基於號誌因子之公車動態旅行時間預估模式研究

Abstract

先進大眾運輸系統(APTS)將運輸管理方法和資訊傳輸及處理技術應用於大眾運輸系統中，其目的為提高運作效率及提升服務水準。因此，如何運用先進大眾運輸系統於預估公車旅行時間，以提高公車服務品質和管理者之營運效率為很重要的問題之一。 本研究之目的為發展公車旅行時間預估模式，並考量公車於號誌化路口停等時的延滯時間，號誌時制依據不同路段、不同時段改變，公車行進過程亦受其影響，故本研究將依時間、空間變動之號誌時制型態納入公車旅行時間預估中，以提供使用者準確且符合即時現況之公車旅行時間。針對公車路段旅行時間，研擬兩種不同預估方法之旅行時間預估模式，再根據預估模式輸出項與當時段之號誌時制進行旅行時間解調。依據公車發車間距區分為小於15分鐘及大於15分鐘，將路段旅行時間預估模式分成兩大類型，前者屬於短期預估，使用α-β-γ濾波器預估法，後者由於發車間距較長，則使用離散傅立葉變換預估方法。 本研究實驗設計根據旅行時間預估方法分成兩部分，離散傅立葉變換預估方法使用公車實測數據結果與預估模式結果兩者進行績效評估，研究結果顯示不論平日或假日離峰時段之預估結果皆較尖峰時段之預估結果準確，故傅立葉轉換預估模式較適用於離峰時刻；由於本研究無法取得即時之公車回傳資料，因此α-β-γ濾波器預估法使用以同一班次之原始值及預估值進行績效評估，研究結果顯示不論平日或假日，當預估時期大於四小時後之預估結果精準度較高，即系統需要四小時之訓練期才足以產出穩定之預估結果。

Advanced public transportation systems (APTS) using transportation management and information technologies to public transportation systems and the purpose of advanced public transportation systems is to increase their efficiency of operation and improve the level of service. Therefore, how to apply advanced public transportation systems in predicting arriving time becomes one of the important issues to improve the service quality and operation efficiency. This study aims to develop the dynamic bus travel time prediction model in considering delay of buses at the intersection. Traffic signal program in variation of time and space is both included to calculate accurate and reliable bus travel time. There are two travel time prediction models under study based on prediction segments: (1) Kalman Filter model for short headway prediction that less than 15 minutes and (2) Discrete Fourier Transform model for long headway prediction beyond 15 minutes. The result shows that Discrete Fourier Transform model has more accurate outcome in weekdays or off-peak hours in holidays than peak-hour segments, and the prediction model has the better performance than the existing algorithm model applied in Taipei City. Besides, the result shows that Kalman Filter model has higher accuracy when prediction segment is longer than 4 hours, which indicates that it will show stable performance after 4-hour period of training.