This paper is based on the past several years of research carried out by transportation research group at the University of Regina, Canada to understand the impact of cold and snow on traffic volume during winter months in Canada. A detailed investigation of highway traffic variations i.e. total traffic, passenger car and truck traffic with severity of cold, the amount of snow, and various combinations of cold and snow intensities is presented here. These investigations were conducted using hourly traffic data from 350 permanent traffic counter sites, 6 Weigh in Motion sites and weather data from 598 weather stations located in the province of Alberta, Canada, from 1995 to 2010. Multiple regression analysis is used in the modeling process. The model parameters include three sets of variables: the amount of snowfall as a quantitative variable, categorized cold as a dummy variable, and an interaction variable formed by the product of these two variables. The study results indicate that the association of highway traffic flow with cold and snow varies with day of week, hour of day, and severity of weather conditions. A reduction of 1% to 2% in total traffic volume for each centimeter of snowfall is observed when the mean temperature is above 0°C. For the days with zero precipitation, reductions in total traffic volume due to mild and severe cold are 1% and 31%, respectively. An additional reduction of 0.5% to 3% per centimeter of snowfall results when snowfall occurs during severe cold conditions. Traffic volumes decreased with increase in the severity of cold temperatures. During extremely cold weather (below -25°C), the average winter daily traffic volume was reduced by about 30%. Weekend traffic volumes were more susceptible to cold than weekday numbers for all types of highways. Commuter and regional commuter roads experienced the lowest variations with cold. The impact of cold was very high for recreational roads and moderate for rural, long distance roads. This study also shows a clear indication in the reduction in daily traffic volumes due to snow (reductions between 7% and 17% for each centimeter of snowfall were observed). When individual vehicle classes were analyzed, it is found that passenger cars are more vulnerable to adverse weather conditions than trucks. Trucks are not as greatly affected as passenger cars by adverse weather conditions. Interestingly, the modeling results for one of the study sites reveal that higher truck traffic volumes can result during heavy snowfall (or other adverse weather conditions) in winter months due to shifting of trucks from secondary highways with poor winter maintenance to primary highways. This is contradictory to observations from other similar studies in the literature. The traffic-weather models developed from the past studies have two majorapplications in transportation engineering. These models have been tested for: (1) imputation of missing traffic data during winter months and (2) estimation of annual average daily traffic (AADT) from short-term traffic counts undertaken during winter months. The imputation efficiency of the traffic-weather models was compared with the most efficient imputation methods used by highway agencies. A new factor approach, incorporating weather conditions, was proposed in this paper to estimate AADT from short duration counts taken during winter months. Therefore, the developed traffic-weather models can be recommended to highway agencies to impute missing traffic data and to estimate AADT from short duration counts taken during winter months.
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Open Access
Jan 1, 2015
Joana Guimaraes + 2
