Rapid population growth, urbanization and increasing mobility demands continue to pose serious challenges for urban transportation systems. Traffic congestion, high costs of transportation, and increased CO2 emission levels contribute to climate change impacts, not only that but also lower user satisfaction. This paper introduces a mathematical model (TCES). The model leverages Pareto optimization and linear programming to simultaneously minimize two major conflicting objectives: travel time, and cost emissions (Pollution) while maximizing user satisfaction. The model takes into account the factors which influence the transportation sector, such as passenger demand, real-time traffic data and environmental considerations. This makes the model a scalable and resilient solution to modern transportation, that can be applied in carpooling, ridesharing and city planning.