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The literature is rich in travel time prediction because of its importance in intelligent transportation systems. Despite the proliferation of advanced methodologies, modeling the uncertainty of traffic conditions is still a challenge, especially during congested situations. Travel time reliability associated with its time-dependent variation provides a way to measure the system performance and has received extensive attention in recent years. In practice, one of the measures for travel time reliability is the identification of prediction intervals (PIs). The PI measurement has many potential applications in the development of systems that aim at disseminating real-time traffic information to travelers. From a management point of view, the PIs forecast the unreliable traffic periods and enable the selection of proper strategies to avoid or release possible traffic congestion. The generalized autoregressive conditional heteroscedasticity …