Predicting the Transportation Activities of Construction Waste Hauling
  Trucks: An Input-Output Hidden Markov Approach
Hongtai Yang Boyi Lei Ke Han Luna Liu
Construction waste hauling trucks (CWHTs), as one of the most commonly seen
heavy-duty vehicles in major cities around the globe, are usually subject to a
series of regulations and spatial-temporal access restrictions because they not
only produce significant NOx and PM emissions but also causes on-road fugitive
dust. The timely and accurate prediction of CWHTs' destinations and dwell times
play a key role in effective environmental management. To address this
challenge, we propose a prediction method based on an interpretable
activity-based model, input-output hidden Markov model (IOHMM), and validate it
on 300 CWHTs in Chengdu, China. Contextual factors are considered in the model
to improve its prediction power. Results show that the IOHMM outperforms
several baseline models, including Markov chains, linear regression, and long
short-term memory. Factors influencing the predictability of CWHTs'
transportation activities are also explored using linear regression models.
Results suggest the proposed model holds promise in assisting authorities by
predicting the upcoming transportation activities of CWHTs and administering
intervention in a timely and effective manner.