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Neural Computing and Applications

, Volume 31, Supplement 2, pp 1013–1028 | Cite as

Recursive least-squares temporal difference learning for adaptive traffic signal control at intersection

  • Biao YinEmail author
  • Mahjoub Dridi
  • Abdellah El Moudni
Original Article
  • 143 Downloads

Abstract

This paper presents a new method to solve the scheduling problem of adaptive traffic signal control at intersection. The method involves recursive least-squares temporal difference (RLS-TD(λ)) learning that is integrated into approximate dynamic programming. The learning mechanism of RLS-TD(λ) is to make an adaptation of linear function approximation by updating its parameters based on environmental feedback. This study investigates the method implementation after modeling a traffic dynamic system at intersection in discrete time. In the model, different traffic control schemes regarding signal phase sequence are considered, especially the defined adaptive phase sequence (APS). By simulating traffic scenarios, RLS-TD(λ) is superior to TD(λ) for updating functional parameters in the approximation, and APS outperforms other conventional control schemes on reducing traffic delay. By comparing with other traffic signal control algorithms, the proposed algorithm yields satisfying results in terms of traffic delay and computation time.

Keywords

Adaptive traffic signal control Recursive least-squares temporal difference Approximate dynamic programming Adaptive phase sequence 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.LVMT-City Mobility Transport Laboratory, École des Ponts ParisTechIFSTTAR, UPEMChamps-sur-MarneFrance
  2. 2.NIT-O2S, Université de technologie de Belfort-MontbéliardBelfortFrance

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