Multiagent Deep Reinforcement Learning Based Incentive Mechanism for Mobile Crowdsensing in Intelligent Transportation Systems

Mobile crowdsensing in intelligent transportation systems is a new data acquisition mode that utilizes vehicles to sense a changing traffic environment, where incentive mechanisms play a vital role in motivating vehicles to participate. Nevertheless, existing incentive mechanisms do not simultaneously consider enhancing the efficiency of task allocation, maximizing the benefits of the entire system, and balancing various costs. Therefore, a two-stage incentive mechanism, considering task aggregation, is designed to maximize the benefits of vehicles and the platform. In the first stage, each vehicle, utilizing the multiagent deep reinforcement learning algorithm, dynamically adjusts its path selection strategy to maximize its net income and then uploads the selected path information to the platform. In the second stage, this platform solves an NP-hard problem of maximizing satisfaction by combining the bid price, time consumption, and task coverage to assign tasks to the appropriate vehicles. In particular, vehicle satisfaction is measured by the analytic hierarchy process, and rewards are allocated accordingly. The proposed mechanism is compared with the state-of-the-art method, the greedy, random, and optimal algorithms. Experiments on the real trajectory dataset manifest that the proposed mechanism can fully use the platform budget to achieve the best net income, platform satisfaction, and task completion rate.