Impact of roadside facility layout on network traffic flow in vehicle-infrastructure cooperation environments: An analysis based on macroscopic fundamental diagram model

Implementing vehicle-infrastructure cooperation technology requires connected and autonomous vehicles (CAVs) and roadside facilities. However, full deployment of CAVs and roadside facilities across urban road networks remains a gradual process. In the mixed traffic environment of CAVs and human-driven vehicles (HVs), the layout strategy for intelligent roadside facilities becomes particularly critical. Based on the macroscopic fundamental diagram (MFD) model, this paper investigates how the roadside facility layout and coverage scale affect network-level traffic efficiency. Two road network models are constructed: a 4 × 4 grid network and a real urban traffic network. The impacts of the spatial layout and deployment scale of roadside facilities on trip completion flow are analysed under varying CAV penetration rates. The analysis yields three key conclusions: (i) At low roadside facility coverage rates (≤25%), the central layout improves the network’s maximum trip completion flow; at high coverage rates (≥75%), the layout effects on MFD characteristics weaken significantly. (ii) Under low CAV penetration rates (≤20%) and low roadside facility coverage rates (≤25%), improving either factor alone is insufficient to continuously enhance the road network performance. Conversely, with high CAV penetration rates (≥80%) or high roadside facility coverage rates (≥75%), increasing any one factor can simultaneously improve road network capacity and operational efficiency. (iii) The grid network is more sensitive to variations in the vehicle-infrastructure cooperation environment, while the real road network shows more stable macroscopic performance due to structural and demand heterogeneities.