Research of Traffic Flow Forecasting Based on Grids

Guozhen Tan (), Hao Liu, Wenjiang Yuan and Chengxu Li
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Guozhen Tan: Dalian University of Technology, Department of Computer Science and Engineering
Hao Liu: Dalian University of Technology, Department of Computer Science and Engineering
Wenjiang Yuan: Dalian University of Technology, Department of Computer Science and Engineering
Chengxu Li: Dalian University of Technology, Department of Computer Science and Engineering

A chapter in Current Trends in High Performance Computing and Its Applications, 2005, pp 451-456 from Springer

Abstract: Summary The complexity of traffic road network and the huge amount of traffic flow data results in the complexity of traffic flow forecasting. Gird computing technology integrates the grid resources and provides the traffic flow forecasting problem with resource sharing and coordination abilities. In this paper, according to the correlation theory, a traffic flow forecasting algorithm based on back-propagation(BP) neural network for single road section has been put forward, followed with a grid computing model to meet the high-performance requirement of the forecasting process. Making full use of the coordination ability between the multi-nodes of the grid computing, this method solves the precious problems in traffic flow forecasting, such as low efficiency, low real-time, etc.

Keywords: neural network; traffic flow forecasting; Gird; coordination (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-27912-9_60

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DOI: 10.1007/3-540-27912-1_60

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