Urban context aware human mobility model based on temporal correlation

The performance of mobile networks is significantly influenced by the mobility patterns of wireless device holders. Human mobility models, which yields synthetic trajectories with essential mobility patterns of crowd, are important for the research and development of mobile networks. However, traditional models are often perceived as not realistic for depicting urban context (such as urban hotspots and direction of crowd flow). This paper proposes a temporal correlation based model that produce large-scale of synthetic trajectories on the basis of a short period of time real human traces. The trajectories generated by our model not only maintain the inherent statistical features of human mobility, but also “learn” the urban context from real traces. For simulating crowd mobility in a city, our model first extract the statistical features and urban context features from the the real traces, then regenerate synthetic trajectories on the basis of temporal correlation of human mobility. We validate our model and findings through three open datasets: taxicabs' traces in San Francisco, public bicycles' traces in Washington D.C. and New York City.