Study on the truck axle load limit for rural roads based on the cost of the concerned entities in China

The axle load limit represents the maximum permissible mass that an axle can carry when a vehicle is operating on a road. This limit is closely associated with the expenses incurred in highway construction, truck transportation, and other related sectors. Consequently, it needs to be determined by carefully weighing the relevant costs. As freight demand continues to rise, many rural roads have failed to meet the requirements for maximizing the social benefits derived from truck transportation in China. To address this issue, this study examines the optimization of the axle load limit for rural roads, taking into consideration advancements in road construction technology, construction funding, and vehicle manufacturing techniques. Initially, the entities affected by the axle load limit, namely, road agencies, truck companies, commodity producers, and consumers, are analyzed. Subsequently, a programming model is developed to optimize the truck axle load limit for rural roads, aiming to maximize the cost gap between the scenarios with the incremental limit and those without the incremental limit. Finally, a case study is conducted to validate the effectiveness of the model, using the rural road network in Guiyang city (China) as an illustrative example. The findings indicate that a limit below 17 tons results in a greater increase in benefits compared to costs. However, if the limit increases to 17 tons, the benefit increment decreases to a smaller value than the cost increment. In conclusion, the optimal axle load limit for the rural road network in Guiyang should be 13 tons, at which point the system cost reduction is maximized, resulting in an annual amount of 17.94 million RMB.

Automated summary

The axle load limit plays a crucial role in determining the costs associated with highway construction and truck transportation. This study examines the optimization of the axle load limit for rural roads, taking into account advancements in road construction technology, construction funding, and vehicle manufacturing techniques. A programming model is developed to maximize the cost gap between scenarios with and without incremental limits. The findings suggest that a limit of 13 tons maximizes the reduction in system costs, resulting in significant annual benefits.