Impact assessments of highway construction work zones (CWZ) are mandated for all federally-funded highway infrastructure improvement projects. However, most existing approaches are ad-hoc or project-specific, thus incapable of being benchmarked for any particular spatial region. A novel multi-contextual approach to modeling the traffic impact of urban highway CWZ is proposed and tested in this paper. The proposed approach is unique because it models the impact of CWZ operations through a multi-contextual quantitative method using big data for improved accuracy. In this study, a machine-learning technique was adopted to predict long-term traffic flow rates and the corresponding truck percentages. Using these predicted values, stereotypical patterns of traffic volume-to-capacity ratios were created for typical urban nighttime closures. Third-order curve-fitting models to achieve potential work zone travel time delays in heavily trafficked large urban cores were then developed and validated. This study will greatly help state and local governments and the general traveling public in major cities know the potential traffic flow due to construction, thereby facilitating progress on highway improvement projects with the better-informed work zone traffic flow and thus improving safety and mobility in and between CWZs.