14 results about "Traffic pollution" patented technology

The invention belongs to the technical field of traffic pollution prediction, and particularly relates to a LightGBM-based multi-source data fusion pollutant concentration prediction method, which comprises the following steps of: 1, integrating and processing multi-source data, and constructing a plurality of feature sets; and 2, carrying out feature analysis on meteorological, pollutant and traffic index variables through correlation analysis and time sequence analysis, and providing support for modeling and model interpretation. And step 3, dividing a training test set according to a time sequence, and training and optimizing the LightGBM pollution prediction model through parameter tuning by taking minimization of RMSE as a target. And 4, verifying the performance of the model from prediction precision, spatial distribution and wind direction influence. And 5, analyzing and displaying the key driving factor and quantifying the contribution of the key driving factor. According to the invention, through feature engineering processing and model optimization of heterogeneous data such as weather and traffic, high-precision prediction and influence factor analysis of lane-level pollutant concentration are realized.

The invention provides a multi-mode remote sensing highway vegetation carbon reserve accurate inversion method based on tail gas stress correction, and belongs to the field of remote sensing images. Comprising the following steps: acquiring satellite remote sensing data, unmanned aerial vehicle data, ground actual measurement data and traffic flow data; vegetation biomass inversion based on multivariate data fusion, including biomass inversion of individual tree scale, sample area scale and road domain scale; converting the road domain biomass into road domain carbon reserves; calculating the traffic pollutant discharge amount; the day-by-day and hour-by-hour pollutant concentration of the vegetation sample plot is simulated according to the traffic pollutant discharge amount; constructing a carbon reserve dynamic correction model, and correcting road domain carbon reserves based on a photosynthetic inhibition effect and a respiration promotion effect; according to the method, satellite remote sensing, unmanned aerial vehicle remote sensing and ground measured data are fused, a multi-modal data closed loop is constructed, the limitation of a single data source in temporal-spatial resolution and coverage range is solved, multi-scale biomass inversion from a single tree to a road domain is realized, and the carbon reserve estimation precision is improved.

The invention discloses an adjacent tunnel fluid channeling ratio simulation test device and method combined with boundary conditions, the adjacent tunnel fluid channeling ratio simulation test device combined with the boundary conditions comprises an upstream tunnel model, a downstream tunnel model and a middle section road surface, and the side surface of the middle section road surface is provided with a crosswind simulation mechanism. And a full-spectrum lamp is arranged above the middle section of the road surface. The invention further relates to an adjacent tunnel fluid channeling ratio simulation test method in combination with the boundary conditions. According to the method, the sulfur hexafluoride (SF6) concentration is used as a basis for quantitatively calculating the fluid channeling ratio, fluid channeling analysis of traffic pollutants and high-temperature fire smoke can be combined, and a more accurate adjacent tunnel pollutant fluid channeling ratio is obtained by combining terrain wind environment boundary conditions and pavement thermal environment boundary conditions; and more reliable experimental data is provided for the design of adjacent tunnels and the type selection of ventilation and fire fighting equipment.

The invention discloses an intelligent traffic engineering ecological monitoring and detection collaborative management and control system. The system comprises an ecological environment dynamic sensing module, a traffic operation state acquisition module, a multi-source data fusion analysis center, a collaborative decision optimization module, a global linkage execution module and a man-machine interaction management and control platform. According to the invention, a sensing-studying-judging-decision-executing-feedback full-chain cooperation mechanism is constructed; an ecological environment dynamic sensing module and a traffic operation state acquisition module form a double-path nerve ending, and environment and traffic coupling data are transmitted to a multi-source data fusion analysis center; the center generates a dynamic environment risk thermodynamic diagram through the pollution conduction sub-model and the public weight factor; the collaborative decision optimization module formulates a hierarchical management and control strategy and a signal lamp pollution suppression algorithm according to the information; meanwhile, the man-machine interaction management and control platform absorbs public reports and feeds back environmental protection points, and finally minute-level closed-loop treatment of'generation-diffusion-blocking 'of traffic pollution is achieved.

The invention relates to the field of urban road traffic pollution assessment, in particular to a city heavy diesel emission pollution field rapid description method based on dynamic point distribution, comprising the following steps: S1, constructing joint input data of road emission spatial features and meteorological conditions; s2, extracting multi-scale features of an emission space based on a residual coding structure; s3, performing adaptive modulation on the multi-scale spatial features according to meteorological conditions; s4, establishing a mapping relationship between the conditionally modulated high-dimensional spatial features and the optimal receptor stationing strategy by using a deep learning model; and the like. Compared with the prior art, on the premise that the spatial resolution of the pollution concentration field is guaranteed, the set number of pollution concentration acceptor points is remarkably reduced, and the overall efficiency of road traffic pollution emission simulation and pollution hot spot recognition is improved; an efficient and reliable technical means is provided for urban traffic environment impact assessment, pollution prevention and control decision and refined traffic management.

The present application relates to the field of urban road traffic pollution assessment, and particularly relates to a city heavy-duty vehicle emission pollution field rapid characterization method based on dynamic point distribution, comprising the following steps: S1. constructing a joint input data of road emission space characteristics and meteorological conditions; S2. extracting multi-scale characteristics of the emission space based on a residual coding structure; S3. adaptively modulating the multi-scale space characteristics according to the meteorological conditions; S4. using a deep learning model to establish a mapping relationship between the high-dimensional space characteristics after the conditional modulation and an optimal receptor point distribution strategy; and the like. Compared with the prior art, the present application significantly reduces the number of pollution concentration receptor points under the premise of ensuring the spatial resolution of the pollution concentration field, improves the overall efficiency of road traffic pollution emission simulation and pollution hotspot identification, and provides an efficient and reliable technical means for urban traffic environmental impact assessment, pollution prevention and control decision-making and fine traffic management.

This invention relates to the field of traffic safety analysis technology, providing a multi-dimensional feature analysis system for the degree of driving delay at urban traffic accident sites. Based on driving delay data at urban traffic accident sites, it extracts road-dimensional features at the time of the accident, proposes targeted data imputation schemes based on the characteristics of missing features, and completes redundant feature screening according to statistical distribution, constructing structured traffic modeling data with four dimensions: weather, road, POI, and time. It systematically models and analyzes the degree of driving delay caused by urban traffic accidents from three aspects: individual heterogeneity, group transferability, and group heterogeneity, exploring the influence of significant variables among the multi-dimensional influencing factors. The aim is to help traffic management departments take preventive measures and emergency response strategies, provide a theoretical basis for road design optimization and management decisions, thereby mitigating the impact of traffic accidents, improving the operational risk level of accident-prone road sections, avoiding secondary accidents, and reducing traffic pollution.

This invention relates to a ventilation control method for traffic tunnel groups in hydropower station hub engineering areas, and pertains to the field of hydropower station technology. It utilizes tunnel intersections and / or pollutant emission points as ventilation nodes, dividing the tunnel group into sections based on these nodes. The method calculates traffic pollutants and excavation pollutants for each section during different construction periods, and, combined with vehicle routes and times, determines the required design air volume for each section during each construction period. This information is used to determine wind speed, wind direction, air outlets, and jet fans, and ventilation is carried out according to the required design air volume. This invention solves the problem of inappropriate ventilation in existing hydropower station hub engineering areas and is applicable to ventilation control of traffic tunnel groups in hydropower station construction.

To prevent the spread of traffic pollution to a health environment and the deterioration of a natural environment since the prevention of the scattering of dust at the time of the traveling of vehicles is left as it is without regulation. There are various installation methods of the dust scattering prevention device, but it is necessary to consider the installation place and the manufacturing cost in the design, the form of the vehicle body, the function, and the like according to the corresponding vehicle type, and it is considered that it is urgent to make the attachment duty of the present device by the dust scattering prevention regulation law similar to the exhaust gas regulation law as a problem, although it can be attached to many vehicle types because of the adsorption, the dust separation, the storage, the same in-device rotation type, and the small device by the electrostatic attraction object function presented this time.SELECTED DRAWING: Figure 2

The invention relates to the field of road risk patrol, in particular to a multi-dimensional road risk patrol method and system based on space-time tracking. The method comprises the steps of obtaining tunnel environment multi-dimensional basic state data, performing multi-source pollution-causing factor analysis on traffic pollution-causing source intensity, air circulation efficiency and rainfall removal capacity based on the tunnel environment multi-dimensional basic state data, and generating a tunnel oil pollution risk factor set; based on the tunnel oil contamination risk factor set, dynamically simulating an oil contamination accumulation or dissipation process by coupling a space-time action mechanism of each factor, and generating tunnel oil contamination accumulation risk information; and performing maintenance priority scheduling and decision support based on the tunnel oil contamination accumulation risk information, and generating a tunnel risk inspection report. According to the method, in the road risk inspection process, the spatial heterogeneous distribution and evolution trend of pavement disease accumulation can be accurately predicted, maintenance resource mismatching and risk response lag are avoided, and driving potential safety hazards are reduced.

The application provides a multi-modal remote sensing highway vegetation carbon storage accurate inversion method based on tail gas stress correction, and belongs to the field of remote sensing images; comprising the following steps: obtaining satellite remote sensing data, unmanned aerial vehicle data, ground measured data and traffic flow data; multi-element data fusion vegetation biomass inversion, including single tree scale, sample area scale and road area scale biomass inversion; converting road area biomass into road area carbon storage; calculating traffic pollutant emissions; simulating the daily and hourly pollutant concentration of the vegetation sample site according to the traffic pollutant emissions; constructing a carbon storage dynamic correction model, correcting the road area carbon storage based on photosynthesis inhibition effect and respiration promotion effect; the application fuses satellite remote sensing, unmanned aerial vehicle remote sensing and ground measured data, constructs a multi-modal data closed loop, solves the limitations of single data source in terms of spatial and temporal resolution and coverage, realizes multi-scale biomass inversion from single tree to road area, and improves the carbon storage estimation accuracy.

The invention relates to the field of traffic pollution positioning, and discloses a high-emission road positioning method, device, equipment and medium based on traffic data, and the method comprises the steps: determining a high-pollution emission sub-period according to the air quality data monitored by a monitoring station in a plurality of continuous sub-periods of a target period; the target area is an area within a set distance range around the monitoring station. And determining a plurality of upwind roads of the monitoring station in the high pollution emission sub-time period based on the wind direction data monitored by the monitoring station in the high pollution emission sub-time period and the road network distribution in the target area. And according to the truck traffic data of each upwind road in the high pollution emission sub-period, determining the pollution emission intensity of each upwind road in the high pollution emission sub-period, and further determining a high emission road in each upwind road. According to the invention, accurate positioning of the high-emission road in the high-pollution emission sub-period in the target area can be effectively realized.

The application discloses a plant maintenance method, system and device based on traffic pollution and a storage medium, and the technical scheme points of the application are as follows: traffic congestion data, meteorological data, air pollution data, soil physicochemical data and plant physiological data of target plants in a target area are acquired; a pollution diffusion coefficient is calculated according to the meteorological data and the air pollution data; a first composite feature is constructed according to the pollution diffusion coefficient, the meteorological data and the air pollution data, a second composite feature is constructed according to the pollution diffusion coefficient, the traffic congestion data and the meteorological data, and a third composite feature is constructed according to the soil physicochemical data and the plant physiological data; the first composite feature, the second composite feature and the third composite feature are input into a pre-trained pollution prediction model to obtain a pollution prediction result; and a plant maintenance system corresponding to the target plants is controlled according to the pollution prediction result. The application helps to improve the ability of the target plants to resist urban traffic pollution and relieve the degree of oxidative damage.

The application discloses a kind of expressway traffic pollutant emission quantity multi-scenario interval prediction method, comprising the following steps: obtaining the traffic distribution observation data set of expressway based on entrance and exit ramp toll collection system in prediction space-time range;Generation policy control scenario mode combination based on different emission standard updating scheme;Calculate baseline pollutant emission factor, obtain the emission factor data set under each scenario;The seasonal SARIMA-SVR model is used to predict the vehicle population under different scenarios in the future, and the pollutant emission in each scenario in the future month is estimated;Evaluate the emission reduction effect of each combination of emission standard control policy.The application realizes the quantitative evaluation of multi-scenario expressway traffic pollutant emission reduction effect under the consideration of different emission standard updating control policy, and improves the seasonal prediction accuracy by using the SARIMA-SVR model, which provides technical support for reasonably formulating carbon peak policy.