A Hybrid Graph-Theoretic and Optimization Model for Sustainable Urban Transportation Planning
Keywords:
Graph theory, transportation optimization, mathematical modelling, shortest path, network analysis, sustainable transportation, multi-objective optimization, urban mobilityAbstract
Urban transportation systems involve complex interactions among roads, intersections, travel demand, congestion, travel distance, energy consumption, and environmental impacts. Conventional transportation planning often considers individual objectives such as shortest travel distance or minimum travel time, whereas sustainable urban transportation requires simultaneous consideration of multiple and sometimes conflicting objectives. The present study develops a hybrid mathematical framework integrating graph theory, shortest-path analysis, network centrality, and multi-objective optimization for sustainable urban transportation planning. The urban road system is represented as a weighted directed graph G = (V, E) where vertices represent intersections and edges represent road segments. Edge weights incorporate travel distance, travel time, congestion level, and estimated environmental cost. A composite transportation cost function is then formulated and optimized subject to network capacity, accessibility, and sustainability constraints. An illustrative transportation network consisting of 20 intersections and 32 road links is developed to demonstrate the proposed model. The model evaluates alternative routing and network-management strategies using travel efficiency, congestion reduction, accessibility, and environmental cost as performance indicators. The proposed framework provides a mathematical basis for identifying transportation interventions while maintaining a balance between mobility and sustainability.
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