To fully exploit the benefits of the new generation of Intelligent Transportation Systems now widely under development, including applications for performance measurement and homeland security, more accurate and appropriate real-time traffic data need to be collected from the urban highway transportation network and communicated to traffic management centers, traffic operations personnel, travelers, and other agencies. This research proposes to deploy and investigate at a corridor level anonymous vehicle tracking techniques that have been pioneered by the authors in previous PATH research. The objective of the research is to investigate and demonstrate real-time freeway and arterial performance measurement in a major real-world setting. This project represents a planned continuation of current PATH Task Order (TO) 4159 on Anonymous Vehicle Tracking for Real-Time Freeway and Arterial Street Performance Measurement. TO 4159 emphasized microscopic simulation in conjunction with individual intersection and freeway segment field implementations to develop and assess methods for tracking vehicles across multiple detector stations in a traffic network, based on real-time acquisition of vehicle inductive signatures, in order to provide improved freeway and arterial (and transit) performance measures to the Caltrans PeMS. Ultimately, however, the utility and effectiveness of such new network-based methods can only be judged through large-scale field implementation, as proposed here.

Though most existing signal systems include some optimization schemes, their performance suffers from inaccurate route travel prediction due to the limitations of data. In the Persistent Traffic Cookies (PTC) system being researched in UC Irvine, the path-based variables including path flow and path travel time can be obtained from the historical trip tables and current movement information stored in individual vehicles. The information is automatically updated by intersection wireless hardware every time the drivers return to those locations, and can be read by the same hardware, unless the drivers choose to withhold it. With such path variables data diary, the improved traffic control schemes can be introduced for a group of intersections called a sub-network. For network-level control, the path flows are estimated from the inference of individual vehicle movement to capture a movement along several intersections. The future path flow is predicted based on the current path flow and historical data. Based on it, we present a scheme to group a series of intersections as a sub-network for signal optimization. First, the interaction between any two intersections is estimated by the path flow between them. After choosing a Critical Intersection in a network, a group of intersections having a certain interaction with it is selected and formed a sub-network. The signal optimization in a sub-network is accomplished by a Mixed Integer Linear Problem with the objective to minimize the total delay and a set of constraints. To our knowledge, this is the first real-time traffic control optimization scheme developed using travel diaries.