7 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 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.

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.