Roadside Units Optimization Considering Path Flow Uncertainty

Abstract

Traffic flow is crucial for the efficient and safe operation of transportation systems. Understanding and managing traffic flow can help alleviate congestion, reduce travel time, and enhance transportation safety. In order to better identify traffic flow in a traffic network, we propose a new method that uses roadside units (RSUs) for path flow reconstruction. Roadside units (RSUs) are vital transportation facilities in cooperative vehicle infrastructure systems. They utilize modern communication technologies to exchange information directly with intelligent connected vehicles and their influence on accurate path flow reconstruction and average travel time are respectively analyzed. Considering the path flow uncertainty in traffic networks, a two-stage stochastic model is formulated, which aims to balance RSU deployment cost and value of reduced travel time. On the first stage, we solve a fully path flow reconstruction problem; On the second stage, we calculates the reduction on average travel time under different scenarios. To effectively handle the characteristics of the second stage model, we employ the integer L-shaped algorithm for solution. Numerical experiments suggest that (1) Expanding the size of scenarios has little impact on experimental results, which indicating that this model has good applicability; (2) some links play important roles in path flow reconstruction.

Keywords

• Urban traffic
• location decision
• two-stage stochastic programming
• roadside unit
• path flow reconstruction