<p>This paper introduces a novel Logistic Burr mixture autoregressive model (LBMAR) for two key bus scheduling and operation problems. First, the LBMAR model effectively analyzes and quantifies the individual and joint impacts of contributing factors on the time-varying bimodal distribution of bus section travel time. Unlike traditional approaches that examine factors between bus sections, our study investigates their impact on the validated bimodal variations in travel time for various operating environments of bus sections. Second, the LBMAR model jointly accounts for contributing factors in point and interval predictions of bus section travel time. Validated using six months of data and 169 bus section travel time observations with passenger information, we meticulously analyze four key contributing factors: day of the week, time of the day, bus occupancy changes, and neighboring bus section's travel time variability. The results show a remarkable correlation between bus occupancy changes and bimodal variations in bus section travel time. Moreover, the LBMAR model exhibits significant improvements in point and interval predictions, particularly for high to medium levels of variations in bus section travel time. These findings have profound implications for real-time bus operation management. By effectively identifying, quantifying, and managing the impact of these contributing factors, bus transportation operators can make informed decisions to optimize their operations, resulting in more efficient and reliable services.</p>