Abstract: An edge computing-based method for fine determination of an urban traffic state includes the following steps: preprocessing lane-level data collected by edge nodes; dividing a complete road segment; computing an average delay per vehicle of a lane by using the edge nodes; inputting the preprocessed and computed data into a fuzzy logic model to determine a lane-level traffic state of an approach region; and based on the characteristic that edge nodes at the intersections can be interconnected, linking upstream and downstream intersection information to compute an average travel speed of a remaining road segment, and determining a traffic state of the remaining road segment.

Abstract: An edge computing-based method for fine determination of an urban traffic state includes the following steps: preprocessing lane-level data collected by edge nodes; dividing a complete road segment; computing an average delay per vehicle of a lane by using the edge nodes; inputting the preprocessed and computed data into a fuzzy logic model to determine a lane-level traffic state of an approach region; and based on the characteristic that edge nodes at the intersections can be interconnected, linking upstream and downstream intersection information to compute an average travel speed of a remaining road segment, and determining a traffic state of the remaining road segment.

Abstract: A method for recognizing multi-dimensional anomalous urban traffic events based on a ternary Gaussian mixture model includes: reading a data sample of urban road traffic events; randomly dividing the data sample into a first subsample and a second subsample; performing modeling based on the first subsample by using the ternary Gaussian mixture model to obtain a second ternary Gaussian mixture model to calculate a distribution probability p of any sample point; clustering the second subsample, recognizing an outlier in the second subsample, and labeling the outlier and a normal point to obtain a labeled subsample; calculating the labeled subsample to obtain the distribution probability p corresponding to each sample point in the labeled subsample; when a new traffic event occurs, obtaining features of three dimensions of the new traffic event, calculating a distribution probability p by using the second model, and recognizing the new traffic event as anomalous if p<t-score.

Abstract: A method for recognizing multi-dimensional anomalous urban traffic events based on a ternary Gaussian mixture model includes: reading a data sample of urban road traffic events; randomly dividing the data sample into a first subsample and a second subsample; performing modeling based on the first subsample by using the ternary Gaussian mixture model to obtain a second ternary Gaussian mixture model to calculate a distribution probability p of any sample point; clustering the second subsample, recognizing an outlier in the second subsample, and labeling the outlier and a normal point to obtain a labeled subsample; calculating the labeled subsample to obtain the distribution probability p corresponding to each sample point in the labeled subsample; when a new traffic event occurs, obtaining features of three dimensions of the new traffic event, calculating a distribution probability p by using the second model, and recognizing the new traffic event as anomalous if p<t-score.

Abstract: A method for extracting road capacity based on traffic big data includes the following steps: selecting a specific traffic flow model; reading massive road lane traffic flow parameters; calibrating a model parameter of the selected traffic flow model by using the road lane traffic flow parameters read in the previous step; and fitting the calibrated model parameter to obtain a fitted traffic flow model. The present invention solves the problems that traditional methods for traffic capacity calibration have a heavy workload, inadequate samples and unreliable results due to their reliance on manual information acquisition, thereby providing support for automatic, long-term, large-scale and precise acquisition of the capacity.