What is the Human Mobility in a New City: Transfer Mobility Knowledge Across Cities

With the advances of web-of-things, human mobility, e.g., GPS trajectories of vehicles, sharing bikes, and mobile devices, reflects people's travel patterns and preferences, which are especially crucial for urban applications such as urban planning and business location selection. However, collecting a large set of human mobility data is not easy because of the privacy and commercial concerns, as well as the high cost to deploy sensors and a long time to collect the data, especially in newly developed cities. Realizing this, in this paper, based on the intuition that the human mobility is driven by the mobility intentions reflected by the origin and destination (or OD) features, as well as the preference to select the path between them, we investigate the problem to generate mobility data for a new target city, by transferring knowledge from mobility data and multi-source data of the source cities. Our framework contains three main stages: 1) mobility intention transfer, which learns a latent unified mobility intention distribution across the source cities, and transfers the model of the distribution to the target city; 2) OD generation, which generates the OD pairs in the target city based on the transferred mobility intention model, and 3) path generation, which generates the paths for each OD pair, based on a utility model learned from the real trajectory data in the source cities. Also, a demo of our trajectory generator is publicly available online for two city regions. Extensive experiment results over four regions in China validate the effectiveness of the proposed solution. Besides, an on-field case study is presented in a newly developed region, i.e., Xiongan, China. With the generated trajectories in the new city, many trajectory mining techniques can be applied.