Construction waste hauling (CWH) trucks are a significant source of air pollution and particulate emissions in urban environments, prompting strict regulatory controls and monitoring. Accurate prediction of their transportation activities, including destinations and arrival times, is critical for improving environmental management and regulatory enforcement. In this study, we present a probabilistic approach that captures the complex spatiotemporal dynamics inherent in the transportation behavior of CWH trucks using the input–output hidden Markov model (IOHMM). This model leverages contextual factors such as historical trajectories, weather conditions, and time‐based patterns to make real‐time predictions of transportation activities with high accuracy. The model is applied to a dataset of 1000 CWH trucks collected over a 5‐month period in Chengdu, China. The model’s performance was evaluated against several baseline methods, including traditional Markov chains, long short‐term memory (LSTM) networks, and DeepMove, an attention‐based deep learning model. Results demonstrated that the IOHMM outperforms these models in both prediction accuracy and interpretability. Specifically, the IOHMM achieved an average destination prediction accuracy of 51.2%, compared to 47.9% for DeepMove, 43.1% for LSTM, and 39.4% for Markov chains. In terms of arrival time prediction, the IOHMM obtained an accuracy of 38.8%, outperforming all other models, with DeepMove at 36.8%, LSTM at 35.6%, and Markov chains at 27.5%. These findings highlight the IOHMM’s ability to effectively incorporate both spatial and temporal factors in predicting transportation dynamics, providing a powerful tool for regulatory agencies to improve real‐time interventions and environmental management of heavy‐duty vehicles.
Liu et al. (Thu,) studied this question.