Hampton Roads Transportation Planning OrganizationHRTPO
Home » News » E Newsletter Articles » Using Accessibility Based Travel Odds Factors as a Performance Measure for the Coordination of Transportation and Land Use to Improve Non Driver Accessibility
Share
Using Accessibility Based Travel Odds Factors as a Performance Measure for the Coordination of Transportation and Land Use to Improve Non Driver Accessibility

Using Accessibility Based Travel Odds Factors as a Performance Measure for the Coordination of Transportation and Land Use to Improve Non Driver Accessibility

At the January 2011 annual meeting of the Transportation Research Board (TRB) in Washington, DC, staff presented a method it developed of measuring the performance of local government in improving non-driver accessibility by coordinating land use and transportation.

Given the widespread use of automobiles for personal transportation in the U.S.—with the associated dispersion of land uses and limited scope of public transportation—the travel of non-drivers is limited in many American municipalities.  On a given day, non-drivers often do not leave their homes at all.


Although some governments have tried to improve non-driver accessibility by coordinating land use and transportation—specifically improving the proximity of bus stops, activity locations, and residences favored by non-drivers—no existing performance measurement technique for these government efforts toward non-drivers was available. 

The technique developed by staff measures accessibility and then uses a model to calculate the impact  that accessibility is expected to have on non-driver travel, called a “travel odds factor”.  A better-walking non-driver living in an area with destinations and bus service within walking distance, and therefore—for example—a travel odds factor of 3.0, is expected to have 3 times the odds of traveling (i.e. leaving the home) on a given day as compared to living in a rural area.  The average travel odds factor of a locality is a measure of that locality’s performance in coordinating transportation and land use to improve non-driver accessibility.

Using the coefficients from the travel model, the impact of bus stops and activity locations on the travel odds of better-walking non-drivers can be calculated for Hampton Roads:

Using 1) the calculated travel odds factors, and 2) the number of non-drivers living in each block, average travel odds factors were calculated for each city/county.  The resulting performance scores—average travel odds factors—for each local government are shown:

To improve performance, local government may further apply this technique to identify the best neighborhoods on which to focus improvement efforts.  These promising neighborhoods (and the technique) are shown in
http://www.hrtpo.org/Documents/Reports/2009/T09-02_NonDriverOppAnalysis.pdf.


Local government can improve non-driver accessibility in these neighborhoods—and thereby efficiently improve non-driver accessibility in its jurisdiction—by using its zoning and budgetary powers to modify land use and expand bus service to achieve the co-positioning of residences favored by non-drivers, activity locations, and bus stops.

Latest News
September 24, 2019 - Kathlene Grauberger, Transportation Planner
October 8, 2019 - Robert B. Case, PhD, PE
Archive