Due to high mobility and intermittent connections in vehicular networks, reliable and efficient vehicular communication is a challenging task. Previous research on Vehicle-to-Vehicle (V2V) communication mostly focuses on achieving reliable transmissions from a given source to a given destination by mining moving patterns of taxicabs. However, to the best of our knowledge, none of them considered the habit-driven regularities of individual taxicabs as well as the urban-layout-driven time-varying regularities of crowds of taxicabs synthetically. With this insight, we model both individual and holistic driving patterns by Markov Chain models, then devise a new method to predict possible driving routes for every single taxicab. In addition, we design a new method to evaluate the probability that a single taxicab retrieves information of a specific road segment while it drives through another road segment during a given time period, and also to quantify the expected probability that a single taxicab obtains the information of a given road segment in the near future. With such information, our solution enables the selection of the optimal data packet transmission scheme. We evaluate our solution on a real-world taxicab dataset. Experimental results demonstrate that our approach outperforms alternative solutions in terms of diffusion speed and success ratio of data retrieval.