The quantitative prediction and understanding of human performances in the responses to speech warnings is an essential component to improve warning effectiveness. Queuing network-model human processor (QN-MHP), as a computational architecture, enables researchers to model dual-task information processing. The current study enhanced QN-MHP by modelling the effect of loudness and semantics on human responses to speech warning messages. The model predictions of crash rate were validated with two empirical studies in collision warning systems with resultant R squares of 0.73 and 0.77, respectively. The developed mathematical model could be further utilized in optimizing the design of speech warnings to achieve most safety benefits.
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