  Construction waste hauling trucks (or `slag trucks') are among the most
commonly seen heavy-duty diesel vehicles in urban streets, which not only
produce significant carbon, NO$_{\textbf{x}}$ and PM$_{\textbf{2.5}}$ emissions
but are also a major source of on-road and on-site fugitive dust. Slag trucks
are subject to a series of spatial and temporal access restrictions by local
traffic and environmental policies. This paper addresses the practical problem
of predicting levels of slag truck activity at a city scale during heavy
pollution episodes, such that environmental law enforcement units can take
timely and proactive measures against localized truck aggregation. A deep
ensemble learning framework (coined AI-Truck) is designed, which employs a soft
vote integrator that utilizes Bi-LSTM, TCN, STGCN, and PDFormer as base
classifiers. AI-Truck employs a combination of downsampling and weighted loss
is employed to address sample imbalance, and utilizes truck trajectories to
extract more accurate and effective geographic features. The framework was
deployed for truck activity prediction at a resolution of
1km$\times$1km$\times$0.5h, in a 255 km$^{\textbf{2}}$ area in Chengdu, China.
As a classifier, AI-Truck achieves a macro F1 of 0.747 in predicting levels of
slag truck activity for 0.5-h prediction time length, and enables personnel to
spot high-activity locations 1.5 hrs ahead with over 80\% accuracy.
