Intelligent Multi-Modal Collaborative Distribution in Urban Smart Transportation

In the context of urban smart transportation, logistics distribution is plagued by persistent challenges such as traffic congestion, suboptimal operational efficiency, and elevated carbon emissions, rendering traditional single-vehicle delivery models increasingly inadequate for surging demands. To address these issues, this paper presents an innovative collaborative distribution approach. By constructing a multi-dimensional knowledge graph, it comprehensively integrates vehicle performance parameters, real-time traffic information, road network constraints, and dynamic task requirements, providing a solid data foundation for intelligent decision-making. An enhanced genetic algorithm, integrated with the Variable Neighborhood Search (VNS) mechanism for local optimization and simulated annealing for multi-objective release point adjustment, is employed to achieve efficient task allocation and seamless coordination of road and air paths. This approach not only takes into account vehicle capacity, delivery time windows, but also incorporates traffic flow dynamics and airspace regulations. Rigorous tests on hybrid datasets, which simulate real-world urban traffic scenarios, demonstrate that the proposed method outperforms traditional algorithms. It significantly reduces overall delivery time, cuts operational costs, decreases carbon emissions, and improves customer satisfaction by enabling more reliable and timely deliveries through intelligent transport synergy.