CN113920727B - Prediction method and system for road congestion caused by construction - Google Patents
Prediction method and system for road congestion caused by construction Download PDFInfo
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Abstract
The invention provides a prediction method for road congestion caused by construction, which comprises the steps of obtaining traffic flow, vehicle following percentage and average vehicle distance in a period of time on a construction road to solve average vehicle group length and average vehicle group interval, and further analyzing the relationship between the average vehicle group length and the average vehicle group interval and the corresponding interval standard deviation by combining the actual running condition of vehicles on the construction road to obtain the vehicle group length standard deviation and the vehicle group interval standard deviation; and combining the average vehicle group length with the vehicle group length standard deviation, combining the average vehicle group interval with the vehicle group interval standard deviation, randomly generating two groups of sample data which are normally distributed, simulating the running condition of the road vehicles, solving the probability of generating congestion by using simulation analysis, and further predicting the congestion or non-congestion of the construction road according to the solved probability of generating congestion. The method and the device realize the prediction of road congestion caused by construction occupying the road, and have the characteristics of rapidness and accuracy.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a prediction method and a prediction system for road congestion caused by construction.
Background
According to statistics of China national highway group limited company, by 12 months in 2019, the total mileage of China expressway exceeds 14 ten thousand kilometers, and the mileage is first in global rank. Because asphalt concrete pavement has the characteristics of low noise, high stability, comfortable running and the like, more than 90% of the asphalt concrete pavement is asphalt concrete pavement. However, it has been found that many road surfaces have not reached their designed service life due to long-term overload, and various diseases such as cracks, irregularities, etc. are generated. Therefore, maintenance of the road is a necessary means for improving the vehicle running experience and prolonging the road service time.
At present, in the road construction maintenance process, the inconvenience of normal traffic operation caused by occupying one side road is an important cause of urban traffic jam. Traffic jam increases people's travel time, reduces labor efficiency, increases the consumption of fuel and increases the degree of difficulty of traffic management, restricts the development of city, brings inconvenience for people's work and life to the cause of jam depends on driver's proficiency to a great extent, and these extra artificial reasons are difficult to carry out the ration, cause very big degree of difficulty for the prediction of jam.
However, there is little research in the prior art on traffic congestion models based on driver behavior, so that there is currently a lack of efficient methods for predicting road congestion caused by construction.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide a prediction method and a system for road congestion caused by construction, which realize the prediction of the road congestion caused by construction occupying the road, have the characteristics of rapidness and accuracy, have guiding significance for road construction and driving route planning of a driver, reduce traffic pressure and bring convenience to work and life of people.
In order to solve the technical problems, the embodiment of the invention provides a prediction method for road congestion caused by construction, which comprises the following steps:
obtaining traffic flow, vehicle following percentage and average vehicle distance in a period of time on a construction road to solve average vehicle group length and average vehicle group interval, and further analyzing the relationship between the average vehicle group length and the vehicle group length standard deviation and the relationship between the average vehicle group interval and the vehicle group interval standard deviation by combining the actual running condition of vehicles on the construction road to obtain the vehicle group length standard deviation and the vehicle group interval standard deviation;
and combining the average vehicle group length with the vehicle group length standard deviation, combining the average vehicle group interval with the vehicle group interval standard deviation, randomly generating two groups of sample data which are normally distributed, simulating the running condition of the road vehicles, solving the probability of generating congestion by using simulation analysis, and predicting the congestion or non-congestion of the construction road according to the solved probability of generating congestion.
Wherein, through the formulaSolving the average vehicle group length L a The method comprises the steps of carrying out a first treatment on the surface of the Wherein,
L f represents the total length of the following vehicle, and L f ＝N×L min N represents the number of following vehicles, n=p×q, L min Representing a minimum following distance; p represents the percentage of following; q represents the traffic flow.
Wherein, through the formulaSolving the average vehicle group interval L d The method comprises the steps of carrying out a first treatment on the surface of the Wherein L represents the average distance between the vehicles which are not in contact with the vehicle.
Wherein the probability of congestion generation is expressed by a formulaWherein sigma d A standard deviation representing the group spacing; sigma (sigma) a Representing the standard deviation of the cluster length.
Wherein, in the calculation of the following percentage, the distance between two vehicles is less than 50m and is regarded as following; the average inter-vehicle distance refers to an average distance between vehicles passing through the construction road within 5 minutes.
The embodiment of the invention also provides a prediction system for road congestion caused by construction, which comprises a road parameter solving unit and a road congestion prediction unit; wherein,
the road parameter solving unit is used for obtaining traffic flow, vehicle following percentage and average vehicle distance in a period of time on a construction road so as to solve the average vehicle group length and the average vehicle group interval, and further analyzing the relationship between the average vehicle group length and the vehicle group length standard deviation and the relationship between the average vehicle group interval and the vehicle group interval standard deviation by combining the actual running condition of vehicles on the construction road so as to obtain the vehicle group length standard deviation and the vehicle group interval standard deviation;
the road congestion prediction unit is used for combining the average vehicle group length and the vehicle group length standard deviation, combining the average vehicle group interval and the vehicle group interval standard deviation, randomly generating two groups of sample data which are normally distributed, simulating the running condition of a road vehicle, calculating the probability of congestion by using simulation analysis, and further predicting the construction road congestion or non-congestion according to the calculated probability of congestion.
Wherein, through the formulaSolving the average vehicle group length L a The method comprises the steps of carrying out a first treatment on the surface of the Wherein,
L f represents the total length of the following vehicle, and L f ＝N×L min N represents the number of following vehicles, n=p×q, L min Representing a minimum following distance; p represents the percentage of following; q represents the traffic flow.
Wherein, through the formulaSolving the average vehicle group interval L d The method comprises the steps of carrying out a first treatment on the surface of the Wherein L represents the average distance between the vehicles which are not in contact with the vehicle.
Wherein the probability of congestion generation is expressed by a formulaWherein sigma d Representing the standard deviation, sigma, of the cluster spacing a And the standard deviation representing the length of the vehicle group is related to the distance between the vehicle groups and the length of the vehicle group according to the actual running condition of the vehicles on the road, and the vehicle groups and the distance conditions of the vehicle groups on the road are collected for analysis and calculation.
Wherein, in the calculation of the following percentage, the distance between two vehicles is less than 50m and is regarded as following; the average inter-vehicle distance refers to an average distance between vehicles passing through the construction road within 5 minutes.
The embodiment of the invention has the following beneficial effects:
the invention collects data such as traffic flow, following percentage, average distance between vehicles and the like as dependent variables to calculate average distance between vehicles and average length of vehicles, combines standard deviation of distance between vehicles and standard deviation of length of vehicles, carries out simulation analysis according to normal distribution of distance between vehicles and length of vehicles, and calculates probability of congestion generation to carry out threshold value comparison, thereby realizing prediction of road congestion caused by construction occupying road.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that it is within the scope of the invention to one skilled in the art to obtain other drawings from these drawings without inventive faculty.
Fig. 1 is a flowchart of a method for predicting road congestion caused by construction according to an embodiment of the present invention;
fig. 2 is a schematic diagram of two vehicle driving states on a construction road in an application scenario of a prediction method for road congestion caused by construction according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a prediction system for road congestion caused by construction according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present invention more apparent.
As shown in fig. 1, in an embodiment of the present invention, a method for predicting road congestion caused by construction is provided, including the following steps:
s1, acquiring traffic flow, vehicle following percentage and average vehicle spacing in a period of time on a construction road to solve average vehicle group length and average vehicle group interval, and further analyzing the relationship between the average vehicle group length and the vehicle group length standard deviation and the relationship between the average vehicle group interval and the vehicle group interval standard deviation by combining the actual running condition of vehicles on the construction road to obtain the vehicle group length standard deviation and the vehicle group interval standard deviation;
and S2, combining the average vehicle group length with the vehicle group length standard deviation, combining the average vehicle group interval with the vehicle group interval standard deviation, randomly generating two groups of sample data which are normally distributed, simulating the running condition of the road vehicles, solving the probability of generating congestion by using simulation analysis, and predicting the congestion or non-congestion of the construction road according to the solved probability of generating congestion.
Specifically, in step S1, data such as the traffic flow Q, the following percentage P, and the average inter-vehicle distance L on the construction road are collected as dependent variables. In one example, a following percentage P is calculated where a two-vehicle distance of less than 50m is considered to be a following vehicle; the average inter-vehicle distance L refers to the average distance between vehicles passing through the construction area within 5 minutes.
By the formulaSolving the average vehicle group length L a The method comprises the steps of carrying out a first treatment on the surface of the Wherein L is f Represents the total length of the following vehicle, and L f ＝N×L min N represents the number of following vehicles, n=p×q, L min Representing a minimum following distance; p represents the percentage of following vehicles; q represents the vehicle flow.
By the formulaSolving the average vehicle group interval L d The method comprises the steps of carrying out a first treatment on the surface of the Wherein L represents the average distance between the vehicles which are not in contact with the vehicle. It should be noted that the group interval solving formula is based on +.>And L is n ＝(1-P)×Q×L d Derived from the above.
Considering the average cluster length solved in step S1L a And average cluster spacing L d Representing only the average value and not actually representing the magnitude of the true value, and thus the magnitude of the congestion probability cannot be judged by directly comparing the magnitude of the average value and the true value.
Based on the above consideration, the train group length L is adopted a And distance between vehicle groups L d And comparing the standard probability distribution of the average value, and judging whether congestion is generated or not by introducing a preset threshold value according to the probability of congestion or not. Thus, a combination analysis was performed by introducing a standard deviation of the cluster interval and a standard deviation of the cluster length.
In step S2, the probability of congestion occurrence can be expressed by the formulaWherein sigma d Standard deviation of vehicle group interval sigma a The standard deviation of the vehicle group length is related to the vehicle group interval and the vehicle group length according to the actual running condition of vehicles on the road, and analysis and calculation are carried out by collecting the vehicle group and the interval condition of the vehicle group on the road.
However, it is difficult to calculate an analytical solution by the above formula, and therefore the probability is calculated by a software simulation method.
Therefore, python software is used to determine the group length L a Standard deviation of vehicle group length, vehicle group interval L d Standard deviation sigma of vehicle group interval d And respectively randomly generating a plurality of (e.g. 10000) sample data conforming to normal distribution, and comparing two by two to determine whether congestion probability is generated.
Setting an upper probability limit (such as 70%) and a lower probability limit (such as 40%) on the basis, and if the obtained probability is higher than the set upper probability limit, considering that congestion is generated; if the obtained probability is lower than the lower probability limit, it is considered that congestion does not occur.
It should be noted that, in the above process, the average value and standard deviation of the vehicle group distance and the vehicle group length are obtained through experiments for different roads, and the setting of the upper limit and the lower limit of the probability is also combined with the actual condition of the road, and the optimal accuracy is used as an index to solve.
As shown in fig. 2, an application scenario of a method for predicting road congestion caused by construction provided in an embodiment of the present invention is further described:
taking the high-speed road section of Jiangsu province in fig. 2 as an example, the road is relatively simple, has only an entrance and an exit, has no bifurcation, and has consistent vehicle entering and exiting quantity.
Data such as traffic flow, percent following, and average inter-vehicle distance are collected as dependent variables. The distance between two vehicles is less than 50m in the calculation of the following percentage, and the following percentage is taken as the following distance; the average inter-vehicle distance refers to the average distance between vehicles passing through the construction area within 5 minutes (or 10 minutes, 15 minutes, etc.). A total of 400 data were collected for the road segment, 312 of which were used as training sets and 88 as prediction sets.
Firstly, a decision tree method is adopted to predict the congestion caused by construction, and the statistics of the result are shown in table 1. For the training set, 109 data are actually congested, 203 data are not congested, the algorithm is finally adopted for prediction, 150 data are congested, 65 prediction is correct, and the accuracy of congestion prediction is 43.33%; for the prediction set, there are 30 data actually congested, 38 are not congested, and finally 48 data are predicted to be congested, wherein 16 predictions are correct, and the accuracy of congestion prediction is 33.33%. It can be seen that the prediction accuracy using this algorithm is not high.
TABLE 1
Then, the method provided by the invention is adopted to predict the congestion caused by construction, the standard deviation of the vehicle group distance and the vehicle group length is defined as half of the average value according to the actual running condition of the road vehicles, the data with the congestion probability higher than 20% is considered to be congested, the congestion probability lower than 10% is considered to be not congested, if the congestion probability is between 10% and 20%, the congestion probability is considered to be difficult to judge, and an expert judges according to the actual condition, and the statistics of the results are shown in table 2. For the training set, 109 data are actually congested, 203 data are not congested, the algorithm is finally adopted for prediction, 97 data are congested, 74 prediction accuracy rates of the congestion prediction are 76.29%, 194 data are not congested, 170 prediction accuracy rates of the congestion prediction accuracy rates are 87.63%; for the prediction set, 30 data are actually congested, 58 data are not congested, and 29 data are finally predicted to be congested, wherein 21 data are predicted correctly, the congestion prediction correct rate is 72.41%, 50 data are predicted to be uncongested, 44 data are predicted correctly, and the congestion prediction correct rate is 88%. It was thus found that better results were obtained using this algorithm.
TABLE 2
As shown in fig. 3, in an embodiment of the present invention, a prediction system for road congestion caused by construction is provided, which includes a road parameter solving unit 110 and a road congestion prediction unit 120; wherein,
the road parameter solving unit 110 is configured to obtain a traffic flow, a following percentage and an average inter-vehicle distance in a period of time on a construction road, so as to solve an average vehicle group length and an average vehicle group interval, and further analyze a relationship between the average vehicle group length and a standard deviation of the vehicle group length and a relationship between the average vehicle group interval and the standard deviation of the vehicle group interval in combination with an actual running condition of vehicles on the construction road, so as to obtain a standard deviation of the vehicle group length and a standard deviation of the vehicle group interval;
the road congestion prediction unit 120 is configured to combine the average vehicle group length and the vehicle group length standard deviation, and combine the average vehicle group interval and the vehicle group interval standard deviation, randomly generate two groups of sample data in normal distribution, simulate the running condition of a road vehicle, calculate the probability of congestion by using simulation analysis, and further predict the construction road congestion or non-congestion according to the calculated probability of congestion.
Wherein, through the formulaSolving the average vehicle group length L a The method comprises the steps of carrying out a first treatment on the surface of the Wherein,
L f represents the total length of the following vehicle, and L f ＝N×L min N represents the number of following vehicles, n=p×q, L min Representing a minimum following distance; p represents the percentage of following; q represents the traffic flow.
Wherein, through the formulaSolving the average vehicle group interval L d The method comprises the steps of carrying out a first treatment on the surface of the Wherein L represents the average distance between the vehicles which are not in contact with the vehicle.
Wherein the probability of congestion generation is expressed by a formulaWherein sigma d Representing the standard deviation, sigma, of the cluster spacing a And the standard deviation representing the length of the vehicle group is related to the distance between the vehicle groups and the length of the vehicle group according to the actual running condition of the vehicles on the road, and the vehicle groups and the distance conditions of the vehicle groups on the road are collected for analysis and calculation.
Wherein, in the calculation of the following percentage, the distance between two vehicles is less than 50m and is regarded as following; the average inter-vehicle distance refers to an average distance between vehicles passing through the construction road within 5 minutes.
The embodiment of the invention has the following beneficial effects:
the invention collects data such as traffic flow, following percentage, average distance between vehicles and the like as dependent variables to calculate the distance between vehicles and the length of the vehicles, carries out simulation analysis on the distance between vehicles and the length of the vehicles as normal distribution, and calculates the probability of congestion generation to carry out threshold value comparison, thereby realizing the prediction of road congestion caused by road occupation during construction.
It should be noted that, in the above system embodiment, each unit included is only divided according to the functional logic, but not limited to the above division, so long as the corresponding function can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in implementing the methods of the above embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc.
The above disclosure is only a preferred embodiment of the present invention, and it is needless to say that the scope of the invention is not limited thereto, and therefore, the equivalent changes according to the claims of the present invention still fall within the scope of the present invention.
Claims (4)
1. The prediction method for road congestion caused by construction is characterized by comprising the following steps:
obtaining traffic flow, vehicle following percentage and average vehicle distance in a period of time on a construction road to solve average vehicle group length and average vehicle group interval, and further analyzing the relationship between the average vehicle group length and the vehicle group length standard deviation and the relationship between the average vehicle group interval and the vehicle group interval standard deviation by combining the actual running condition of vehicles on the construction road to obtain the vehicle group length standard deviation and the vehicle group interval standard deviation;
combining the average vehicle group length with the vehicle group length standard deviation, combining the average vehicle group interval with the vehicle group interval standard deviation, randomly generating two groups of sample data which are normally distributed, simulating the running condition of the road vehicles, calculating the probability of generating congestion by using simulation analysis, and predicting the congestion or non-congestion of a construction road according to the calculated probability of generating congestion;
by the formulaSolving the average vehicle group length L a The method comprises the steps of carrying out a first treatment on the surface of the Wherein,
L f represents the total length of the following vehicle, and L f ＝N×L min N represents the number of following vehicles, n=p×q, L min Representing a minimum following distance; p represents the percentage of following; q represents the traffic flow;
by the formulaSolving the average vehicle group interval L d The method comprises the steps of carrying out a first treatment on the surface of the Wherein L represents the average distance between vehicles which are not in contact with the vehicle;
the probability of congestion is expressed by a formulaWherein sigma d A standard deviation representing the cluster spacing; sigma (sigma) a Representing the standard deviation of the cluster length.
2. The method for predicting congestion caused by construction according to claim 1, wherein the calculation of the following percentage considers that the following distance is less than 50 m; the average inter-vehicle distance refers to an average distance between vehicles passing through the construction road within 5 minutes.
3. The prediction system for road congestion caused by construction is characterized by comprising a road parameter solving unit and a road congestion prediction unit; wherein,
the road parameter solving unit is used for obtaining traffic flow, vehicle following percentage and average vehicle distance in a period of time on a construction road so as to solve the average vehicle group length and the average vehicle group interval, and further analyzing the relationship between the average vehicle group length and the vehicle group length standard deviation and the relationship between the average vehicle group interval and the vehicle group interval standard deviation by combining the actual running condition of vehicles on the construction road so as to obtain the vehicle group length standard deviation and the vehicle group interval standard deviation;
the road congestion prediction unit is used for combining the average vehicle group length and the vehicle group length standard deviation, combining the average vehicle group interval and the vehicle group interval standard deviation, randomly generating two groups of sample data which are normally distributed, simulating the running condition of a road vehicle, calculating the probability of congestion by using simulation analysis, and further predicting the construction road congestion or non-congestion according to the calculated probability of congestion;
by the formulaSolving the average vehicle group length L a The method comprises the steps of carrying out a first treatment on the surface of the Wherein,
L f representing the total length of the following vehicle, andn represents the number of following vehicles, n=p×q, L min Representing a minimum following distance; p represents the percentage of following; q represents the traffic flow;
by the formulaSolving the average vehicle group interval L d The method comprises the steps of carrying out a first treatment on the surface of the Wherein L represents the average distance between vehicles which are not in contact with the vehicle;
the probability of congestion is expressed by a formulaWherein sigma d A standard deviation representing the cluster spacing; sigma (sigma) a Representing the standard deviation of the cluster length.
4. A prediction system for construction-induced road congestion according to claim 3, wherein the calculation of the following percentage considers that the following percentage is less than 50 m; the average inter-vehicle distance refers to an average distance between vehicles passing through the construction road within 5 minutes.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020077742A1 (en) * | 1999-03-08 | 2002-06-20 | Josef Mintz | Method and system for mapping traffic congestion |
| CN103038117A (en) * | 2010-06-29 | 2013-04-10 | 本田技研工业株式会社 | Method of traffic congestion estimation |
| CN103249627A (en) * | 2010-12-15 | 2013-08-14 | 本田技研工业株式会社 | Vehicular driving assist device |
| CN103262135A (en) * | 2010-12-15 | 2013-08-21 | 本田技研工业株式会社 | Traffic congestion prediction method |
| CN104268415A (en) * | 2014-09-30 | 2015-01-07 | 北京交通发展研究中心 | Travel congestion probability estimation method |
| CN104919509A (en) * | 2013-03-11 | 2015-09-16 | 本田技研工业株式会社 | Server device, congestion prediction information display system, congestion prediction information distribution method, congestion prediction information display method, and program |
| CN112466119A (en) * | 2020-11-26 | 2021-03-09 | 清华大学 | Method and system for predicting vehicle following speed of vehicle by using vehicle-road cooperative data |
-
2021
- 2021-10-08 CN CN202111173074.1A patent/CN113920727B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020077742A1 (en) * | 1999-03-08 | 2002-06-20 | Josef Mintz | Method and system for mapping traffic congestion |
| CN103038117A (en) * | 2010-06-29 | 2013-04-10 | 本田技研工业株式会社 | Method of traffic congestion estimation |
| CN103249627A (en) * | 2010-12-15 | 2013-08-14 | 本田技研工业株式会社 | Vehicular driving assist device |
| CN103262135A (en) * | 2010-12-15 | 2013-08-21 | 本田技研工业株式会社 | Traffic congestion prediction method |
| CN104919509A (en) * | 2013-03-11 | 2015-09-16 | 本田技研工业株式会社 | Server device, congestion prediction information display system, congestion prediction information distribution method, congestion prediction information display method, and program |
| CN104268415A (en) * | 2014-09-30 | 2015-01-07 | 北京交通发展研究中心 | Travel congestion probability estimation method |
| CN112466119A (en) * | 2020-11-26 | 2021-03-09 | 清华大学 | Method and system for predicting vehicle following speed of vehicle by using vehicle-road cooperative data |
Non-Patent Citations (2)
| Title |
|---|
| 王殿海 ; 李凤 ; 宋现敏 ; .一种新的车队离散模型及其应用.吉林大学学报(工学版).2009,(第04期),全文. * |
| 纪廷婷 ; 陈少军 ; 陈雨人 ; .在虚拟环境下分析车群行为安全的方法.重庆交通大学学报(自然科学版).2010,(第01期),全文. * |
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