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Title	Can You Do Both? Balancing Order Serving And Crowdsensing For Ride-Hailing Vehicles
ID_Doc	13345
Authors	Rao B.; Zhang X.; Zhu T.; You Y.; Li Y.; Duan J.; Zhou Z.; Chen X.
Year	2024
Published	IEEE International Workshop on Quality of Service, IWQoS
DOI	http://dx.doi.org/10.1109/IWQoS61813.2024.10682936
Abstract	Given the high mobility and sensor-carrying capability, vehicle crowdsensing (VCS) has become a significant part of urban crowdsensing tasks in the development of smart cities. Ride-hailing vehicles, which are widely distributed in cities, can be a powerful tool for carrying out VCS. However, dispatching the vehicles to jointly benefit VCS and order serving is challenging, as the goals of these two tasks may not be consistent or even conflict. The distribution of ride orders and the distribution of point-of-interests (PoIs) may not coincide in time and geography. In addition, these orders and data PoIs have distinct forms of timeliness: prolonged waiting makes orders invalid and data with a larger age-of-information (AoI) has lower utility. We propose an online framework by extending multi-agent reinforcement learning (MARL) with careful augmentation to optimize the profit of order-serving and the data utility of crowdsensing. A new quality-of-service (QoS) metric is designed to characterize the utility of the two joint tasks, and formal mathematical modeling drives our MARL design. In particular, we integrated graph neural networks (GNN) to enhance state representations and capture the graph-structured dependencies among vehicles. We developed a simulator and conducted extensive experiments utilizing the New York City Taxi dataset. Experimental results demonstrate the advantage of our method in QoS improvement. © 2024 IEEE.
Author Keywords	age-of-information; graph neural networks; multi-agent reinforcement learning; points-of-interest; smart city; vehicle crowdsensing

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