從眾源停走資料擷取時序資訊之演算法

Abstract

從眾源資源資料中擷取出需要的資訊已成為目前的新趨勢，與傳統方法相比， 此方法不需要建設大量的硬體設備。而在眾源資料中，如何擷取出需求的資訊與 滲透率的問題一直是被強力地關注與討論。在本篇論文中，我們首先介紹關於震 動波此一現象，並且詳細地介紹當此一現象在都市中由紅綠燈和車輛所引起時的 特殊情形，最後在說明此震動波中所隱含的紅綠燈號誌資訊與車流資訊。而本論 文的重點就是透過眾源資料偵測出震動波，並且計算出震動波所隱含的號誌資 訊。為了要降低計算出震動波的滲透率，我們使用了折疊技術以大幅將低滲透率 的需求。在本篇論文中，我們模擬了多次車輛在移動情形，並且分析其結果。結 果顯示透過眾源資訊所找出的震動波其隱含的號誌資訊有著極高的準確度，而且 摺疊技術也能夠有效地降低滲透率的需求。

Crowdsourcing is a new trend for pervasively discovering traffic information due to its low deployment and maintenance cost as compared with traditional infrastructurebased approaches, e.g., loop detectors and CCTV. Mining techniques and the penetration rate of participators in the discovery process are two major issues in such approaches. In this work, we first point out the shockwave phenomenon occurring in signalized traffic can be used to discover useful traffic information including traffic light information and vehicle flow information. To reduce the requirement on the penetration rate, a folding heuristic is proposed. The proposed concepts are verified via extensive simulations, especially on the penetration rate issue. Our results show that shockwave models are useful to extract traffic information from crowd sourced data, and the folding technique can effectively reduce the requirement on the penetration rate.