Real-time Prediction of Construction Waste Hauling Trucks’ Transportation Activities: An Input‒Output Hidden Markov Modelling Approach

Abstract 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 access restrictions because they not only produce significant NOx and PM emissions but also causes on-road fugitive dust. To limit their environmental impact, specific spatial-temporal access restrictions are implemented, and timely and accurate detection of possible infringements has become a key challenge facing many municipal managers. To address this challenge, we propose a prediction method based on an interpretable activity-based model, input-output hidden Markov model (IOHMM), and apply it to trajectories of 300 CWHTs in Chengdu, China for evaluation. Contextual factors including weather conditions, recent transportation activities, and historical work statistics are considered in the model to improve its prediction power. Results show that the IOHMM has an average percentage of 64% for correctly predicting the destination and an average \({R}^{2}\) value of 69% for predicting the duration of the transportation activity. These values are higher than the results of the baseline models, including Markov chains, linear regression, and long short-term memory. To investigate the factors that influence the predictability of CWHTs’ transportation activities, linear regression models are constructed using the percentage of correct destination predictions and \({R}^{2}\) of the duration prediction model as the dependent variables, respectively. Our findings indicate that the number of active days and the proportion of days with nighttime activities are positively correlated with the prediction accuracy of both models. The average number of daily transportation activities and the number of different staying activities have positive and negative effects on the prediction accuracy of the destination model, respectively. In conclusion, the proposed model holds promise in assisting government agencies by predicting the next transportation activities of CWHTs and detecting infringements in a timely and effective manner.