CN116110218A - Traffic accident congestion queuing dynamic prediction and control method for extra-long tunnel - Google Patents
Traffic accident congestion queuing dynamic prediction and control method for extra-long tunnel Download PDFInfo
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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Abstract
The invention discloses a traffic accident congestion queuing dynamic prediction and control method for a super-long tunnel, and relates to the technical field of traffic congestion prediction. The invention divides the tunnel into n sections of roads, and carries out filtering treatment on real-time flow and density data of each section of road where accidents occur; the vehicle queuing lengths in tunnels at different stages of accident occurrence are analyzed, and the types of the accidents are judged to determine the relation between the background traffic volume and the residual traffic capacity of the tunnels; predicting the maximum queuing length of the vehicle; a moving average filtering method is introduced to construct a real-time vehicle queuing length estimation model and calculate; judging the queuing condition of vehicles in the tunnel; different management and control measures are adopted for different queuing length development stages. According to the invention, the real-time prediction model of the vehicle queuing length is built, so that the change condition of the vehicle queuing length in the tunnel can be reflected in real time, theoretical basis is provided for the management and control department to take next step of management and control measures, and feedback is provided for the management and control department in real time.
Description
Technical Field
The invention relates to the technical field of traffic jam prediction, in particular to a traffic accident jam queuing dynamic prediction and control method for a special tunnel.
Background
At present, the research method for queuing vehicles at home and abroad mainly focuses on the following aspects: the research method based on the shock wave theory, the probability method, the research method based on the input and output model, the machine learning method and the like, wherein the traffic flow theory is taken as a core traffic wave model to always occupy a more important position, and meanwhile, with the development of an estimator technology and an intelligent algorithm, an artificial intelligent algorithm is also popular gradually.
The researches are mainly carried out on expressways and city main roads, so that the accident queuing length research is less, and the real-time dynamic prediction research on the vehicle queuing length in the whole processes of congestion generation, spreading and dissipation is less in the process of relieving the accident; therefore, the invention provides a method for dynamically predicting and controlling traffic accident congestion queuing of a very long tunnel.
Disclosure of Invention
The invention aims to provide a traffic accident congestion queuing dynamic prediction and control method for a very long tunnel, explore queuing length evolution characteristics and rules of the whole process of congestion generation, propagation and dissipation under the traffic accident of the very long tunnel, establish a vehicle maximum queuing length model and a vehicle queuing length real-time estimation model of the whole process of congestion generation, propagation and dissipation under the traffic accident of the very long tunnel, master the queuing length of the vehicle in real time, provide a key technical method and data support for traffic control of the tunnel, and are beneficial to improving the traffic operation efficiency of the tunnel and reducing the operation safety risk.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a traffic accident congestion queuing dynamic prediction and control method for a special tunnel, which comprises the steps of firstly collecting real-time flow and density data of each section of a tunnel after an accident, then based on a traffic flow shock wave theory, combining the following steps to realize the evolution analysis and modeling of the traffic accident vehicle queuing length of the special tunnel, wherein the steps comprise;
s1: dividing the tunnel into n sections, filtering the real-time flow and density data of each section with accidents to obtain smoothed data, and then qualitatively analyzing the change characteristics of the process from spreading to dissipation of the vehicle queuing under the traffic accidents, and theoretically laying a foundation for S3;
s2: analyzing the queuing lengths of vehicles in the tunnels at different stages of accident occurrence, judging the types of the accidents to determine the relation between the background traffic volume and the residual traffic capacity of the tunnels, and if the background traffic volume is larger than the residual traffic capacity, performing S3;
s3: constructing a tunnel traffic accident vehicle queuing length model based on a shock wave theory to predict the maximum queuing length of the vehicle;
s4: combining traffic flow data collected in the tunnel, introducing a moving average filtering method, constructing a vehicle queuing length real-time estimation model, and calculating;
S5: s4, real-time estimating the relation between the real-time queuing lengths of the vehicles and the time relation is utilized to judge the queuing situation of the vehicles in the tunnel;
s6: different management and control measures are adopted for different queuing length development stages.
The S1-S2 comprises vehicle queuing generation in a tunnel, vehicle queuing spreading in the tunnel and vehicle queuing dissipation in the tunnel, wherein the vehicle queuing generation in the tunnel is based on a traffic flow basic diagram, traffic flow states are divided by a flow and density relation, after an accident occurs, a tunnel management department can implement flow control at a flow control point, so that congestion aggravation caused by the fact that a large number of vehicles are gathered in is avoided, and secondary accidents are avoided;
the S3 comprises geometric analysis and model establishment, wherein the geometric analysis is combined with traffic flow shock wave theory and the change characteristics of flow, density and speed in the whole process in the vehicle queuing length evolution process under the traffic accident of the previous section analysis, a vehicle time-space running track line diagram of the whole accident process is made, and the model establishment is carried out by establishing a maximum queuing length model taking the flow, density and speed, the distance between the accident occurrence position and the hole as variables after the geometric analysis of the whole queuing change process;
The S4-S5 middle node comprises a vehicle queuing length real-time estimation implementation flow, a vehicle queuing length real-time estimation model, a time interval determining method and an introduced moving average filtering improvement model, wherein after an accident occurs in a tunnel in the vehicle queuing length real-time estimation implementation flow, when the upstream traffic volume is larger than the residual traffic capacity of an accident section, an accident point forms a traffic bottleneck due to the decrease of the traffic capacity, a large number of vehicles cannot pass through timely and smoothly, a congestion phenomenon can be generated, otherwise, the vehicle queuing length real-time estimation model does not influence the shock wave speed and the wave speed direction between a congestion area and a non-congestion area of the queuing length change through analysis of the vehicle queuing length real-time estimation model, after the accident occurs, the queuing length is reduced when the shock wave propagates towards the direction of the traffic flow, the queuing length is increased when the shock wave propagates towards the opposite direction of the traffic flow, the time interval determining method has randomness due to the arrival of the vehicles, the extracted flow and density data in different time intervals are obvious, the real-time length calculated by the introduced moving average filtering improvement model comprises a moving average filtering improvement model and an accuracy comparison model.
The method comprises the steps of S6, carrying out dynamic control strategy analysis under the traffic accident of the special tunnel, dynamic control model constraint conditions under the traffic accident of the special tunnel and dynamic control model construction under the traffic accident of the special tunnel, wherein the dynamic control strategy analysis under the traffic accident of the special tunnel specifically comprises queuing increasing period, queuing dissipation period and queuing ending period, the dynamic control model constraint conditions under the traffic accident of the special tunnel specifically comprises traffic efficiency function and operation risk function, and the dynamic control model construction under the traffic accident of the special tunnel specifically comprises target constraint conditions and model construction.
The invention has the following beneficial effects:
the traffic accident congestion queuing dynamic prediction and control method for the extra-long tunnel can reveal the process mechanism of vehicle queuing generation, spreading and dissipation under traffic accidents in the extra-long tunnel. And qualitatively carrying out state division on traffic flows on different road sections in the tunnel, and explaining the rule of influence of shock waves generated among the traffic flows in different traffic states on the queuing length of the vehicles according to the traffic flow shock wave theory.
According to the traffic accident congestion queuing dynamic prediction and control method for the extra-long tunnel, a vehicle queuing length real-time prediction model is established, the accuracy of the predicted maximum queuing length can reach 95.62%, the accuracy of the whole queuing length reaches 84.34%, the model can reflect the change condition of the vehicle queuing length in the tunnel in real time, theoretical basis is provided for a management department to take next management and control measures, and feedback is provided for the management department in real time.
The traffic accident congestion queuing dynamic prediction and control method for the extra-long tunnel realizes the regulation and control of the traffic flow running state under the traffic accident, and the dynamic control model can control 120s in advance on the premise of ensuring the running safety in the tunnel, so that the improved passing efficiency can reach 7526 veh.km.h -1 The method provides scientific and accurate decision support for tunnel control, so that the tunnel and the section to which the tunnel belongs can be quickly restored to high-efficiency and safe operation, and the tunnel can exert the greatest economic benefit and social benefit.
Of course, it is not necessary for any one product to practice the invention to achieve all of the advantages set forth above at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a technical flow chart of a traffic accident congestion queuing dynamic prediction and control method for a very long tunnel of the invention;
FIG. 2 is a schematic flow-density diagram of the traffic flow in the tunnel of the present invention in a normal operation state;
FIG. 3 is a schematic diagram of the normal operation of traffic flow in the tunnel according to the present invention;
FIG. 4 is a flow-density schematic diagram of the present invention at the occurrence of a traffic accident in a tunnel;
FIG. 5 is a schematic diagram of the operation of traffic flow in a tunnel in the event of an accident according to the present invention;
FIG. 6 is a schematic flow-density diagram of the present invention after a traffic accident in a tunnel;
FIG. 7 is a schematic diagram of the present invention for managing traffic flow in a post-control tunnel;
FIG. 8 is a flow-density schematic diagram of the present invention after a traffic accident in a tunnel;
FIG. 9 is a schematic illustration of the flow of vehicles when the managed vehicles meet the queuing vehicles according to the present invention;
FIG. 10 is a flow-density schematic diagram of the present invention after a traffic accident in a tunnel is cleared;
FIG. 11 is a schematic illustration of the operation of the traffic flow after accident clearing in accordance with the present invention;
FIG. 12 is a flow-density schematic diagram of the present invention after a traffic accident in a tunnel is cleared;
FIG. 13 is a schematic illustration of the flow of traffic during queuing for dissipation in accordance with the present invention;
FIG. 14 is a diagram of a vehicle time-space trajectory of the present invention;
FIG. 15 is a flow-density relationship diagram of the present invention;
FIG. 16 is a flow chart of an embodiment of the vehicle queue length estimation in the event of a very long tunnel accident according to the present invention;
FIG. 17 is a schematic diagram of a shock wave model vehicle queue length estimation of the present invention;
FIG. 18 is a graph showing the comparison of queuing lengths at different time intervals T in accordance with the present invention;
FIG. 19 is a graph showing the comparison of queuing lengths of 30s at time intervals T according to the present invention;
FIG. 20 is a schematic diagram of queue length growth according to the present invention;
FIG. 21 is a schematic diagram of queue length dissipation according to the present invention;
FIG. 22 is a dynamic control flow chart of the present invention;
FIG. 23 is a schematic view of the traffic efficiency of the present invention;
FIG. 24 is a schematic view of the velocity encounter process of the present invention;
FIG. 25 is a flow chart illustrating the operation of the method for dynamically predicting and controlling traffic accident congestion queuing in a very long tunnel according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Please refer to fig. 1 and 25: the invention relates to a traffic accident congestion queuing dynamic prediction and control method for a special tunnel, which comprises the steps of firstly collecting real-time flow and density data of each section of a tunnel after an accident, then based on a traffic flow shock wave theory, combining the following steps to realize the evolution analysis and modeling of the traffic accident vehicle queuing length of the special tunnel, wherein the steps comprise;
S1: dividing the tunnel into n sections, filtering the real-time flow and density data of each section with accidents to obtain smoothed data, and then qualitatively analyzing the change characteristics of the process from spreading to dissipation of the vehicle queuing under the traffic accidents;
s2: analyzing the queuing lengths of vehicles in the tunnels at different stages of accident occurrence, judging the types of the accidents to determine the relation between the background traffic volume and the residual traffic capacity of the tunnels, and if the background traffic volume is larger than the residual traffic capacity, performing S3;
the traffic accident queuing length in the extra-long tunnel can be divided into three stages of queuing generation, queuing propagation and queuing dissipation.
1. In-tunnel vehicle queuing generation
Firstly, based on the basic diagram of traffic flow, the traffic flow state is divided by the relationship of flow and density, as shown in figure 2, q m1 K is the normal traffic capacity in the tunnel m1 K is the optimal density when the traffic capacity in the tunnel is maximum j1 Is the blocking density; defined at k m1 The traffic state on the left side is smooth, and the traffic state on the right side is congestion. At this time, the running condition of the traffic flow in the tunnel is shown in fig. 3, and the traffic flow in the tunnel runs smoothly and steadily.
When accident happens, the accident vehicle occupies the road and the tunnelThe relationship between the internal normal flow-density and the flow-density of the accident section is shown in figure 4, the traffic capacity of the accident section is suddenly reduced, and the traffic capacity of the accident section in the tunnel is represented by q m1 Reduced to q m2 ，k m2 For the optimal density, k, of the maximum traffic capacity of accident road sections in the tunnel j1 The blocking density of the accident road section; at this time, the running condition of the traffic flow in the tunnel is shown in fig. 5, state 1 is an unblocked state, state 2' is a congested state, state 2 is a congested state, and state 2 "is an unblocked state.
When an accident occurs, the traffic flow of the accident road section is changed from the original state 1 to the state 2 because the traffic flow of the accident road section is larger than the residual traffic capacity of the accident road section, the accident road section forms a traffic bottleneck, vehicles which cannot pass through in time start to be queued, the traffic flow running state of the adjacent road section at the upstream of the accident section is changed from the original state 1 to the state 2', and the traffic flows in different traffic states meet to generate shock waves w 2'1 As shown by the arrowed lines 1-2' in fig. 6. The shockwave w is generated between the accident road section state 2 and the upstream state 2 22' A shockwave w is generated between the accident road section state 2 and the downstream state 2 22” As indicated by the arrow-headed line 2-2 "in fig. 6.
At this time, because of Q 2 ＝Q 2' ,K 2 ＜K 2' The shockwave w is obtained from shockwave formula 22' Wave velocity of (2)
The method comprises the following steps:
shock wave w 22' Wave speed of 0, will not propagate upstream or downstream, and is equivalent to shock wave w 22” The wave speed is also zero. Because of Q 1 ＞Q 2' ,K 1 ＜K 2' Shock wave w 2'1 Wave velocity of (2) 
The method comprises the following steps:
shock wave w 2'1 The wave velocity being less than 0 is negative, indicating that the shock wave propagates in a direction opposite to the direction of the traffic flow, i.e. upstream, congestion occurs and vehicle queuing begins to occur.
2. Vehicle queuing propagation in tunnels
After the accident happens, the tunnel management department can implement flow control at the flow control point (the flow control point is assumed to be positioned at the tunnel entrance), so that the congestion aggravation caused by the convergence of a large number of vehicles is avoided, even secondary accidents occur, and the like. After the flow control point performs flow control, the traffic flow entering the tunnel starts to decrease, and at this time, the traffic flow running state in the tunnel is shown in fig. 7, and the flow-density of each state is shown in fig. 6.
After the flow control management is implemented, the traffic flow running state from the accident section to the flow control point can be divided into five types (traffic flow states beyond the flow control point are not considered), namely an unblocked state 2", an unblocked state 2 with the flow equal to the residual traffic capacity of the accident section, a state 2' of congestion queuing, an unblocked state 1 for keeping normal running before the accident and an unblocked state 3 with the flow reduced after the flow control are sequentially carried out. Generating shock wave w when the state 3 traffic flow meets the state 1 traffic flow 13 Because of Q 3 ＜Q 1 ,K 3 ＜K 1 Shock wave w 13 Wave velocity of (2)
The method comprises the following steps:
shock wave w 13 The wave velocity is greater than 0, which indicates that the shock wave propagates along the same direction as the direction of the traffic flow, namely downstream, and the traffic flow is not promoted to generate a congestion phenomenon. At this time, the wave w is concentrated 2'1 Continuing to propagate upstream, the flow of state 1 gradually transitions to state 2' with an increase in vehicle queue length.
3. Vehicle queuing dissipation in tunnel
When wave w is concentrated 2'1 After passing through all the vehicles in state 1, the running conditions of the traffic flow in the tunnel are shown in fig. 9, and the flow-density of each state is shown in fig. 8.
When wave w is concentrated 2'1 After passing through all the vehicles in the state 1, the traffic flow in the state 1 is completely converted into the state 2', the traffic flow running state from the accident section to the flow control point can be divided into four types, namely an unblocked state 2", an unblocked state 2 with the flow equal to the residual traffic capacity of the accident section, a state 2' of congestion queuing and an unblocked state 3 with the flow reduced after flow control. At the moment, the state 3 traffic flow with smaller flow meets the state 2' traffic flow with congestion queuing, and the traffic flows with different states meet to generate shock waves w 32' As shown by line 3-2' in fig. 7. Generating shock wave w when state 3 traffic meets state 2' traffic 32' Because of Q 3 ＜Q 2' ,K 3 ＜K 2' Shock wave w 32' Wave velocity of (2)
The method comprises the following steps:
shock wave
Wave speed greater than 0, indicating a shockwave w 32' Properties and shockwave w 13 The same phenomenon can not be continuously generated at the tail of the queue, and the shock wave w 32' For forward waves, the queue length no longer increases and the queue tail position begins to move forward, i.e., the vehicle queue length begins to decrease.
When the accident vehicles occupying the road are cleared, the traffic capacity of the accident section is recovered to be normal, the running state of the traffic flow in the tunnel is shown in fig. 11, and the flow-density of each state is shown in fig. 10.
When the accident vehicles occupying the road are cleared, the traffic capacity of the accident section is represented by q m2 Increase in sizeIs q m1 The traffic capacity is recovered to be normal, at the moment, the traffic flow of the accident section begins to increase, and the traffic flow running state from the accident section to the flow control point can be divided into three types, namely, a state 4 reaching the saturated flow rate, a state 2' of congestion queuing and a state 3 with smaller traffic flow, on the assumption that the traffic flow state of the accident section reaches saturation. Since the vehicles with backlogged upstream lines start to gradually pass through at a speed increasing, the accident road section and downstream are changed from the original state 2 to the state 4, and the state 2' is converted into the state 4 to generate shock waves w 42' As shown by line 2' -4 in fig. 10.
At this time, Q 4 ＞Q 2' ,K 4 ＜K 2' Shock wave w 42' Wave velocity of (2)
The method comprises the following steps:
shock wave w 42' Wave speed less than 0, indicating a shockwave w 42' To backward wave, propagating in opposite direction to the traffic flow until reaching the shock wave
Meeting;
the controlled state 3 traffic flow meets the saturated traffic flow of state 4, at this time, the traffic flow running state in the tunnel is shown in fig. 13, and the flow-density of each state is shown in fig. 12.
At this time, Q 4 ＞Q 3 ,K 4 ＞K 3 Shock wave w 43 Wave velocity of (2)
The method comprises the following steps: />
Shock wave w 43 Wave speed greater than 0, indicating a shockwave w 43 In order to advance the wave in the direction of the wave,and (3) spreading the vehicle flow in the running direction, and moving the tail position of the queue forwards until the end of dissipating the congestion queue generated by the accident when the tail vehicle passes through the accident road section.
S3: constructing a tunnel traffic accident vehicle queuing length model based on a shock wave theory to predict the maximum queuing length of the vehicle;
1. geometric resolution
In combination with traffic flow shockwave theory, vehicle queuing length evolution process under traffic accident analyzed in the previous section and change characteristics of flow, density and speed in the whole process, a vehicle time-space running track diagram of the whole accident process is made, as shown in fig. 14, the horizontal axis represents time, and the vertical axis represents distance between an accident occurrence point and a tunnel entrance.
In fig. 14: t is t A Indicating accident occurrence time; t is t B Representing a flow control start time; t is t C Indicating the meeting time of the traffic flow and the queuing traffic flow after management and control, namely the time for the queuing length to reach the maximum; t is t D Indicating the accident clearing time; t is t E Representing the shock wave w 32' And shock wave w 42' The time of the encounter; t is t F Indicating the time of maximum queuing length for the tail vehicle to pass the accident section. The different sparsity of lines on the graph represents different traffic flow states, the area 1 represents the traffic flow state of normal running of traffic flow in a tunnel before an accident, the area 2 'represents the traffic flow state of vehicle queuing congestion phenomenon generated by the fact that the traffic flow is larger than the residual traffic capacity of an accident section after the accident occurs, the area 2' represents the traffic flow state of the downstream of the accident section, the area 3 represents the traffic flow state of reduced traffic flow after the flow control point takes flow control measures after the accident occurs, and the area 4 represents the traffic flow state after the accident section traffic capacity is recovered after the accident is cleared.
On the vehicle time-space trajectory plot, line AC may represent the shockwave w generated when states 1 and 2' meet 2'1 I.e. the path along which the vehicle starts to queue. t is t C And the traffic flow meets the queuing traffic flow after the control at the moment, the flow reached at the moment is smaller than the flow driven away by the accident section, the queuing is not increased any more, and the queuing length reaches the maximum. Line CE may represent a shapeShock wave w generated by state 3 and state 2' meeting 32' Is provided for the propagation path of (a). Line DE represents the shock wave w generated by the meeting of state 2' and state 4 2'1 Is provided for the propagation path of (a). Line EF represents the shock wave w generated by the meeting of state 3 and state 4 43 Is provided for the propagation path of (a). The flow-density relationship for each state is shown in fig. 15.
The estimated formula of each shock wave velocity (taking absolute value) is as follows:
q in i For the flow in the traffic state i, veh/h, i=1, 2, …, n; k (K) i For density in traffic state i, veh/km, i=1, 2, …, n; w (w) ij A shockwave generated for the flow state i and the flow state j to meet, i=1, 2, …, n, j=1, 2, …, n;
as a shock wave w ij I=1, 2, …, n, j=1, 2, …, n.
Time t for meeting traffic flow and queuing traffic flow after control C The estimation formula is:
The geometric analysis of the vehicle time-space running track line graph can be obtained by:
due to t A In order to be the moment of occurrence of the accident,
the simplification is as follows:
in the above, l max Is the maximum queuing length, km; l is the accident location distance, defined herein as the distance of the accident location from the tunnel entrance, km; v (V) 3 For controlling the speed of the vehicle after the speed is controlled, km/h; t is t A The accident occurrence time; t is t B Controlling a start time for the flow control; t is t C The time for the meeting of the post-traffic and the queuing traffic, namely the time for the queuing length to reach the maximum is controlled; t is t D Is the accident end time; t is t E The time when the two evanescent waves meet;
is a concentrated wave w 2'1 Speed of km/h. The time units are in one-to-one correspondence with the time in the flow, density and speed units.
Bringing maximum queuing length into
The time t of maximum queuing length generation can be obtained C . In addition, the geometrical analysis of the vehicle time-space running track diagram can also be used for obtaining the moment t of two evanescent waves meeting E Time t of maximum queuing length for tail vehicles to pass through accident section F And duration of the accident impact, as follows:
t max ＝t A -t F
in the above, l max Is the maximum queuing length; t is t C The time for the meeting of the post-traffic and the queuing traffic, namely the time for the queuing length to reach the maximum is controlled; t is t D Is the accident end time; t is t E : the time when the two evanescent waves meet; t is t F The time for the tail vehicle to pass through the accident section is the maximum queuing length; t is t max Duration of impact for an accident;
as a shock wave w 32' Is a speed of (2); />
As a shockwave w 42' Is a speed of (2); />
As a shockwave w 43 Is a function of the speed of the machine.
Through the above estimation, it is possible toObtaining the space range l of accident influence max And duration t of the accident effect max 。
2. Model building
After the geometric analysis of the whole queuing change process, a maximum queuing length model taking the flow, density, speed and distance between the accident occurrence position and the hole as variables can be constructed, and the maximum queuing length model is shown as the following formula:
l max ＝(q u -q jam )[L+v u (t B -t A )]/(v u k u -v u k jam +q u -q jam )
and the following steps:
v u k u ＝q u ，Δv＝t B -t A
thus formula l max ＝(q u -q jam )[L+v u (t B -t A )]/(v u k u -v u k jam +q u -q jam ) The simplification is as follows:
l max ＝(q u -q jam )(L+v u Δt)/(2q u -v u k jam -q jam )
in the above, q u The veh/h is the upstream traffic volume in the tunnel; v u The speed is the upstream speed in the tunnel, km/h; k (k) u The density of the upstream traffic in the tunnel is veh/km; q jam The residual capacity is the accident point, veh/km; k (k) jam For blocking density, veh/km; l is the accident location distance, defined herein as the distance of the accident location from the tunnel entrance, km; Δt is the control start time, which is the difference between the start control time and the accident occurrence time.
From l max ＝(q u -q jam )(L+v u Δt)/(2q u -v u k jam -q jam ) It is known that the maximum queuing length is limited by upstream traffic volume (flow-speed model relationship between upstream speed and flow obeying greenhields, and therefore no longer explored the influence of upstream speed alone), remaining traffic capacity, blocking density, accident location and tunnel exit The hole distance and the control starting time.
S4: combining traffic flow data collected in the tunnel, introducing a moving average filtering method, constructing a vehicle queuing length real-time estimation model, and calculating;
s5: s4, real-time estimating the relation between the real-time queuing lengths of the vehicles and the time relation is utilized to judge the queuing situation of the vehicles in the tunnel;
1. real-time vehicle queuing length estimation implementation flow
When the traffic volume of the upstream is larger than the residual traffic capacity of the accident section after the accident occurs in the tunnel, the accident point forms a traffic bottleneck due to the decrease of the traffic capacity, and a large number of vehicles cannot pass through timely and smoothly to generate the congestion phenomenon, otherwise, the traffic bottleneck cannot occur. The queuing phenomenon of the vehicles in the accident situation in the tunnel is different from the queuing phenomenon of the signalized intersection and the like, and the vehicles in the signalized intersection are completely stationary, and traffic parameters such as flow and speed are zero at the moment, so that the queuing situation of the vehicles can be judged according to the change of the traffic flow parameters, and the queuing situation of the vehicles in the accident situation in the tunnel is not completely stationary, and cannot be accurately judged according to the change of the traffic flow parameters. Therefore, the invention establishes a real-time estimation model of the queuing length of the vehicle based on the shock wave theory, and the concrete implementation flow is shown in fig. 16.
After an accident occurs, the first step is to judge whether the upstream traffic volume of the accident road section is higher than the residual traffic capacity of the accident section so as to judge whether the queuing phenomenon of vehicles can occur in the tunnel. When the upstream traffic volume of the accident section is higher than the residual traffic capacity of the accident section, the second step is started, and the queuing length of the vehicle is estimated in real time. The queuing length value changes along with the change of time, when the queuing length value is smaller than 0, the queuing is considered to be completely dissipated, and the estimation of the vehicle queuing length is finished.
2. Real-time estimation model for queuing length of vehicle
By analyzing the evolution process of the queuing length of the vehicle, the impact on the change of the queuing length is mainly the wave speed and the wave speed direction of the shock wave between the congestion area and the non-congestion area, as shown in fig. 17, when the shock wave propagates towards the direction of the traffic flow after the accident occurs, the queuing length is reduced, and when the shock wave propagates towards the opposite direction of the traffic flow, the queuing length is increased.
The real-time queuing length estimation needs to rely on real-time input of traffic flow parameters, and the real-time flow, density and other data in a certain time interval can be obtained through a video detector in a tunnel, so that the real-time wave velocity estimation is carried out on shock waves generated when traffic flows in different traffic states meet. For convenience in acquiring data, detectors from an accident point (or near upstream) to a flow control point are numbered 1,2, …, S, an average distance between adjacent detectors is S, time after accident occurrence is divided into 1,2, …, m time periods, and a time period interval is T. And selecting the upstream detector flow close to the tail position of the congestion queuing queue as the background traffic demand at the moment, and taking the average flow of each detector in the congestion area as the queuing flow at the moment, wherein the density parameters are the same.
After an accident occurs, when the background traffic demand is greater than the residual traffic capacity of the accident section, queuing starts to be generated, and in the mth time interval T, the queuing flow and the queuing density in the congestion area are respectively as follows:
in the above
Representing queuing flow at the mth time interval of the congestion area, and veh/h; />
Queuing density, veh/km of the congestion area at the mth time interval; q (S, T) m ) Representing the flow rate of the position number S detector in the mth time interval, veh/h; k (S, T) m ) Representing bits in the mth time intervalThe density of the detector with the number S, veh/km; s is the detector number, s=1, 2, …, n; t (T) m For the mth time interval, m=1, 2, …, n. />
The number of detectors representing the positions of the tail of the queue in the congestion area is also the number of detectors in the congestion area.
The background traffic and density in the non-congested area are respectively:
in the above
Traffic flow at T representing non-congested zone status j m Flow in time interval veh/h, j=1, 2, …, n; />
Traffic flow at T representing non-congested zone status j m Density over time interval veh/km, j=1, 2, …, n;
representing the detector number within the non-congested area.
At this time, shock wave w ij The wave speed and the vehicle queuing length which changes in the time interval are respectively as follows:
In the above
Representing the shock wave w ij At T m Wave speed in time interval km/h, i=1, 2, …, n, j=1, 2, …, n; l (T) m ) Indicating the queuing length variation in the mth time interval T, km.
To sum up, the queuing length real-time estimation model is:
wherein l (t) represents real-time queuing length, km; n represents the number of time intervals; q u Representing the upstream flow of the tunnel, veh/h; q jam And the residual traffic capacity of the tunnel accident point is represented by veh/h.
The Accuracy (AC) of the vehicle queue length estimate is derived by estimating the Relative Error (RE). The relative error is a simple statistical measurement method for estimating the error between the estimated value and the actual value, and the relative error estimation formula of the maximum queuing length is that
The corresponding accuracy estimation formula is->
Wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the relative error of the maximum queuing length of the vehicle; />
An estimate representing a maximum queuing length of the vehicle; />
An actual measurement representing the maximum queuing length of the vehicle; />
Indicating the accuracy of the maximum queuing length of the vehicle.
The relative error estimation formula of the real-time queuing length is as follows
The whole process vehicle queuing length accuracy rate estimation formula is +.>
Wherein RE queue (T m ) Representing the relative error in the vehicle queue length at the mth time interval; 
An estimated value representing a vehicle queuing length at an mth time interval; />
An actual measurement representing the length of the vehicle queue at the mth time interval; AC (alternating current) queue Indicating the accuracy of the queuing length of the whole process vehicle.
When queuing occurs, the shock wave velocity is a negative value, and the estimated queuing length of the vehicle is also a negative value; when the shock wave velocity is positive, the vehicle queue length begins to shorten until the queue length shortens to zero. Therefore, the queuing length estimated by the model is a negative value, and absolute value processing is carried out on the queuing length of the vehicle for the convenience of understanding. Meanwhile, in order to ensure the accuracy of the model, an initial calibration value of the queuing length needs to be given, namely, the first queuing length value estimated by the model needs to be calibrated according to the actual measurement value.
3. Time interval determining method
Because of the randomness of the vehicle arrival, the extracted flow and density data in different time intervals are obviously different, and the estimated real-time queuing lengths in different time intervals T are different. In order to improve the accuracy of the model, the optimal time interval T is explored, and the invention estimates the queuing length by taking 5s, 15s, 30s and 60s at the time interval T respectively through a queuing length real-time estimation model based on accident data generated by a typical special tunnel. The time interval value and the estimated time are different, the queuing length initial calibration value is different, the estimated result is shown in fig. 18, and the accuracy of different time intervals T is shown in the following table.
Comparison of T accuracy at different time intervals
As can be seen from fig. 18, when the time interval T is taken for 30s, the estimated queuing length is matched with the actual queuing length. As can be seen from Table 5.1, the maximum queuing length accuracy was 95.62% and the overall queuing length accuracy was 84.34% when the time interval T was taken for 30 seconds. In general, when the time interval T is 30s, the maximum queuing length accuracy is highest, and the overall queuing length accuracy is also highest; accuracy of 60s and 15s is inferior.
4. Introduction of a moving average filter improvement model
(1) Principle of moving average filtering
The principle of the moving average filtering is as follows: the dynamic test data is divided into a deterministic component and a random component, the deterministic component data is a measurement result or an effective signal with high accuracy, the random component data is a random fluctuation test error or noise, and after discretization sampling, the dynamic test data can be expressed as:
y j ＝f j +e j j＝1,2,…,N
y j is dynamic test data; f (f) j Deterministic components of the dynamic test data; e, e j Is a random component of the dynamic test data.
(2) Improved model building
In order to obtain more accurate dynamic test data, error influence caused by random components needs to be restrained as much as possible on the premise of guaranteeing deterministic components of the dynamic test data. Thus, dynamic test data y j Often through smoothingProcessing and filtering to reduce random error e j Is a function of (a) and (b). The specific method is that for a certain non-stable data y j Considered near stationary on appropriate inter-cell, and some local average is made to reduce e j The resulting random fluctuations. In this way, a smoother measurement f can be obtained by continuous local averaging across each cell along the full length N data j While filtering out random errors that are frequently fluctuating. Thus, to reduce random errors in flow, density data, an improved vehicle queuing length real-time estimation model incorporating moving average filtering is shown as follows:
wherein e (r) is a random component in the dynamic test data; r is the moving average window length.
(3) Comparison of accuracy
The window length of the sliding average is respectively 30s, 60s and 90s, and queuing length estimation results obtained by introducing an improved model of the sliding average filtering to the three groups of flow and density data after the smoothing processing are obtained, so that whether random errors and optimal smoothing processing window length can be reduced after the smoothing processing is researched.
And comparing the queuing length obtained after the non-smoothing treatment, the smoothing treatment for 30s, the smoothing treatment for 60s and the smoothing treatment for 90s with the actual queuing length for analysis. As can be seen from fig. 19, when the time interval T is taken for 30s, the queuing length obtained after the smoothing process is higher than the actual queuing length, and the queuing length obtained without the smoothing process is closer to the actual queuing length.
Queuing length accuracy contrast
The table above can be combined: after smoothing processing is carried out on the 5s data, the accuracy is obviously improved when the time interval T takes 5s, the results of smoothing 30s, 60s and 90s are close, the maximum queuing length accuracy is 94.86% at the highest, and the overall queuing length accuracy is 81.89% at the highest; the time interval T is consistent with the time interval 5s when 15s is taken, the maximum queuing length accuracy is 93.22% at the highest, and the overall queuing length accuracy is 82.23% at the highest. The accuracy of 30s and 60s taken at time interval T is reduced. When the time interval T is 5s and 15s, the flow and density data are smoothed, the model estimation accuracy can be improved, and the difference of window length values of a sliding average of 30s, 60s and 90s is not large.
In general, when the time interval T is 30s, the overall accuracy of the data without smoothing is highest, and the accuracy of the maximum queuing length and the overall queuing length is 92.22% and 84.34% respectively. The highest accuracy of the model is not improved by the data smoothing processing, but the highest accuracy is greatly improved when the time interval is 5s and 15s, and different sliding window lengths have no obvious influence on the result.
S6: different management and control measures are adopted for different queuing length development stages.
1. Dynamic control strategy analysis under traffic accident of extra-long tunnel
(1) Queue increasing period
As shown in fig. 20, the period from the queuing generation to the queuing length reaching the maximum value is defined as a queuing increase period, and the determination condition of the queuing increase period can be obtained by a vehicle queuing length real-time estimation model:
t A indicating accident occurrence time; t represents the time interval estimated in real time.
After an accident occurs, an accident point forms a traffic bottleneck, so that an upstream vehicle in the tunnel forms congestion queuing, and in the period of increasing the queuing length, in order to ensure the safety of the tunnel, a lane should be closed outside the tunnel, the input of the vehicle is controlled, and the congestion is prevented from being aggravated after the vehicle enters the tunnel.
(2) Dissipation period of queuing
As shown in fig. 21, the period from when the queuing length reaches the maximum value to when the queuing length falls to zero is defined as a queuing dissipation period, and the determination condition of the queuing dissipation period can be obtained through a vehicle queuing length real-time estimation model:
in the dissipation period of the queuing length, on the premise that a tunnel lane is closed, a blank road section continuously grows from a hole to the tail position of the queuing, the maximum value of the blank road section is related to the accident position, and the farther the accident position is away from the tunnel entrance, the larger the length value of the blank road section is, so that space-time resource waste is more serious. Therefore, on the premise of ensuring the safety of the tunnel, the method can be considered to open the lane in advance outside the hole in the queuing dissipation period, allow part of vehicles to enter the tunnel and reduce space-time resource waste.
This control concept requires that conditions of optimum traffic efficiency and minimum running risk be satisfied.
(3) End of queuing
And when the queuing is finished, the normal operation in the tunnel is restored, and the traffic control is finished.
The dynamic control flow taking into account the queuing length variation is shown in fig. 22.
2. Dynamic control model constraint condition under traffic accident of extra-long tunnel
(1) Traffic efficiency function
In order to meet the condition of optimal traffic efficiency, the invention constructs a traffic efficiency function and obtains an optimal solution thereof. Brilon proposes a traffic efficiency evaluation index referencing the physical medium-power and power concepts, and the expression form is as follows:
E＝qvt
wherein E is a traffic efficiency index, veh.km.h -1 The method comprises the steps of carrying out a first treatment on the surface of the q is the flow rate in unit time t; v is the average vehicle speed, km/h.
The traffic efficiency is related to flow and speed per unit time as indicated by the above equation, and the speed versus flow relationship can be described by the greensells' flow-speed model (shown below).
Combining the flow-speed model with the traffic efficiency evaluation index to obtain a traffic efficiency objective function taking speed and time as variables, wherein the traffic efficiency objective function is as follows:
by varying the blocking density k j Smooth velocity v f And (3) calibrating the same parameters, and taking the time t as unit time, namely, constant, so as to obtain the maximum value of the traffic efficiency function. Fig. 23 is a graph of traffic efficiency obtained by calibrating accident traffic flow parameters in a typical extra-long tunnel, and the graph shows that the traffic efficiency gradually increases with increasing speed, and starts to decrease with increasing speed after increasing to a maximum value. Indicating that the greater the non-speed, the greater the traffic efficiency, when the speed is equal to the traffic efficiency optimum speed v e And the traffic efficiency is maximum.
(2) Running risk function
The risk of operation increases when there is a large speed difference between the vehicle that later enters the tunnel and the vehicle in the tunnel. TTC (time to collision) is widely used in the field of traffic safety assessment, defined as the remaining time before a collision if two vehicles maintain this speed difference when the speed of the rear vehicle is greater than that of the front vehicle, and the TTC estimation formula is as follows:
wherein Δl represents the vehicle distance; v n Indicating the speed of the nth vehicle; v n-1 Indicating the speed of the n-1 vehicle.
The risk of operation within the tunnel can thus be characterized by the speed difference between the meeting vehicles, where the traffic flow is defined as a macroscopically steady flow, ignoring the differences between individual vehicles, constructing a risk of operation objective function with speed as a variable, as shown in the following equation:
minf 1 (x)＝Δv＝v i -v j Δv≥0
wherein Deltav represents the speed difference, v i Representing average speed, v of traffic flow of accident affected road section in tunnel j Indicating the average speed of the traffic outside the tunnel that will enter the tunnel. The greater the speed differential, the greater the risk of operation.
3. Construction of dynamic control model under traffic accident of extra-long tunnel
(1) Target constraint
The target constraints include traffic efficiency constraints and operational risk constraints. To ensure the optimal passing efficiency of the tunnel, the method can pass through the formula 
Solving the maximum value to obtain the optimal speed v of the passing efficiency e I.e. the control speed preferably approaches the optimum speed v of traffic efficiency e 。
In terms of operational risk, the speed of the incoming tunnel should meet the following conditions: the driving safety in the tunnel is ensured, and the traffic flow speed entering the tunnel from the outside of the tunnel is not higher than the highest speed limit value and not lower than the lowest speed limit value, so that the following formula is satisfied:
v min ≤v j ≤v max
ensuring smooth connection of traffic flow in the tunnel, wherein the traffic flow speed entering the tunnel from outside the tunnel is not higher than the traffic flow speed in the tunnel:
v j ≤v i
the optimal passing efficiency is ensured, and the speed of the traffic flow entering the tunnel from the outside of the tunnel approaches to the optimal passing efficiency speed:
v j →v e
wherein the tail position speed v of the queuing traffic flow in the tunnel in the queuing dissipation period tail The speed of the tail of the vehicle is gradually increased from zero to the smooth speed in the process from the stop queuing to the queuing dissipation along with the time change, and the speed change rule has a certain linear relation with the time. In the process of accelerating the vehicle flow, besides the self acceleration factor of the vehicle, the vehicle flow is also intersected in the tunnelThe influence of the environment can be expressed by means of the viscosity coefficient h in physics, and the influence of the environment in the extra-long tunnel on the acceleration of traffic flow from congestion and stopping to the smooth process can be described. Thus, the traffic flow tail position velocity v considering the viscosity of the traffic flow of the extra-long tunnel is constructed tail The calculation formula is as follows:
wherein a represents the acceleration of the vehicle, which can be accelerated from zero to 100km/h in 10 seconds, and the average acceleration is 2.78m/s 2, and the acceleration is defined as constant; h represents the viscosity coefficient in the accelerating process of traffic flow in the extra-long tunnel, and can be obtained through parameter calibration.
In addition, whether the pre-release is performed in the queuing dissipation period also needs to meet the requirement of the distance from the accident occurrence position to the opening. When the speed difference of the traffic flow outside the tunnel entering the tunnel and the traffic flow in the tunnel is less than or equal to zero, the running risk is the lowest, namely the position speed of the traffic flow tail part in the tunnel is equal to v tail Has been increased to the clear running speed v in the tunnel, satisfying the constraint of the following equation, as shown in fig. 24.
Therefore, on the premise of ensuring the running safety of tunnel traffic, the distance L from the accident occurrence position to the tunnel portal needs to meet the following conditions:
(2) model construction
To sum up, the following dynamic control model is established:
c in the formula t (q, v) a recommended control value for flow q and velocity v at time t; q m1 The normal traffic capacity of the basic road section of the tunnel is represented; q jam Indicating the remaining traffic capacity of the accident road section.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (5)
1. A traffic accident congestion queuing dynamic prediction and control method for a special tunnel is characterized in that firstly, real-time flow and density data of each section of a tunnel after an accident are collected, and then, based on a traffic flow shock wave theory, the following steps are combined to realize the evolution analysis and modeling of the traffic accident vehicle queuing length of the special tunnel, and the steps comprise;
s1: dividing the tunnel into n sections, filtering the real-time flow and density data of each section with accidents to obtain smoothed data, and then qualitatively analyzing the change characteristics of the process from spreading to dissipation of the vehicle queuing under the traffic accidents;
S2: analyzing the queuing lengths of vehicles in the tunnels at different stages of accident occurrence, judging the types of the accidents to determine the relation between the background traffic volume and the residual traffic capacity of the tunnels, and if the background traffic volume is larger than the residual traffic capacity, performing S3;
s3: constructing a tunnel traffic accident vehicle queuing length model based on a shock wave theory to predict the maximum queuing length of the vehicle;
s4: combining traffic flow data collected in the tunnel, introducing a moving average filtering method, constructing a vehicle queuing length real-time estimation model, and calculating;
s5: s4, real-time estimating the relation between the real-time queuing lengths of the vehicles and the time relation is utilized to judge the queuing situation of the vehicles in the tunnel;
s6: different management and control measures are adopted for different queuing length development stages.
2. The method for dynamically predicting and controlling traffic accident congestion queuing in extra-long tunnels according to claim 1, wherein the steps S1-S2 comprise vehicle queuing generation in tunnels, vehicle queuing spreading in tunnels and vehicle queuing dissipation in tunnels, the vehicle queuing generation in tunnels is based on a traffic flow basic diagram, traffic flow states are divided by flow and density relations, and after an accident occurs, a tunnel management department can implement flow control at a flow control point to avoid congestion aggravation caused by the integration of a large number of vehicles and avoid secondary accidents.
3. The method for dynamically predicting and controlling traffic accident congestion queuing in extra-long tunnels according to claim 1, wherein the step S3 comprises geometric analysis and model establishment, wherein the geometric analysis is combined with traffic flow shock wave theory, vehicle queuing length evolution process under traffic accidents analyzed in the previous section and change characteristics of flow, density and speed in the whole process, a vehicle time-space running track line diagram of the whole process of the accident is made, and the model establishment is carried out by establishing a maximum queuing length model taking the distance between the flow, density, speed, accident occurrence position and an opening as variables after the geometric analysis of the whole process of the queuing change.
4. The method for dynamically predicting and controlling traffic accident congestion queuing in extra-long tunnels according to claim 1, wherein the sections in the S4-S5 comprise a vehicle queuing length real-time estimation implementation process, a vehicle queuing length real-time estimation model, a time interval determination method and a sliding average filter improvement model, wherein when an accident occurs in a tunnel in the vehicle queuing length real-time estimation implementation process, when the upstream traffic volume is larger than the residual traffic capacity of an accident section, the accident point forms a traffic bottleneck due to the decrease of the traffic capacity, a large number of vehicles cannot pass through timely and smoothly, and congestion phenomenon can not occur otherwise;
According to the vehicle queuing length real-time estimation model, the impact on the change of the queuing length is mainly the shock wave speed and the wave speed direction between a congestion area and a non-congestion area, after an accident occurs, the queuing length is reduced when the shock wave propagates in the direction of the vehicle flow, and the queuing length is increased when the shock wave propagates in the opposite direction of the vehicle flow.
5. The method for dynamically predicting and controlling congestion queuing of a special tunnel traffic accident according to claim 1, wherein the step S6 comprises performing special tunnel traffic accident dynamic control policy analysis, special tunnel traffic accident dynamic control model constraint conditions and special tunnel traffic accident dynamic control model construction, the special tunnel traffic accident dynamic control policy analysis specifically comprises queuing increase period, queuing dissipation period and queuing end, the special tunnel traffic accident dynamic control model constraint conditions specifically comprises traffic efficiency function and operation risk function, and the special tunnel traffic accident dynamic control model construction specifically comprises target constraint conditions and model construction.
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