一個先進旅行者資訊系統(ATIS)要能有效控制路網上車流的運作，合理且準確的旅行時間預測系統將是關鍵。傳統上，交控中心可以藉由線圈偵測器或影像偵測器蒐集交通流量資料與交通狀況，以對旅行時間進行預測與推估，但由於線圈偵測器無法識別所偵測之車輛，且過去的預測模式往往並未詳細考慮車流續進與延滯之特性，預估之旅行時間仍為瞬間旅行時間。為了考慮車流續進的過程以及彰顯車輛旅行時間與車流狀態間之關係並簡化預測模式之處理，本文藉由資料採集技術與迴歸分析設計出一套能預測高速公路交流道間旅行時間的預測模式，首先以集群分析方法對每個線圈偵測器之歷史資料作交通狀態分類處理，再經由迴歸分析構建不同交通狀態類別對旅行時間影響之旅行時間預測模式。最後則以ETC (Electronic Toll Collection)車輛通行於收費站間之通行資料所計算出之旅行時間，作為旅行時間預測模式之校估依據。模式結果顯示其不但有相當良好之預測能力，校估之係數值亦可提供系統管理者豐富的訊息，以更了解各路段之幾何與交通特性對擁擠交通狀態所造成旅行時間增加之原由，進而研提有效的管理策略。

A travel time prediction system with a satisfactory level of prediction accuracy plays a vital role in Advanced Travel Information Systems (ATIS) to effectively control traffic flow on a highway network. Traditionally, travel time estimation and prediction in a Traffic Management Center is mostly based on the data obtained from loop and/or image detectors. A prediction model solely based on these data, however, is difficult to consider the dynamic transformation and delay of traffic flow. To partially resolve this issue, this paper proposes a novel travel time prediction framework with the capability to predict inter-ramp travel time at a satisfactory level of prediction performance. First, historical traffic data collected by each loop detector were classified into different traffic states. For each state, regression techniques were then applied to build up a travel time prediction model. Finally, the travel time of vehicles passing Electronic Toll Collect (ETC tall) booths was considered to adjust the predicted traffic states and link travel time. The results showed satisfactory performance of the proposed models. More importantly, the estimated traffic parameters could provide system managers with fruitful information about how travel time is increased by different road geometry and traffic characteristics. Consequently, effective control strategies could be devised.