CN114783193A - Expressway abnormal event queuing length prediction method considering large-sized vehicles - Google Patents
Expressway abnormal event queuing length prediction method considering large-sized vehicles Download PDFInfo
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Abstract
The invention provides a method for predicting the queuing length of an expressway abnormal event considering large vehicles, and belongs to the field of traffic data analysis and processing. The invention comprises the following steps: firstly, under the condition that abnormal events occur on an observation road section of a highway and large vehicles are mixed in, improving the dynamic space occupancy of the observation road section according to different sensitivity degrees of different drivers to the large vehicles and the mixing rate of the large vehicles; then, introducing a traffic density concept and a greenshiels linear relation model, and analyzing the traffic parameters of the observed road section; then, introducing the wave speed of the traffic wave, analyzing the propagation process of the traffic wave of the observed road section, and constructing a traffic wave model; and finally, predicting the influence time and the influence range of the abnormal events on the expressway with the large vehicles. The method can accurately predict the queuing length change trend and the traffic evolution trend under the condition that a large vehicle is mixed and an abnormal event occurs, provides a reference basis for traffic control personnel to carry out traffic guidance, and improves the service level of the highway.
Description
Technical Field
The invention belongs to the field of traffic data analysis and processing, and particularly relates to a method for predicting the queuing length of an abnormal event on a highway by considering a large-sized vehicle, which can be suitable for predicting the queuing length of the abnormal event on the highway with a vehicle detector and an ETC portal device.
Background
Due to the occurrence of abnormal events such as traffic accidents, abnormal weather, construction and the like on the expressway, the expressway is more prone to abnormal congestion and queuing. The expressway is different from a common road and has the characteristics of rapidness and closure. Once abnormal congestion occurs, the spreading speed is extremely high, if the traffic congestion evolution trend cannot be controlled, correct control measures are taken in time, secondary congestion is easy to generate, and great economic loss is brought.
Current methods for queue length prediction can be broadly divided into two categories, mathematical model based and data based. The mathematical model is mainly based on a traffic wave model or a queuing theory, and is improved on the basis of the traffic wave model or the queuing theory. The patent CN108765981A discloses a lane-dividing real-time queuing length prediction method, which is based on the traffic wave theory, obtains the upstream real-time passing number through video vehicle detector data, and obtains the lane-dividing traffic flow ratio from the upstream to the downstream by using license plate data, thereby performing lane-dividing real-time prediction on the queuing length. The method comprehensively considers factors such as signal design, road section length, vehicle discrete arrival characteristics and the like of an upstream intersection and a downstream intersection, but does not consider microscopic factors such as large-sized vehicles and driver differences.
At present, few researches on the aspect of predicting the queuing length of large-sized vehicles are considered, and the related researches only convert the large-sized vehicles into standard equivalent vehicles through static conversion factors. However, studies have shown that the static scaling factor does not accurately describe the interaction between vehicles in the congestion evolution process, and it does not fully consider that the interaction between vehicles and roads caused by the interaction between vehicles in different traffic states is dynamically changed. Therefore, in order to adapt to the evolution of dynamic traffic, a method capable of dynamically depicting the influence of large vehicles in different traffic states is provided, so that the change trend of the queuing length of an abnormal event can be accurately analyzed, decision support can be provided for traffic managers, and the service level of an expressway is improved.
Disclosure of Invention
In view of the above, the invention provides a method for predicting the queuing length of an abnormal event on an expressway, which considers the mixing of large-sized vehicles, and solves the problems that the prior art cannot accurately describe the dynamic change of the mutual influence among the large-sized vehicles, the vehicles and roads in the congestion evolution process of the expressway, cannot provide decision support for traffic managers, and cannot improve the service level of the expressway.
To achieve the above object, the present invention comprises the steps of:
1) under the condition that abnormal events occur in the observation section of the expressway and large vehicles are mixed in, improving the dynamic space occupancy of the observation section according to different sensitivity degrees of different drivers to the large vehicles and the mixing rate of the large vehicles;
2) based on the dynamic space occupancy improved in the step 1), introducing a traffic density concept and a Greenshields linear relation model, and analyzing traffic parameters of abnormal events on an observation road section of the expressway mixed with a large vehicle;
3) introducing traffic wave speed based on the dynamic space occupancy improved in the step 1) and the traffic parameters in the step 2), analyzing a traffic wave propagation process of abnormal events on an observation road section of a highway mixed with a large vehicle, and constructing a traffic wave model;
4) and (3) predicting the influence time and the influence range of the abnormal events on the expressway with the large vehicles on the basis of the traffic wave model in the step 3).
Further, the step 1) specifically comprises the following steps:
11) the dynamic space occupancy of the observation section of the single-lane expressway with the large vehicle mixed in can be expressed by the following formula:
in the formula, OrRepresenting the dynamic space occupancy of the observation road section; l is a radical of an alcoholi' represents the actual occupied length of the ith vehicle on the single-lane observation road section, namely the vehicle length; l isi"represents the virtual occupancy length of the ith vehicle on the single-lane observation road section; l represents the length of a single-lane observation road section; n represents the number of vehicles on the single-lane observation road section; v. of0The initial speed of the vehicle when the ith vehicle brakes on the observation road section can be replaced by the average running speed of the vehicle in the observation road section under the normal condition; t is t0Representing the reaction time of the ith vehicle driver on the observation road section; a is amaxRepresenting the maximum deceleration of the ith vehicle on the observation road section;
12) the virtual occupancy of the ith vehicle on the single lane observation stretch can therefore be expressed by the following equation, depending on the different driver sensitivities to the large vehicle:
wherein b represents a driver characteristic factor, and 0< b ≦ 1;
13) the dynamic space occupancy of the observation road section of the expressway with m lanes mixed by the large vehicle can be expressed by the following formula:
in the formula, nmRepresenting the number of vehicles on the mth lane; l isim' represents the actual occupied length of the ith vehicle on the mth lane, namely the vehicle length; l isim"represents the virtual occupancy length of the ith vehicle on the mth lane;
14) setting the occupation proportion of the oversize vehicle in the mth lane of the observation road section of the highway to be lambdamThe dynamic space occupancy can be expressed by the following formula:
in the formula of lambdamRepresenting the occupation ratio of the large vehicle on the mth lane;
representing the average value of the actual and virtual occupied lengths of the large vehicle in the mth lane;
representing the average value of the actual and virtual occupied lengths of the small vehicle in the mth lane;
15) further, regardless of the difference of each lane, the dynamic space occupancy of the observed road section can be represented by the following formula:
in the formula (I), the compound is shown in the specification,
representing the average value of the proportion of the oversize vehicles in each lane of the observed road section, namely the mixing rate of the oversize vehicles in the observed road section;
representing the average value of the actual and virtual occupied lengths of the large-scale vehicle on the observation road section;
representing the average of the actual and virtual occupancy lengths of the miniature vehicles on the observed road section.
Further, the step 2) specifically comprises the following steps:
21) introduction of concept of traffic density
Substituting the dynamic space occupancy obtained in step 15) can be represented by the following formula:
the relationship between traffic density and dynamic space occupancy can be found as:
in the formula, k represents the traffic density of an observed road section;
22) when the traffic density reaches the jam density, the observation road section is completely occupied by the vehicles, and the dynamic space occupancy rate O is increasedrThe occupied length of the vehicle on the observation road section is 1, namely the actual vehicle length of the vehicle:
l 'in the formula'hRepresenting the actual occupied length of the large-sized vehicle on the single-lane observation road section; l'cRepresenting the actual occupied length of the small-sized vehicle on the single-lane observation road section;
thereby, the blocking density kjCan be expressed by the following formula:
in the formula, kjRepresenting an observed road segment blockage density;
23) introducing a Greenshields linear relation model formula
The following equation can be obtained:
wherein q represents a traffic flow; k represents traffic density; v represents vehicle speed; v. offRepresents the free-stream vehicle speed, when k → 0, vf＝v。
Further, the step 3) specifically includes the following steps:
31) substituting the traffic flow q and the traffic density k obtained in the step 23) into a traffic wave velocity formula to obtain the traffic wave velocity as follows:
in the formula, O1rRepresented by the traffic flow q1Traffic density of k1Vehicle speed v1Dynamic space occupancy in traffic state of (1); o is2rIs represented by a traffic flow of q2A traffic density of k2Vehicle speed v2Dynamic space occupancy in traffic state of (1);
represented by the traffic flow q1A traffic density of k1Vehicle speed v1The average value of the total length occupied by the large vehicle in the traffic state of (3);
is represented by a traffic flow of q2A traffic density of k2Vehicle speed v2The average value of the total length occupied by the large vehicle in the traffic state of (1);
represented by the traffic flow q1A traffic density of k1Vehicle speed v1The average value of the total length occupied by the small cars in the traffic state of (3);
represented by the traffic flow q2A traffic density of k2V vehicle speed v2The average value of the total length occupied by the small cars in the traffic state of (3);
32) in the process of abnormal events, the traffic flow of an observation road section of the highway forms aggregate waves, starting waves and dissipation waves; based on the dynamic space occupancy of the step 1), calculating the wave velocities of the aggregate wave, the start wave and the evanescent wave according to the step 3.1); and constructing a new traffic wave model on the basis of the original traffic wave model through the calculated wave speed of each traffic wave.
Further, the step 4) specifically includes: based on the traffic wave model constructed in the step 3) and the dynamic space occupancy improved in the step 2), the duration t of the abnormal event of the expressway can be obtained according to the dissipation and aggregation processes of the traffic wave of the abnormal event of the expresswaymMaximum queue length xmAnd the time t for the traffic state to return to normalnIt can be expressed by the following formula:
in the formula, teThe time is the end time of the abnormal event, namely the closing release time of the lane; x is the number ofmRepresents the maximum queue length upstream during the occurrence of the exception event; t is tmThe starting wave at the abnormal event occurrence position x-0 catches up with the aggregate wave, namely the time when the influence of the abnormal event starts to dissipate; t is tnThe time when the abnormal event occurrence position x is 0 and the normal traffic state is recovered, that is, the abnormal event influence complete end time.
Advantageous effects
The invention provides a method for predicting the queuing length of an abnormal event on a highway in consideration of the mixing of large vehicles, which is characterized in that the influence of the large vehicles in different traffic states is represented by considering the mixing of the large vehicles and the microscopic factors of a driver on the basis of the existing dynamic space occupancy, and a queuing length prediction model is established by means of a traffic wave theory to accurately analyze the variation trend of the queuing length of the abnormal event under the condition of the mixing of the large vehicles. The method can accurately predict the change trend of the queuing length under the condition that large vehicles are mixed, and predict the traffic evolution trend under the abnormal events of the expressway, thereby providing a reference basis for traffic control personnel to carry out traffic guidance and improving the service level of the expressway.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram illustrating dynamic space occupancy in accordance with the present invention;
FIG. 3 is a schematic diagram of the process of collecting and dissipating traffic waves during an abnormal event according to the present invention.
Detailed Description
In order to make the technical solutions, advantages and objects of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It should be apparent that the described embodiments are only some of the embodiments of the present invention, and not all of them. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the present application.
The invention is further illustrated by the following figures and examples.
Example 1
As shown in fig. 1 to 3, the present embodiment provides a method for predicting a queuing length of an abnormal event on a highway in consideration of a large-sized vehicle, including the steps of:
step 1) under the condition that abnormal events occur on the observation section of the expressway and large vehicles are mixed in, improving the dynamic space occupancy of the observation section according to different sensitivity degrees of different drivers to the large vehicles and the mixing rate of the large vehicles.
The method specifically comprises the following steps:
step 1.1) the dynamic space occupancy is defined as the proportion of the sum of the actual occupied area and the virtual occupied area of all vehicles in a certain area to the total area of the area, and the occupied area of the vehicles to roads can be expressed by length, so that the dynamic space occupancy of a single-lane expressway observation road section mixed with large vehicles can be expressed by the following formula:
in the formula, OrRepresenting the dynamic space occupancy of the observation road section; l isi' represents the actual occupied length of the ith vehicle on the single-lane observation road section, namely the vehicle length; l is a radical of an alcoholi"represents the virtual occupied length of the ith vehicle on the single-lane observation road section, specifically refers to the distance that the vehicle travels in the whole process by braking in time to avoid collision when the driver notices that an obstacle suddenly appears in front of the road; l represents the length of a single-lane observation road section; n represents the number of vehicles on the single-lane observation road section; v. of0The initial speed of the vehicle when the ith vehicle brakes on the observation road section can be replaced by the average running speed of the vehicle in the observation road section under the normal condition; t is t0Representing the reaction time of the ith vehicle driver on the observation road section; a ismaxRepresenting the maximum deceleration of the ith vehicle on the observed road segment.
Step 1.2) takes into account that different drivers have different degrees of sensitivity to large vehicles, not all of which are at the maximum deceleration a during decelerationmaxThe deceleration, and therefore the virtual occupancy length of the ith vehicle on the single-lane observation link, can be expressed by the following equation:
wherein b represents a driver characteristic factor, and 0< b ≦ 1.
Step 1.3) the dynamic space occupancy of the observation road section of the expressway with m lanes mixed with the large vehicle can be expressed by the following formula:
in the formula, nmRepresenting the number of vehicles on the mth lane; l is a radical of an alcoholim' represents the actual occupied length of the ith vehicle on the mth lane, namely the vehicle length; l is a radical of an alcoholim"represents the virtual occupancy length of the i-th vehicle on the m-th lane.
Step 1.4) setting the occupation ratio of the oversize vehicle in the mth lane of the observation road section of the expressway to be lambdamThe dynamic space occupancy of the observation road section can be represented by the following formula:
and is provided with a plurality of groups of the materials,
Lh＝L′h+L″h
Lc＝L′c+L″c
in the formula, λmRepresenting the occupation ratio of the large vehicle on the mth lane;
representing the average value of the actual and virtual occupied lengths of the large vehicle in the mth lane;
representing the average value of the actual and virtual occupied lengths of the mini-car in the mth lane; l ishRepresenting the actual and virtual total occupied length of the large-sized vehicle on the single-lane observation road section; l'hRepresenting the actual occupied length of the large-sized vehicle on the single-lane observation road section; lhRepresenting the virtual occupied length of the large-sized vehicle on the single-lane observation road section; l iscRepresenting the actual and virtual total occupied length of the small vehicles on the single-lane observation road section; l'cRepresenting the actual occupied length of the small-sized vehicle on the single-lane observation road section; lcThe virtual occupied length of the small vehicles on the single-lane observation road section is represented.
Step 1.5) further, the dynamic space occupancy can be expressed by the following formula without considering the difference of each lane:
in the formula (I), the compound is shown in the specification,
representing the average value of the occupation ratio of the large vehicles on each lane of the observed road section, namely the mixing rate of the large vehicles on the observed road section;
representing the average value of the actual and virtual occupied lengths of the large vehicles on the observation road section;
representing the average of the actual and virtual occupancy lengths of the miniature vehicles on the observed road section.
In step 1.5
The method comprises the following steps:
step 1.5.1) under the premise that the traffic flow of the observed road section is uniform, various vehicle types and various drivers are uniformly distributed on the observed road section,
by finding the mathematical expectation, it can be expressed by the following formula:
in the formula, ahmaxRepresenting the maximum deceleration of the ith vehicle on the observation road section, and taking 6 m/s; a iscmaxThe maximum deceleration of the ith vehicle on the representative observation road section is 5 m/s; p is a radical of formulaiRepresenting the proportion of the ith style driver in the crowd in the observation road section; biAnd representing the driver characteristic factor corresponding to the ith style driver of the observed road section.
Step 1.5.2) according to the existing research, when analyzing the characteristics of the driver from two angles of the style of the driver and the sensitivity degree to the large-scale vehicle, the driver can be divided into four types: the driver is conservative and sensitive, not conservative but sensitive, conservative but insensitive, not conservative and insensitive, and the proportion of drivers in four styles can be obtained through cluster analysis. The characteristic factors of drivers corresponding to the drivers with four styles are respectively set as b1、b2、b3、b4And calibrating the value of the driving simulator by collecting experimental data of the driving simulator, wherein the calibration result is as follows:
in the formula, b1Characteristic factors of drivers with conservative and sensitive styles to large vehicles; b2Characteristic factors of drivers which are not conservative but sensitive to large vehicles; b3Is a conservative but insensitive style driver characteristic factor to large vehicles; b4Is a style driver characteristic factor that is not conservative and insensitive to large vehicles.
The formula of step 1.5.1 can be replaced by the following formula:
in the formula, p1Is a conservative and proportion of drivers with a sensitive style to large vehicles; p is a radical of formula2Is a non-conservative but proportion of drivers with a sensitive style for large vehicles; p is a radical of3Is a conservative but insensitive proportion of style drivers to large vehicles; p is a radical of formula4Is the proportion of style drivers which are not conservative and are not sensitive to large vehicles; p is a radical of5Is the proportion of conservative style drivers; p is a radical of6Is the proportion of drivers with a non-conservative style.
Wherein p is1、p2、p3、p4、p5、p6Should satisfy the following relations:
step 1.5.3) step 1.5.2
The calculation formula of (2) is simplified, and the following formula can be obtained:
further simplification is as follows:
where ρ represents an introduced intermediate variable, and is set
And 2) introducing a traffic density concept and a greenshiels linear relation model based on the dynamic space occupancy improved in the step 1), and analyzing traffic parameters of abnormal events on the observed highway section mixed with the large-scale vehicle. The method specifically comprises the following steps:
step 2.1) introduction of concept of traffic density
Substituting the dynamic space occupancy obtained in the step 1.5) can be represented by the following formula:
in the formula, k represents the traffic density of an observed road section;
the relationship between traffic density and dynamic space occupancy can be found as follows:
step 2.2) as observation roadWhen the section traffic density reaches the blocking density, the observation road section is completely occupied by the vehicle, and the dynamic space occupancy rate O of the observation road section is obtainedrThe occupied length of the vehicle on the observation road section is 1, namely the vehicle length:
thus, the blocking density can be expressed by the following equation:
in the formula, kjRepresenting the observed road segment congestion density.
Step 2.3) introducing a Greenshirds linear relation model formula
The following equation can be obtained:
wherein q represents the traffic flow of the observation road section; k represents the traffic density of the observed road section; v represents the vehicle speed on the observation section; q, k and v form three elements of the traffic flow of the observed road section; v. offRepresenting the speed of free flow on the observed road, when k → 0, vf＝v。
And 3) introducing the wave speed of the traffic wave based on the dynamic space occupancy improved in the step 1) and the traffic parameters in the step 2), analyzing the propagation process of the traffic wave of the abnormal event on the observed highway section with the large-scale vehicle, and constructing a traffic wave model.
The method specifically comprises the following steps:
step 3.1) substituting the traffic flow q and the traffic density k of the observed road section obtained in the step 2.3) into a traffic wave velocity formula to obtain the following traffic wave velocity:
in the formula, O1rRepresented by the traffic flow q1Traffic density of k1Vehicle speed v1Dynamic space occupancy under traffic conditions; o is2rIs represented by a traffic flow of q2Traffic density of k2V vehicle speed v2Dynamic space occupancy under traffic conditions;
is represented by a traffic flow of q1Traffic density of k1V vehicle speed v1The average value of the total length occupied by the large vehicle in the traffic state of (1);
is represented by a traffic flow of q2A traffic density of k2V vehicle speed v2The average value of the total length occupied by the large vehicle in the traffic state of (3);
represented by the traffic flow q1A traffic density of k1Vehicle speed v1The average value of the total length occupied by the small cars in the traffic state of (1);
represented by the traffic flow q2Traffic density of k2Vehicle speed v2The small cars occupy the average value of the total length in the traffic state of (1).
And 3.2) forming aggregate waves, starting waves and dissipation waves by the traffic flow of the observed road section of the expressway in the abnormal event occurrence process. Based on the dynamic space occupancy of the step 1), calculating the wave velocities of the aggregate wave, the start wave and the evanescent wave according to the step 3.1). And constructing a new traffic wave model on the basis of the original traffic wave model through the calculated wave speed of each traffic wave.
The wave velocity calculation steps of the aggregate wave, the start wave and the evanescent wave are as follows:
step 3.2.1) setting the abnormal event occurrence position as x-0 and the abnormal event occurrence time as t 00. The aggregate wave velocity can be expressed by the following equation:
and has the following components:
0<t<tm
wherein:
q1＝0.35q0
in the formula, q0Representing the actual traffic flow of the observed road section under the normal condition; q. q of1Representing the traffic flow under the condition that an abnormal event occurs on the observed road section; k is a radical of formula0Representing the actual traffic density of the observed road section under the normal condition; k is a radical of1Representing the traffic density under the condition that an abnormal event occurs on the observation road section; t is tmAnd the time when the start wave of the observation road section at the abnormal event occurrence position x-0 catches up with the aggregate wave, namely the abnormal event duration is represented.
Step 3.2.2) setting teAnd the time is the end time of the abnormal event, namely the observed section lane closure release time. After the abnormal event is ended, the traffic capacity of the expressway is gradually recovered, at the moment, a starting wave is generated at the position x, which is equal to 0, of the abnormal event, and the wave speed of the starting wave can be expressed by the following formula:
and comprises the following components:
te<t<tm
in the formula, q2Representing the traffic flow of the observed road section after the abnormal event is ended, and taking the maximum allowed traffic flow of the observed road section under the current service level; k is a radical of2And representing the traffic density after the abnormal event is ended, and taking the maximum traffic density allowed to pass through the observation road section under the current service level.
Step 3.2.3) at time tmThen, the shock wave formed at the upstream of the abnormal event occurrence position x being 0 starts to propagate forwards, which indicates that the traffic on the observed road section starts to recover to normal, and at the moment, the evanescent wave is generated under the combined action of the starting wave and the collecting wave. The wave velocity of the evanescent wave can be expressed by the following formula:
and has the following components:
tm<t
and 4) predicting the influence time and the influence range of the abnormal events on the expressway with the large vehicles on the basis of the traffic wave model in the step 3). The method specifically comprises the following steps:
step 4.1) according to the traffic wave propagation process under the condition that the abnormal events occur on the observed highway section analyzed in the step 3), the following relational expression can be obtained:
u1(tm-te)＝u0(tm-0)
further simplification, can obtain:
substituting the wave velocity calculation formula of the aggregate wave and the starting wave into the formula to obtain the following result:
step 4.2) the abnormal event occurs at the x-0 position, the queued vehicles are all accumulated at the upstream of the x-0 position of the abnormal event, the position is negative, and the t-t position ismWhen the queue length of the upstream vehicle reaches the maximum, the queue length can be expressed by the following formula:
xm＝u0tm
in the formula, xmRepresenting the maximum length of the vehicle queue at the upstream of the observation road section;
substituting the aggregate wave velocity calculation formula into the formula to obtain:
step 4.3) at time tmThen, the shock wave formed upstream of the abnormal event occurrence position x of 0 starts propagating forward, and the time for returning the traffic to the normal state at the abnormal event occurrence position x of 0 is set to tn。
Function of position xtCan be expressed by the following formula:
xt＝xm+u2(tn-tm)
let the position function xtWhen 0, the above formula is modified as:
in conclusion, the invention provides a method for predicting the queuing length of the abnormal events on the expressway in consideration of the mixing of large vehicles, which is characterized in that the influence of the large vehicles in different traffic states is further represented by considering the micro factors of the large vehicles and drivers on the basis of the existing dynamic space occupancy, and a queuing length prediction model is established by means of a traffic wave theory, so that the variation trend of the queuing length of the abnormal events under the condition of the mixing of the large vehicles is accurately analyzed. The method and the device can accurately predict the change trend of the queuing length under the condition that the large vehicles are mixed, and predict the traffic evolution trend under the abnormal events of the expressway, thereby providing a reference basis for traffic control personnel to carry out traffic guidance and improving the service level of the expressway.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered in the protection scope of the present invention.
Claims (5)
1. A method for predicting the queuing length of an abnormal event on a highway by considering large-sized vehicles is characterized by comprising the following steps of: the method comprises the following steps:
1) under the condition that abnormal events occur in the observation section of the expressway and large vehicles are mixed in, improving the dynamic space occupancy of the observation section according to different sensitivity degrees of different drivers to the large vehicles and the mixing rate of the large vehicles;
2) introducing a traffic density concept and a greenshirds linear relation model based on the dynamic space occupancy improved in the step 1), and analyzing traffic parameters of abnormal events on an observation road section of a highway mixed with a large vehicle;
3) introducing traffic wave speed based on the dynamic space occupancy improved in the step 1) and the traffic parameters in the step 2), analyzing a traffic wave propagation process of abnormal events on an observation road section of a highway mixed with a large vehicle, and constructing a traffic wave model;
4) and (3) predicting the influence time and the influence range of the abnormal events on the expressway with the large vehicles on the basis of the traffic wave model in the step 3).
2. The method for predicting the abnormal event queue length of the expressway according to claim 1, wherein: the step 1) specifically comprises the following steps:
11) the dynamic space occupancy of a single-lane highway observation section with a large vehicle mixed therein can be represented by the following formula:
in the formula, OrRepresenting the dynamic space occupancy of the observation road section; l isi' represents the actual occupied length of the ith vehicle on the single-lane observation road section, namely the vehicle length; l isi"represents the virtual occupation length of the ith vehicle on the single-lane observation road section; l represents the length of a single-lane observation road section; n represents the number of vehicles on the single-lane observation road section; v. of0The initial speed of the vehicle when the ith vehicle on the observation road section is braked can be replaced by the average running speed of the vehicle in the observation road section under the normal condition; t is t0Representing the reaction time of the ith vehicle driver on the observation road section; a ismaxRepresenting the maximum deceleration of the ith vehicle on the observation road section;
12) the virtual occupancy of the ith vehicle on the single lane observation stretch can therefore be expressed by the following equation, depending on the different driver sensitivities to the large vehicle:
wherein b represents a driver characteristic factor, and 0< b ≦ 1;
13) the dynamic space occupancy of the observation road section of the expressway with the m lanes mixed by the large vehicle can be represented by the following formula:
in the formula, nmRepresenting the number of vehicles on the mth lane; l isim' represents the actual occupied length of the ith vehicle on the mth lane, namely the vehicle length; l is a radical of an alcoholim"represents the virtual occupancy length of the ith vehicle on the mth lane;
14) setting the occupation ratio of the large vehicle on the mth lane of the observation section of the highway to be lambdamThe dynamic space occupancy can be expressed by the following formula:
in the formula of lambdamRepresenting the occupation ratio of the large vehicle on the mth lane;
representing the average value of the actual and virtual occupied lengths of the large vehicle in the mth lane;
representing the average value of the actual and virtual occupied lengths of the mini-car in the mth lane;
15) further, regardless of the difference of each lane, the dynamic space occupancy of the observed road section can be represented by the following formula:
in the formula (I), the compound is shown in the specification,
representing the average value of the occupation ratio of the large vehicles on each lane of the observed road section, namely the mixing rate of the large vehicles on the observed road section;
representing the average value of the actual and virtual occupied lengths of the large-scale vehicle on the observation road section;
representing the average of the actual and virtual occupancy lengths of the miniature vehicles on the observed road section.
3. The method for predicting the abnormal event queue length of the expressway according to claim 2, wherein: the step 2) specifically comprises the following steps:
21) introduction of concept of traffic density
Substituting the dynamic space occupancy obtained in step 15) can be represented by the following formula:
the relationship between traffic density and dynamic space occupancy can be found as:
in the formula, k represents the traffic density of an observed road section;
22) when the traffic density reaches the jam density, the observation road section is completely occupied by the vehicles, and the dynamic space occupancy rate O is increasedrThe occupied length of the vehicle on the observation road section is 1, namely:
of formula (II) to'hRepresenting the actual occupied length of the large-sized vehicle on the single-lane observation road section; l'cRepresenting the actual occupied length of the small vehicles on the single-lane observation road section;
thereby, the blocking density kjCan be expressed by the following formula:
in the formula, kjRepresenting an observed road segment blocking density;
23) introducing a Greenshields linear relation model formula
The following formula can be obtained:
wherein q represents a traffic flow; k represents traffic density; v represents vehicle speed; v. offRepresents the free-flow vehicle speed, when k → 0, vf＝v。
4. The method for predicting the abnormal event queue length of the expressway according to claim 3, wherein: the step 3) specifically comprises the following steps:
31) substituting the traffic flow q and the traffic density k obtained in the step 23) into a traffic wave velocity formula to obtain a traffic wave velocity formula:
in the formula, O1rIs represented by a traffic flow of q1Traffic density of k1Vehicle speed v1Dynamic space occupancy under traffic conditions; o is2rRepresented by the traffic flow q2Traffic density of k2V vehicle speed v2Dynamic space occupancy in traffic state of (1);
is represented by a traffic flow of q1Traffic density of k1V vehicle speed v1The average value of the total length occupied by the large vehicle in the traffic state of (3);
is represented by a traffic flow of q2Traffic density of k2V vehicle speed v2The average value of the total length occupied by the large vehicle in the traffic state of (3);
represented by the traffic flow q1A traffic density of k1V vehicle speed v1The average value of the total length occupied by the small cars in the traffic state of (1);
represented by the traffic flow q2Traffic density of k2Vehicle speed v2The average value of the total length occupied by the small cars in the traffic state of (1);
32) in the process of abnormal events, the traffic flow of an observation road section of the highway forms aggregate waves, starting waves and dissipation waves; based on the dynamic space occupancy of the step 1), calculating the wave velocities of the aggregate wave, the start wave and the evanescent wave according to the step 3.1); and constructing a new traffic wave model on the basis of the original traffic wave model through the calculated wave speed of each traffic wave.
5. The method for predicting the abnormal event queue length of the expressway according to claim 4, wherein: the step 4) specifically comprises the following steps: based on the traffic wave model constructed in the step 3) and the dynamic space occupancy improved in the step 2), the duration t of the abnormal event of the expressway can be obtained according to the dissipation and aggregation processes of the traffic wave of the abnormal event of the expresswaymMaximum queue length xmAnd the traffic state is recovered to normalTime t ofnIt can be expressed by the following formula:
in the formula, teThe time is the end time of the abnormal event, namely the lane closure release time; x is the number ofmRepresents the maximum queue length upstream during the occurrence of the exception event; t is tmThe starting wave at the abnormal event occurrence position x-0 catches up with the aggregate wave, namely the time when the influence of the abnormal event starts to dissipate; t is tnThe time when the abnormal event occurrence position x is 0 and the normal traffic state is recovered, that is, the abnormal event influence complete end time.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102496264A (en) * | 2011-11-11 | 2012-06-13 | 东南大学 | Method capable of determining influence scope of highway emergent traffic incident |
| CN105023433A (en) * | 2015-07-01 | 2015-11-04 | 重庆大学 | Method for predicting range influenced by abnormal traffic event of highway |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102496264A (en) * | 2011-11-11 | 2012-06-13 | 东南大学 | Method capable of determining influence scope of highway emergent traffic incident |
| CN105023433A (en) * | 2015-07-01 | 2015-11-04 | 重庆大学 | Method for predicting range influenced by abnormal traffic event of highway |
Non-Patent Citations (1)
| Title |
|---|
| 李维佳: "路网环境下考虑大型车混入率的事故疏散诱导模型", 《中国公路学报 》, vol. 33, no. 11, 30 November 2020 (2020-11-30), pages 276 - 280 * |
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