摘要
Purpose–Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents.Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management.Previous studies have proposed models for traffic incident duration prediction;however,most of these studies focus on the total duration and could not update prediction results in real-time.From a traveler’s perspective,the relevant factor is the residual duration of the impact of the traffic incident.Besides,few(if any)studies have used dynamic trafficflow parameters in the prediction models.This paper aims to propose a framework tofill these gaps.Design/methodology/approach–This paper proposes a framework based on the multi-layer perception(MLP)and long short-term memory(LSTM)model.The proposed methodology integrates traffic incident-related factors and real-time trafficflow parameters to predict the residual traffic incident duration.To validate the effectiveness of the framework,traffic incident data and trafficflow data from Shanghai Zhonghuan Expressway are used for modeling training and testing.Findings–Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best.The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75.These indicators demonstrated that the model is appropriate for this study context.The model provides new insights into traffic incident duration prediction.Research limitations/implications–The incident samples applied by this study might not be enough and the variables are not abundant.The number of injuries and casualties,more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively.The framework needs to be further validated through a sufficiently large number of variables and locations.Practical implications–The framework can help reduce the impacts of incidents
