A spatial panel regression model to measure the effect of weather events on freight truck traffic

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).

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