Bi-objective optimization approach to sustainable decisions in a ridesharing traffic network

Reducing traffic congestion and air pollution has been a hot topic, and ridesharing mobility is considered to be one of the most effective ways to solve these problems. Previous models generally suffer from insufficient decision elements, difficulty in solving, and difficulty in practical application. We determine route schedules with the aim of reducing both the overall process cost and total CO2 emissions to a minimum. We propose a new algorithm, the inexact accelerated augmented lagrangian algorithm, and compare the new algorithm with the original algorithm in terms of both theoretical proof and numerical experiments to derive the efficiency of the new algorithm in solving the traffic flow allocation problem.