Is dynamic traffic sensor network profitable for network-level real-time information prediction?

Abstract Optimally deploying sensors into a complex highway network can significantly enhance the socio-economic benefits through accurately providing system users and operators with the latest traffic state information. This paper presents a linear optimization framework for designing a sensor network for travel time surveillance. The presented framework consists of a traditional static network model and a novel dynamic network model as well as an integer cut-based speed-up solution approach. The aims of the study are twofold. First, the proposed optimization model maximizes the network-level travel time surveillance benefits by taking into account the values of real-time measurements induced from both temporal and spatial traffic state patterns. Second, the mechanism of sensor relocation operations is designed and incorporated into the proposed model. This mechanism enables one to evaluate whether appropriately relocating sensors can optimize the utilization of the surveillance resources and further enhance the surveillance benefits over a given time horizon. We applied the proposed optimization framework to a mixed arterial-freeway commuting network located in Washington. D.C.-Baltimore metropolitan area. The study empirically revealed and illustrated two interesting findings: (1) relocation operations do bring additional surveillance benefits on travel time prediction, and the marginal benefit is highly correlated with the sensor fleet size; (2) spatial measurement is not a trivial part in travel time prediction, and one should fully recognize the effectiveness of temporal and spatial measurements when designing a traffic sensor network for real-time travel time surveillance.