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

机译：在加拿大艾伯塔省主要公路路段的寒冷地区冬季天气交通模型的时间传递性评估

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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.
机译：本文评估了恶劣天气条件对加拿大艾伯塔省主要公路上三类车辆出行需求的影响。需求变量是乘用车，卡车和总流量。从先前的研究中众所周知，低温和大雪等不利天气条件会导致交通流型的变化。开发了基于虚拟变量回归模型的冬季天气模型，以量化由于降雪和温度变化导致的交通量变化。为了建立关系，从六个动态称重（WIM）站点收集了车辆数据，并从附近的气象站收集了与WIM站点关联的天气数据。研究表明，由于恶劣的天气条件，卡车交通的变化与乘用车交通的变化相比微不足道。这项研究还调查了已开发的冬季天气模型的时间转移性，以测试该模型是否可以应用而与开发时间无关。此外，尝试检查模型系数是否可以针对不同的交通类别进行不同的优化，以估计正确的交通变化。为了评估可传递性，研究了虚拟变量回归模型和纯模型（无虚拟变量）的性能。结果表明，虚拟变量回归模型对乘用车和总交通量显示出更好的性能，而天真的冬季天气模型对卡车交通量提供了更好的结果。

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