Research on the Impact of Ant Colony Algorithm Optimization Based on Highway Enterprise Operation Data on Cost Control of Logistics Path Planning

This study investigates logistics path planning cost control through an optimized Ant Colony Algorithm (ACA) driven by highway enterprise operational data. Real-time transportation information, including traffic flow, vehicle speed, and road condition data, is integrated into pheromone update mechanisms and heuristic factor adjustments to enhance the adaptability of the algorithm in dynamic logistics environments. A data-driven optimization framework is developed to support intelligent route selection under continuously changing traffic conditions. Case-study results demonstrate that the proposed method significantly improves route planning efficiency and logistics cost control performance. Compared with traditional experience-based planning methods and the standard ACA, the optimized approach reduces total logistics distribution costs by 26.45%, transportation costs by 31.2%, and average travel distance by 12.12%. The framework exhibits strong robustness, adaptability, and operational efficiency in large-scale logistics networks. The proposed methodology is particularly applicable to intelligent transportation systems supported by wireless communication infrastructures and antenna-enabled sensing networks, where reliable real-time data acquisition and low-latency information transmission are essential for dynamic route optimization and operational decision-making. This research provides an effective engineering solution for intelligent logistics management, transportation optimization, and data-driven supply chain operations.