Traffic information systems play an important role in the world as numerous people rely on the road transportation network for their most important daily functions. This dissertation proposes a general system architecture for processing traffic data and for disseminating accurate, timely traffic information via the internet. It also specifically addresses the challenges with estimating arterial traffic conditions using only data from GPS probe vehicles. GPS probe data promises to be the most ubiquitous source of traffic data for years to come as transit agencies decrease their investment in traditional fixed-location sensing infrastructure.

The dissertation introduces the architecture design and implementation of the Mobile Millennium system. A joint project between UC Berkeley, Nokia and Navteq, Mobile Millennium aggregates data from numerous sources, runs state of the art estimation and forecast algorithms, and provides timely traffic information to drivers and other targets. This system took over two years to build and the result is a robust framework for any traffic estimation researcher to access vast stores of data quickly and easily as well as test any number of estimation algorithms.

For estimating arterial traffic conditions, this dissertation proposes a hybrid approach leveraging advances in the fields of machine learning and traffic theory (based on hydrodynamic theory). This approach provides a foundation for any arterial traffic estimation model. A variety of model/algorithm approaches are presented, with one ultimately proving to be superior to the rest and the one that should be carried forward as research in this area continues.

The growing need of the driving public for accurate traffic information has spurred the deployment of large scale dedicated monitoring infrastructure systems, which mainly consist in the use of inductive loop detectors and video cameras. On-board electronic devices have been proposed as an alternative traffic sensing infrastructure, as they usually provide a cost-effective way to collect traffic data, leveraging existing communication infrastructure such as the cellular phone network. A traffic monitoring system based on GPS-enabled smartphones exploits the extensive coverage provided by the cellular network, the high accuracy in position and velocity measurements provided by GPS devices, and the existing infrastructure of the communication network. This article presents a field experiment nicknamed Mobile Century, which was conceived as a proof of concept of such a system. Mobile Century included 100 vehicles carrying a GPS-enabled Nokia N95 phone driving loops on a 10-mile stretch of I-880 near Union City, California, for 8 hours. Data were collected using virtual trip lines, which are geographical markers stored in the handset that probabilistically trigger position and speed updates when the handset crosses them. The proposed prototype system provided sufficient data for traffic monitoring purposes while managing the privacy of participants. The data obtained in the experiment were processed in real-time and successfully broadcast on the internet, demonstrating the feasibility of the proposed system for real-time traffic monitoring. Results suggest that a 2-3% penetration of cell phones in the driver population is enough to provide accurate measurements of the velocity of the traffic flow.

PATH Task ORder 6328 addresses the optimal deployment of traffic detectors on freeway to ensure that adequate information is collected at the lowest possible cost. The project team produced a study framework and tools that can be applied locally to test the sensitivity of traffic data quality to detectors location and spacing, and ultimately recommend a deployment plan.

Various types of traffic detectors, including loop detectors, radars, toll tag readers and video cameras are deployed on highways. They provide the data needed to run traffic management applications such as ramp metering control, bottleneck identification, and travel times estimation. However, few studies have systematically analyzed the data requirements of these applications in terms of detector spacing and location. In other words, the trade-offs between the cost of the detectors and their benefits for traffic estimation accuracy are not well known. As a result, most highway detectors are installed using ad hoc guidelines or on a case-by-base basis, rather than through the application of measurable objectives. This in turn makes it difficult for practitioners to justify equipment and maintenance expenditures, often slowing deployment.

The product of this research is two-fold. First, we developed a framework to study the sensitivity of traffic information to sensor location and spacing and reached general conclusions. Second, the team created practical tools to assist practitioners at the local level with optimal sensor deployment. These tools include recommendations for rural areas and an Excel-based model for urban areas.