  This paper evaluates the benefit of integrating vehicle-based mobile
crowd-sensing tasks into the ride-hailing system through the collaboration
between the data user and the ride-hailing platform. In such a system, the
ride-hailing platform commissions high-valued sensing tasks to idle drivers who
can undertake either ride-hailing or sensing requests. Considering the
different service requirements and time windows between sensing and
ride-hailing requests, we design a staggered operation strategy for
ride-hailing order matching and the sensing task assignment. The auction-based
mechanisms are employed to minimize costs while incentivizing driver
participation in mobile sensing. To address the budget deficit problem of the
primal VCG-based task assignment mechanism, we refine the driver selection
approach and tailor the payment rule by imposing additional budget constraints.
We demonstrate the benefits of our proposed mechanism through a series of
numerical experiments using the NYC Taxi data. Experimental results reveal the
potential of the mechanism for achieving high completion rates of sensing tasks
at low social costs without degrading ride-hailing services. Furthermore,
drivers who participate in both mobile sensing tasks and ride-hailing requests
may gain higher income, but this advantage may diminish with an increasing
number of such drivers and higher demand for ride-hailing services.
