4253 results about "Urban road" patented technology

The invention belongs to the field of intelligent transportation, and discloses a traffic prediction method based on an attention temporal graph convolutional network. The method includes the following steps that: firstly, an urban road network is modeled as a graph structure, nodes of the graph represent road sections, edges are connection relationships between the road sections, and the time series of each road section is described as attribute characteristics of the nodes; secondly, the temporal and spatial characteristics of the traffic flow are captured by using an attention temporal graph convolutional network model, the temporal variation trend of the traffic flow on urban roads is learned by using gated cycle units to capture the time dependence, and the global temporal variation trend of the traffic flow is learned by using an attention mechanism; and then, the traffic flow state at different times on each road section is obtained by using a fully connected layer; and finally,different evaluation indexes are used to estimate the difference between the real value and the predicted value of the traffic flow on the urban roads and further estimate the prediction ability of the model. Experiments prove that the method provided by the invention can effectively realize tasks of predicting the traffic flow on the urban roads.

The invention discloses a remote sensing monitoring system for automotive exhaust emission of an urban road network. The system is mainly composed of a remote measurement device layer, a site selection and position arrangement layer, and a data processing layer. Through mobile, horizontal and vertical exhaust remote measurement devices, real-time data of the automotive exhaust emission in running is obtained; by adopting an advanced site selection and position arrangement method, the remote measurement devices are scientifically networked; and in combination with external data of weather, traffic, geographic information and the like, the real-time remote measurement data of the automotive exhaust emission is subjected to intelligent analysis and data mining by adopting big data processing and analysis technologies such as deep learning and the like, and key indexes and statistical data with optimal identification performance are obtained, so that effective support is provided for government departments to make related decisions.

The invention discloses a traffic flow running rate recognizing method based on bus GPS data and relates to a traffic information collecting and processing technology in the field of intelligent traffics. The method comprises the following solving steps of: carrying out grade division on an urban road section by a GIS; confirming a speed threshold value K1 and a speed threshold value K2 of all grades of roads; carrying out sub-road section division on the urban roads by the GIS; obtaining an average value of the speed that all buses pass through a sub-road section in a certain time interval, which is collected by a bus vehicle-mounted GPS system; comparing the average value of the speed with the threshold value K1 and the threshold value K2 of the sub-road section and confirming the traffic flow running rate of the sub-road section. The traffic flow running rate recognizing method based on bus GPS data can obviously improve the recognizing precision of the traffic flow running rate, reduce the time delay and provide the type of traffic jam simultaneously, thereby providing a basis for selecting more convenient traveling line for a traveler and proving more powerful decision support for establishing a jam facilitating scheme for a traffic management department.

The invention discloses a floating car state real-time traffic information processing method based on GPS data, which includes the following steps: 1. GPS point data pre-processing and map pre-processing measures; 2. conduct point matching and select alternative road section sets according to projection distance and azimuth angles; 3. determine correct matching road sections and find out travel routes according to an improved optical route selection method considering topological relation between front and rear points (including a special regional node processing method for urban complicate road network); 4. calculate the route average travel speed, and conduct statistics in a speed formula to generate a road network speed thematic map. The floating car state real-time traffic information processing method based on GPS data of the invention is well applicable the modern complicated urban road network full of overpasses and staggered main and side roads, which can not only meet the speed requirement for large-data-quantity GPS data real-time calculation, but also obtain quite high matching precision.

The invention relates to a method for evaluating traffic jam, congestion and smooth state of urban roads by using floating vehicle data. Concretely, the method comprises the following steps: analyzing the transmission frequency and the traveling tracks of data based on the prior GPS data by using network state estimation technique based on GPS data of floating vehicles in detail; selecting a comprehensive evaluation value of the calculation result of a time shortest path algorithm as the traveling speed of the road in consideration of the OD characteristics of the urban traffic; and comprehensively evaluating the traffic state of the road by using fuzzy reasoning technique based on the combination of the traveling speed and the use function of the road. The improved evaluation method has the advantages of easily satisfied operation condition, easy implementation, and accurate result.

The invention discloses an urban road traffic information detecting and issuing system, which comprises a traffic flow information collection sub-system used for collecting the road traffic information; a traffic flow analyzing and processing sub-system which is connected with the traffic flow information collection sub-system through a communication sub-system and used for extracting the traffic information with the same characteristic to conduct analysis and processing; a dynamic traffic information platform which is connected with the traffic flow analyzing and processing sub-system, used for storing real-time and historical data and can implement information sharing and exchanging; and a traffic information issuing sub-system which is connected with the dynamic traffic information platform through the communication sub-system and used for issuing the processed result internally and externally in a form of road network traffic state index system. The urban road traffic information detecting and issuing system can detect and issue the road traffic information in real time and is convenient for the effective management of the road traffic.

The invention discloses a lane changing trajectory planning method for an unmanned vehicle based on vehicle-to-vehicle cooperation. In consideration of the vehicle lane changing complexity in an unmanned driving environment and characteristics of frequent turning and lane changing in urban road sections, a vehicle-to-vehicle cooperation policy and a trajectory planning method during the lane changing process are provided. With a lane changing cooperation policy and a quintic polynomial trajectory planning method as a basis and with vehicle kinematics and comfort as control conditions, a main vehicle lane changing trajectory optimization model under different cooperation degrees of a rear vehicle in the target lane is built; and besides, in consideration of defects of traditional elliptic and circular vehicle simulation models, through analyzing a boundary relationship between a possible collision point and a vehicle contour, a collision avoidance boundary condition under a rectangular vehicle model is built, and the vehicle lane changing trajectory model is tested through a scene. Under the vehicle-to-vehicle cooperation condition, safe vehicle lane changing under the unmanned driving environment can be completed, and requirements of lane changing comfort and kinematics can be met.

The invention discloses a discriminating method based on a section detector for urban traffic state. The existing traffic state discriminating method has low accuracy and less reliability. Based on the three traffic flow parameters of traffic flow, speed and time occupancy rate, the invention constructs a comprehensive congestion evaluation index, namely a traffic congestion index which is used for discriminating the road traffic state. The traffic state discriminating method provided by the invention comprises the steps of acquiring section traffic flow data at information publishing intervals, smoothing traffic flow parameters, calculating a speed congestion index and a occupancy congestion index, calculating a critical speed congestion index, calculating the traffic congestion index and discriminating the road traffic state. Based on the traffic information of a certain detecting section of a road and the overall consideration of various traffic flow parameters, the discriminating method based on the section detector for urban traffic state can automatically discriminate the traffic state of the road, and meanwhile utilize discriminating threshold values as few as possible and make full use of available resources, thereby facilitating the realization of a project.

The invention discloses an automobile autopilot system comprising an automobile control unit, a positioning system, an information transmit-receive unit and a steering-by-wire system; wherein the automobile control unit is used for recording driving routes by the positioning system, for exchanging information between the automobile autopilot system and urban road monitoring center by the information transmit-receive unit, for correcting road information provided by urban road monitoring center, for recording the movement of steering wheels by the steering-by-wire system, and for correcting steering data by combining the positioning function of the positioning system. When the autopilot function is activated, the automobile control unit is used for automobile automatic piloting control according to road information, traffic report and steering data of a selected section of road. Real-time data interchange between the automobile autopilot system and urban road monitoring center is provided, so that road information can be updated timely; navigation accuracy is increased; traffic violations such as speeding, running red lights are decreased; car accident rate is decreased, and the safety of drivers is ensured in a certain level.

The invention relates to a real-time dynamic traffic information processing method based on probe vehicle techniques. The method comprises the following steps: (1) reticulating an urban road system, and recording the shape and section numbers of all roads located inside or crossed with each grid; (2) grouping the data sets of a probe vehicle within one period according to the timer sequencing, determining a road aggregate R where the probe vehicle can be located in the mean while according to the geometrical characteristics of the data, calculating the road identification confidence of each road to generate a candidate identified road aggregate Rs, combining a plurality of Rs aggregates to establish a candidate path aggregate: (Rk<*>)(k=1)<K>, searching the candidate path aggregate, identifying several paths from the start-point GPS to the end-point GPS, performing the comprehensive judgment of road identification confidence and length of the paths to obtain the moving track of the probe vehicle; and (3) calculating the average speed per hour on the road according to the length of the moving track between the start-point and the end-point of the probe vehicle, the running time and the road condition history.

The embodiment of the invention discloses a laser-point-cloud-based urban road identification method and apparatus. The method comprises: a corresponding road edge model is constructed according to laser point clouds collected by a laser sensor; a height of a mobile carrier with the laser sensor is determined and a corresponding road surface model is constructed based on the height and the laser point; and according to the road edge model and the road surface model, a road surface point cloud and a road edge point cloud in the laser point cloud are eliminated, the rest of laser point clouds are segmented by using a point cloud segmentation algorithm, and an object corresponding to the a segmentation result is identified. The height of the mobile carrier is estimated based on the laser point cloud and the road surface model corresponding to the laser point cloud is constructed by using the height, so that the construction efficiency and accuracy of the road surface model are improved. Therefore, the identification efficiency and accuracy of the corresponding object are improved.

The invention discloses a traffic state prediction method for the urban road network based on key road sections, which is characterized by comprising the steps of first, carrying out data preprocessing; second, establishing a spatial weight matrix of the road network; third, establishing a time correlation matrix; fourth, recognizing key road sections by using a time-space correlation matrix; andfifth, establishing a deep convolution neural network, predicting the state of the road network in the future, and carrying out evaluation on a prediction model. The traffic state prediction method predicts the urban traffic flow state from a level of the wide-range road network, thereby being conducive to guiding the traffic flow from a macroscopic perspective, and fully exploring time-space correlation characteristics of the traffic flow. The key road sections in the road network are recognized, so that the training time of the model can be greatly reduced compared with a method of taking historical states of all road sections as input data, and the prediction efficiency is improved; and the convolution neural network is adopted to serve as the prediction model, and the prediction resultis also more accurate.

The invention discloses an urban road marker automatic sorting method based on vehicle-mounted laser scanning point cloud. The method includes: S1) a step of subjecting original point cloud data to road surface segmentation based on wheel path data to obtain road surface point cloud data; S2) a step of subjecting the obtained road surface point cloud data to binarization processing and extracting road marker points; S3) a step of clustering the road marker points and separating independent road marker targets; S4) a step of sorting large road markers and small road markers according to the dimensions of the obtained road marker targets; S5) a step of subjecting the large road markers to sorting processing based on wheel paths and road edge lines; and S6) a step of subjecting the small road markers to sorting processing based on deep learning and principal component analysis. The method rapidly and accurately extracts and sorts urban road markers, largely reduces the time and labor cost for data processing, and effectively guarantees traffic safety and intelligent drive reliability.

The invention discloses a lane line predicating and early warning method suitable for a city road. According to the method, firstly, down sampling is performed on a collected road image, a detection region of interest is set and grayed, filtering enhancement is performed on the region of interest, edge detection is performed through a Canny operator, binarization is performed on the image after edge detection, then the image is mapped on a Hough plane, and lane lines are extracted through improved Hough conversion; then, parameters of the lane line are predicated by the adoption of a classical Kalman filtering algorithm, a region of interest is established, and the lane lines in the region of interest are fitted through a least square method to obtain the optimal predication value; finally, road surface information and current position information of automobiles are combined to establish a lane line departure warning model based on transverse distance, and therefore departure warning is achieved. The recognition method for the lane lines is good in real-time performance and robustness, according to the lane line departure warning model, calculation is easy, and warning is accurate.

The invention provides a traffic flow surveying and handling method based on an unmanned aerial vehicle high-definition video. The method comprises the steps of video capture, wherein an unmanned aerial vehicle is made to hover over a selected urban road intersection for high-definition video shooting; image stabilization and pre-processing, wherein the high-definition video is copied, stabilization and pre-processing are carried out on images, and then an image sequence is output; detection and tracking, wherein detection and tracking are carried out on moving objects with the image sequence treated with image stabilization as source data; analysis and statistics, wherein traffic flow analysis and statistics are carried out on a target ID which is tracked down and the current coordinates of motion; output, wherein traffic flow statistical data are transmitted to a client-side graphical interface for display, and then data and statements are generated. The traffic flow surveying and handling method based on the unmanned aerial vehicle high-definition video is capable of obtaining various high-accuracy traffic data, reducing the workload of field survey remarkably, preventing the traffic from being affected, obtaining statistic data of all traffic flows in twelve directions of the intersection, and providing data support for congestion control such as intersection signal timing optimization and traffic channeling improvement.

The present invention discloses a road boundary detection method based on a three-dimensional laser radar. In the process of intelligent vehicle driving, point cloud data collected by a vehicle-mounted three-dimensional laser radar is subjected to rasterizing processing to generate a binary raster graphic. The binary raster graphic is subjected to a distance conversion operation to obtain a distance grey-scale map, a filing distance is smaller than the narrow space between obstacle points of certain thresholds, the overall contour of the obstacle points is not changed, and an obstacle area contour map is obtained. A region growing method is used, with the position of an intelligent vehicle as a start point, the region growing is carried out forward, the passable area contour map of a road is obtained, and combined with the original binary raster graphic, a road area contour map is obtained. The contours of two sides of the road area contour map are extracted, the second function fitting is carried out, and a road boundary is obtained. The method is applicable to an urban road, a rural road and other roads, the influence of obstacles on a detection effect is small, the time complexity is low, the real-time processing is achieved, day and night work is achieved, and the algorithm robustness is good.

The invention relates to the technical field of traffic control, and especially relates to a road network tide traffic flow variable guide lane control method. The road network tide traffic flow variable guide lane control method comprises the steps of a, monitoring a road network traffic flow in real time through the video and performing intersection modeling; b, analyzing intersection entrance traffic flow turning imbalance and a variable guide lane; c, building a variable guide lane intersection upstream vehicle smooth transition mode; and d, setting and controlling a tide congestion intersection variable guide lane in a linkage manner. According to the invention, the operating state of urban road network traffic flows is monitored online in real time by adopting unmanned aerial vehicle video, and urban road network traffic flow and intersection modeling is performed for tide congestion intersections and key road sections, so that a new mode for variable guide lane control can be realized; and the road network tide traffic flow variable guide lane control method adapts to the intersection traffic flow turning imbalance and reduces disturbance for traffic running of road sections according to an interaction relation between traffic flow dynamic requirements and traffic facility static supply.

The invention discloses an urban traffic flow prediction method with regional vitality integrated. The method comprises steps: S1, regional division is carried out on an urban road network, and the traffic flow in each region is calculated; S2, a regional vitality model is designed: by using distribution of urban points of interest, holidays and weather information, a 3D convolution neural network(3D CNN) is used to study the dynamic changes of the vitality in each urban region; S3, a flow prediction model is designed: the regional vitality and the traffic flow are integrated, and a convolution long short-term memory network (ConvLSTM) is used for flow prediction; and S4, according to the historical data, the regional vitality model and the flow prediction model are trained at the same time, and the well-trained models are used for predicting the traffic flow in each region in real time. Through integrating the regional vitality and considering influences from a driving force behind crowd activities and external factors, high prediction precision can be acquired.

The invention discloses a day-dimension regional traffic index prediction method considering influences of multiple factors. The method comprises the steps that regions are divided and aggregated; regional traffic index original data preprocessing is carried out; the influences of multiple factors are considered, and regional traffic index prediction under the day dimension is carried out. According to the specific technical scheme of the method, on the basis of traffic cell division, traffic cells with the same aggregation property are aggregated, and regional traffic indexes are calculated;on the basis of road network operation early warning requirements, a prediction time period and a prediction cycle are determined; regional traffic data is extracted, made up for and removed, and preprocessing such as comprehensive building of a historical data factor attribute set from different angles is conducted on the data; on the basis of a decision tree theory, regional road network operation congestion state prediction is carried out; a final prediction result of the regional traffic indexes is determined by means of the square euclidean distance. By means of the method, on the one hand, monitoring and application of the urban road network operation state is deepened, and on the other hand, technical support is provided for early warning and forecasting work of the road network operation state.

The invention discloses an optimizing control method for a single intersection signal in a saturated traffic state, which belongs to the field of traffic detection and traffic control of urban roads. The method of the invention comprises the following steps of: detecting and recognizing intersection upstream and downstream vehicle queue setting areas and intersection central area traffic events by high-definition video and carrying out analysis processing on detected traffic data; and by a way based on the combination of the setting areas and rules, carrying out optimizing control on the traffic of the single intersection in the saturated traffic state in a plurality of aspects of intersection central area event judgment, phase downstream traffic flow queue detection and corresponding control processing, phase upstream traffic flow queue detection and corresponding control processing, and the like so that the intersection traffic in the saturated traffic state is run in order, and the traffic capacity of the intersection is improved. The method of the invention can further prevent traffic jam of the intersection in the saturated traffic state, eliminate the traffic jam as soon as possible and avoid the generation of a deadlock phenomenon of the intersection traffic, thereby remitting the problem of urban traffic jam and improving the running efficiency of urban traffic.

The invention discloses a high-precision vehicle positioning method for fusing multi-source information under a GPS (global positioning system) blind area and a device. By a strap-down matrix algorithm of the method, INS (inertial navigation system) vehicle position information and position information obtained by a pavement matching technology are calculated according to angular rate information output by a gyroscope and acceleration information output by an accelerometer and are fused by a kalman filtering algorithm to output final fused positioning information. As an INS positioning algorithm has an accumulated error, vehicle position information is recalibrated by arranging an anchor node beside a road. According to the method, stable and reliable high-precision vehicle position information can be obtained; the method is suitable for non-GPS-signal environments such as urban roads with dense buildings, mountain areas and tunnels.

The invention relates to an urban road congestion degree prediction method based on time sequence traffic events. The method comprises the steps of: S1, acquiring historical traffic event data, real-time traffic event data and video monitoring data of an urban road section; S2, identifying traffic congestion forewarning events in the video data through the 3D CNN, and performing data space-time fusion according to the historical traffic events; S3, determining congestion degree classification labels, constructing a time sequence traffic congestion event data dictionary, and screening a training set, a verification set and a test set; S4, establishing an LSTM sequence data classification model, inputting the training set, and iteratively updating model parameters by utilizing a gradient descent method; S5, inputting the verification set into the model with updated parameters, optimizing and adjusting hyper-parameters, and selecting an optimal model; and S6, inputting the test set into the optimal training model, checking the effectiveness of the model, and carrying out road congestion prediction according to real-time traffic monitoring data. According to the method, a sequence dataclassification model is established by using LSTM, and the urban road congestion degree is predicted based on time sequence traffic events.

The method is suitable for the technical field of traffic prediction, and provides a traffic flow prediction method and system, and a terminal device. The method comprises the following steps of: acquiring traffic data in a specified time period of a target area, performing graph convolution network training on the traffic data, capturing a topological structure of an urban road network to obtainspatial features, inputting the time sequence of the spatial features into a gating cycle unit model, and obtaining dynamic changes through information transmission between units to capture time features; and establishing a prediction model according to the spatial features and the time features so as to predict the traffic flow of the target area according to the prediction model. According to the invention, a graph convolutional neural network based on an attention mechanism is applied to the prediction problem in the traffic field, high-dimensional features in the road traffic network can be better mined in a complex road structure, the accuracy of traffic flow prediction is improved in cooperation with the improved recurrent neural network, and the improvement effect is obvious especially in medium-term and long-term prediction.

The invention relates to a short-time forecasting method for a traffic flow based on a macroscopic road network model, which comprises the following steps: (1) obtaining the input flow of the source node of a road network at a forecasting period, extracting the average speed of each road section at a previous forecasting period and determining the flow ratio of different turning directions at each intersection; (2) calculating the time of vehicles running to the tail of a queued vehicle queue, which are input on a road so as to obtain the number of the vehicles arriving at the tail of the queue at an iterative period; (3) determining the number of the vehicles which are correspondingly turned to leave the intersections by the conditions of the number of the vehicles queued at the intersections, the saturated leaving flow and the like; (4) accumulating to obtain the total number of the vehicles leaving the intersections at one forecasting period and converting to obtain the traffic flow within the forecasting period; and (5) updating the number of the queued vehicles as known data for iterative forecasting at the next time. The short-time forecasting method for the traffic flow based on the macroscopic road network model aims at the defects that the adaptability of the road network is poor, a great deal of training data are needed, the operation quantity in a microscopic model is large and the like, which exist in the prior art. The spatial information of an urban road network is fully utilized. The short-time forecasting method for the traffic flow based on the macroscopic road network model is based on a macroscopic traffic flow model, and the forecasting of the traffic flow of a road with high accuracy and good real-time property can be realized. Moreover, the short-time forecasting method for the traffic flow based on the macroscopic road network model is suitable for most of urban road networks.

The invention belongs to the field of intelligent traffic video image monitoring and video image analysis, and in particular relates to a method and a system for automatically identifying an urban traffic accident. The method for automatically identifying the traffic accident comprises the following steps of: acquiring an urban road video image sequence; performing foreground vehicle separation based on a mixed Gaussian background model; performing a multi-target vehicle tracing algorithm based on a Camshift algorithm and a kalman filtering combination; extracting traffic accident determiningparameters such as speed variation, horizontal position variation, vertical position variation, moving direction variation and the like; and proposing a multi-featured weighted fusion automatic accident identification algorithm. Traffic accident information is transmitted to a traffic control center in time by a transmission unit and a display unit, so that the traffic accident can be quickly treated, an effective and flexible road traffic monitoring means with high cost performance is provided for traffic management, and new thought is provided for the development of a high-efficiency intelligent video traffic accident system.

The invention discloses a method of map matching and route estimation, and a device and a system thereof, and pertain to the processing and application field of urban road traffic situation information, and aim at solving the problem of low operating efficiency of the map matching and route estimation in the prior art. The device and the system preprocess the original map data in the map matching and route estimation: first, pre-generating a route, then dividing a road net into smaller grids, re-organizing the data of each new grid, and finally compiling the data into a file according to a defined data structure for the following map matching and route estimation, thus enhancing the operating efficiency. The method, the device and the system can directly position the grid at which a GPS point is located, and as the grids are smaller, Link numbers in the grids are fewer, the GPS point matching rate is extremely fast; and no frequent route exploration is needed in the following route estimation, which greatly improves the route estimation efficiency, thus an algorithm of the method is simple.

The invention discloses a method for extracting real-time urban road traffic flow data based on mobile phone positioning data. The method comprises the following steps of: 1, preprocessing the mobile phone positioning data and map data; 2, determining a candidate matching road section of the mobile phone positioning data and a rough matching point set of each road section; 3, extracting vehicle-mounted mobile phone data, namely determining a fine matching point set of each road section; and 4, calculating space mean speed of the road section by a space speed weighted average method. The method is a dynamic and real-time traffic flow data acquisition method with a large coverage area and low acquisition cost; a traffic information acquisition mode and a source of an intelligent transportation system are enriched; and the method has positive significance to constructing the urban intelligent transportation system and improving the road operation management level.

The invention relates to an urban road traffic real-time state distinguishing and guidance system, consisting of a traffic information acquisition subsystem, a communication subsystem, a traffic information integrated processing platform, a traffic information release subsystem and a closed circuit television monitoring subsystem. The traffic information acquisition subsystem acquires the on-the-spot traffic information and transmits the traffic information to the traffic information integrated processing platform through the communication subsystem; the traffic information integrated processing platform adopts a traffic real-time state distinguishing algorithm to determine the traffic state of a specified section of the road according to a traffic flow, a speed, an occupancy and a periodic data packet, and transmits the determined result real-timely to the information release subsystem which then sends the result to a vehicle-oriented traffic scene. The urban road traffic real-time state distinguishing and guidance system of the invention can increase traffic efficiency of a traffic system, and improve the problem of the traffic jams.

The invention discloses a method for constructing driving condition of an electric vehicle, which can solve the problem that the conventional driving conditions cannot accurately evaluate various performance indexes and energy consumption of the electric vehicle, establishes a driver best travel manner model based on urban road network information and daily traffic flow, establishes a calculationmethod and an allocation rule of the sample capacity required for a test, and plans a test route accordingly, and determines the test time. A large amount of experimental data is obtained by using GPS / IMU equipment. According to the working characteristics of an electric motor of the electrical vehicle and its operating characteristics in urban roads, the rules of data preprocessing and data analysis are formulated. Finally, the driving condition of the electric vehicle is constructed based on the Markov-static Monte Carlo simulation method.

The invention relates to a method for computing dynamical traffic energy consumption and emission of urban road networks. The method includes acquiring energy consumption, exhaust emission and working condition data of sample vehicles; creating VSP-Bin (vehicle specific power-Bin) distribution rate databases; creating emission rate databases according to the exhaust gas emission data and the working condition data; combining gradient information of the road networks with dynamic traffic flow information of the road networks and computing dynamic traffic energy consumption and emission data of road sections and regions. The VSP-Bin distribution rate databases include VSP-Bin proportions under the conditions of multiple road grades, multiple vehicle models and multiple speed intervals. The emission rate databases include emission rates of pollutants in vehicle exhaust gas corresponding to various VSP-Bin intervals, the pollutants in the vehicle exhaust gas include NOx (nitric oxide), HC (hydrocarbon compounds) and CO (carbon oxide).