Appraisal of Temporal Transferability of Cold Region Winter Weather Traffic Models for Major Highway Segments in Alberta Canada

• Main Authors: Hyuk-Jae Roh, Furqan A. Bhat, Prasanta K. Sahu, Satish Sharma, Babak Mehran, Ata M. Khan, Orlando Rodriguez
• Format: Article
• Language: English
• Published: MDPI AG 2019-03-01
• Series: Geosciences
• Subjects: weigh-in-motion; vehicle classification; winter weather traffic model; temporal transferability
• Online Access: https://www.mdpi.com/2076-3263/9/3/137

This paper evaluates the effect of inclement weather conditions on the travel demand for three classes of vehicles for a primary highway in the province of Alberta, Canada. The demand variables are passenger cars, trucks, and total traffic. It is well known from previous studies that adverse weather conditions such as low temperatures and heavy snowfall cause variation in traffic flow patterns. A winter weather model, based on the dummy variable regression model, was developed to quantify the variations in traffic volume due to snowfall and temperature changes. To establish the relationships, vehicular data was collected from six weigh-in-motion (WIM) sites, and the weather data associated with the WIM sites was collected from nearby weather stations. The study revealed that the variation in truck traffic, due to inclement weather conditions, was insignificant compared to variation in passenger car traffic. This study also investigated the temporal transferability of the developed winter weather model to test if a model can be applied irrespective of the time when it was developed. In addition, an attempt was made to check if the model coefficients could be optimized differently for different classes of traffic for estimating correct traffic variations. To evaluate transferability, the performance of both dummy variable regression and naive (without dummy variables) models was investigated. The results revealed that the dummy variable regression models show better performance for passenger car traffic and total traffic and naive winter weather models give better results for truck traffic.