NDLI: Short-term traffic flow prediction using fuzzy information granulation approach under different time intervals

Content Provider	IET Digital Library
Author	Guo, Jianhua Liu, Zhao Huang, Wei Wei, Yun Cao, Jinde
Abstract	Short-term traffic flow forecasting has been regarded as essential for intelligent transportation systems, including both point prediction and interval prediction. Compared with point prediction, interval prediction of traffic flow in the future will be critical for traffic managers to make reasonable decisions. This study applies the fuzzy information granulation method to obtain the dispersion range of the collected traffic flow time series, and classical forecasting approaches of K-nearest neighbours, back-propagation neural network, and support vector regression are applied on the dispersion range and the original series itself, constituting a short-term traffic flow forecasting system with the capability of joint point and interval prediction. Using real-world traffic flow data collected from a field transportation system in America, the proposed forecasting system is shown to generate workable point prediction and associated prediction interval, demonstrating the validity of the proposed forecasting system. In addition, for unravelling the impact of time interval on the forecasting system, different time intervals are investigated, showing that with the increase in time interval, the stability of the forecasting system increases. Discussions are provided for the proposed approach, and future works are expected to enhance the proposed forecasting system.
Starting Page	143
Ending Page	150
Page Count	8
ISSN	1751956X
Volume Number	12
e-ISSN	17519578
Issue Number	Issue 2, Mar (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/12/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2017.0144
Journal	IET Intelligent Transport Systems
Publisher Date	2017-11-29
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Back-propagation Neural Network Backpropagation Fuzzy Information Granulation Method Interval Prediction K-nearest Neighbours Knowledge Engineering Technique Neural Computing Technique Neural Nets Point Prediction Regression Analysis Short-term Traffic Flow Forecasting System Short-term Traffic Flow Prediction Stability Statistics Support Vector Machine Support Vector Regression Time Series Traffic Engineering Computing Traffic Flow Forecasting Traffic Flow Time Series Traffic Information System
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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