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Efficient data delivery is a great challenge in vehicular networks because of frequent network disruption, fast topological change and mobility uncertainty. The vehicular trajectory knowledge plays a key role in data delivery. Existing algorithms have largely made predictions on the trajectory with coarse-grained patterns such as spatial distribution or/and the inter-meeting time distribution, which has led to poor data delivery performance. In this paper, the authors mine the extensive trace datasets of vehicles in an urban environment through conditional entropy analysis, they find that there exists strong spatiotemporal regularity.
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