In this study, one of the most frequently studied problems in the field of operations research, the traveling salesman problem and the transportation problem, are considered, and a new solution algorithm that takes advantage of the relationship between them is proposed. Many initial solution algorithms have been proposed for transportation problems. In this study, with a similar approach and intuition, an approach called TPORT is proposed for the initial solution of the symmetric traveling salesman problem. This method has been proposed to contribute to years of research to solve the traveling salesman problem more effectively. In the proposed method, the traveling salesman distance matrix is treated as a transportation table, and a special normalization process on the matrix provides the initial solution of the traveling salesman problem. Then, the performance of the initial solutions is improved with 2-Opt algorithm. As the developed heuristic has similarities with the nearest neighbor algorithm, the solution performances of the traveling salesman problems were compared with the solutions of the nearest neighbor algorithm and the solutions of the directly applied 2-Opt algorithm. The proposed approach has been analyzed by the well-known traveling salesman test instances and a group of test instances from the literature. The average solution performance of the proposed method has 26% deviation from the optimal, whereas the performance of the nearest neighbor algorithm has a 16% deviation from the optimal. However, when the solutions of TPORT and nearest neighbor algorithm were improved with 2-Opt, the average deviation was obtained as 4% and 3%, respectively. In this context, it can be argued that the proposed solution approach is competitive in terms of solution performance. Also, there is a huge difference between the proposed method and the nearest neighbor algorithm in terms of solution times. As a result, it has been shown that the proposed method is superior in terms of solution speed and competitive in terms of solution quality. In particular, as the problem size is increased, the solution time of the comparable methods is almost constant, while the solution time of the nearest neighbor algorithm shows an exponential increasing trend.