ACP-Based Modeling of the Parallel Vehicular Crowd Sensing System: Framework, Components and an Application Example

As an emerging paradigm for urban sensing, vehicular crowd sensing (VCS) has received increasing attention in recent years. Unlike traditional sensing paradigms, VCS leverages ubiquitous connected vehicles (CVs) and diverse onboard sensors to efficiently collect city-scale data. Despite the considerable benefits of CVs, the fast-changing traffic environment and attendant human and social factors bring significant complexity to the VCS system and make it a typical cyber-physical-social system (CPSS), followed by the challenge of robust and efficient modeling of VCS systems. To cope with the complexity of social dimensions and optimize the decision-making process in the physical VCS, this article introduces the artificial societies, computational experiments, and the parallel execution (ACP) approach to the VCS system and develops a novel framework called parallel VCS (P-VCS). Three key components empower P-VCS to balance the physical environment, cyber networks, and human and social factors, namely, an artificial system that is used to parametrically describe the physical VCS, two types of computational experiments that simulate the decision process and evaluate different strategies, and the parallel execution mechanism that is used to characterize the system operation. To demonstrate the feasibility of the framework, we take participant selection under traffic events as an application example. Experimental results illustrate that the P-VCS-based parallel learning strategy maintains competitive performance in all cases.