Large-scale dynamic traffic simulations generate a sizeable amount of raw data that needs to be managed for analysis. Typically, big data reduction techniques are used to decrease redundant, inconsistent and noisy data as these are perceived to be more useful than the raw data itself. However, these methods are normally performed independently so it wouldn’t compete with the simulation’s computational and memory resources. In this paper, we propose a data reduction technique that will be integrated into a simulation process and executed numerous times. Our interest is in reducing the size of each link’s time-dependent travel time data in a large-scale dynamic traffic simulation. The objective is to approximate the time-dependent link travel times along the y - axis to reduce memory consumption while insignificantly affecting the simulation results. An important aspect of the algorithm is its capability to restrict the maximum absolute error bound which avoids theoretically inconsistent results which may not have been accounted for by the dynamic traffic simulation model. One major advantage of the algorithm is its efficiency’s independence from the input data complexity such as the number of sampled data points, sampled data’s shape and irregularity of sampling intervals. Using a 10Â ×Â 10 grid network with variable time-dependent link travel time data complexities and absolute error bounds, the dynamic traffic simulation results show that the algorithm achieves around 80%–90% of link travel time data reduction using a small amount of computational resource.