A node optimization model based on the spatiotemporal characteristics of the road network for urban traffic mobile crowd sensing

Abstract

Urban traffic mobile crowd sensing (Urban Traffic MCS) has emerged as a new effective paradigm of sensing and collecting data by means of vehicles equipped with various sensors in urban areas. In an Urban Traffic MCS system, the utility directly reflects the effectiveness of the sensing results, and it is essential to maximize the utility of the collected data. Some studies have shown that utility can be effectively improved by optimizing the selection of sensing nodes. However, most previous research has considered only the coverage and critical links of the road network while neglecting the spatiotemporal characteristics of the traffic flow, although the latter are essential for node selection optimization and significantly impact the utility of Urban Traffic MCS. Therefore, most existing methods are not suitable for Urban Traffic MCS systems. In this paper, a novel node optimization model based on the spatiotemporal characteristics of the road network is proposed. First, we introduce the Urban Traffic MCS system, and dynamic accessibility is introduced to describe the spatiotemporal characteristics of the whole road network. Then, the utility function for Urban Traffic MCS is redefined based on the effective coverage and dynamic accessibility to consider both the topological structure of the road network and the dynamic changes in traffic flow. On this basis, a node selection method with the aim of maximizing the utility of Urban Traffic MCS is proposed. Finally, the results of simulation experiments show that the node selection method in this paper can effectively achieve increased utility for an Urban Traffic MCS system.