資料融合技術應用於旅行時間推估之研究

A Study on Travel Time Estimation Applications of Data Fusion Techniques

蔡百里(Bai-Li Tsai)

指導教授 ： 羅孝賢

淡江大學/管理學院/運輸管理學系碩士班/碩士(2006年)

https://doi.org/10.6846/TKU.2006.00439

摘要

近年政府大力推行智慧型運輸系統（Intelligent Information System, ITS）九大服務領域中之先進旅行者資訊系統（Advanced Traveler Information System, ATIS）的發展。為提供用路人準確的資訊，以做為路徑、運具選擇之依據，路徑旅行時間的推估是一項重要的議題，其作法可透過固定式偵測器與移動式探針車蒐集路段資料，如流量、佔有率，以及速度等，以推估路段之旅行時間。然而目前國內道路車輛偵測器佈設仍稀，堪用者亦寥寥可數，在資源及經費有限的情況下，廣佈偵測器並非易事，短期可考慮以增加探針車數量以彌補資訊獲取之不足。
本研究擬自車流速率空間分佈之概念，探討探針車之數量規模以描述或推估路段上之旅行時間，以路段或路段含路口為範圍，依抽樣分佈的概念來反映母體特性。由於探針車可視為一瞬間固定式之偵測器，透過探針車之瞬時速度與位置，形成一抽樣之速率空間分佈，據以決定探針車數量，反映母體特性以推估瞬時旅行時間。並以此瞬間抽樣方式與偵測器資料進行資料融合測試，以探討此方式之可行性。
本研究以真實路網資料校估並驗證模擬軟體，據以構建模擬路網，透過模擬方式蒐集車輛偵測器與探針車資料，進行資料融合以推估旅行時間。偵測器以流量與佔有率推估密度，並結合OH與Webster模式推估旅行時間，配合探針車之旅行時間資料進行資料融合。研究內容包括：(1)探針車數量演算法探討與測試；(2)資料融合之比較與適用情境；(3)資料融合推估旅行時間，以提供較精確之旅行資訊予用路人。
研究結果顯示，本研究以瞬間速率空間分佈推估所需探針車數量較過去之研究為多，依路段長度與流量不同所需探針車數量比率各異，其分佈大致為1至6成，整體平均結果與Tetsuhiro (2005)所提40%可不停地蒐集交通資訊之結論相近。另以此瞬間抽樣方式進行資料融合，測試結果顯示一路段以加權平均法推估績效較佳；二路段(含路口)以類神經網路推估績效較佳，且透過資料融合可有效降低個別推估之誤差。在資料融合比較方面，結果顯示，加權平均法較適用於路段長度400公尺以下，探針車數量比率達10%以上，更新時間3分鐘較為即時之狀態；類神經網路則較適用於路段長度400公尺以上，探針車數量比率10%以下，且更新時間5分鐘相對較長時間。最後，提出兩者之優劣比較，以供相關應用參考。

關鍵字

移動式探針車 ； 固定式偵測器 ； 探針車數量 ； 資料融合 ； 旅行時間

並列摘要

In recent years, Government tries to carry out the development of Advanced Traveler Information System - among the nine service domains of Intelligent Information System. In order to provide accurate information for road users, to stand on the choices of routes and transportation, estimating the path travel time is an important issue. To estimate travel time, vehicle detectors and probe vehicles collecting information (e.g., flow, occupancy and speed, etc.) are being used. For the moment, there is quite few vehicle detectors can still be used. Under the insufficient resource and budget, it is uneasy to set up vehicle detectors widely, otherwise, to add probe vehicles in the short term to make up for the shortage of information gathering.
This study applies the concept of vehicle speed distributes in space of roadway segment and intends to investigate how many probe vehicles are enough to describe or estimate travel time for a roadway segment. The aspect of investigation is a roadway segment or a roadway segment containing intersection, according to the concept of a sample distribution which reflects population characteristics. As a result, probe vehicle can be considered as an instantaneous fixed vehicle detector by using the instantaneous speed and position of probe vehicles and it sets up a speed distribution of samples, from the inside, explores the size of probe vehicles and reflects population to estimate instantaneous travel time. Furthermore, by using the instantaneous sample method and vehicle detector data to test the data fusion, the feasibility of this method will be determined.
After conferring the size of probe vehicle, data collection through real network and establishment of the simulation network can be used when parameters are evaluated. To collect data from vehicle detectors and probe vehicles through simulation, and then carrying out data fusion to estimate travel time. Vehicle detector estimates density by using flow and occupancy rate, accords with OH and Webster model to estimate travel time, and matches up the travel time which probe vehicles drive end of the roadway segment. For this reason, this study contains: (1) Investigate and test the algorithm of probe vehicle size. (2) The comparison and suitable situation of data fusion. (3) Estimate travel time using data fusion, and hope to provide more accurate travel information for road user.
The result of this study exhibits that sizes of the probe vehicle are more than other studies by using the instantaneous distribution of speed. According to different length and flow rate of roadway segment with different probe vehicle size, it distributes about ten to sixty percent, and the average is similar to Tetsuhiro (2005) who brought up that forty percent probe vehicles can collect traffic information nonstop. Besides, the test of data fusion uses instantaneous sampling method and the result exhibits that Weighted Average is better in the one roadway segment case, Artificial Neural Network is better in the two roadway segments case, and data fusion can reduce the travel time errors from each detector has estimated.
The result of data fusion exhibits that Weighted Average is suitable for the road length under 400 meters, probe vehicle rate upon 10 percent, and update in 3 minutes (i.e., real time); Artificial Neural Network is suitable for the road length upon 400 meters, probe vehicle rate under 10 percent, and update in 5 minutes (i.e., comparatively longer time). Finally, advantages and disadvantages of two methods are provided for the related applications.

並列關鍵字

Probe Vehicle ； Vehicle Detector ； The Size of Probe Vehicle ； Data Fusion ； Travel Time Estimation

參考文獻

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