As reported in the literature for the applications of intelligent transportation systems with traffic detectors, various missing data patterns are frequently observed in such systems and may dramatically degrade their performance. This study presents two imputation approaches for contending with the missing data issues in travel time prediction. The first model is based on the concept of multiple imputation technique to predict directly the travel times under various missing data patterns. The second model that serves as the supplemental component is to estimate the missing detector values using neighboring detector data and historical traffic patterns. Both models have been incorporated with reliability indicators so as to assess the quality of imputed data and its applicability for use in prediction. The numerical example based on 10 roadside detectors on I-70 in Maryland has demonstrated that both developed models outperformed existing methods and offer the potential for field implementation.