運用於車載網路中群組平均預估行車時間演算法

Travel-Time Prediction in Vehicular Ad-hoc Network with Grouping-and-Average Algorithm

彭治平(Chih-Ping Peng)

指導教授 ： 劉宏煥

中原大學/工學院/電子工程研究所/碩士(2010年)

https://doi.org/10.6840/cycu201000466

摘要

車載網路是一個結合車輛與無線通訊裝置的技術，提供無所不在的連線與車輛之間的通訊，並且能夠即時的整合交通資訊增進道路的效率與安全性，是智慧型傳輸系統的一大進步。提供精確的預估行車時間可以讓駕駛人在行駛之前，規劃一條到目的地最佳的路線，有效的減少時間，對於社會有助於改善交通擁塞的問題。
車載網路克服傳統預估行車時間演算法不能即時反應突發事件的特性，但在演算法方面仍然有許多問題。其中最普遍的SA (Segment Average) 演算法，在叉路口將所有車輛平均計算會產生較大的誤差，因為此區域的車輛位置與速度會依據駕駛人的目的地而有所差異，將所有車輛都視為相同行為是不合理。本研究提出GA(Grouping-and-Average)演算法改善上述的缺點，將所有車輛作群組分類與定義群組行為，利用過濾群組的方式擷取正確的車輛資訊計算預估行車時間。最後利用交通軟體整合系統驗證比較兩演算法，從結果顯示GA演算法改善SA演算法的問題，並且成功降低預估行車時間的誤差。

關鍵字

群組平均演算法 ； 交通軟體整合系統 ； 區段平均演算法 ； 車載隨意網路 ； 預估行車時間演算法

並列摘要

VANET is a technology integrating vehicles and wireless communication devices to provide ubiquitous connectivity, inter-vehicle communication and integration of real-time traffic information to enhance the efficiency and safety of road. It's a big progress for ITS (Intelligent Transport Systems). Before moving, providing accurate travel-time prediction can help driver to plan an optimal route to the destination to reduce travel time and ease the traffic congestion problems.
VANET overcomes that traditional travel-time prediction algorithms cannot immediate response to emergencies, but algorithms still have many problems. The most common algorithm is SA (Segment Average) algorithm. Averaging speeds of all vehicles can have large error at intersections, because the velocities of vehicles are different if the destination of the vehicles is different. All vehicles regarded as the same behavior is irrational. This paper proposes GA (Grouping and Average) algorithm to overcome the problem. All vehicles are grouped and defined behavior in unit of groups. To retrieve the correct vehicle information by filtering groups to predicate travel-time. Finally, we use a well-known traffic simulation software TSIS to verify and compare these two algorithms. The results showed that GA algorithm improves the problem of the SA algorithm, and successfully reduces the inaccuracy of travel-time prediction.

並列關鍵字

Travel-time Prediction ； TSIS ； VANET ； Segment Average Algorithm ； Grouping-and-Average Algorithm

參考文獻

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