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Balancing Supply and Demand for Mobile Crowdsourcing Services

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13740)


Mobile crowdsourcing (MC) which has been developed rapidly in recent years is playing an increasingly indispensable role in people’s daily lives such as taxi-hailing, food delivery and other services. The geographic equilibrium of service supply and demand is crucial so that the MC system could guarantee more promising matches in a more regionally balanced way. However, due to the spatial dynamic of MC environments, the emergence of supply and demand is unpredictable, asymmetric, and constantly changing among different regions and throughout the day, presenting considerable challenges to the MC platform. In this paper, we propose a hybrid reinforcement learning and transformer-based balancing framework (HRB) to achieve geographically balanced coverage of MC services, considering both the imbalanced state of service supply-demand geographical distribution and the moving willingness of MC participants. The HRB framework is developed based on the Deep Deterministic Policy Gradient strategy, which includes an actor-critic network for generating migration strategies and a Willingness Transformer (WiT) model for predicting the migration willingness of both mobile service providers and demanders among different regions. Experimental results have validated the effectiveness by comparing the proposed approach with other algorithms under multiple indicators.


  • Mobile crowdsourcing service
  • Supply and demand balance
  • Reinforcement learning
  • Transformer
  • Migration willingness

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  • DOI: 10.1007/978-3-031-20984-0_20
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This work was supported by National Key R &D Program of China (No.2021YFF0900802), Natural Science Foundation of China (No. 91846205) and Natural Science Foundation of Shandong Province (No. ZR2019LZH008).

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Correspondence to Wei He or Lizhen Cui .

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Li, Z., He, W., Liu, N., Xu, Y., Cui, L., Qi, K. (2022). Balancing Supply and Demand for Mobile Crowdsourcing Services. In: Troya, J., Medjahed, B., Piattini, M., Yao, L., Fernández, P., Ruiz-Cortés, A. (eds) Service-Oriented Computing. ICSOC 2022. Lecture Notes in Computer Science, vol 13740. Springer, Cham.

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