A Study of Multi-Step Ahead Travel Time Prediction on Freeway
Date Issued
2014
Date
2014
Author(s)
Hsueh, Ya-Yun
Abstract
The primary objective of Advanced Traveler Information System (ATIS) is providing travelers the necessary traffic information as decision guidance of route selection, and travel time information is one of the most important traffic information; querying travel time based on trip departing time is indispensable demand when travelers doing pre-trip planning. Hence, in order to provide traffic information which meets the need of travelers, this study proposes a "Hybrid Travel time Prediction Model" to forecast multi-step ahead travel time, applying different prediction methods within different prediction periods to conduct travel time prediction to improve prediction accuracy.
Hybrid travel time prediction model includes abnormal data processing module, data fusion module and travel time prediction module. The abnormal data processing module is for addressing outlier and missing data interpolation. The data fusion module fuses VD data and ETC data to improve the accuracy of input data. The travel time prediction module respectively constructs two Kalman Filter prediction models (KF1, KF2) with historical data and real-time data, and a Fourier Transform prediction model with historical data (DTFT). This study proposes hybrid prediction modelⅠwith KF1 and DTFT, hybrid prediction modelⅡwith KF2 and DTFT, dividing prediction length into short-term and long-term, and comparing performance of prediction models to decide the threshold and applicable prediction models for both short-term and long term prediction.
With respect to the tests for different daily traffic profiles (weekday, weekend), the result shows that the performance of the two hybrid travel time prediction models is significantly better than the single prediction model. The result also supports applying the hybrid prediction modelⅠ in weekday and the hybrid prediction modelⅡ in weekend.
Subjects
多時階
旅行時間預測
複合式模式
資料融合
Type
thesis
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