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

Abstract

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.

1. Introduction

In recent years, with the development of information technology and the rapid popularization of mobile terminal devices, spatial crowdsourcing (SC) has become an effective model for accomplishing spatiotemporal tasks in the real world (Guo et al., 2018). This approach has gradually attracted widespread attention from both industry and academia (Wang L et al., 2018). SC leverages the mobile Internet to integrate and schedule idle offline resources through online recruitment, aiming for efficient sharing of resources (Chen et al., 2014). By using SC platforms, mobile workers can be recruited to handle various complex, large-scale, and distributed spatiotemporal tasks that are difficult for computers or a few experts to accomplish (Gong et al., 2020).

With the rapid development of the online-to-offline business models, SC has been widely applied across various industries (Tong et al., 2017). These diverse SC platforms are gradually becoming integrated into people’s daily lives, playing a significant role in areas like social governance and traffic management. In terms of transportation management, the time constraints and spatial location changes involved in residents’ travel can be regarded as typical spatiotemporal tasks (Mazzetto, 2024). Shared mobility platforms of SC have reshaped the urban transportation industry ecosystem, and numerous related SC platforms have emerged, such as Uber, DiDi, and Gofun (Ray et al., 2023). According to the urban traffic operation report released by DiDi, the number of daily trips facilitated by the platform has exceeded 30 million people (Feng and Xiao, 2024).