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
Several factors, such as cold exposure, aging, the number of experiences and viral infection, have been shown to affect learning ability in different organisms. Wol- bachia has been found worldwide as an arthropod par...Several factors, such as cold exposure, aging, the number of experiences and viral infection, have been shown to affect learning ability in different organisms. Wol- bachia has been found worldwide as an arthropod parasite/mutualist symbiont in a wide range of species, including insects. Differing effects have been identified on physiology and behavior by Wolbachia. However, the effect of Wolbachia infection on the learning ability of their host had never previously been studied. The current study carried out to compare learning ability and memory duration in 2 strains of the parasitoid Trichogramma brassicae： 1 uninfected and I infected by Wolbachia. Both strains were able to associate the novel odors with the reward of an oviposition into a host egg. However, the percentage of females that responded to the experimental design and displayed an ability to learn in these conditions was higher in the uninfected strain. Memory duration was longer in uninfected wasps （23.8 and 21.4 h after conditioning with peppermint and lemon, respectively） than in infected wasps （18.9 and 16.2 h after conditioning with peppermint and lemon, respec- tively）. Memory retention increased in response to the number of conditioning sessions in both strains, but memory retention was always shorter in the infected wasps than in the uninfected ones. Wolbachia infection may select for reduced memory retention because shorter memory induces infected wasps to disperse in new environments and avoid compe- tition with uninfected wasps by forgetting cues related to previously visited environments, thus increasing transmission of Wolbachia in new environments.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Several factors, such as cold exposure, aging, the number of experiences and viral infection, have been shown to affect learning ability in different organisms. Wol- bachia has been found worldwide as an arthropod parasite/mutualist symbiont in a wide range of species, including insects. Differing effects have been identified on physiology and behavior by Wolbachia. However, the effect of Wolbachia infection on the learning ability of their host had never previously been studied. The current study carried out to compare learning ability and memory duration in 2 strains of the parasitoid Trichogramma brassicae： 1 uninfected and I infected by Wolbachia. Both strains were able to associate the novel odors with the reward of an oviposition into a host egg. However, the percentage of females that responded to the experimental design and displayed an ability to learn in these conditions was higher in the uninfected strain. Memory duration was longer in uninfected wasps （23.8 and 21.4 h after conditioning with peppermint and lemon, respectively） than in infected wasps （18.9 and 16.2 h after conditioning with peppermint and lemon, respec- tively）. Memory retention increased in response to the number of conditioning sessions in both strains, but memory retention was always shorter in the infected wasps than in the uninfected ones. Wolbachia infection may select for reduced memory retention because shorter memory induces infected wasps to disperse in new environments and avoid compe- tition with uninfected wasps by forgetting cues related to previously visited environments, thus increasing transmission of Wolbachia in new environments.
