An MFD approach to route guidance with consideration of fairness

Abstract

This paper introduces two fair and efficient route guidance (RG) advisory control schemes for proactive control of a large-size urban network. The two developed fairness-centered concepts are proportional fairness and anticipatory control. These concepts are developed as distinct control frameworks to address inequity issues of earlier RG schemes in a model predictive control (MPC) scheme for a heterogeneous urban network, which is divided into multiple pockets of congestion. The modelling approach uses a macroscopic fundamental diagram (MFD), which relates aggregated traffic variables, such as vehicle accumulation and trip completion rate. The proportionally fair RG control (FC) scheme is developed as a two-level RG advisory controller that focuses on maximizing the proportion of travellers utilities to effectively prevent the network from becoming congested while also considering fairness in the distribution of network resources. In addition, an anticipatory control (AC) RG scheme is devised as a two-level optimization model by incorporating road users routing behaviour as an integral part of the control scheme. Intensive sensitivity analysis is conducted under high-demand profiles and for different compliance rates and MFD parameters to analyze and evaluate the performance of these two fairness-centered routing schemes compared to a basic MPC based control scenario. The results indicate that FC improves fairness in an urban network by increasing homogeneity while maintaining efficiency. For all examined compliance levels, even as low as 30%, a more homogenized traffic condition is achieved under FC and AC. When comparing the two schemes, AC advises a smoother RG ratios compared to FC.