The load factor is an essential indicator of vehicle utilization efficiency. This paper is the first attempt to explore the factors impacting Less-than-Truckload (LTL) freight load factors at a disaggregated level to the best of our knowledge. The paper analyzes the operation data provided by a leading LTL logistics company in China, using the beta regression finite mixture models. The results show that the under-loaded tours and over-loaded tours behave differently in terms of load factor determination. All variables' impacts vary across the different components, confirming the need to analyze these two scenarios separately using finite mixture models. For under-loaded tours, high shipping demand and the use of small vehicles are always positively and significantly associated with the load factor. For over-loaded LTL tours, the effects of transportation distance and leased vehicles are always positive and significant. Finally, implications for operations strategies and public policies are discussed based on the estimated results. These findings shed light on an in-depth understanding of LTL freight load factors and provide essential references for LTL logistics companies, transportation planners, policymakers, and researchers.