風景區交通壅塞狀況動態預警研析-以日月潭國家風景區為例

Dynamic traffic jam forecasting of scenic area - A case study of Sun Moon Lake

郭思漢(Tzu-Han Kuo)

指導教授 ： 劉士仙

淡江大學/管理學院/運輸管理學系碩士班/碩士(2012年)

https://doi.org/10.6846/TKU.2012.01017

摘要

近年來，知名之觀光遊憩區於周休及連續假期時常湧入大量人潮造成壅塞；政府為了提升景區交通服務水準，因此擬導入先進的智慧型運輸系統(Intelligent Transportation System, ITS) 科技，使景區之交通壅塞能事前預警，防範於未然。本研究以日月潭國家風景區為例，利用現有車輛偵測器資訊提前預測景點壅塞狀況，並嘗試比較路網中不同之路段(節線)或景點(節點)資訊於景區壅塞預測之準確與可靠度績效，做為未來評估與選擇交通管制之參考。
以路段速率透過加權平滑推估進行預測，並以卡門濾波法演算進行停車場流量動態推估，並嘗試以時空數列方法對日月潭路網進行時空分析，最後以VISSIM軟體模擬日月潭交通路網。
結果顯示，以近景區之停車場評估景區壅塞較週邊路段更具優勢，且根據壅塞門檻以預測壅塞預警，可以提早60分鐘準備應便，準確率約為80%。

關鍵字

智慧型運輸系統 ； 壅塞預警 ； 卡門濾波 ； 加權趨勢平滑

並列摘要

Due to the overwhelming tourists, congestion in some scenic area became a dead lock recently, especially in long vocation. To improve the quality of well-known scenic spot, authority introduces some advanced technologies of Intelligent Transport System (ITS) into this area, and hope that it could response the incident in advance before it gets worse. Sun Moon Lake National Scenic Area is on the top list of well-known spots cross the Taiwan Strait to be the first choice of this study area. With density layout vehicle detector (VD) data, we compare with types of link and node information in order to provide the effective performance index for traffic control in the near future.
This study applies weighted average method and extended Kalman Filter for link and node performance assessments separately. Furthermore, to explore the spatial relationships of traffic congestion of upstream and downstream, STARIMA is also included based on a full data by a traffic simulation software, VISSIM.
After discussion in details, the congestion of parking lot near major scenic spot is best for traffic control than those from links in surrounded area. Based on the standard threshold of congestion index, the warning call could make one hour in advance with accuracy of 80%.

並列關鍵字

ITS ； warning call ； Kalman Filter ； moving average method

參考文獻

賴長偉(2006)，「用路人動態擁擠感知行為分析」，淡江大學運輸管理科學系碩士論文。

葉昱宏(2010)，「以電子收費系統資訊推估高速公路動態旅次起迄之研究」，淡江大學運輸科學研究所碩士論文。

Arem, B. V., Kirby, H. R., Van Der Vlist, M. J. M. & Whittaker, J. C. (1997), Recent advances and applications in the field of short-term traffic forecasting, International Journal of Forecasting, 13(1), 1–12.

Chen, H. B. & Grant-Muller, S. (2001), Use of sequential learning for short-term traffic flow forecasting, Transportation Research Part C, 9(5), 319–36.

Dickey, D. A. & Fuller, W. A. (1979) Distribution of the Estimators for Autoregressive Time Series With a Unit Root. Journal of the American Statistical Association, 74, 427-431.