✦ LIBER ✦

Use of sequential learning for short-term traffic flow forecasting

✍ Scribed by Haibo Chen; Susan Grant-Muller

Book ID: 104368807
Publisher: Elsevier Science
Year: 2001
Tongue: English
Weight: 552 KB
Volume: 9
Category: Article
ISSN: 0968-090X
DOI: 10.1016/s0968-090x(00)00039-5

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✦ Synopsis

Accurate short-term trac ¯ow forecasting has become a crucial step in the overall goal of better road network management. Previous research [H. Kirby, M. Dougherty, S. Watson, Should we use neural networks or statistical models for short term motorway trac forecasting, International Journal of Forecasting 13 (1997) 43±50.] has demonstrated that a straightforward application of neural networks can be used to forecast trac ¯ows along a motorway link. The objective of this paper is to report on the application and performance of an alternative neural computing algorithm which involves Ôsequential or dynamic learningÕ of the trac ¯ow process.

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