Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/74181
Title: Freeway traffic prediction using neural networks
Authors: Cheu, Ruey-Long 
Issue Date: 1998
Source: Cheu, Ruey-Long (1998). Freeway traffic prediction using neural networks. Proceedings of the International Conference on Applications of Advanced Technologies in Transportation Engineering : 247-254. ScholarBank@NUS Repository.
Abstract: This paper presents the design of multilayer feedforward neural networks to predict freeway traffic conditions at a loop detector station. The neural networks make use of 30-second volume, occupancy and speed averaged across all lanes in the past 2 intervals as inputs, and predict the same set of local parameters in the next 1 or 2 time intervals. Networks with various design and training parameters have been trained and evaluated with 2 weeks of morning data collected at I-880 Freeway in the San Francisco Bay Area. The results show that the neural nets have high accuracy in volume, occupancy and speed predictions during low, moderate and perhaps high volume conditions, including recurring congestion and possibly during incidents.
Source Title: Proceedings of the International Conference on Applications of Advanced Technologies in Transportation Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/74181
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

23
checked on Jan 19, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.