Abstract
Ramp metering is one of the cornerstone solutions for Smart Freeway systems, which means regulating traffic inflow at on-ramps to prevent flow breakdowns on the freeway so it can remain reliable while delivering higher throughput. This iMOVE project investigates the possibility of implementing perimeter control in the Perth CBD, which is based on similar principles but operates at the zonal level to optimize the overall flow at a network level.
Perimeter control (also known as gating) works by dividing the network into zones and regulating their flow exchange at the boundaries. It aims at load-balancing between zones across the network to achieve a stable and optimal operation at the global level. Controllers prevent overflow of traffic into busy zones by leveraging spare capacity in less busy zones as temporary storage space. This is different to local congestion relief strategies that focus on individual pinch points.
Effective implementation of perimeter control requires a good understanding of the behaviour of each zone. Macroscopic Fundamental Diagrams (MFDs) are commonly used for such a purpose. They describe the underlying relationship between a zone’s speed, flow, and density at the aggregate level and are believed to be stable under different traffic demands. The accurate measurement of MFDs has become possible in recent years with the advent of ‘big data’. Our previous iMOVE Project 1-003 demonstrated that metropolitan Perth can be divided into zones with well-behaved MFDs (Lester et al., 2020). It has also shown the feasibility and potential benefit of applying perimeter control to multiple zones using an abstract mathematical model.
This work extends the MFD zoning project by testing MFD-based perimeter control on Main Roads’ more realistic Aimsun model for the Perth CBD area. The Perth CBD network was simulated from 6:45 am-11:45 am based on Main Roads’ previously validated mesoscopic simulation from 6:45 am-9:45 am. The MFDs generated did not reach a congestion threshold under the base demand levels so they were increased by 20% to allow for experimentation on a system that could benefit from perimeter control. The increase can be seen as future growth or daily variation – the model reflects the traffic condition of a typical day but traffic sometimes deviates from it. When the deviation is significant enough, the benefit of perimeter control can be realised.
Perimeter control (also known as gating) works by dividing the network into zones and regulating their flow exchange at the boundaries. It aims at load-balancing between zones across the network to achieve a stable and optimal operation at the global level. Controllers prevent overflow of traffic into busy zones by leveraging spare capacity in less busy zones as temporary storage space. This is different to local congestion relief strategies that focus on individual pinch points.
Effective implementation of perimeter control requires a good understanding of the behaviour of each zone. Macroscopic Fundamental Diagrams (MFDs) are commonly used for such a purpose. They describe the underlying relationship between a zone’s speed, flow, and density at the aggregate level and are believed to be stable under different traffic demands. The accurate measurement of MFDs has become possible in recent years with the advent of ‘big data’. Our previous iMOVE Project 1-003 demonstrated that metropolitan Perth can be divided into zones with well-behaved MFDs (Lester et al., 2020). It has also shown the feasibility and potential benefit of applying perimeter control to multiple zones using an abstract mathematical model.
This work extends the MFD zoning project by testing MFD-based perimeter control on Main Roads’ more realistic Aimsun model for the Perth CBD area. The Perth CBD network was simulated from 6:45 am-11:45 am based on Main Roads’ previously validated mesoscopic simulation from 6:45 am-9:45 am. The MFDs generated did not reach a congestion threshold under the base demand levels so they were increased by 20% to allow for experimentation on a system that could benefit from perimeter control. The increase can be seen as future growth or daily variation – the model reflects the traffic condition of a typical day but traffic sometimes deviates from it. When the deviation is significant enough, the benefit of perimeter control can be realised.
| Original language | English |
|---|---|
| Place of Publication | Perth |
| Publisher | Planning and Transport Research Centre, University of Western Australia |
| Number of pages | 32 |
| Publication status | Published - Mar 2022 |
