HOT-package: Health-Oriented Transportation (HOT) Model
In GHI-UW/HOT: The Health-Oriented Transportation (HOT) Model

Description Details Author(s) References See Also Examples

HOT is a statistical model that makes projections about attributable health risks from (in)active travel data. Based on population and travel scenarios, the model has been used to calculate the health impacts of walking and bicycling short distances usually traveled by car or driving low-emission automobiles. Please Cite: Younkin, S. (2019).

The model uses comparative risk assessment through which it formulates a change in the disease burden, resulting from the shift in the exposure distribution from a baseline scenario to an alternative scenario.

HOT characterizes exposure distributions in several ways:

– Physical Activity – Described as quintiles of a log-normal distribution on the basis of the mean weekly active transport time per person, its standard deviation and coefficient of variation (the standard deviation divided by the mean), mean weekly non-transport physical activity, and the ratio between bicycling and walking times. The activity times were multiplied by weights to give metabolic-equivalent task hours (METS), which reflect energy expenditures for walking and cycling at average speeds and for performing occupational tasks.

Descriptive statistics were obtained from published research on walking and bicycling speeds and analysis of travel and health surveys with large probability samples for the Bay Area. HOT characterizes exposure distributions in several ways: – Physical Activity – Described as quintiles of a log-normal distribution on the basis of the mean weekly active transport time per person, its standard deviation and coefficient of variation (the standard deviation divided by the mean), mean weekly non-transport physical activity, and the ratio between bicycling and walking times. The activity times were multiplied by weights to give metabolic-equivalent task hours (METS), which reflect energy expenditures for walking and cycling at average speeds and for performing occupational tasks. Time spent in either mode (walking or bicycling) was taken from London travel survey data.

Samuel G. Younkin syounkin@wisc.edu

https://ghi.wisc.edu/health-climate-cities/health-oriented-transportation/

CRA, getTravelActivity, getMeans

# Parametric
HOT:::CRA.function(meanlog.baseline = log(10), meanlog.scenario = log(11), type = "parametric")
HOT:::CRA.function(participation.baseline = 0.75, participation.scenario = 0.74, type = "parametric")

# Non-parametric
n <- 1e4
P <- qlnorm(p = 1/n*(1:(n-1)), meanlog = log(10))
Q <- qlnorm(p = 1/n*(1:(n-1)), meanlog = log(11))
HOT:::CRA.function(P = P, Q = Q, type = "non-parametric")
HOT:::CRA.function(meanlog.baseline = log(10), meanlog.scenario = log(11), participation.baseline = 0, participation.scenario = 0, meanlog.Tc = 1e-6, n = n)