Adaptive Network Routing Algorithm for Intelligent and Sustainable Smart Transportation Systems

Modern transportation systems face challenges such as traffic congestion excessive carbon emissions and long waiting times because of significant growth in vehicle density and urbanization.The conventional routing algorithms frequently do not work efficiently in this kind of environment because they are mostly rigid and do not consider dynamic elements like congestion, environmental effect and real time traffic density.In this paper, an Adaptive Network routing algorithm (ANRA) is proposed especially designed for intelligent and sustainable smart transportation systems.This algorithm inclines towards overcoming the problems faced by existing systems.The model proposes to minimize the CO2 emissions and round-trip times by intelligently combining the multi-objective optimization and reinforcement learning.This approach utilizes an emission aware reward function which is novel way of improving the ability to explore the best routing policies under different network environments.The proposed algorithm is simulated with grid-based network wherein it has proven to be more efficient and sustainable as compared to traditional approaches such as A*, Dijkstra and random walk approaches.To support intelligent navigation, environment sustainable solutions and dynamic traffic management frameworks, ANRA provides a scalable routing and real time solution that can be integrated with smart city infrastructures.The proposed algorithm-ANRA shows drastic improvement in the performance parameters such as travel time, density, emission and cost.This approach aids to the realization of sustainable, environment friendly and more effective urban mobility systems.