Understanding loading patterns of construction waste hauling trucks: triangulation between big quantitative and informative qualitative data.

This research aims to understand the loading patterns of construction waste hauling trucks in Hong Kong and the factors shaping these patterns. It does so by triangulating the analytical results of big data collected from secondary sources and qualitative data from interviews. Firstly, based on the literature review and our engagement with the industry, four hypotheses on the nexus between "loading pattern" and the factors of (1) vehicle, (2) permitted gross vehicle weight, (3) commodity, and (4) ownership. Then, the hypotheses are tested with combined null hypothesis significance test and effect size measure using 13 million construction waste transportation records. Finally, the results are triangulated with interview data to empirically validate the nexus while providing sensible explanations to them. We find that the four hypotheses are all supported. Distinct loading patterns are presented by different types of (1) construction waste hauling trucks with different (2) permitted gross vehicle weights, (3) types of construction waste transported, and (4) ownership. These findings provide valuable evidence for more targeted interventions, e.g., introducing public policies or hauling operation optimization through the avoidance of excessive underloading or overloading.