In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so o...In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations(PN-DO) that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.展开更多
Traffic modeling is a key step in several intelligent transportation systems（ITS） applications. This paper regards the traffic modeling through the enhancement of the cell transmission model. It considers the traffi...Traffic modeling is a key step in several intelligent transportation systems（ITS） applications. This paper regards the traffic modeling through the enhancement of the cell transmission model. It considers the traffic flow as a hybrid dynamic system and proposes a piecewise switched linear traffic model. The latter allows an accurate modeling of the traffic flow in a given section by considering its geometry. On the other hand, the piecewise switched linear traffic model handles more than one congestion wave and has the advantage to be modular. The measurements at upstream and downstream boundaries are also used in this model in order to decouple the traffic flow dynamics of successive road portions. Finally, real magnetic sensor data, provided by the performance measurement system on a portion of the Californian SR60-E highway are used to validate the proposed model.展开更多
The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare...The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare. In recent years, the related technologies of Intelligent Transportation System (ITS) re</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">presented by the Vehicles to Everything (V2X) technology have been developing rapidly. Utilizing the related technologies of ITS, the large-scale vehicle microscopic trajectory data with high quality can be acquired, which provides the research foundation for modeling the car-following behavior based on the data-driven methods. According to this point, a data-driven car-following model based on the Random Forest (RF) method was constructed in this work, and the Next Generation Simulation (NGSIM) dataset was used to calibrate and train the constructed model. The Artificial Neural Network (ANN) model, GM model, and Full Velocity Difference (FVD) model are em</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">ployed to comparatively verify the proposed model. The research results suggest that the model proposed in this work can accurately describe the car-</span><span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">following behavior with better performance under multiple performance indicators.展开更多
基金supported in part by the National Key R&D Program of China(2017YFB1001804)Shanghai Science and Technology Innovation Action Plan Project(16511100900)
文摘In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations(PN-DO) that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.
文摘Traffic modeling is a key step in several intelligent transportation systems（ITS） applications. This paper regards the traffic modeling through the enhancement of the cell transmission model. It considers the traffic flow as a hybrid dynamic system and proposes a piecewise switched linear traffic model. The latter allows an accurate modeling of the traffic flow in a given section by considering its geometry. On the other hand, the piecewise switched linear traffic model handles more than one congestion wave and has the advantage to be modular. The measurements at upstream and downstream boundaries are also used in this model in order to decouple the traffic flow dynamics of successive road portions. Finally, real magnetic sensor data, provided by the performance measurement system on a portion of the Californian SR60-E highway are used to validate the proposed model.
文摘The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare. In recent years, the related technologies of Intelligent Transportation System (ITS) re</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">presented by the Vehicles to Everything (V2X) technology have been developing rapidly. Utilizing the related technologies of ITS, the large-scale vehicle microscopic trajectory data with high quality can be acquired, which provides the research foundation for modeling the car-following behavior based on the data-driven methods. According to this point, a data-driven car-following model based on the Random Forest (RF) method was constructed in this work, and the Next Generation Simulation (NGSIM) dataset was used to calibrate and train the constructed model. The Artificial Neural Network (ANN) model, GM model, and Full Velocity Difference (FVD) model are em</span><span style="font-family:Verdana;">- </span><span style="font-family:Verdana;">ployed to comparatively verify the proposed model. The research results suggest that the model proposed in this work can accurately describe the car-</span><span style="font-family:Verdana;"> </span><span style="font-family:Verdana;">following behavior with better performance under multiple performance indicators.
