CN113327418A - Expressway congestion risk grading real-time prediction method - Google Patents
Expressway congestion risk grading real-time prediction method Download PDFInfo
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Abstract
The invention relates to a method for grading and predicting an expressway congestion risk in real time, which comprises the following steps of: 1) dividing road section units of the target express way according to road geometric data of the target express way; 2) acquiring the free flow speed of a target road section according to the traffic flow historical data of each road section unit; 3) calculating a road traffic index, and dividing congestion levels of each road section unit according to the road traffic index; 4) carrying out variable screening according to the congestion level, the road geometric data, the traffic flow data and the weather data, then constructing a congestion risk real-time prediction model, and carrying out congestion risk probability prediction; 5) and judging whether traffic control measures need to be taken or not according to the congestion risk probability prediction result, if so, taking corresponding control measures to realize dynamic adjustment, and otherwise, returning to the step 4). Compared with the prior art, the method grades the congestion degree, fully considers the sequence relation among the congestion levels, and has the advantages of high accuracy, good interpretability, strong availability and the like.
Description
Technical Field
The invention relates to the technical field of prediction of traffic running states of express ways, in particular to a method for predicting congestion risks of express ways in a grading and real-time manner.
Background
The urban expressway is an important component of a modern traffic system and bears most of medium and long distance traffic and transit traffic. Once traffic jam occurs, the traffic jam usually shows that the intensity of the jam is high, the number of jammed road sections is large, the jam time is long, and the traffic jam is very easy to spread to ground roads. In various measures for reducing the influence of congestion of the expressway, active traffic control is the key point, and accurate congestion risk prediction is the premise of active traffic control. The congestion risk prediction result can be used as an input of active traffic control, so that the type and the grade of active traffic control measures are determined.
The congestion risk real-time prediction technology is rapidly developed in recent years, and the result of a happy person is obtained, but certain defects still exist, and the technology specifically comprises the following two points:
the existing prediction technology does not consider the sequence relation among all congestion levels, and the 4 results which are irrelevant to each other are regarded as the existing prediction technology, so that the prediction precision is low;
secondly, in the existing prediction technology, only traffic flow parameters are considered in input data, factors such as weather and road section geometric characteristics are not considered in a range, and the consideration is not comprehensive enough.
Thirdly, the existing prediction technology focuses on the prediction precision, is deficient in the analysis of the congestion cause and the influence degree thereof, and cannot effectively provide a proper management and control direction for traffic management and control.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for predicting the risk classification of the expressway congestion in real time.
The purpose of the invention can be realized by the following technical scheme:
a real-time prediction method for expressway congestion risk classification comprises the following steps:
1) dividing road section units of the target express way according to road geometric data of the target express way;
2) acquiring the free flow speed of a target road section according to the traffic flow historical data of each road section unit;
3) calculating a road traffic index according to the free flow speed of each road section unit and the space average speed of each road section unit acquired in real time, and dividing the congestion level of each road section unit according to the road traffic index;
4) carrying out variable screening according to the congestion level, the road geometric data, the traffic flow data and the weather data, then constructing a congestion risk real-time prediction model, and carrying out congestion risk probability prediction;
5) and judging whether traffic control measures need to be taken or not according to the congestion risk probability prediction result, if so, taking corresponding control measures to realize dynamic adjustment, and otherwise, returning to the step 4).
In the step 1), the concrete process of dividing the road section units is as follows:
and manually confirming the origin-destination point of the road section unit according to the arrangement position of the detector and the ramp afflux position information.
In the step 2), the express way average speed when the density tends to 0 is taken as the free flow speed of the target road section.
In the step 3), the calculation formula of the road traffic index is as follows:
wherein TSI is road traffic index, vfFor free flow velocity, viIs the spatial average velocity.
The congestion levels of all the road section units are divided according to the road traffic index TSI, the congestion levels comprise 4 levels of smooth, comparatively smooth, congested and serious congestion, and the specific corresponding division relationship is as follows:
| road traffic index | [0，30) | [30，50) | [50，70) | [70，100] |
| Road traffic index grading | Level 1 | Stage 2 | Grade 3 | 4 stage |
| Grade of traffic operating state | Clear | Is relatively unblocked | Congestion | Severe congestion |
。
In the step 4), the road geometric data includes lane change conditions, the number of ramp layout, the number of express way lanes and road section length, the weather data includes visibility conditions and precipitation, and the traffic flow data includes space average speed, flow and change conditions thereof.
In the step 4), the congestion risk real-time prediction model is specifically an ordered Logit model, and the expression is as follows:
p1+p2+p3+p4＝1
speed is the space average Speed of a target road section in first preset time, Speed _ diff _ abs _ ratio is the absolute value of the change proportion of the space average Speed of the target road section in first preset time and second preset time, Vol _1 is the flow of a first detector at the upstream of the target road section in the first preset time, Vol _ diff _1 is the change of the flow of the first detector at the upstream of the target road section in the first preset time and the second preset time, Vol _ diff _2 is the change of the flow of the first detector at the downstream of the target road section in the first preset time and the second preset time, Lane _ drop is the reduction of the number of lanes on the target road section, Mist is the visibility condition of an express way, p is the visibility condition of the express way, and1、p2、p3、p4respectively, the predicted probabilities corresponding to the congestion levels, a1、…、a10All are training parameters, and the above 7 parameters are parameters after screening.
When the real-time congestion risk prediction model is trained, sample data comprises congestion levels, road geometric data, traffic flow data and weather data, and the sample data is trained after random sampling, data cleaning and independent variable screening.
The method comprises the steps that weather data are updated according to preset time granularity, the time granularity corresponding to the weather data is 5min, traffic flow data are obtained in real time based on a video card port and are updated according to the preset time granularity, the time granularity corresponding to the traffic flow data is 5min, a congestion risk prediction result is updated by a congestion risk prediction model according to preset time intervals, and the preset time intervals are 5 min.
In the step 5), the traffic control measures comprise road side control measures and vehicle-mounted control measures, the road side control measures comprise variable speed limit control and ramp control, the vehicle-mounted control measures comprise speed coordination control, and if the reason for causing the congestion risk predicted value to rise is that the traffic volume is large and the occupancy rate is high, the variable speed limit control and the ramp control are adopted to reduce short-term traffic volume; and if the reason for causing the congestion risk predicted value to rise is that the vehicle speed difference is large, adopting vehicle-mounted management and control measures.
Compared with the prior art, the invention has the following advantages:
the method and the device consider the sequence of the congestion levels of the expressway, analyze the internal relation among the congestion levels, and establish a congestion risk prediction model, so that the prediction accuracy and the model availability of the model are remarkably improved.
On the basis of traffic flow parameters, the influences of road section geometric characteristics and weather characteristics on a congestion formation mechanism are also considered, the interpretability of the prediction model is improved, the stability of the prediction performance of the prediction model is improved by increasing input variable types, management and control of managers are facilitated, and the service quality of the system is improved.
And thirdly, related research is developed based on a statistical analysis method, so that the method is beneficial to determining the obvious cause and the influence degree of the congestion, provides model explanatory property and provides a proper management and control direction for a traffic management and control system.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
FIG. 2 is a flow chart of selection of input variables of the predictive model.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
The invention provides a method for predicting the risk classification of expressway traffic jam in real time, which is shown in figure 1 and is used for fully considering the sequential relationship among jam grades and improving the accuracy and the availability of the technology in the field, and the method comprises the following steps:
step S1: acquiring road geometric data of a target express way, dividing road section units of the target express way according to the road geometric data, and carrying out congestion mechanism analysis based on each road section unit;
step S2: acquiring traffic flow historical data of each road section unit, and selecting the average speed of the express way when the density tends to 0 as the free flow speed of the target road section;
step S3: acquiring the space average speed of each road section unit in real time based on the free flow speed of each road section unit, calculating a road traffic index according to the space average speed, and dividing the congestion level of each road section unit;
step S4: establishing a congestion risk real-time prediction model based on congestion levels of all road section units, acquiring traffic flow data and weather data of a target express way in real time, inputting road geometric data, the traffic flow data and the weather data into the congestion risk prediction model, and outputting a congestion risk prediction result, wherein the road geometric data comprises lane change conditions, the number of laid ramps, the number of lanes of the express way and road section length; weather data includes visibility conditions, precipitation; the traffic flow data comprises space average speed, flow and change conditions thereof;
step S5: and judging whether traffic control measures need to be taken or not according to the congestion risk prediction result, if so, taking corresponding appropriate control measures to realize dynamic adjustment, and otherwise, returning to the step S4.
In step S1, the road geometry data of the target express way is collected by the ArcGIS software, and the specific process of dividing the road segment unit is as follows:
and manually confirming the origin-destination point of the road section unit according to the data such as the arrangement position of the detector, the ramp convergence position and the like, wherein the length of the road section unit is kept in a proper range and is not suitable to be too long or too short.
In step S3, the specific expression for calculating the road traffic index is as follows:
wherein TSI is road traffic index, vfFor free flow velocity, viIs the spatial average speed of the road segment i.
The congestion levels comprise 4 levels of unblocked, comparatively unblocked, congested and serious congestion, and the congestion degrees of the express way corresponding to different TSI values are shown in a table 1:
table 1 congestion level division table
| Road traffic index | [0，30) | [30，50) | [50，70) | [70，100] |
| Road traffic index grading | Level 1 | Stage 2 | Grade 3 | 4 stage |
| Grade of traffic operating state | Clear | Is relatively unblocked | Congestion | Severe congestion |
Step S4 specifically includes the following steps:
step S41: according to case group: control group 1: 5, extracting data according to the proportion;
step S42: carrying out data cleaning;
step S43: performing variable reconstruction based on the spatio-temporal correlation;
step S44: selecting independent variables which are obviously related to the dependent variables and can provide the best predictive performance;
step S45: and (3) establishing a congestion risk prediction model based on the ordered Logit theory by using the screening result, wherein the ordered Logit model is specifically expressed as follows:
p1+p2+p3+p4＝1
wherein p is1、p2、p3、p4Respectively, the predicted probability of the corresponding congestion level, Speed is the spatial average Speed of the target road section in a first preset time, Speed _ diff _ abs _ ratio is the absolute value of the change proportion of the spatial average Speed of the target road section in the first preset time and a second preset time, Vol _1 is the flow of the first detector at the upstream of the target road section in the first preset time, Vol _ diff _1 is the change of the flow of the first detector at the upstream of the target road section in the first preset time and the second preset time, Vol _ diff _2 is the change of the flow of the first detector at the downstream of the target road section in the first preset time and the second preset time, Lane _ drop is the reduction of the number of lanes on the target road section, and Mist is the visibility condition of the express way.
In this embodiment, the first preset time is 0 to 5min, the second preset time is 5 to 10min, a Lane number change value of a road segment corresponding to Lane _ drop of 1 indicates that the Lane number is reduced, and a Lane number change value of 0 indicates that the Lane number does not change. The visibility condition value corresponding to the Mist is 1, which indicates that the weather is foggy and the visibility is limited, and the visibility is good when the visibility condition value is 0, which indicates that the weather is fogless.
In step S41, the control group data is extracted by random sampling, i.e. the sample sampling space of the control group is not required to be controlled.
The data cleansing operation in step S42 includes invalid sample elimination, missing value detection and padding, and abnormal value detection and padding.
In step S43, a variable reflecting temporal and spatial differences in traffic flow state is constructed on the basis of the existing variable.
In step S45, as shown in fig. 2, an independent variable that is significantly related to a dependent variable and provides the best predictive performance is selected, which overcomes the problem of multiple collinearity in the model and the problem of more dimensions in the modeling process, and in the present embodiment, 7 modeling variables, i.e., Speed _ diff _ abs _ ratio, Vol _1, Vol _ diff _2, Lane _ drop, and Mist, are screened and retained.
Independent variables in the prediction model based on the ordered Logit theory are obtained by screening in a correlation analysis result, an ordered Logistic regression result and a random forest algorithm result.
The weather data comprises visibility, is obtained in real time based on various data sources, and is updated according to preset time granularity, and in the embodiment, the time granularity corresponding to the weather data is 5 min.
Traffic flow data is obtained in real time based on a video checkpoint, variable construction is carried out, variable information required by model input is obtained, updating is carried out according to preset time granularity, and the updated time granularity is sent to a congestion risk prediction model.
The congestion risk prediction model updates the congestion risk prediction result in real time according to a preset time interval, which is 5min in this embodiment.
In step S5, the traffic control measures include road side control measures and vehicle control measures, the road side control measures include variable speed limit control and ramp control, the vehicle control measures include speed coordination control, and if the cause of congestion risk prediction value increase is mainly large traffic volume and high occupancy, the variable speed limit control and ramp control are adopted to reduce short-term traffic volume; if the reason for causing the congestion risk predicted value to rise is mainly that the vehicle speed difference is large, vehicle-mounted traffic control measures are adopted, and corresponding suggested speed is given to each intelligent internet vehicle (CAV) to carry out speed coordination control on the intelligent internet vehicles. For various scenes, road side management and control measures and vehicle-mounted management and control measures are combined to effectively reduce the congestion risk.
And after a new congestion situation appears, updating the congestion risk prediction model according to the new congestion situation.
In addition, it should be noted that the specific implementation examples described in this specification may have different names, and the above contents described in this specification are only illustrations of the structures of the present invention. All equivalent or simple changes in the structure, characteristics and principles of the invention are included in the protection scope of the invention. Various modifications or additions may be made to the described embodiments or methods may be similarly employed by those skilled in the art without departing from the scope of the invention as defined in the appending claims.
Claims (10)
1. A real-time prediction method for expressway congestion risk classification is characterized by comprising the following steps:
1) dividing road section units of the target express way according to road geometric data of the target express way;
2) acquiring the free flow speed of a target road section according to the traffic flow historical data of each road section unit;
3) calculating a road traffic index according to the free flow speed of each road section unit and the space average speed of each road section unit acquired in real time, and dividing the congestion level of each road section unit according to the road traffic index;
4) carrying out variable screening according to the congestion level, the road geometric data, the traffic flow data and the weather data, then constructing a congestion risk real-time prediction model, and carrying out congestion risk probability prediction;
5) and judging whether traffic control measures need to be taken or not according to the congestion risk probability prediction result, if so, taking corresponding control measures to realize dynamic adjustment, and otherwise, returning to the step 4).
2. The method for real-time prediction of expressway congestion risk classification as recited in claim 1, wherein in the step 1), the specific process of dividing the road segment units is as follows:
and manually confirming the origin-destination point of the road section unit according to the arrangement position of the detector and the ramp afflux position information.
3. The method for real-time prediction of expressway congestion risk classification as claimed in claim 1, wherein in the step 2), the expressway average vehicle speed with the density tending to 0 is taken as the free flow speed of the target road section.
4. The method as claimed in claim 3, wherein the calculation formula of the road traffic index in step 3) is:
wherein TSI is road traffic index, vfFor free flow velocity, viIs the spatial average velocity.
5. The expressway congestion risk grading real-time prediction method according to claim 4, wherein the congestion levels of each road section unit are divided according to a road traffic index TSI, the congestion levels comprise 4 levels of unblocked, comparatively unblocked, congested and severe congestion, and the specific corresponding division relationship is as follows:
。
6. The method as claimed in claim 4, wherein in the step 4), the road geometry data includes lane change condition, number of ramp layout, number of lanes of the expressway and length of road section, the weather data includes visibility condition and precipitation, and the traffic flow data includes spatial average speed, flow and change condition thereof.
7. The method as claimed in claim 6, wherein in the step 4), the congestion risk real-time prediction model is specifically an ordered Logit model, and an expression of the model is as follows:
pl+p2+p3+p4＝1
speed is the space average Speed of a target road section in first preset time, Speed _ diff _ abs _ ratio is the absolute value of the change proportion of the space average Speed of the target road section in first preset time and second preset time, Vol _1 is the flow of a first detector at the upstream of the target road section in the first preset time, Vol _ diff _1 is the change of the flow of the first detector at the upstream of the target road section in the first preset time and the second preset time, Vol _ diff _2 is the change of the flow of the first detector at the downstream of the target road section in the first preset time and the second preset time, Lane _ drop is the reduction of the number of lanes on the target road section, Mist is the visibility condition of an express way, p is the visibility condition of the express way, and1、p2、p3、p4respectively, the predicted probabilities corresponding to the congestion levels, a1、...、a10Are all training parameters.
8. The method as claimed in claim 7, wherein during the training of the real-time congestion risk prediction model, the sample data includes congestion level, road geometry data, traffic flow data and weather data, and the sample data is trained after random sampling, data cleaning and independent variable screening.
9. The method as claimed in claim 1, wherein the weather data is updated according to a preset time granularity, the time granularity corresponding to the weather data is 5min, the traffic flow data is obtained in real time based on a video portal and is updated according to the preset time granularity, the time granularity corresponding to the traffic flow data is 5min, the congestion risk prediction model updates the congestion risk prediction result in real time according to a preset time interval, and the preset time interval is 5 min.
10. The method for graded real-time prediction of expressway congestion risks according to claim 1, wherein in the step 5), the traffic control measures comprise roadside control measures and vehicle-mounted control measures, the roadside control measures comprise variable speed limit control and ramp control, the vehicle-mounted control measures comprise speed coordination control, and if the reason for the rise of the congestion risk predicted value is that traffic volume is large and occupancy rate is high, the variable speed limit control and the ramp control are adopted to reduce short-term traffic volume; and if the reason for causing the congestion risk predicted value to rise is that the vehicle speed difference is large, adopting vehicle-mounted management and control measures.
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| CN117173899A (en) * | 2023-11-03 | 2023-12-05 | 武汉网信安全技术股份有限公司 | Smart city data processing method |
| CN117173899B (en) * | 2023-11-03 | 2024-01-30 | 武汉网信安全技术股份有限公司 | Smart city traffic travel quality evaluation method |
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