The research aims to develop vehicle trip reduction rates for Traffic Impact Analysis (TIA) reviews by collecting and analyzing data from 21 sites across nine cities in North Carolina. Factors such as land use, infrastructure density, and proximity to trip generators guided the selection of sites. Data collection utilized ground-based cameras, manual counting, and mobile device data to capture variations in travel behavior.
Overall, 85.7% of all trips observed were made by vehicles, with walking (13.6%) as the most common non-auto mode. Public transit and cycling were minimally observed. A comparison of field data and Institute of Transportation Engineers (ITE) trip generation estimates found that field data reported fewer trips, especially in the AM, reflecting localized variations in travel behavior not captured by ITE data and models. The study also identifies proximity to the central business district (CBD) as a significant factor influencing multimodal usage and walkability. Sites with higher Walk Scores (between 50 and 90) exhibited more multimodal activity. Locations with higher walkability and those closer to or within the CBD are more likely to have a higher proportion of non-auto trips, while more remote areas with lower Walk Scores tend to rely less on multimodal trips. These findings underscore the importance of built-environment characteristics in promoting sustainable, multimodal transportation and provide recommendations for incorporating multimodal considerations into TIA reviews.
The research team recommends considerations of the Walk Score and location relative to the Central Business District (CBD) for sites being reviewed for a TIA. As a continuous variable on a scale of 0 to 100, the Walk Score showed promise as an indicator of multimodal trips in the sites evaluated for this study. As an example of Walk Score and non-auto trip percentages, at the four sites with Walk Scores rounded to 50, the non-auto percentage was 8% (AM) and 11% (PM), while the five sites with Walk Scores rounded to 90, the non-auto percentage was 32% (AM) and 31% (PM). Considering the locations relative to the CBD as a binary variable of proximity to the CBD, the average non-auto percentage observed was 27% (AM) and 30% (PM) for sites in or adjacent to the CBD, and 6% (AM) and 8% (PM) for sites outside or not adjacent to the CBD. To refine these estimates, the research team also recommends additional data collection at sites in and adjacent to CBDs to expand on the 11 sites observed in this study.
