NDLI: Short-term forecasting of travel time based on license plate matching data

Content Provider	IEEE Xplore Digital Library
Author	Zhipeng Li Nan Li Fuqiang Liu Yuncai Liu
Copyright Year	2008
Description	Author affiliation: Dept. of Inf. & Commun. Eng., Tongji Univ., Shanghai (Zhipeng Li; Nan Li; Fuqiang Liu)
Abstract	In this paper, we review the state of art of short-term traffic forecasting models, outlining their basic ideas, related works, advantages and disadvantages of each model. An improved adaptive exponential smoothing (IAES) model is also proposed to overcome the drawbacks of previous adaptive exponential smoothing model. Then comparing experiments are carried out under normal traffic condition and abnormal traffic condition to evaluate the performance of four main branches of forecasting models on direct travel time data obtained by license plate matching. The results of experiments show the forecasting performance of IASE is superior to other models in shorter forecasting horizon (one and two step forecasting).
Starting Page	1390
Ending Page	1395
File Size	316541
Page Count	6
File Format	PDF
ISBN	9781424425020
DOI	10.1109/ICAL.2008.4636371
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Smoothing methods Travel time Time series analysis Adaptation model Artificial neural networks Predictive models Data models Short-term forecasting Forecasting
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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