@article{ref1,
title="Varying influences of the built environment on daily and hourly pedestrian crossing volumes at signalized intersections estimated from traffic signal controller event data",
journal="Journal of transport geography",
year="2021",
author="Singleton, Patrick A. and Park, Keunhyun and Lee, Doo Hong",
volume="93",
number="",
pages="e103067-e103067",
abstract="Direct-demand models of pedestrian volumes (identifying relationships with built environment characteristics) require pedestrian data, typically from short-duration manual counts at a limited number of locations. We overcome these limitations using a novel source of pedestrian data: estimated pedestrian crossing volumes based on push-button event data recorded in traffic signal controller logs. These continuous data allow us to study more sites (1494 signalized intersections throughout Utah, US) over a much longer time period (one year) than in previous models, including the ability to detect variations across days-of-week and times-of-day. Specifically, we develop direct demand (log-linear regression) models that represent relationships between built environment variables (calculated at ¼- and ½-mile network buffers) and annual average daily and hourly pedestrian metrics. We control spatial autocorrelation through the use of spatial error models. All results confirm theorized relationships: There is more pedestrian activity at intersections with greater population and employment densities, a larger proportion of commercial and residential land uses, more connected street networks, more nearby services and amenities, and in lower-income neighborhoods with larger households. Notably, we also find relevant day-of-week and time-of-day differences. For example, schools attract pedestrian activity, but only on weekdays during daytime hours, and the coefficient for places of worship is higher in the weekend model. K-fold cross-validation results show the predictive power of our models. <br><br>RESULTS demonstrate the value of these novel pedestrian signal data for planning purposes and offer support for built environment interventions and land use policies to encourage walkable communities.<p /> <p>Language: en</p>",
language="en",
issn="0966-6923",
doi="10.1016/j.jtrangeo.2021.103067",
url="http://dx.doi.org/10.1016/j.jtrangeo.2021.103067"
}