Dempster-Shafer 理 論 於 交 通 資 料 整 合 技 術 之 應 用

Abstract

隨著智慧型運輸系統(ITS)的蓬勃發展，道路上可以收集到的交通資料越來越多，對於交通控制中心而言，這些資料都有其不同的來源，例如探偵車、道路偵測器、CCTV等，而因為其來源不同，格式、準確率以及更新頻率都不同，所以交控中心處理起來就有其困難。因此如何將不同資訊來源加以整合並提供給用路人就成為一個重要的課題，而此種資料整合技術通常稱為資料融合。 資料融合技術開始於1980年晚期，起初多半應用於軍事領域。近幾年才開始應用在智慧型運輸系統的相關產業，其中主要應用在先進旅行者資訊系統(ATIS)和先進交通管理系統(ATMS)。 本研究應用Dempster-Shafer理論提出一個新的模式，這個模式可以給予不同資料來源不同的權重，透過此權重可以去整合來自不同資料來源的交通原始資料。對於用路人而言，交通壅塞程度是不容易表示的，所以本研究會將速率資訊轉成不同的區間，然後再透過我們的模式，將這些資料轉成道路服務水準的模式讓交控中心提供給用路者。 為了評估本研究的準確性和合理性，除了實際資料的測試，我們還會利用資料模擬的方式來模擬不同的情境。測試結果證實本研究所提出的資料融合方法在實務上具有可行性。

With the wide implementation of Intelligent Transportation Systems (ITS), there are many raw traffic data collected by various devices. In a traffic information center, the traffic data may come from different sources, such as probe vehicles, CCTV and loop detector, with different formats, accuracy and updating frequency. An important issue for this traffic information center is to integrate the data from multiple sources into single one and broadcast to users. This data integration is usually called data fusion. Data fusion technology started in the late 1980s and has continued to the present but usually used in military surveillance. In recent years, ITS industry starts to use data fusion technique, especially in Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS). This study proposes a new model using Dempster-Shafer Theory to combine various raw traffic data from multiple sensors and assign different weights to distinguish multiple sensors. Because the level of traffic congestion to the drivers is hard to quantify, we develop a method to categorize speed data into different intervals. Then, we develop a efficient data processing method in order to provide real-time road service level to the traffic center. In order to evaluate the accuracy and rationality of our model, beside the real data testing, we generate simulated data for scenarios simulation. The testing results show that our proposed entropy data fusion technique is suitable in practice.