11 results about "Traffic emission" patented technology

The invention discloses an air pollution intelligent early warning method and system based on machine learning, and the method comprises the steps: obtaining the multi-source data of environment, wind direction, geography, traffic, emission, population and the like, carrying out the multi-scale decomposition of the environment data, extracting the short-term fluctuation and long-term trend, and achieving the time series prediction in combination with a historical mode; constructing a regional association graph by using a graph convolutional network, identifying cross-regional diffusion features, and fusing wind direction and space information to determine a high-risk region and a potential diffusion path; carrying out weighted calculation in combination with traffic and emission data to obtain a comprehensive risk score, and completing region division and early warning level determination; and finally, generating a regional visual early warning report. According to the invention, pollution trend accurate prediction and diffusion path identification can be realized.

The invention relates to the technical field of environment evaluation, and discloses a method, a device, equipment and a medium for evaluating the influence of traffic emission of a mountain tourism city on NO2 pollution, which can obtain the characteristic data of a plurality of city center grids and mountain grids in a plurality of continuous time steps in the mountain tourism city, pre-process each characteristic data, and obtain the NO2 pollution influence of the city center grids and the mountain grids. And each piece of processed feature data is obtained. And based on the processed characteristic data of the grid in each time step, determining a first SHAP value of each processed traffic flow of the grid to the processed NO2 concentration, thereby determining evaluation data of the influence of the traffic emission of the grid to the NO2 concentration, and evaluating the influence of the traffic emission of different terrain regions to NO2 pollution. According to the method, based on a gridding SHAP value analysis mode, the influence of traffic emission of the urban center region and the mountain region on NO2 pollution is evaluated, the influence of traffic emission of different terrain regions on NO2 pollution can be compared, and the evaluation accuracy of the influence of traffic emission of different terrain regions in a mountain tourism city on NO2 pollution is improved.

The invention relates to an urban traffic road network CO2 emission and haze grade prediction method and system based on LSTM. The prediction method comprises the following steps: collecting road monitoring data and vehicle feature data as input data; performing feature analysis on the input data by using principal component analysis and a correlation coefficient method, and screening out feature variables related to CO2 emission and haze grades as input feature variables; performing normalization processing on the data set of the input characteristic variables to obtain a normalized characteristic variable data set; dividing the normalized characteristic variable data set into a training set and a test set; constructing an LSTM traffic emission prediction model, wherein the LSTM traffic emission prediction model comprises a sequence input layer, an LSTM layer, a Dropout layer, a ReLU layer, a full connection layer and a regression output layer; configuring hyper-parameters of the LSTM traffic emission prediction model by using an Adam gradient descent algorithm, wherein the hyper-parameters comprise an initial learning rate, a batch size, an iteration frequency and a gradient threshold; and inputting the normalized characteristic variable data set into an LSTM traffic emission prediction model with set hyper-parameters for training to obtain a prediction model for the CO2 emission and the haze grade of the urban traffic network, and evaluating the prediction model for the CO2 emission and the haze grade by using an evaluation index. The LSTM-based urban traffic network CO2 emission and haze grade prediction method provided by the invention gets rid of the subjective factor influence of a traditional experience-based prediction model, and the LSTM model with strong time sequence can automatically learn key features, thereby avoiding the limitation of artificial experience.

The invention relates to the technical field of spatio-temporal data modeling, and discloses a causal-driven hierarchical spatio-temporal network-based traffic emission prediction method, which comprises the following steps of: performing heterogeneity-guided spatio-temporal enhancement on a road network emission sequence from two aspects of node attributes and edge attributes; in the hierarchical prediction stage, an original emission sequence and an enhanced sequence are mapped into a feature embedding space through a soft clustering convolution layer, space-time contrast clustering is carried out, a clustering result of enhanced sequence features serves as a self-supervision signal of original feature clustering, and a regional-level macroscopic emission mode is obtained; space-time coding is carried out on the road section level emission and the area level emission respectively, and space-time representations of different levels are obtained; in the causal learning stage, potential causal association is learned based on spatio-temporal characterization of different levels, macroscopic emission semantics with the causal relationship are identified, and information fusion of different levels is realized by utilizing cross-scale causal fusion; and carrying out iterative training on the hierarchical prediction stage and the causal learning stage, optimizing parameters of the space-time network and the causal graph, and obtaining an emission value of the road network in a future time period through an output layer. According to the invention, more accurate and effective road-level traffic emission prediction can be realized.

The application discloses a feature selection method for traffic emission prediction, comprising the following steps: selecting automatic or manual feature grouping, performing missing completion, anomaly detection and normalization processing on traffic emission input data, and initializing feature weights; calculating intra-group and inter-group feature correlation, and updating feature weights; using a machine learning method, combining SHAP analysis and model inherent feature importance method, iteratively updating feature weights; and screening optimal feature combinations and verifying. Through the above steps, the application balances the key feature selection, data dimension reduction and interpretability problem under the premise of ensuring that the traffic emission prediction accuracy is not reduced, and can achieve high efficiency to achieve the purpose of selecting an optimal feature subset.

The invention discloses a pollution and carbon reduction space function partitioning method based on multi-modal deep learning. The method comprises the following steps: firstly, constructing a multi-modal spatial index system covering environmental bearing capacity, environmental pressure and pollution reduction and carbon reduction potential, including an air diffusion index, an ecological carbon sink index, and a traffic emission pressure index and pollution reduction and carbon reduction potential considering a road network, population, lamplight and function mixing degree; secondly, uniformly resampling, standardizing and synthesizing the multi-source spatial indexes into a multi-band raster data set; then a multi-branch convolutional neural network model is constructed, the multi-branch convolutional neural network model comprises three independent convolution branches corresponding to bearing capacity, pressure and potential index groups respectively, and after branch output features are fused, the probability that each space unit belongs to different function partitions is calculated in a low-dimensional embedding space through a deep embedding clustering algorithm; and finally, generating a pollution-reducing and carbon-reducing space function partition grid map according to the maximum probability. According to the method, deep fusion, automation and refined partitioning of the multi-source heterogeneous spatial data are realized.

The invention relates to an air quality prediction management method and system for air pollution prevention and control, and relates to the technical field of environment monitoring. The method mainly comprises the following steps: respectively calculating air quality prediction data of a target city and adjacent cities according to industrial emission data, traffic emission data and real-time pollutant concentration monitoring data of the target city and the adjacent cities; updating the air quality prediction data of the target city through the meteorological prediction data and geographic information data of the target city and the adjacent cities thereof and the air quality prediction data of the adjacent cities to obtain final air quality prediction data of the target city; determining whether the final air quality prediction data of the target city is greater than a preset abnormal value; if the air quality is larger than the preset abnormal value, industrial emission and traffic emission of the target city or adjacent cities are managed and controlled according to the generation reason of the final air quality prediction data.

This application relates to the field of intelligent transportation technology, and discloses a method, device, system, and storage medium for real-time collaborative optimization of traffic emissions. The method includes collecting vehicle operation data to calculate vehicle power, constructing a two-dimensional bin-structured index of the instantaneous emission rate as a baseline, and correcting it in real-time using pre-fixed rules to output single-vehicle, single-step emissions at the microscopic level; aggregating emission data according to spatial hierarchical logic and simultaneously calculating performance indicators such as average commuting time; calling templates to generate strategies when indicators exceed limits; compiling the strategies into a set of six-tuple standard actions and performing fairness and conflict checks; using an exactly-once mechanism and consistency window to ensure atomic execution across devices; and evaluating strategies based on in-loop monitoring, combined with a third-generation non-dominated sorting genetic algorithm and a conditional risk-value risk term, outputting a Pareto optimal solution set that is continuously updated. This invention balances traffic efficiency, environmental benefits, and system execution stability through a high-precision calculation model and robust optimization algorithm.

The invention relates to the field of urban road traffic emission evaluation, in particular to a road heavy truck NOX emission calculation method based on an improved hidden Markov model, which comprises the following steps: S1, identifying inner and outer tracks of a road and extracting tracks on the road; s2, track segmentation based on datum line offset; s3, track key point extraction based on offset distance statistics; s4, performing candidate road network reduction based on micro-region segmentation; s5, track matching based on the hidden Markov model; s6, road-level vehicle operation condition calculation; and S7, calculating and summarizing the NOX emissions of the road-level vehicles. According to the method, the calculation process of the traditional hidden Markov model is optimized, and the calculation complexity is remarkably reduced while the track matching precision is ensured. Compared with the prior art, the method has the advantages that the problem of low road-level emission calculation efficiency under the condition of low sampling frequency GPS data can be effectively solved, and the road traffic pollution emission simulation process is more efficient.

The application relates to the field of urban road traffic emission evaluation, in particular to a road heavy truck NO X emission calculation method based on an improved hidden Markov model. The method comprises the following steps: S1. Road internal and external track identification and in-road track extraction; S2. Track segmentation based on baseline offset; S3. Track key point extraction based on offset distance statistics; S4. Candidate road network reduction based on micro-area segmentation; S5. Track matching based on a hidden Markov model; S6. Road-level vehicle operating condition calculation; S7. Road-level vehicle NO X emission calculation and summarization. The application optimizes the calculation process of a traditional hidden Markov model, ensures the track matching accuracy, and significantly reduces the calculation complexity. Compared with the prior art, the application can effectively solve the problem of low road-level emission calculation efficiency under the condition of low sampling frequency GPS data, and makes the road traffic pollution emission simulation process more efficient.

The invention discloses an airport grade evaluation and flight redistribution method under the perspective of carbon emission, and the method comprises the following steps: (1) constructing an air traffic complex network by taking airports as nodes and taking route emission as an edge weight on the basis of measuring and calculating air traffic emission according to the flight path data of flights, the number of take-off and landing passengers of each airport and other information; (2) based on four node indexes and network efficiency indexes of each airport node in the network, grading the airports by using a K-means clustering algorithm to solve the classification homogenization problem; and (3) from the angle of sky-train cooperation, the aim of reducing emission of air traffic is achieved by redistributing flights of the branch airports. The method is helpful for solving the technical problems of over-high aviation carbon emission and low network operation efficiency caused by neglect of carbon emission and network efficiency in existing airport grade division and unreasonable flight resource allocation, and provides technical support for relieving hub airport congestion and promoting low-carbon and high-efficiency planning of air transportation.