This database would meet a variety of data needs for different stakeholder groups. One of its principal purposes would be to allow safety professionals at the state and local levels to estimate pedestrian exposure to risk at specific sites.

Since exposure data is essentially equivalent to facility usage data, a pedestrian exposure data would be used for many purposes beyond risk analysis. Facility usage data might be used by municipalities to pinpoint new infrastructure needs, or to determine whether new infrastructure encourages more pedestrian activity. Facility usage data might also be used by advocacy groups as a means to promote new facility investments.

If the database includes information beyond pedestrian volumes, such as facility characteristics (e.g. the availability of sidewalks and intersection crossings) or planning variables (e.g. land uses and population densities), it may be used as a means to improve pedestrian demand modeling techniques or to investigate the relationship between pedestrian environmental quality and pedestrian demand. Furthermore, if facility funding data are included, the database may also be used as a means to track spending on pedestrian projects.

In short, there is a wide range of usage for a pedestrian volume database. In designing the database, it is important to maximize its utility to pedestrian stakeholder groups while recognizing the costs associated with increased complexity.

Creation of a pedestrian volume database for the state of California involves several major decision points. This report examines these decision points and provides a range of database approaches given different funding and institutional constraints, and describes the challenges that will need to be addressed in the database development process.

Chapter 2 discusses the technical and institutional challenges inherent in creation of a pedestrian exposure database. Chapter 3 discusses the need for an inventory of the pedestrian network as a starting point for the database, and present two existing sources for the network. Chapter 4 presents a range of approaches to data collection process, and suggests data points that might be appropriate for inclusion in the data collection process. Chapter 5 discusses how pedestrian demand modeling might be used to estimate pedestrian volumes with limited data inputs. Chapter 6 summarizes the report and provides recommendations for future development of the database.