suitable paths and the time to start off. The forecasting of the future travel time adopted real-time traffic data are essential for ATIS. This research adopted the traffic flow simulation method via microscopically view to create data pertaining to vehicle detector and probe vehicle and then constructed two forecasting models. The first model is floating summary travel time using single data source from probe vehicle, and the second model is data fusion using artificial neural network travel time. Base on these two models, it consider the alternative combination including flow type, data collection interval and probe vehicle percentage. Furthermore, taking the actual data to validate the output . After repeatedly correcting and testing, the performance of floating summary travel time forecasting model and data fusion by artificial neural network travel time forecasting model that both constructed in the research belong to “high-precise”. This research can be provided to forecast travel time in real-time highway travel time estimation.
