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Twelve months of automated hourly pedestrian counts in downtown Montpelier, Vermont (population 8,035), were analyzed along with weather data (temperature, relative humidity, precipitation, and wind) to determine the factors affecting count variability. This study is unique in that a large amount of data in a single location was collected in a locale with an extreme range of weather conditions. Results indicate consistent patterns in relative volumes by hour of the day and month of the year that show that good adjustment factors can be developed to use with time-limited counts to estimate usage and pedestrian exposure to accidents. Predictive relationships were found between weather variables, season, and pedestrian volumes (30% of the variation is accounted for). Consistent hourly patterns within a day and the consistency of day type (weekday or Saturday versus holiday or Sunday) suggest that correction factors and forecasting methods are feasible for pedestrian traffic volumes. The results indicate that weather such as cold temperatures or precipitation consistently reduces aggregate levels of walking by only a moderate amount (less than 20%). Precipitation and season are found to affect pedestrian levels even when time of day and day of week are controlled, but other, larger, unmeasured factors are at play.
Aultman-Hall et al. (Thu,) studied this question.