Spatial crowdsourcing task allocation for heterogeneous multi-task hybrid scenarios: a model-embedded role division approach

Spatial crowdsourcing (SC), as an effective paradigm for accomplishing spatiotemporal tasks, has gradually attracted widespread attention from both industry and academia. With the advancement of mobile technology, the service modes of SC have become more diversified and flexible, aiming to better meet the variable requirements of users. However, most research has focused on homogeneous task allocation problems under a single service model, without considering the individual differences among task requirements and workers. Consequently, many of these studies fail to achieve satisfactory outcomes in real scenarios. Based on real service scenarios, in this study, we investigate a heterogeneous multi-task allocation (HMTA) problem for hybrid scenarios and provide a formal description and definition of the problem. To solve the problem, we propose a role division approach embedded with an individual sorting model (RD-ISM). This approach is implemented based on a batch-based mode (BBM) and consists of two parts. First, an individual sorting model is introduced to determine the sequence of objects based on spatiotemporal attributes, prioritizing tasks and workers. Second, a role division model is designed based on an attraction–repulsion mechanism to match tasks and workers. Following several iterations over multiple batches, the approach obtains the final matching results. The effectiveness of the approach is verified using real and synthetic datasets and its performance is demonstrated through comparisons with other algorithms. Additionally, the impact of different parameters within the approach is investigated, confirming its scalability.