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.
A Green Intelligent Transport Model for Urban Mobility
Communic.Comp.Inf.Science
International Symposium on Intelligent Technology for Future Transportation ; 2024 ; Helsinki, Finland October 20, 2024 - October 22, 2024
2025-03-12
16 pages
Article/Chapter (Book)
Electronic Resource
English
Optimization model , Environmental impact , Traffic congestion , Pareto optimization , Intelligent , Urban transportation , Sustainable mobility , Micro-mobility , Green , User satisfaction , Carpooling , Transportation costs , Multi-objective optimization , Urban mobility solutions , Public transportation integration , Ride-sharing Computer Applications , Computer Science , Data Mining and Knowledge Discovery , Special Purpose and Application-Based Systems , Transportation Technology and Traffic Engineering , Geography, general