Competing risk mixture model and text analysis for sequential incident duration prediction

• Competing risks mixture model is used in the traffic incident duration analysis and prediction.
• Topic modeling converts textual data from traffic incident log into attributes of a hazard-based model.
• Different factors significantly affect the duration of incidents with different clearance methods.
• Prediction accuracy gradually improved as more messages arrived.
• The prediction results of the mixture model are better than that of nonmixture model.

Predicting the duration of traffic incidents sequentially during the incident clearance period is helpful in deploying efficient measures and minimizing traffic congestion related to such incidents. This study proposes a competing risk mixture hazard-based model to analyze the effect of various factors on traffic incident duration and predict the duration sequentially. First, topic modeling, a text analysis technique, is used to process the textual features of the traffic incident to extract time-dependent topics. Given four specific clearance methods and the uncertainty of these methods when used during traffic incidents, the proposed mixture model uses the multinomial logistic model and parametric hazard-based model to assess the influence of covariates on the probability of clearance methods and on the duration of the incident. Subsequently, the performance of estimated mixture model in sequentially predicting the incident duration is compared with that of the non-mixture model. The prediction results show that the presented mixture model outperforms the non-mixture model.