Method for vehicle route planning in large-scale construction sites based on low-density floating car data

Large-scale construction sites have frequently changing and complex road networks, which can easily lead to inefficiencies and even traffic accidents due to poor vehicle path planning. This study addresses the gap in studying route planning for large-scale construction site vehicles. By constructing cost calculation model based on vehicle length, segment length, and low-density floating vehicle speed, the limitation of relying on high-density floating vehicle data for effective calculations is overcome. This combination ensures the safety by adjusting routes in a timely manner based on changes in data. An enhanced Dijkstra-ACO algorithm is proposed to obtain the final route based on the improvements of search range determination and modes of pheromone change. Experiments is conducted using real large-scale construction sites, and comparisons are made with mainstream methods, all of which were optimized by the present method. Meanwhile, compared to the classic ACO algorithm, average convergence speed is improved by 5.1%. Furthermore, accuracy and search range are guaranteed. The results demonstrate that the proposed method not only ensures transportation efficiency but also enhances safety effectively.