Truck drivers adhere to delivery schedules making them more likely to reroute rather than cancel a trip when faced with inclement weather. While previous studies modeled the direct effects of adverse weather on total traffic volumes, none considered the particular implications for trucks. The ability to predict spatial and temporal shifts in truck traffic resulting from adverse weather is novel and useful for decision makers tasked with long-range freight planning and for the trucking industry. With deeper insights into rerouting around adverse weather, the trucking industry will be able to more efficiently plan and accurately estimate billable miles. Thus, this study applied dynamic spatial panel regression that captures rerouting behavior of trucks due to adverse weather conditions. Results showed that changes in truck traffic volume due to adverse weather conditions, e.g. surface runoff, snow mass, and humidity, exhibited spatial (direct and indirect) and temporal shifts (short and long term effects).

中文翻译：

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测量天气事件对货车交通影响的空间面板回归模型

卡车司机遵守交货时间表，这使他们在遇到恶劣天气时更有可能改变路线而不是取消行程。虽然之前的研究模拟了恶劣天气对总交通量的直接影响，但没有人考虑对卡车的特殊影响。预测由恶劣天气导致的卡车交通空间和时间变化的能力对于负责长期货运规划和卡车运输行业的决策者来说是新颖且有用的。随着对恶劣天气改道的深入了解，卡车运输业将能够更有效地规划和准确估算计费里程。因此，本研究应用动态空间面板回归来捕捉由于恶劣天气条件导致的卡车改道行为。