A Recurrent Neural Network Approach to Network-wide Traffic Signal Control.

Author(s)
Li, Y. & Mueck, J.
Year
Abstract

This paper presents a recurrent neural network approach to network-wide traffic control, referred to as NEURA-NESC. The system is based on a traffic flow model, which comprises two recurrent neural networks. The control model of NEURA-NESC extends this traffic model by two error-propagation networks that are related to the most essential parameters of traffic signal control: green time split and offset. The time values correspond directly with link capacities that are adjusted according to the minimization of some given objective function. Simulation investigations demonstrate the efficiency of the proposed approach. Furthermore, in the complete model a very close cooperation between network-wide dynamic traffic assignment and signal control is achieved.

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Publication

Library number
C 48013 (In: C 47949 DVD) /73 / ITRD E854018
Source

In: Compendium of papers DVD 89th Annual Meeting of the Transportation Research Board TRB, Washington, D.C., January 10-14, 2010, 23 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.