Multi-Task Seq2Seq Framework for Highway Incident Duration Prediction Incorporating Response Steps and Time Offsets

Highway traffic incident management is a dynamic and time-dependent process, and rapidly and accurately predicting its complete sequence of actions and corresponding time schedule is essential for improving the refinement and intelligence of traffic control systems. To address the limitations of existing studies that predominantly focus on predicting the total duration while lacking fine-grained modeling of the response procedure, this study proposed a multi-task sequence-to-sequence (Seq2Seq) framework based on a BERT encoder and Transformer decoder to jointly predict incident response steps and their associated time offsets. The model first leveraged a pretrained BERT to encode the incident type and alarm description text, followed by an autoregressive Transformer decoder that generated a sequence of response actions. An action-aware temporal prediction module was incorporated to predict the time offset of each step in parallel, and an adaptive weighted multitask loss was adopted to optimize both action classification and time regression tasks. Experiments based on 4128 real records of highway incident handling in Yunnan Province demonstrated that the proposed model achieved improved performance in duration prediction, outperforming baseline approaches in RMSE (18.05), MAE (14.69), MAPE (37.13%), MedAE (13.23), and SMAPE (33.55%). In addition, the model attained BLEU-4 and ROUGE-L scores of 62.33% and 82.04% in procedure text generation, which confirmed its capability to effectively learn procedural logic and temporal patterns from textual data and offered an interpretable decision-support approach for traffic incident duration prediction. The findings of this study could further support intelligent traffic management systems by enhancing incident response planning, real-time control strategies, and resource allocation for expressway operations.