Sustainability-Oriented Task Recommendation in Spatial Crowdsourcing

With the rapid evolution of sensing techniques and the proliferation of mobile devices, spatial crowdsourcing (SC) has gained significant attention in both academia and industry. SC involves assigning location-based tasks to mobile workers, with task recommendation playing a key role in helping workers identify suitable and appealing tasks. However, most existing studies focus on task completion rate, worker satisfaction, or efficiency, without consideration of the environmental impact, e.g., pollutant emissions from the increased vehicle usage associated with SC applications like Uber, Lyft, and FoodPanda. In this study, we consider a novel problem of sustainable task recommendation in SC, which aims to minimize the environmental footprint (i.e., pollution) while maintaining acceptable levels of task completion, worker satisfaction, and overall task recommendation efficiency. We develop an innovative Sustainability-Oriented Task Recommendation framework encompassing two major components: speed-driven pollutant emission estimation and task recommendation. Specifically, the pollutant emission estimation component aims to estimate future pollutant emissions based on worker trajectories and speeds, using a context-enhanced spatio-temporal network for road speed prediction. In the task recommendation component, we provide a completion-sensitive recommendation algorithm to maximize the expected number of completed tasks. Further, we design an efficient emission-optimized KM ranking algorithm to minimize emissions. Experiments on real data offer insight into the effectiveness and efficiency of the proposals, providing valuable insights into its potential for sustainable spatial crowdsourcing.