Combining model predictive intersection control with Green Light Optimal Speed Advisory in a connected vehicle environment

In recent years there has been much interest in Connected Vehicle (CV) technology. Vehicle-to-infrastructure (V2I) communication has the potential to reduce delays, stoppage time, fuel usage and emissions, because it allows fine-grained traffic movement data to be shared with greater frequency. Previously, traffic control algorithms have been based on macroscopic, fluid mechanical traffic models, but since V2I communication allows for fine-grained traffic data, a more accurate, microscopic, car-following traffic model will be used instead in this paper. At an intersection there are essentially two ways to improve traffic conditions – by improving the intersection control schedule, and by modifying vehicle approach trajectories. In order to best utilise traffic data that varies second-by-second, it is proposed that the optimal control schedule that minimises delay can be found via model predictive control (MPC) with suitable state space reduction techniques. In addition, since the control algorithm utilises an underlying microscopic model, entering vehicles’ trajectories can be modified with Green Light Optimal Speed Advisory (GLOSA). This allows drivers to adjust their speed profiles in order to have an efficient approach trajectory. CV technology allows MPC to be integrated with GLOSA, making the best use of this future technology to improve traffic conditions for all motorists.

Language

  • English

Media Info

  • Pagination: 15p
  • Monograph Title: 38th Australasian Transport Research Forum (ATRF 2016), Melbourne, 16th - 18th November 2016

Subject/Index Terms

Filing Info

  • Accession Number: 01627450
  • Record Type: Publication
  • Source Agency: ARRB
  • Files: ITRD, ATRI
  • Created Date: Feb 27 2017 10:10AM