基於行動定位服務的即時旅行時間知識庫預測系統

Abstract

發展智慧型運輸系統(ITS) 的精神和目的就是利用先進的通訊技術、交通控制與資訊來達到便利、經濟以及安全的交通環境。在ITS領域中，即時旅行時間預測一直是被探討的重要題目。因為在ITS九大領域之中，即時旅行時間預測涵蓋了其中的四個子領域：先進交通管理系統(ATMS)、先進旅行者資訊系統(ATIS)、商用車輛營運系統(CVO)與急難救助系統(EMS)。並且它代表著交通路況的有用資訊指標。 然而，在很多過去文獻中，旅行時間都被預測在高速公路和少數幹道路網上。因為即時旅行時間是較難以預測在都會路網上，其中有四種原因是: 都會路網的複雜度和繞路的問題，即時交通資訊如何取得的成本問題，有限的交通時間和空間資訊收集問題，以及缺乏交通事件反應問題。本論文提出一個即時旅行時間知識庫預測系統，其利用資料探勘技術與行動定位服務來找出一些過去的交通樣本，並利用這些樣本和即時交通資訊來預測即時旅行時間。當交通事件發生在一些路段上時，系統會觸發元規則(Meta-rule)來動態整合過去和現在的旅行時間預測。此系統被實作在台北都會路網上，且實驗數據顯示動態整合歷史和即時的旅行時間預測會比單一預測在過去或即時路況上得到較佳的結果。

The purpose and the essence of developing Intelligent Transportation System (ITS) are to utilize advanced communication techniques, traffic control and information to achieve a convenient, economic benefits and safety traffic environment. In ITS area, real-time travel time prediction (TTP) topic has been discussed recently, because this important topic covers four of nine research subjects in ITS domain. Such as：Advance Traffic Management System, Advance Traveler Information System, Commercial Vehicle Operation and Emergency Medical Services. Also, it presents an index of real-time traffic condition and useful traffic information. However, most previous researches focus on the predicting the travel time on freeway or simple arterial network. The real-time TTP in urban network is hard to be achieved in four reasons: complexity and routing problem in road network, sensor data is either not available in real time or is not cost-effective to get in real time, spatiotemporal data coverage problem of sensor based or vehicle based travel time prediction, and lost precision because lack of traffic event response mechanism. In this thesis, the knowledge based real-time TTP system is proposed, which uses data mining technique to discover some target traffic patterns/rules with location based service (LBS), and then uses inference engine with previous traffic pattern/rules and real-time traffic information to predict the real-time travel time. When traffic events occur in some road sections, the meta-rules are triggered by the system to dynamically combine real-time and historical travel time predictors. The proposed system is implemented for Taipei urban network, and experiment results show that weighted combination of real-time and historical predictors outperforms either single predictor.