The efficiency and robustness of different real-time dynamic origin-destination (O-D) matrix adjustment algorithms were investigated when implemented in large-scale transportation networks. The proposed algorithms produce time-dependent O-D trip matrices based on the maximum-entropy trip departure times with simulated and actual observed link flows. Implementation of the algorithms, which are coupled with a quasi-dynamic traffic assignment model, indicated their convergent behavior and their potential for handling realistic urban-scale network problems in terms of both accuracy and computational time. The main factors influencing the numerical performance of each algorithm were identified and analyzed. Their relative efficiency was found to be particularly dependent on the level at which the assigned flows approximate the observed link flows. These results may provide insights into the suitability of each algorithm for diverse application domains, including freeways, small networks, and large-scale urban networks, where a different quality of O-D information is usually available.