CN114783183A - Monitoring method and system based on traffic situation algorithm - Google Patents
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Abstract
The invention provides a monitoring method and a system based on a traffic situation algorithm, wherein the method comprises a step of calculating the regional traffic situation in real time, which comprises the steps of acquiring real-time and historical traffic data, representing the change rule of regional traffic flow by using a traffic flow model, calculating to obtain the characteristic value of the traffic flow model, evaluating the regional traffic running state based on the average speed and monitoring the change of the road running state in real time; and an abnormal congestion early warning step, namely establishing a short-time traffic speed prediction model based on a traffic situation algorithm by utilizing a convolutional neural network and a long-time and short-time memory network and combining an attention mechanism, performing iterative training on the established short-time traffic speed prediction model, and calculating the change condition of traffic situation, so as to predict the speed condition of a future target road section, further judge the congestion condition of the future target road section according to the preset average speed in a grading manner, and early warn the congestion condition in time, so that the traffic congestion is conveniently dredged.
Description
Technical Field
The invention relates to the technical field of intelligent highways, in particular to a monitoring method and a monitoring system based on a traffic situation algorithm.
Background
Under the situation of rapid development of the internet information industry, great effort is made on the aspects of research and construction of the smart highway in China, the pace is accelerated continuously, the policy and system environment, the resource condition and the like of the smart highway construction are mature day by day, the smart highway enters a new era of rapid development, and the core of the smart highway is a cloud control platform. In recent years, with the rapid increase of the number of motor vehicles in China, highway traffic congestion becomes a common phenomenon, and time delay, environmental pollution and the like caused by congestion cause huge economic loss to the society. Although many provinces have increased the investment in traffic infrastructure construction, it is not possible to fundamentally solve the contradiction between the increasing traffic demand and the relatively low traffic utilization.
In the aspect of traffic situation calculation, an existing algorithm is to judge whether a traffic event exists in the road section range by calculating the occupancy of adjacent coils and comparing the occupancy with a calibrated threshold value. The calibration of the threshold in the algorithm is particularly difficult, particularly in a large-scale road network, each independent threshold must be set according to different road geometric conditions, and the algorithm cannot represent specific traffic modes on road sections such as an entrance ramp, an interwoven section and an ascending slope, so that the false alarm rate is increased. There is also the McMaster situation recognition algorithm that derives a traffic-occupancy template that consists of four zones, each representing a particular traffic situation. The algorithm takes into account changes in traffic flow and road geometry and alignment when defining the blocking and non-blocking boundaries, and therefore requires redefinition of different locations and different data sets, which results in poor portability, and secondly the predetermined blocking and non-blocking boundaries cannot change over time during real-time operation.
The real-time traffic situation of the current road section published by the prior art cannot accurately show the current traffic condition, so that the research on the traffic situation can provide important basis for the path selection of travelers and the traffic dispersion of traffic managers, and the method is an important application in the technical field of intelligent roads.
Disclosure of Invention
The invention provides a monitoring method based on a traffic situation algorithm, which aims to solve the problems of low real-time performance of the traffic situation and low abnormal congestion early warning accuracy rate of the existing traffic situation calculation. The invention also relates to a monitoring method and a system based on the traffic situation algorithm.
The technical scheme of the invention is as follows:
a monitoring method based on a traffic situation algorithm is characterized by comprising the following steps:
the method comprises the steps of calculating regional traffic situation in real time, acquiring real-time and historical traffic data, representing the change rule of regional traffic flow by using a traffic flow model in the expression form of a scatter diagram and a curve graph, obtaining a function relation among flow, speed and density in a data fitting mode, calculating to obtain a characteristic value of the traffic flow model, evaluating the regional traffic running state based on average vehicle speed, and monitoring the change of the road running state in real time;
and an abnormal congestion early warning step, namely, based on the calculated average speed, utilizing a convolutional neural network and a long-time and short-time memory network, and combining an attention mechanism to establish a short-time traffic speed prediction model based on a traffic situation algorithm, performing iterative training on the established short-time traffic speed prediction model, extracting time and space characteristics of the actual speed of the target road section at each window moment, mapping and outputting hidden space correlation characteristics of the target road section at the window moment, calculating the change condition of the traffic situation, predicting the speed condition of the future target road section, judging the congestion condition of the future target road section according to the preset average speed in a grading manner, and early warning the congestion condition.
Preferably, in the step of calculating the regional traffic situation in real time, after the characteristic value of the traffic flow model is calculated, the regional traffic operation state is comprehensively evaluated based on the average speed and by combining the calculated road network saturation and the calculated traffic saturation.
Preferably, in the step of calculating the regional traffic situation in real time, the calculated average vehicle speed is further graded according to preset average speed, and the grades include five grades of free flow, basic smooth traffic, light congestion, medium congestion and heavy congestion, the road network saturation is the ratio of the mileage of a road section with heavy congestion and medium congestion processed in the road network to the total mileage of the road network, the traffic saturation is the ratio of the actual vehicle flow to the saturated traffic capacity, and different types of vehicles are converted into standard vehicle equivalents in the calculation.
Preferably, in the abnormal congestion early warning step, when the change situation of the traffic situation is calculated, the time intervals of congestion observed in the area are counted, the number of all the observed time intervals is compared, the congestion proportion is obtained, the area is divided into an over-utilization area, a reasonable utilization area and a low utilization area, the difference value of the congestion proportion between the peak time interval and the peak balance time interval of the over-utilization area exceeds a set threshold value, the difference value of the congestion proportion between the peak time interval and the peak balance time interval of the reasonable utilization area is within a set threshold value range, and the difference value of the congestion proportion between the peak time interval and the peak balance time interval of the low utilization area is lower than the set threshold value.
Preferably, in the abnormal congestion early warning step, the preset average speed is classified into five grades of free flow, basic smooth, light congestion, medium congestion and severe congestion, and the congestion index of the future target road section is judged according to the preset average speed in a grading manner so as to obtain the congestion condition of the future target road section.
Preferably, in the abnormal congestion early warning step, early warning is carried out on congestion conditions, and future road network operation indexes, congestion lengths and congestion time are predicted; and the calculation of the congestion length in the future is directly carried out by adopting a congestion road section length summation mode.
A monitoring system based on a traffic situation algorithm is characterized by comprising a regional traffic situation real-time calculation module and an abnormal jam early warning module which are mutually connected,
the regional traffic situation real-time calculation module is used for acquiring real-time and historical traffic data, representing the change rule of regional traffic flow by using a traffic flow model in the expression form of a scatter diagram and a curve graph, obtaining a function relation among flow, speed and density in a data fitting mode, calculating to obtain a characteristic value of the traffic flow model, evaluating the regional traffic running state based on the average speed and monitoring the change of the road running state in real time;
and the abnormal jam early warning module is used for establishing a short-time traffic speed prediction model based on a traffic situation algorithm by utilizing a convolutional neural network and a long-time and short-time memory network based on the average vehicle speed obtained by calculation and combining an attention mechanism, performing iterative training on the established short-time traffic speed prediction model, extracting time and space characteristics of the actual speed of the target road section at each window moment, mapping and outputting hidden space correlation characteristics of the target road section at the window moment, calculating the change condition of the traffic situation, predicting the vehicle speed condition of the future target road section, judging the jam condition of the future target road section according to the preset average speed grade, and early warning the jam condition.
Preferably, in the real-time regional traffic situation calculation module, the calculated average vehicle speed is graded according to a preset average speed, wherein the five grades comprise free flow, basic smooth, light congestion, medium congestion and severe congestion, and the regional traffic running state is comprehensively evaluated based on the average speed by combining the calculated road network saturation and the traffic saturation; the road network saturation is the ratio of the mileage of the road sections with serious congestion and moderate congestion in the road network to the total mileage of the road network, and the traffic saturation is the ratio of the actual traffic flow to the saturated traffic capacity and converts different types of vehicles into the equivalent number of standard vehicles during calculation.
Preferably, in the abnormal congestion early warning module, when the change situation of the traffic situation is calculated, the congestion time periods observed in the area are counted, the number of all observation time periods is compared, the congestion proportion is obtained, the area is divided into an over-utilization area, a reasonable utilization area and a low utilization area, the congestion proportion difference between the peak time period and the peak balance time period of the over-utilization area exceeds a set threshold value, the congestion proportion difference between the peak time period and the peak balance time period of the reasonable utilization area is within a set threshold value range, and the congestion proportion difference between the peak time period and the peak balance time period of the low utilization area is lower than the set threshold value.
Preferably, in the abnormal congestion early warning module, early warning is carried out on congestion conditions, and future road network operation indexes, congestion lengths and congestion time are predicted; and the calculation of the congestion length in the future is directly carried out by adopting a congestion road section length summation mode.
The invention has the beneficial effects that:
the invention provides a monitoring method based on a traffic situation algorithm, which comprises the steps of representing the change rule of regional traffic flow by using a traffic flow model according to real-time and duration traffic data, calculating to obtain a characteristic value of the traffic flow model, namely calculating to obtain the overall traffic flow characteristic of a road through the algorithm, evaluating the traffic running state of the road, and monitoring the change of the running state of the road in real time; meanwhile, according to the real-time traffic information, the change situation of the future traffic situation is calculated by establishing a short-time traffic speed prediction model based on a traffic situation algorithm, the speed situation of the future target road section is predicted, the congestion situation of the future target road section is judged in a grading mode according to the preset average speed, the congestion situation in a period of time in the future is warned in time, the possible congestion index, the congestion length and the congestion time can be further predicted, and the traffic congestion can be conveniently relieved. The real-time traffic situation calculation method has real-time performance, the real-time traffic situation is calculated by using a traffic map model according to real-time traffic information, the real-time traffic situation is calculated by an algorithm, and the output delay of the algorithm is less than 5 seconds; the real-time road condition updating time is less than 2 minutes. The short-time traffic road condition prediction algorithm has the output delay of less than 5 seconds; and (5) predicting the traffic flow and the running speed in the short-time road section by taking 15min and 1h as time intervals. The method has convenience, and only needs to use the existing traffic data of the highway: such as highway access & exit data, ETC portal data, internet traffic data etc. need not to increase new equipment. The prediction of the future traffic jam condition of a certain road section is firstly required to predict the speed condition of the road section in the future, and the congestion condition of a road network is further judged according to the speed, the prediction of the future speed is completed by adopting a short-time traffic speed prediction model GCN-LSTM based on a traffic situation algorithm, the convolutional neural network GCN is combined with a long-short term memory network LSTM, by modeling the highway network and extracting the spatial characteristics of the actual speed of the target road section at each window moment by utilizing the GCN neural network, and mapping hidden space correlation characteristics of the target road section at the moment of the output window, wherein the speed has time correlation in time dimension, and the LSTM is used as a common model for extracting time series characteristics and is used as a key method for extracting time characteristics to calculate the change condition of traffic situation, and predicting the vehicle speed condition of the future target road section, and further judging the congestion condition of the future target road section according to the preset average speed grade. The method has accuracy, and the early warning accuracy rate of real-time road conditions and abnormal congestion reaches 80%; the accuracy rate of predicting the road condition in 15 minutes is 80 percent; the accuracy rate of road congestion prediction within one hour reaches 90%. According to the method, the road information and the multi-dimensional internet data are fused, the capabilities of finding, identifying, understanding, analyzing and responding to and handling the traffic jam are improved from a global view angle, and meanwhile, the result of algorithm calculation can be displayed to a manager in real time so as to facilitate the manager to make a decision in time.
The invention also relates to a monitoring system based on the traffic situation algorithm, which corresponds to the monitoring method based on the traffic situation algorithm and can be understood as a system for realizing the monitoring method based on the traffic situation algorithm, the monitoring system comprises a regional traffic situation real-time calculation module and an abnormal jam early warning module, the modules work cooperatively, regional traffic situation real-time calculation is carried out through a traffic flow model, road running state change is monitored in real time, future vehicle speed is predicted by combining a short-time traffic speed prediction model GCN-LSTM based on the traffic situation algorithm, and then the future jam condition of a certain road section can be predicted, the traffic situation algorithm can obtain detailed traffic running condition data, a road manager can conveniently monitor the traffic condition in real time, the predicted road section traffic condition can provide basis for the path selection of a traveler, alleviate traffic jam, promoted the prediction accuracy, and convenient to use.
Drawings
FIG. 1 is a flow chart of a traffic situation algorithm based monitoring method of the present invention.
Fig. 2 is a preferred flow chart of the traffic situation algorithm based monitoring method of the present invention.
FIG. 3 is a flow chart of the short-term traffic speed prediction model GCN-LSTM according to the present invention.
Detailed Description
The present invention will be described with reference to the accompanying drawings.
The invention relates to a monitoring method based on a traffic situation algorithm, and a flow chart of the method is shown in figure 1, and comprises the following steps:
the regional traffic situation real-time calculation step comprises the steps of dividing a highway network into a plurality of grid regions, acquiring real-time and historical traffic data, representing the change rule of regional traffic flow by using a traffic flow model in the expression form of a scatter diagram and a curve diagram, obtaining a function relation among flow, speed and density in a data fitting mode, calculating characteristic values of the traffic flow model, evaluating the regional traffic running state based on average vehicle speed, and monitoring the change of the road running state in real time. The traffic data mainly comprises an expressway exit flow water meter, an expressway entrance flow water meter, ETC portal plate identification data, internet dynamic road condition data, traffic incident data, traffic jam early warning, weather data and the like.
In the running process of the traffic flow of the expressway, the traffic state changes all the time, and a plurality of parameters capable of representing the traffic state comprise flow, speed, density, occupancy, time interval, travel time and the like. The method is based on a statistical traffic flow basic graph model (namely a traffic flow model, a Van-aerode model) to calculate regional traffic flow, describes the relation among three traffic elements of flow, speed and density, represents the state and the change rule of the traffic flow in the expression form of a scatter diagram and a curve diagram, and obtains a function relation among the flow, the speed and the density in a data fitting mode.
The invention collects the speed and the passing direction of the vehicle in real time and describes the regional traffic flow by using a Van-aerode model, and the model formula is as follows:
in the formula: s represents a locomotive head spacing (km); v represents the running speed (km/h); v. offRepresenting the free stream velocity (km/h); c. C1Representing a locomotive head spacing parameter (km); c. C2Representing variable following parameter (km)2/h)；c3Representing headway parameter (h)-1)。
Parameter c1、c2、c3The calculation method of (2) is as follows:
c1＝vf(2vm-vf)/(kjvm 2) (2)
c2＝vf(vf-vm)/(kjvm 2) (3)
in the formula: q. q ofmMaximum flow rate (veh/h); v. ofmThe speed (km/h) corresponding to the maximum flow rate; v. offIs the free stream velocity (km/h); k is a radical ofjIs the occlusion density (veh/km).
Density k is the reciprocal of the headway:
the flow q can be derived from the velocity, density:
q＝kv (6)
and calibrating the model parameters by acquiring the vehicle data of the highway in real time to obtain a function, and further obtaining a flow rate-speed curve, a flow rate-density curve and a speed-density curve.
And after the overall regional traffic flow characteristics are obtained, the regional traffic operation state is evaluated, and whether the traffic flow is smooth and whether traffic jam occurs or not is analyzed. The average speed is an index which can represent the running state of the vehicle most, the average speed is the average travel speed of the road network, and the average travel speed refers to the ratio of the total driving mileage of all vehicles on a road or the road network to the total travel time, wherein the travel time comprises intermediate parking time and queuing time. The speed is high, which indicates that the traffic flow runs freely and is not influenced by the environment and the road traffic condition; the low speed indicates that the traffic flow is obstructed and cannot keep running at a higher speed.
The traffic situation, in other words, the congestion situation, is divided into 5 levels according to the average speed according to the different speed limits (km/h), as shown in the average speed ranking table of table 1.
TABLE 1
The average speed is the average travel speed of the highway section, is mainly calculated according to the water meter at the exit of the highway, and is used for distinguishing vehicle types and rejecting bad data. The main process is as follows:
(1) selecting data for a respective interval and a respective time period (real-time);
(2) matching the length s of the running road section of each vehicle according to the serial number of the toll station at the entrance and exit of the vehicleiAnd calculates the travel time t of each vehicleiOutbound time-inbound time;
(3) setting a travel time tiThe range is 0-24 h, and data beyond the range are removed;
(4) according to
Calculating the average speed of each vehicle, setting the range of the average speed to be 0-150 km/h, and eliminating data beyond the range;
(5) according to
And calculating the average speed corresponding to each road section. Wherein n is in the time interval of the road sectionThe number of vehicles.
Further, as shown in fig. 2, after the characteristic value of the traffic flow model is obtained through calculation, the regional traffic operation state is comprehensively evaluated based on the average speed and by combining the calculated road network saturation and the calculated traffic saturation. The traffic flow is mainly calculated according to ETC portal plate identification data, vehicle types and up-down movement are distinguished simultaneously, and the number plate numbers in a certain time period are directly summed. And grading the calculated average vehicle speed according to a preset average speed, wherein the grades comprise five grades of free flow, basic smooth, light congestion, medium congestion and severe congestion.
The road network saturation is also commonly called road load degree and road network capacity fitness, and is the ratio of the mileage of a road section in a severe congestion state and a moderate congestion state in a road network to the total mileage of the road network, and the calculation formula is as follows:
wherein M is road network saturation; p is the mileage (km) of the road sections in the network under the severe congestion state and the moderate congestion state; x is the total mileage (km) of the road network.
The traffic saturation is the ratio of the actual traffic flow to the saturated traffic capacity, and different types of vehicles are converted into standard vehicle equivalent numbers in calculation, namely VC ratio, and the calculation formula is as follows:
wherein V is an actual traffic flow (pcu); c is the saturated traffic capacity (pcu) related to the designed speed and number of lanes of the highway, where C is the single lane traffic capacity per number of lanes, and the single lane traffic capacity can be referred to the relevant design specifications, as shown in table 2.
TABLE 2
| Design speed (km/h) | Traffic capacity (pcu/h/ln) |
| 120 | 2200 |
| 100 | 2100 |
| 80 | 2000 |
| 60 | 1800 |
In the above calculations, it is necessary to convert the different types of vehicles into standard vehicle equivalents (pcu), as shown in table 3 with reference to the relevant standards.
TABLE 3
Specifically, the vehicle types are classified as follows: 01-first type passenger car 2-second type passenger car 3-third type passenger car 4-fourth type passenger car 11-first type truck 12-second type truck 13-third type truck 14-fourth type truck 15-fifth type truck 16-sixth type truck 21-first type special working vehicle 22-second type special working vehicle 23-third type special working vehicle 24-fourth type special working vehicle 25-fifth type special working vehicle 26-sixth type special working vehicle. The conversion factor corresponding to each vehicle type was set as shown in table 4.
TABLE 4
| Vehicle model | Conversion factor | Vehicleclass number |
| Small passenger/goods vehicle | 1.0 | 0，1，2，11，21 |
| Large passenger car/medium-sized truck | 1.5 | 3，4，12，13，22，23 |
| Large truck | 2.0 | 14，24 |
| Super-huge truck | 3.0 | 15，16，25，26 |
And an abnormal jam early warning step, namely establishing a short-time traffic speed prediction model based on a traffic situation algorithm by using a convolutional neural network and a long-time and short-time memory network based on the average vehicle speed obtained by calculation and combining an attention mechanism, performing iterative training on the established short-time traffic speed prediction model, extracting time and space characteristics of the actual speed of the target road section at each window moment, mapping and outputting hidden space correlation characteristics of the target road section at the window moment, calculating the change condition of the traffic situation, predicting the vehicle speed condition of the future target road section, judging the jam condition of the future target road section according to the preset average speed grade, and early warning the jam condition.
Predicting the congestion condition of a road section in the future first needs to predict the vehicle speed condition of the road section in the future, and then judges the congestion condition of the road network according to the vehicle speed. The prediction of the future vehicle speed is completed by adopting a short-time traffic speed prediction model GCN-LSTM based on a graph convolution neural network, a flow chart is shown in figure 3, real-time traffic data and historical traffic data are preprocessed, and then test data are added for model training and testing to obtain the real-time traffic speed prediction. The overall framework of the model is mainly divided into 5 modules: the system comprises an input layer, a spatial correlation feature extraction module, a temporal correlation feature extraction module, an attention mechanism module and a prediction module. The model utilizes a GCN convolutional neural network to extract the spatial feature of the actual speed of the target road section at each window moment, and maps and outputs the hidden spatial correlation feature of the target road section at the window moment. The speed has time correlation in a time dimension, and an LSTM long-time memory network is used as a common model for time series feature extraction and is used as a key method for time feature extraction.
The parameters of the GCN-LSTM model mainly comprise: batch size, learning rate, iteration times, forgetting offset and LSTM unit number, and obtaining parameter configuration with highest prediction precision through continuous parameter debugging. For the model input layer, the training set (80%) was used as input to the training process, and the remaining data (20%) was used as the test set. The GCN-LSTM model can be trained using an Adam optimizer. During the training process, the ultimate goal is to minimize the difference between the actual traffic speed and the predicted speed on the road, using VtAnd
representing the actual and predicted speeds, respectively, the loss function of the GCN-LSTM model is as follows:
in the formula, LregIs an L2 regular term to prevent overfitting, λ is a hyper-parameter.
The evaluation index is to evaluate the prediction performance of the model, and the actual speed (V) is evaluated using the following three indexest) And predicting speed
The error between.
(1) Root Mean Square Error (RMSE)
(2) Mean Absolute Error (MAE)
(3) Determining the coefficient (R)2)
Specifically, RMSE and MAE are used to measure the prediction error, with smaller values being better and smaller representative errors; and R is2The larger the better, R when the prediction model does not make any errors2May be 1, but is often not present in reality.
The driving speed is predicted by subdividing the road section, and then the congestion index of the road section in the future is judged by a table 1 (average speed grading table), and the road section is also divided into 5 grades: free flow, basic smooth, light congestion, moderate congestion and severe congestion. For the congestion situation possibly caused in the future, the method predicts the speed of the road section in the future, judges the congestion situation according to the speed, and directly calculates the congestion length by adopting a congestion road section adding and summing mode. The traffic situation algorithm can obtain detailed traffic running condition data, so that a road manager can conveniently monitor the traffic condition in real time, the predicted future traffic condition of the road section can provide a basis for the path selection of travelers, and the traffic jam is relieved.
And calculating the change condition of the traffic situation according to the real-time traffic information of the traffic, giving early warning on the congestion condition in time, and predicting the road network operation index, the congestion length and the congestion time. Each zone is described in units of 2 minutes in time, and the change trend of the running state of the zone in time is obtained. And researching the congestion rule of each region, and analyzing the frequent congestion and the occasional congestion to obtain the region traffic property. The time intervals of the congestion observed in the region are counted, the number of all the observed time intervals is compared to obtain the congestion proportion, and the region is divided into three types as shown in table 5.
TABLE 5
| Region type | The following steps are described: |
| over-utilization area | The difference between the jam proportions in the peak time period and the average time period is large |
| Rational utilization of area | The difference value of the jam proportion between the peak time and the flat time is in a reasonable range |
| Low utilization area | The congestion ratio at the peak period is hardly increased compared to that at the flat period |
The invention also relates to a monitoring system based on the traffic situation algorithm, which corresponds to the monitoring method based on the traffic situation algorithm and can be understood as a system for realizing the monitoring method based on the traffic situation algorithm, the system comprises a regional traffic situation real-time calculation module and an abnormal jam early warning module,
the regional traffic situation real-time calculation module is used for acquiring real-time and historical traffic data, representing the change rule of regional traffic flow by using a traffic flow model in the expression form of a scatter diagram and a curve graph, obtaining a function relation among flow, speed and density in a data fitting mode, calculating to obtain a characteristic value of the traffic flow model, evaluating the regional traffic running state based on the average vehicle speed and monitoring the change of the road running state in real time. Preferably, the calculated average vehicle speed is graded according to a preset average speed, the grade comprises five grades of free flow, basic smooth, light congestion, medium congestion and severe congestion, and the regional traffic running state is comprehensively evaluated based on the average speed by combining the road network saturation and the traffic saturation; the road network saturation is the ratio of the mileage of a road section with severe congestion and moderate congestion processed in the road network to the total mileage of the road network, and the traffic saturation is the ratio of the actual traffic flow to the saturated traffic capacity and converts different types of vehicles into the equivalent number of standard vehicles during calculation.
The abnormal jam early warning module is used for establishing a short-time traffic speed prediction model based on a traffic situation algorithm by utilizing a convolutional neural network and a long-time and short-time memory network based on the average vehicle speed obtained through calculation and combining an attention mechanism, performing iterative training on the established short-time traffic speed prediction model, extracting time and space characteristics of the actual speed of the target road section at each window moment, mapping and outputting hidden space correlation characteristics of the target road section at the window moment, calculating the change condition of the traffic situation, predicting the vehicle speed condition of the target road section in the future, judging the jam condition of the target road section in the future according to the preset average speed grade, and early warning the jam condition.
Further, in the abnormal congestion early warning module, when the change condition of the traffic situation is calculated, the time intervals of congestion observed in the area are counted, the number of all observation time intervals is compared, the congestion proportion is obtained, the area is divided into an over-utilization area, a reasonable utilization area and a low utilization area, the congestion proportion difference value between the peak time interval and the flat time interval of the over-utilization area exceeds a set threshold value, the congestion proportion difference value between the peak time interval and the flat time interval of the reasonable utilization area is within the range of the set threshold value, and the congestion proportion difference value between the peak time interval and the flat time interval of the low utilization area is lower than the set threshold value.
Further, in the abnormal congestion early warning module, the preset average speed is classified into five grades of free flow, basic smooth, light congestion, medium congestion and severe congestion, and the congestion index of the future target road section is judged according to the preset average speed in a grading manner so as to obtain the congestion condition of the future target road section. The operation index, congestion length and congestion time of the road network can be predicted; the calculation of the future congestion length directly adopts a congestion section length summation mode.
It should be noted that the above-described embodiments may enable those skilled in the art to more fully understand the present invention, but do not limit the present invention in any way. Therefore, although the present invention has been described in detail with reference to the drawings and examples, it will be understood by those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention.
Claims (10)
1. A monitoring method based on a traffic situation algorithm is characterized by comprising the following steps:
the method comprises the steps of calculating regional traffic situation in real time, acquiring real-time and historical traffic data, representing the change rule of regional traffic flow by using a traffic flow model in the expression form of a scatter diagram and a curve graph, obtaining a function relation among flow, speed and density in a data fitting mode, calculating to obtain a characteristic value of the traffic flow model, evaluating the regional traffic running state based on average vehicle speed, and monitoring the change of the road running state in real time;
and an abnormal congestion early warning step, namely, based on the calculated average speed, utilizing a convolutional neural network and a long-time and short-time memory network, and combining an attention mechanism to establish a short-time traffic speed prediction model based on a traffic situation algorithm, performing iterative training on the established short-time traffic speed prediction model, extracting time and space characteristics of the actual speed of the target road section at each window moment, mapping and outputting hidden space correlation characteristics of the target road section at the window moment, calculating the change condition of the traffic situation, predicting the speed condition of the future target road section, judging the congestion condition of the future target road section according to the preset average speed in a grading manner, and early warning the congestion condition.
2. The monitoring method based on the traffic situation algorithm as claimed in claim 1, wherein in the step of calculating the regional traffic situation in real time, after calculating the characteristic value of the traffic flow model, the regional traffic operation state is comprehensively evaluated based on the average speed and by combining the calculation of the road network saturation and the traffic saturation.
3. The monitoring method based on the traffic situation algorithm as claimed in claim 2, wherein in the step of calculating the regional traffic situation in real time, the calculated average vehicle speed is further graded according to preset average speed, the grade includes five grades of free flow, basic clear, light congestion, medium congestion and severe congestion, the road network saturation is a ratio of mileage of a road section handling severe congestion and medium congestion in the road network to total road network mileage, the traffic saturation is a ratio of actual vehicle flow to saturated traffic capacity, and different types of vehicles are converted into standard vehicle equivalent numbers in calculation.
4. The monitoring method based on the traffic situation algorithm according to one of claims 1 to 3, characterized in that in the abnormal congestion early warning step, when the change situation of the traffic situation is calculated, the time interval of the congestion observed in the area is counted, the number of all observed time intervals is compared to obtain the congestion ratio, the area is divided into an over-utilization area, a reasonable utilization area and a low utilization area, the difference value of the congestion ratio between the peak time interval and the peak average time interval of the over-utilization area exceeds a set threshold value, the difference value of the congestion ratio between the peak time interval and the peak average time interval of the reasonable utilization area is within a set threshold value range, and the difference value of the congestion ratio between the peak time interval and the peak average time interval of the low utilization area is lower than the set threshold value.
5. The monitoring method based on the traffic situation algorithm according to claim 1 or 2, characterized in that in the abnormal congestion early warning step, the preset average speed is classified into five levels of free flow, basic smooth, light congestion, medium congestion and severe congestion, and the congestion index of the future target road section is judged according to the preset average speed in a classified manner so as to obtain the congestion condition of the future target road section.
6. The monitoring method based on the traffic situation algorithm as claimed in claim 5, wherein in the abnormal congestion early warning step, early warning is performed on congestion conditions, and a future road network operation index, congestion length and congestion time are predicted; and the calculation of the congestion length in the future is directly carried out by adopting a congestion road section length summation mode.
7. A monitoring system based on a traffic situation algorithm is characterized by comprising a regional traffic situation real-time calculation module and an abnormal jam early warning module which are mutually connected,
the regional traffic situation real-time calculation module is used for acquiring real-time and historical traffic data, representing the change rule of regional traffic flow by using a traffic flow model in the expression form of a scatter diagram and a curve diagram, obtaining a function relation among flow, speed and density in a data fitting mode, calculating to obtain a characteristic value of the traffic flow model, evaluating the regional traffic running state based on the average speed and monitoring the change of the road running state in real time;
the abnormal jam early warning module is used for establishing a short-time traffic speed prediction model based on a traffic situation algorithm by utilizing a convolutional neural network and a long-time and short-time memory network based on the average vehicle speed obtained through calculation and combining an attention mechanism, performing iterative training on the established short-time traffic speed prediction model, extracting time and space characteristics of the actual speed of the target road section at each window moment, mapping and outputting hidden space correlation characteristics of the target road section at the window moment, calculating the change condition of the traffic situation, predicting the vehicle speed condition of the target road section in the future, judging the jam condition of the target road section in the future according to the preset average speed grade, and early warning the jam condition.
8. The monitoring system based on the traffic situation algorithm as claimed in claim 7, wherein in the regional traffic situation real-time calculation module, the calculated average vehicle speed is further graded according to a preset average speed, including five grades of free flow, basic smooth, light congestion, medium congestion and severe congestion, and the regional traffic running state is comprehensively evaluated based on the average speed and by combining the calculated road network saturation and the traffic saturation; the road network saturation is the ratio of the mileage of the road sections with serious congestion and moderate congestion in the road network to the total mileage of the road network, and the traffic saturation is the ratio of the actual traffic flow to the saturated traffic capacity and converts different types of vehicles into the equivalent number of standard vehicles during calculation.
9. The monitoring system based on the traffic situation algorithm according to claim 7 or 8, wherein in the abnormal congestion warning module, when the change situation of the traffic situation is calculated, the time interval of the congestion observed in the area is counted, the number of all observation time intervals is compared to obtain the congestion proportion, the area is divided into an over-utilization area, a reasonable utilization area and a low utilization area, the congestion proportion difference between the peak time interval and the flat time interval of the over-utilization area exceeds a set threshold, the congestion proportion difference between the peak time interval and the flat time interval of the reasonable utilization area is within a set threshold range, and the congestion proportion difference between the peak time interval and the flat time interval of the low utilization area is lower than the set threshold.
10. The monitoring system based on the traffic situation algorithm as claimed in claim 9, wherein in the abnormal congestion early warning module, early warning is performed on congestion conditions, and a future road network operation index, congestion length and congestion time are predicted; and the calculation of the congestion length in the future is directly carried out by adopting a congestion road section length summation mode.
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