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Abstract

Origin–destination tolls, destination-based parking charges, and regulated ridesharing fares are typically designed separately, although their effects on congestion and traveler choices are interdependent. This study formulates their coordinated design as a multi-objective equilibrium-constrained optimization problem that minimizes total generalized user cost and network-wide emissions. Traveler responses are represented by a Logit-based stochastic user equilibrium formulated as a fixed-point problem over simultaneous mode–route choices among solo driving, ridesharing driving, and ridesharing riding. The rider-to-driver capacity constraint implicitly couples the pricing decisions through the equilibrium response. By exploiting this structure, we derive a closed-form lower bound on ridesharing fares that enables feasibility-by-construction during the search. Building on this analytical result, we develop a nested solution framework that combines particle swarm optimization with repeated equilibrium assignments using a Barzilai–Borwein-based fixed-point procedure. Numerical experiments on the Wang, Nguyen–Dupuis, and Sioux Falls networks evaluate equilibrium consistency, computational behavior, and economic–environmental trade-offs. On the Nguyen–Dupuis network, the user-cost-oriented extreme reduces total user cost by 29% and emissions by approximately 50% relative to the unpriced equilibrium, whereas the emission-oriented extreme reduces emissions by 80% while increasing user cost by approximately 10%. Results across the benchmark networks show that the preferred coordinated policy depends on congestion conditions and the decision maker’s objective, while joint pricing can alter mode shares, vehicle flows, and congestion without changing total travel demand.