Preserving equity: multi-objective connected and automated vehicle (CAV) lane deployment in mixed traffic

This paper investigates deploying connected and automated vehicle (CAV) lanes in transportation networks with a focus on measuring and preserving equity among travelers. A new metric is proposed to characterize equity based on (1) generalized travel cost per unit origin-destination (OD) distance for travelers on each OD pair and using each vehicle type and (2) maximum deviation of the standardized unit generalized travel cost from system average. A bi-level bi-objective program is developed to simultaneously minimize system travel cost and inequity while deploying CAV lanes. A solution algorithm that combines non-dominated sorting genetic algorithm II and variable neighborhood search is designed. Through extensive numerical experiments, we find (1) inequity is more prominent when travel demand is high; (2) human-driven vehicle travelers become more disadvantageous with lower CAV price and higher CAV automation; and (3) subsidy is effective in mitigating inequity, but a fee for using CAV lanes is less promising.