Travel Time Prediction under Missing Data
Date Issued
2009
Date
2009
Author(s)
Li, Yi-Ru
Abstract
The development of Intelligence Transportation System among numerous advanced countries in the world has made a remarkable progress on improving traffic jams, energy conservation, and the promotion of transportation safety. The core of ITS is called Advanced Transportation Management System, ATMS, which is mainly used for the immediate traffic condition detection and control, etc. Along with recent Internet technological improvement, Advanced Traveler Information Systems, ATIS is also a key point in the growth of ITS. Providing the efficient and correct immediate traffic information to the users is the major function of ATIS. n this research provide two module to avoid missing data, the first is on-time interpolate data which including cubic interpolation, linear interpolation, neighbor interpolation. The second module is real-time interpolate data which including mean interpolation, Furier interpolation, mean interpolation combine with α−β−γ filter, Furier interpolation combine withα−β−γ filter.croding the experiment design, the linear is the best results in on-time interpolation and there were no difference between the four methods of interpolating in real-time interpolation. It depend the authority demend to choose interpolation method.
Subjects
Data Missing
Interpolation
Travel Time Prediction
Kalman Filter
DFT
SDGs
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-98-R96521527-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):bbc14af61d56a84b3359097bd1f2157c