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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84042

    Title: 應用資料探勘技術與結合eTag車輛偵測器於市區道路旅行時間預測;Applying Data Mining Techniques Combined with eTag Vehicle Detector for Urban Road Travel Time Prediction
    Authors: 吳忠賢;Wu, Chung-Shian
    Contributors: 資訊管理學系在職專班
    Keywords: 旅行時間預測;eTag;市區道路;資料探勘;Travel time prediction;eTag;urban road;data mining
    Date: 2020-06-30
    Issue Date: 2020-09-02 17:58:00 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著經濟迅速發展,使得現有道路供給量不足以負荷急速成長之交通量,使得交通壅塞發生情形成為各都市普遍現象,然而道路開闢跟不上運具成長速度,運輸管制成為有效降低交通壅塞之手段,若能提供準確之旅行時間預估給用路人,使用路人選擇行駛交通順暢路段,進而達到分散車流,紓解交通壅塞之情形,惟旅行時間預測相關研究多應用於高速公路,國內亦少有針對市區道路之旅行時間預測相關研究及應用。

    本研究提出一套應用資料探勘技術並結合eTag車輛偵測器之市區道路旅行時間預測方法,使用Weka資料探勘軟體以最近鄰居法(K-NN)、支持向量迴歸(SVR)、多層感知器(MLP)、迴歸樹(Regression Tree)及隨機森林(Random Forest)等不同演算法進行實驗分析,其中過去相關研究之車流量累積區間多僅考慮短時間內之車流量,而本研究發現車流量累計區間若大幅拉長,可大幅提高預測準確度,另外本研究亦發現大型車通行量確實對於旅行時間預測有顯著之影響。本研究利用桃園市政府交通局之eTag車輛偵測器作為資料來源,並對桃園春日路南向之平假日尖峰時段進行旅行時間預測,根據實測結果,使用本研究方法其預測表現在MAPE評估標準中皆屬於高精準的預測,可有效應用於市區道路車輛分流管理策略。
    ;With the economic development, the existing road supply is not enough to load the rapid growth of traffic, making traffic congestion a common phenomenon in various cities. However, road supply can’t keep up with the growth rate of transport vehicles, and transportation control has become an effective way to reduce traffic congestion. If we provide accurate travel time estimates to drivers, drivers can choose substitute route to achieve traffic flow distribution and reduce traffic congestion. However, studies on travel time prediction are mostly applied to expressways and few studies related to travel time prediction for urban roads in domestic.

    This study proposes a urban road travel time prediction method that use data mining techniques combined with eTag vehicle detectors. By using Weka data e mining software to test nearest neighbor method, support vector regression, multi-layer perceptron, regression tree and random forest etc. algorithms for experimental analysis. Among them, in the past related research, the traffic flow accumulation factor mostly considered only the short-term traffic flow, in this study, we found that if the traffic flow accumulation interval is greatly lengthened, the prediction accuracy can be greatly improved. In addition, this study also found that the traffic volume of large vehicles does have a significant impact on the travel time prediction. This study uses the eTag vehicle detector of the Taoyuan City Government Department of Transportation as the data source, and predicts the travel time of peak hours on weekdays and weekends of the south bound Taoyuan Chunri Road. Based on the actual measurement results, the prediction performance of this research method is a high-precision prediction in the MAPE evaluation standard, which can be effectively applied to urban road vehicle diversion management strategies.
    Appears in Collections:[資訊管理學系碩士在職專班 ] 博碩士論文

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