Optimal Design of Highway Traffic Counting Stations for OD Matrix Estimation: A Case Study in Thailand

This study proposes a rigorous optimization framework for the design of traffic counting station locations in large-scale highway networks, with specific application to Thailand’s national highway system. A mixed-integer linear programming (MILP) model is developed to determine the optimal sensor placement under budget-constrained scenarios while explicitly incorporating existing infrastructure. The model aims to maximize origin–destination (OD) flow observability and minimize estimation error, measured by the percentage of OD flows intercepted and root mean square error (RMSE). The proposed framework is validated using a real-world network. The results demonstrate that the optimized design significantly outperforms conventional approaches, including random and high-flow-based selection methods, achieving over 70% reduction in estimation error and 93% of OD flows intercepted with a feasible number of stations. Furthermore, the statistical representativeness of the selected locations is validated across spatial, functional, and traffic characteristics and traffic measurement errors. The findings provide a scalable and cost-effective decision-support tool for transport authorities in developing countries seeking to modernize transportation planning, traffic management, and infrastructure development under limited resources.