Origin-Destination (OD) demand is one of the important requirements in transportation planning. Estimating OD demand could be an expensive and time consuming procedure. These days using vehicle identification sensors for OD estimation has become very common because of its low cost and high accuracy. In this paper, we focus on solving two location problems of these sensors: one to observe and one to estimate path flows. These problems have only been solved for small-scale networks until recently due to being computationally expensive. Therefore, we try to present a method to solve these models for large-scale networks. Due to resemblance of these models and set covering problem, we used heuristic and meta-heuristic methods based on set covering problem. For this purpose, we defined our new set covering matrix based on prime matrix. In order to determine which method is more appropriate, we chose a large-scale and six medium-scale networks. The results represent that through heuristic methods and meta-heuristic methods a greedy algorithm and a Tabu search are more appropriate respectively.