<p>With the rapid proliferation of intelligent vehicles and mobile sensing technologies, vehicular crowdsensing (VCS) has emerged as a scalable paradigm for urban data collection. However, ensuring high data quality and sustained vehicle participation remains challenging due to heterogeneous vehicle behaviors and dynamic vehicular environments. This paper proposes an incentive mechanism that integrates trust evaluation and reference dependence to enhance platform utility and data quality for truth discovery in VCS. First, a membership-based incentive scheme is introduced to offer fee discounts to vehicles with high trust values, and reference dependence is designed to account for vehicles’ expected benefits and encourage their engagement in high-value tasks. Second, the platform prioritizes the recruitment of trusted vehicles and member vehicles to improve the reliability of submitted data. Third, a trust-aware truth discovery algorithm is developed to refine the data aggregation process, complemented by a dynamic trust update model that adjusts vehicles’ trust values based on the quality of their contributed data. Experimental results demonstrate that the proposed mechanism not only improves platform utility but also significantly enhances the accuracy of sensing data, thereby supporting robust truth discovery in VCS.</p>