Traffic volume forecasting of urban highway in Shanghai.

Author(s)
Chen, Z. Qiao, H. & Fei, S.
Year
Abstract

Forecasting traffic volume is an important task in guiding drivers' routes controlling urban highways, and providing real-time transportation information. In this study, neural network models are used to forecasting traffic volume. First the Rescaled Range (R/S) analysis is used to identifying traffic volume data trends, fluctuations and randomness. Then Time-delayed recurrent network is used to forecast the traffic volume in the next 15 minutes. The experiments show that the traffic volume forecasting based on recurrent model has a good performance.

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Publication

Library number
C 31719 (In: C 31321 CD-ROM) /72 / ITRD E826480
Source

In: ITS - enriching our lives : proceedings of the 9th World Congress on Intelligent Transportation Systems ITS, Chicago, Illinois, October 14-17, 2002, 7 p.

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This publication is one of our other publications, and part of our extensive collection of road safety literature, that also includes the SWOV publications.