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 manag...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.展开更多
Machine learning techniques such as artificial neural networks are seeing increased use in the examination of communication network research questions.Central to many of these research questions is the need to classif...Machine learning techniques such as artificial neural networks are seeing increased use in the examination of communication network research questions.Central to many of these research questions is the need to classify packets and improve visibility.Multi-Layer Perceptron(MLP)neural networks and Convolutional Neural Networks(CNNs)have been used to successfully identify individual packets.However,some datasets create instability in neural network models.Machine learning can also be subject to data injection and misclassification problems.In addition,when attempting to address complex communication network challenges,extremely high classification accuracy is required.Neural network ensembles can work towards minimizing or even eliminating some of these problems by comparing results from multiple models.After ensembles tuning,training time can be reduced,and a viable and effective architecture can be obtained.Because of their effectiveness,ensembles can be utilized to defend against data poisoning attacks attempting to create classification errors.In this work,ensemble tuning and several voting strategies are explored that consistently result in classification accuracy above 99%.In addition,ensembles are shown to be effective against these types of attack by maintaining accuracy above 98%.展开更多
文摘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.
文摘Machine learning techniques such as artificial neural networks are seeing increased use in the examination of communication network research questions.Central to many of these research questions is the need to classify packets and improve visibility.Multi-Layer Perceptron(MLP)neural networks and Convolutional Neural Networks(CNNs)have been used to successfully identify individual packets.However,some datasets create instability in neural network models.Machine learning can also be subject to data injection and misclassification problems.In addition,when attempting to address complex communication network challenges,extremely high classification accuracy is required.Neural network ensembles can work towards minimizing or even eliminating some of these problems by comparing results from multiple models.After ensembles tuning,training time can be reduced,and a viable and effective architecture can be obtained.Because of their effectiveness,ensembles can be utilized to defend against data poisoning attacks attempting to create classification errors.In this work,ensemble tuning and several voting strategies are explored that consistently result in classification accuracy above 99%.In addition,ensembles are shown to be effective against these types of attack by maintaining accuracy above 98%.
