Fuzzy group-based intersection control via vehicular networks for smart transportations

Jialang Cheng, Weigang Wu, Jiannong Cao, Keqin Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

25 Citations (Scopus)

Abstract

Vehicular network has been recently used to achieve high efficient and flexible traffic scheduling at intersection roads for smart transportation systems. Different from existing works, where traffic signal is used to schedule waiting vehicles at each lane, we propose to divide vehicles in the same lane into small groups and schedule vehicle groups via wireless communication rather than traffic lights. Such direct scheduling of vehicles can reduce waiting time and improve fairness, especially when the traffic volume in different lanes is imbalanced. The key challenge in such a design lies in determining appropriate size of groups with respect to real-time traffic conditions. To cope with this issue, we propose a neuro-fuzzy network-based grouping mechanism, where the network is trained using reinforcement learning technique. Also, vehicle groups are scheduled via a neuro-fuzzy network. Simulations using ns3 are conducted to evaluate the performance of our algorithm and compare it with similar works. The results show that our algorithm can reduce waiting time and at the same time improve fairness in various cases, and the advantage against traffic light algorithms can be up to 40%.
Original languageEnglish
Article number7508968
Pages (from-to)751-758
Number of pages8
JournalIEEE Transactions on Industrial Informatics
Volume13
Issue number2
DOIs
Publication statusPublished - 1 Apr 2017

Keywords

  • Fuzzy neural networks
  • intelligent transportation system (ITS)
  • intersection control
  • machine learning
  • Vehicular Ad hoc NETworks (VANETs)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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