Location prediction impacts a wide range of research areas in mobile environment. The abundant mobility data, produced by mobile devices, make this research area attractive. Randomness makes people's future whereabouts hard to predict, although studies have proved that human mobility shows strong regularity. Most previous works, in general, tend to discover an association between a user's social relations in real world and variances in trajectory and then utilize this association to model the user's mobility which is used for location prediction. However, these methods normally require some specific data, which make them hard to be migrated to other platforms. Moreover, by focusing on social relations, these methods neglect the potential value of the associations among strangers' trajectory. Based on this argument, this article has proposed a novel location prediction approach trajectory similarity-based location prediction. It applies the social contagion theory and introduces a novel similarity computing-based trajectory method along with the trajectory sampling, which is achieved by covering algorithm to accelerate the process of computing similarity. Experiment results on real dataset show that trajectory similarity-based location prediction achieves higher accuracy and stability comparing to the state-of-the-art approaches.

We consider the problem of mobilizing community effort to reposition indiscriminantly parked shared bikes in urban environments through crowdsourcing. We propose an ethically aligned incentive optimization approach WSLS which maximizes the rate of success for bike repositioning while minimizing cost and prioritizing users’ wellbeing. Realistic simulations based on a dataset from Singapore demonstrate that WSLS significantly outperforms existing approaches.