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We propose a multiagent, large-scale, vehicle routing modeling framework for the simulation of transportation system. The goal of this paper is twofold. Firstly, we investigate how individual and social knowledge interact and ultimately influence the effectiveness of resulting traffic flow. Secondly, we evaluate how different discrete-event simulation designs (delays vs. queuing) affect conclusions within the model. We present a new agent-based model that combines the efficient discrete-event approach to modeling with the intelligent drivers who are capable to learn about their environment in the long-term perspective from both, individual experience, and widely available social knowledge. The approach is illustrated as practical application to modeling commuter behavior in the city of Winnipeg, Manitoba, Canada. All simulations in the paper are fully reproducible as they have been carried out by utilizing a set of opensource libraries and tools that we have developed for the Julia programming language and that are openly available on GitHub. [ABSTRACT FROM AUTHOR]