於未來高速公路路網建置完成後，適當交通資訊之提供對用路人行為決策更顯得其重要，且就由高速公路旅行時間之提供而言，不僅可做為駕駛者選擇適當之路徑與出發時間，用路者亦可藉此選擇最短之旅行時間到達目的地，以真正發揮高速公路路網之整體績效，再者，利用即時之交通資料預測未來旅行時間，乃是先進旅行者資訊系統不可或缺之交通資訊。 本研究係針對國內高速公路用路者之變換車道行為與變換車道時間進行探討與推導相關公式，並進一步撰寫模擬程式，進而探討不同預測時間、流量、探測車混合比例與區段長度等相關參數之實驗組合，再者，利用探測車所收集之相關資料，透過類神經網路進行旅行時間之預測，以期提供精準之旅行時間預測，藉此作為用路人路徑選擇或是出發時間決策判斷之依據。 經由反覆的校估與測試之結果可知，本研究所構建旅行時間預測模式是屬於「高精準預測」，因此於高速公路旅行時間之提供方面，可做為交通相關單位參考。 In the future, after freeway network have been build. When drivers have to make a decision, it is more important for drivers to use duly traffic information. Traffic information will allow drivers to select appropriate routes and departure time to avoid congestion and arrive the destination using the shortest time. With the advent of Advanced Traveler Information System, the prediction of short-term link and corridor travel time has become increasingly important. Therefore, it is necessary to forecast future travel time effectively for Advanced Traveler Information System and users. This research is therefore aimed at establishing a microscopic simulation method to obtain the optimal forecasting highway travel time and use backpropagation algorithm to build forecasting highway travel time models. The simulation model discussed different traffic flow; different percent of probe vehicle and different interval length. Using simulation to produce the relative data of traffic character detected by probe vehicle sequentially for the base of artificial neural network training and testing. After repeatedly correcting and testing, the effect of forecasting model constructed in the research is very well .As to the result, this research can be provided to forecast travel time in real-time highway travel time estimation.
