Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

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 on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications.Originality/value–This study uses two artificial neural network methods,MLP and LSTM,to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers.This study will contribute to the deployment of emergency management and urban traffic navigation planning.

Evaluating Influential Factors on the Duration of Vehicle Fire Incidents Using Grey Relational Analysis

The duration of vehicle fire incidents has been closely associated with incidents loss. Understanding the influential priority of factors is significant to take targeted countermeasures for the managements. Based on the database from WSDOT （Washington Department of Transportation） in USA, we analyze the probability distribution of the vehicle fire accidents＇ duration. Then we classify the influential factors into the first-grade factors including three categories： time, incident type, operation and the second-grade factors including eight categories： quarter, week and day time, etc. Then GILA （grey relational analysis） model is applied to calculate grey relational grades of the influential factors. The results show that the most important factor of the first-grade factors is incident type, vehicles involved and agencies involved are the major factors among the second-grade factors.