Using Mobile Probes to Inform and Measure the Effectiveness of Macroscoopic Traffic Control Strategies on Urban Networks

Urban traffic congestion is a problem that plagues many cities in the United States. Devising and testing strategies to alleviate this congestion is especially challenging due to the difficulty of modeling these complex urban traffic networks. However, recent work has shown that these complicated systems can be modeled in relatively simple ways by leveraging consistent relationships that exist between network-wide averages of pertinent traffic properties, such as average network flow, network density and the rate at which trips are completed. Using these 'macroscopic' traffic models, various control strategies can be developed to mitigate congestion and improve network performance. However, the effectiveness of many of these strategies depends on the ability to estimate traffic conditions on the network in real-time. This jointly proposed research between Penn State and Virginia Tech seeks to investigate how real-time mobile vehicle probes can be combined with macroscopic urban traffic models to implement more efficient network-wide traffic control strategies. Additionally, this work will examine how the effectiveness of these strategies can be directly measured in the field using only mobile vehicle probe data. These two efforts can lead to more efficient control of downtown traffic networks and a reduction in vehicular delay during rush hour periods.