2 Multimodal Accessibility Assessment This chapter reviews methods and findings of a multimodal accessibility analysis conducted for the study area. The purpose of the analysis is to evaluate network connectivity and gaps and identify barriers to safe multimodal transportation. Modes evaluated include walking, cycling, and transit using indices generated for a grid of cells covering the entire study area. The indices represent both accessibility and potential. Accessibility accounts for the availability of existing infrastructure to support these transportation modes and is addressed by incorporating factors such as sidewalk length, bicycle lane length, and roadway network density.
Potential is a function of both the relative population and the number of services that can be reached within a reasonable distance using the identified transportation mode.
The following section details the methodology used to generate grid cells, identify indices, and calculate bus travel time distance. The chapter concludes with results from the analysis and a summary of key findings.
2.1 Methods
A grid of cells was generated for the study area with length and width set to ¼ mile (see Figure 1) 1 . This produced 2,528 cells. For each grid cell, indices were calculated based on certain criteria within the cell, and within a ¼ mile radius of the center of the cell for walking and transit, and within a 1 mile radius of the center of the cell for cycling (see Table 1 through Table 8 for a list of criteria). To calculate the accessibility indices, each criterion was scaled between 0 and 1 enabling an equivalent comparison among factors. A discount of 50% was applied to each of the penalty criteria to reduce their overall impact. A discount factor was needed so that the penalty criteria did not overpower the overall accessibility score. However, too small a discount (e.g. 25%) tended to overstate the walkability or bikeability of an area. A discount of 50% was selected as it produced the most reasonable results in terms of overall score in relation to observed conditions. For the transit accessibility criterion, the walking time to the nearest bus stop was used. Several limitations to this analysis have been identified. The ¼ by ¼ cells used to develop the study area grid cannot account for the curvature of roadways or discretely capture property lines. Therefore, results should be considered at a regional scale, looking at the overall accessibility. In areas where development is currently occurring, time-lapses between GIS data availability, development, and the construction of infrastructure can result in an underestimation of accessibility.
1 This methodology is based on the Livable Polk Initiative by the Polk Transportation Planning Organization (TPO) in Polk County, Florida.
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