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The project team reviewed over 280 research reports, journal articles, and other technical documents on risk and exposure, and in particular, pedestrian and bicyclist risk and exposure. The findings of this review were summarized in Chapters 2 through 5:
This chapter provides overall conclusions based on the findings summarized in the previous chapters.
Chapter 2 noted the importance of geographic scale in estimating exposure. In this report, exposure scale was defined as the most granular geographic level for which an exposure measure is desired. For example, is an exposure measure sought for a selected number of individual street crossings? Is an exposure measure sought for certain roadway segments? Or is an exposure measure sought for a defined areawide geography, such as TAZs, Census tracts, or Census block groups?
The exposure scale will likely influence how the theoretical definition of exposure can be operationalized in a practical way, since exposure data cannot be collected on all pedestrian, bicyclist, and motor vehicle movements at all locations at all times. Therefore, the exposure scale will determine what data source and methods are practical and feasible, and what exposure measures can be readily estimated or calculated from these data sources and methods.
Future methodological development in this project could benefit from the use of clear, unambiguous terms for various scales. The U.S. Census provides standardized terms and definitions for several different areawide geography scales. Similarly, the HCM provides terms and definitions for various scales of roadway system elements. To encourage widespread consistency and adoption by practitioners, existing terms and definitions should be drawn from widely used manuals, guidebooks, or references.
The units used in areawide exposure measures varied widely, despite many analyses using the same two national travel surveys (i.e., ACS and NHTS) as their base data source. Many analyses used the number of pedestrian and/or bicyclist trips. However, some analyses focused on only journey-to-work trips (directly from ACS data), whereas other analyses included total trips (from NHTS data or combining ACS and NHTS data). In other analyses, the pedestrian and bicyclist trips were converted to pedestrian and bicyclist miles of travel using estimated trip length data.
If areawide exposure measures use the same base travel survey data, one might expect an emerging consensus on the best approach for using the same or similar trip data to calculate areawide exposure measures. The number of pedestrian and bicyclist trips was a common exposure measure (see Table 1 in Chapter 3), but even with this measure, some analyses reported only on work trips whereas some reported on all trips. Even if consensus on a single areawide exposure measure cannot be achieved, future methodological development in this project should focus on identifying a few good measures that are designated as a best practice for estimating areawide exposure.
One of the most common approaches to estimate facility-specific exposure has been to combine pedestrian and bicyclist counts with estimation models, such that exposure can be estimated for all facilities within a defined geographic area (typically citywide). Even though many cities are now directly collecting pedestrian and bicyclist count data on an annual basis for multiple purposes, these counts are collected at a very limited number of locations. Therefore, estimation models must be used to estimate counts (and exposure) for all the remaining locations where pedestrians and bicyclists cannot be directly counted.
Direct demand models have been the most widely used models for facility-specific exposure estimation so far, and typically use regression analysis to relate directly measured counts to other measured attributes of the adjacent environment (e.g., land use and form, street type, etc.). Assuming that these measured attributes are available citywide, the regression model allows one to extend the sample of facility-specific counts to all facilities citywide. Aside from direct demand models, there are many estimation models in use, including regional travel demand models, GIS-based accessibility models, network analysis models, and simulation-based traffic models.
Given the wide variety of estimation models in place, it will be difficult to single out a single best practice for future methodological development in this project. This project could focus on providing additional guidance on the most common estimation model, the direct demand model, while still acknowledging and providing high-level details on other estimation model approaches.
Similar to areawide exposure measures, the units used in facility-specific exposure measures varied widely. This is also despite the fact that direct measurement and estimation models both produce the same basic data item: counts of pedestrians and/or bicyclists at a point or along a street segment for a defined time interval. As with areawide exposure measures, it may be difficult to achieve consensus on a single facility-specific exposure measure. However, there would be value in defining a few good measures that are designated as a best practice for estimating facility-specific exposure.
Based on the findings and conclusions in this Task 3 report, the TTI-led project team will develop a conceptual framework and design for risk exposure estimation at several different geographic scales (Task 4.A. of this project). The conceptual framework will be based on best practices as identified in this report, as well as other practices and processes that may be in development (such as those from NCHRP 17-73, Systemic Pedestrian Safety Analyses). The first draft of the conceptual framework will be available for review in May 2017.
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