With increased complexity of customers choice behaviors, practical optimization approaches often involve decomposing a network revenue management problem into multiple single-leg problems. While dynamic programming approaches can be used to solve single-leg problems exactly, they are not scalable and require precise information about the customers' arrival rates. On the other hand, the traditional heuristics are often static which do not explicitly consider the remaining time horizon in the optimization. This motivates us to find scalable and efficient dynamic heuristics that work well with the complex customers choice models. We develop two expected marginal seat revenue type heuristics for the single-leg dynamic revenue management problems in airline industry and evaluate its performances using Monte Carlo simulation. The initial simulation results indicate that our proposed heuristics are computationally efficient and fairly robust. This study provides a foundation for potential future extensions to solve larger network problems.