Automatic vehicle identification (AVI) systems are increasingly used for the collection of traffic data in urban and freeway networks. Several methods have been proposed for the estimation of travel times from AVI data, mainly for freeways. The problem of estimation of travel times in urban networks was examined. The main difference between freeway and urban networks is that urban network AVI data are often extremely noisy. A major part of that noise is attributed to vehicles that do not traverse the monitored section directly but stop for various reasons. A mixture model was proposed to capture the underlying states of the measurements of AVI travel times in urban areas. The hypothesis was that travel times are drawn from two (or more) populations, one representing normal movement through the network and one representing vehicles that stop for whatever reason. The method was applied with AVI data (collected through a system for automatic recognition of number plates) from a number of corridors in central Stockholm, Sweden. The model was estimated as a mixture of two lognormal distributions, and bootstrap standard errors were calculated. The results illustrate the robustness of the method and its ability to identify the underlying distribution of the latent populations consistent with the characteristics of each route, while standard methods for outlier removal fail.