國內先進公共運輸服務(Advanced Public Transportation Services, APTS)系統主要運用歷史平均站間旅行時間推估公車路線預估時間，透過歷史資料進行平假日分群，再依據車輛位置於車輛離站時重新推估後續站點旅行時間，但預估準確性仍受尖離峰車流、行駛距離及駕駛行為影響。且少有針對車載機的GPS資料再利用並提升旅行時間品質的研究。

因此本研究參考並改良貝氏回歸理論(Bayes' Regression theorem)應用於歷史到站時間推估旅行時間的模型，建構各公車路線不同時段之旅行時間模式。本研究收集每班次的歷史進離站數據計算站間旅行時間並依星期別之尖離峰時區分為四群，透過歸納分析路線上多個管制站點進離站旅行時間數據，依分佈最集中的數據進行推估。並事先將導入的數據進行過濾，主要移除偏移路線、重複回傳或倒序的GPS資料，使分析結果趨近實際狀況。

本研究中以台中市公車53路線太原火車站至省議會站資料進行分析，透過大量數據收集車輛進離站資料、運用貝氏回歸演算，有效將預估旅行時間誤差控制在可接受之範圍內且有效近似於公車抵達時間。

Advanced public transport services use historical average travel time data between bus stops to estimate the estimated time of bus routes, use historical data grouped by holiday and workdays, and then reassess the travel time of subsequent stations. The vehicle leaves the station based on the location of the vehicle, but the estimated time is affected by road traffic and driving behavior.
There are few studies on improving the travel time quality of on-board equipment. Therefore, this study has made reference and improvement on Bayesian regression theory.
And apply historical data to construct the travel time mode of each bus line in different time periods. According to the peak time and the general time, it is divided into four groups of peak time and general time, and then divided into morning peak hours: 6:00 am to 9:00 am. In the afternoon, the peak time is from 4:00 pm to 8:00 pm, and the general time is from 9:00 am to 16:00 pm and from 8:00 pm to 6:00 pm. The most concentrated data is used to estimate and clear outliers, and the data is close to the actual situation.
This study takes the Taichung Bus 53 route as the analysis object, and estimates that the estimated time is similar to the actual arrival time.

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中文文獻
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