Reaction time of a snow removal project, which is defined as the duration between the time that snow begins accumulating at a road section and the time that snow is plowed, is a project performance indicator that can be used to evaluate the effectiveness of truck allocation strategies. While sensors, such as truck GPS (global positioning system) and weather RWIS (road weather information system), which track working hours and weather conditions, respectively, are used to collect large amounts of data, these data are not fully utilized to optimize reaction times of snow removal projects. In this research, the relationship between truck performance and weather information was analyzed. Sensor data were extracted, clustered, and refined; stochastic truck travelling speed and stochastic plowing speed were then mined and associated with the weather conditions of corresponding road sections. A data-driven, simulation-based optimization approach, which uses this mined data as input, was also developed to minimize reaction time. A practical case study of a project in Alberta, Canada, was conducted to validate and demonstrate the functionality of the proposed approach, which was simulated and optimized using the in-house simulation software, Simphony.NET. The resultant model allows project managers to predict the impact various truck allocation strategies on project time and cost to ensure that maximum project reaction time is minimized.