CN114582127A - Traffic flow model simulation method and system and abnormal traffic event prediction method - Google Patents
Traffic flow model simulation method and system and abnormal traffic event prediction method Download PDFInfo
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Abstract
The invention discloses a traffic flow model simulation method, a system and an abnormal traffic event prediction method, which relate to the technical field of traffic management, and firstly construct a traffic flow model based on a cellular automaton theory; initializing a traffic flow model, setting an initial time T to be 1, and setting a simulation termination time to be T; traversing the vehicle information array by using a traffic flow model, and determining the attribute of the currently occupied cellular vehicle; updating the vehicle state based on the position of the current cellular in the road network according to the attribute of the vehicle occupying the cellular currently; judging whether T < T is true, if so, making T equal to T +1, and returning to the model traversal step; if not, ending the simulation. The traffic flow model simulation method provided by the invention can simulate the evolution law of traffic flow under typical traffic events, so as to prevent, control and reduce the influence caused by traffic accidents.
Description
Technical Field
The invention relates to the technical field of traffic management, in particular to a traffic flow model simulation method and system and an abnormal traffic event prediction method.
Background
The highway tunnel is in semi-enclosed space, and light condition is relatively poor, and tail gas dust is more, and traffic environment is complicated, and driving influence factor is many, and the occurence of failure probability is great, and after the occurence of failure simultaneously, the rescue is relatively more difficult, causes the tunnel to block up or the secondary accident easily, and accident severity is higher. Traffic change in the highway tunnel has certain influence on traffic operation of road sections outside the tunnel, when congestion or accidents occur in the tunnel, traffic congestion of the whole highway is usually caused, and the traffic operation efficiency and the safety level of the road sections in the highway tunnel area are greatly reduced.
At present, although many electromechanical facilities are equipped in a highway tunnel region in China, a large lifting space exists in the aspect of operation safety lifting, driving safety monitoring and emergency disposal for the tunnel region are far from enough, correlation analysis on traffic related data of the tunnel region is lacked, prevention and control on abnormal traffic events cannot be performed, timely and accurate judgment on the abnormal traffic events is lacked, and the accident influence range is expanded.
Therefore, how to perform simulation on high-speed traffic, especially traffic in a tunnel area, and further perform prevention and control on abnormal traffic events, so as to reduce the influence caused by traffic accidents, is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a traffic flow model simulation method, a traffic flow model simulation system and an abnormal traffic event prediction method.
In order to achieve the above purpose, the invention provides the following technical scheme:
a traffic flow model simulation method based on cellular automata comprises the following steps:
step 2, initializing a traffic flow model, setting an initial time T to be 1, and setting a simulation termination time to be T;
step 3, traversing the vehicle information array by using a traffic flow model, and determining the attribute of the vehicle occupying the cellular at present;
step 4, updating the vehicle state based on the position of the current cellular in the road network according to the attribute of the vehicle occupying the cellular currently;
step 5, judging whether T < T is true, if so, making T equal to T +1, and returning to the step 3; if not, ending the simulation.
Optionally, in step 1, road section cells and multi-lane cells are set, such that the total length of the highway is L, the width of the highway is W, N lanes are arranged in each direction, and the length of the tunnel region of the highway is LtL on a highwayTAnd obtaining a traffic flow model based on the cellular automaton.
Optionally, in step 2, the initialized content includes: setting initial values for speed limit, random slowing probability, lane changing probability and the like of each road section, initially generating a vehicle information array on the road section, setting initial time T to be 1, and setting simulation termination time to be T.
Optionally, in step 3, the attributes of the currently occupied cellular vehicle include: vehicle length, vehicle maximum travel speed, etc.
Optionally, in step 4, the specific step of updating the vehicle state includes:
step 4.1, determining the positions of the current cells in the road network, wherein the positions comprise ramps, main roads, tunnels and the like;
step 4.2, according to the position, different updating strategies are executed on the vehicle state; if the current cellular is positioned on the ramp, executing an updating strategy A; if the current cell is located in the main channel, executing an updating strategy B; and if the current cell is positioned in the tunnel, executing an updating strategy C.
Optionally, the update policy a is: carrying out vehicle acceleration calculation, vehicle deceleration calculation and vehicle random slowing calculation on the occupied cellular vehicles, judging whether the positions of the occupied cellular vehicles are positioned at the ends of ramps or not, if so, merging into a main road and carrying out vehicle position updating, and if not, directly carrying out vehicle position updating;
the updating strategy B is as follows: determining a random slowing probability and a lane changing probability for the vehicles occupying the cellular cells, if the lane changing probability and the lane changing probability are in accordance with a lane changing condition, carrying out vehicle lane changing and vehicle position updating, and if the lane changing probability and the lane changing condition are not in accordance with the lane changing condition, carrying out vehicle acceleration calculation, vehicle deceleration calculation, vehicle random slowing calculation and vehicle position updating;
the updating strategy C is as follows: and forbidding lane change when the occupied cellular vehicle is in the tunnel, carrying out vehicle acceleration calculation, vehicle deceleration calculation and vehicle random slowing calculation on the occupied cellular vehicle, and updating the position of the vehicle.
Based on the simulation method, the invention also provides a method for predicting the abnormal traffic event, which simulates the traffic evolution rule by using any one of the cellular automata-based traffic flow model simulation methods, compares the evolution result with the pre-stored traffic flow state parameters under the scene of the abnormal traffic event and determines whether the abnormal traffic event occurs at present.
Optionally, the abnormal traffic event includes a fire, an interruption, a jam, a disturbance, and the like.
Optionally, the traffic flow state parameters include lane change probability, in-tunnel slowdown probability, out-tunnel slowdown probability, relative density, number of closed lanes, duration, and the like.
The invention also provides a traffic flow model simulation system based on the cellular automata, which comprises the following components:
the model building module is used for building a traffic flow model;
the model initialization module is used for initializing the traffic flow model, setting the initial time T to be 1 and setting the simulation termination time to be T;
the model traversal module is used for traversing the vehicle information array by using a traffic flow model and determining the attribute of the currently occupied cellular vehicle;
the vehicle state updating module is used for updating the vehicle state based on the position of the current cellular in the road network according to the attribute of the vehicle occupying the cellular currently;
the loop judgment module is used for judging whether T < T is true, if so, making T equal to T +1, and returning to the model traversal module; if not, ending the simulation.
According to the technical scheme, the invention discloses a traffic flow model simulation method, a traffic flow model simulation system and an abnormal traffic event prediction method, and compared with the prior art, the method has the following beneficial effects:
the traffic flow model simulation method provided by the invention can simulate the evolution law of traffic flow under typical traffic events, simulate the operating characteristics of various vehicles through short-time prediction of traffic parameters, acquire traffic state data under typical event scenes, lay a foundation for prediction of the influence range of the traffic events, provide timely and accurate abnormal traffic event information for managers, improve the traffic monitoring level and emergency handling capacity of tunnel areas, take measures in time, prevent secondary accidents and ensure safe and smooth traffic of vehicles in the tunnel areas of highways.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic diagram of a simulation method of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention;
FIG. 3 is a basic flow-density diagram of simulated and actual values according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention discloses a traffic flow model simulation method based on a cellular automaton, which is shown in figure 1 and specifically comprises the following steps:
according to the cellular automaton model of the multilane, the whole field of the expressway is made to be L, N lanes are arranged in each direction, and the number of lanes is LcThere is a ramp merge point where the vehicle must change lanes into the outer lane. Length of LtHighway tunnel region is located L of highwayTAnd the specified tunnel interior forbids lane change. When the vehicle is located on a normal road section, the vehicle can freely run and randomly change the road. When the vehicle is located on a tunnel section, the random slowdown probability of the vehicle increases, and lane change is prohibited. When the vehicle is positioned at the junction of the ramps, the vehicle must change lanes to enter an outer lane (for example, the traffic flow on the ramps converges into the main lane, and the opposite traffic flow runs reversely); and (3) at the shunting position, entering a ramp from a main road with a certain probability, respectively updating the speed and position information of the vehicles in the road network according to the arrival sequence, and simulating the whole process from the entering road section to the tunnel and to the leaving of the vehicles. The control flow of the vehicle running simulation model in the normal state is as follows:
setting road section cells and multi-lane cells, wherein the total length of the highway is L, the width of the highway is W, N lanes are arranged in each direction, and the length of the highway tunnel area is LtL on a highwayTWhen the vehicle is located on a normal road section, the vehicle can freely run and randomly change the road. When the vehicles are located on the tunnel road section, the random slowing probability of the vehicles is improved, and lane changing is forbidden, so that a traffic flow model based on the cellular automaton is obtained.
Step 2, initializing a traffic flow model: the initialized content comprises the following steps: setting initial values for the speed limit, the random slowing probability and the lane changing probability of each road section, initially generating a vehicle information array on the road section, setting the initial time T to be 1, and setting the simulation termination time to be T.
And 3, traversing the vehicle information array by using a traffic flow model, determining the attribute of the currently occupied cellular vehicle, and determining the length of the vehicle and the highest driving speed of the vehicle.
Step 4, updating the vehicle state based on the position of the current cellular in the road network according to the attribute of the vehicle occupying the cellular currently, specifically:
step 4.1, determining the position of the current cellular in a road network, wherein the position comprises a ramp, a main road and a tunnel;
step 4.2, according to the position, different updating strategies are executed on the vehicle state;
if the current cellular is positioned on the ramp, executing an updating strategy A; if the current cell is located in the main channel, executing an updating strategy B; and if the current cell is positioned in the tunnel, executing an updating strategy C.
The updating strategy A is as follows: carrying out vehicle acceleration calculation, vehicle deceleration calculation and vehicle random slowing calculation on the occupied cellular vehicles, judging whether the positions of the occupied cellular vehicles are positioned at the ends of ramps or not, if so, merging into a main road and carrying out vehicle position updating, and if not, directly carrying out vehicle position updating;
the updating strategy B is as follows: determining random slowing-down probability and lane changing probability for the occupied cellular vehicles, if the lane changing conditions are met, performing vehicle lane changing and vehicle position updating, and if the lane changing conditions are not met, performing vehicle acceleration calculation, vehicle deceleration calculation, vehicle random slowing-down calculation and vehicle position updating;
the updating strategy C is as follows: and forbidding lane change when the occupied cellular vehicle is in the tunnel, carrying out vehicle acceleration calculation, vehicle deceleration calculation and vehicle random slowing calculation on the occupied cellular vehicle, and updating the position of the vehicle.
In step 3, correlation calculations are performed according to the following algorithm model:
1) acceleration rules
vm,n(t+1)＝min[vm,n(t)+1,vmax]
In the formula, vm,n(t +1) represents the speed of the nth vehicle of vehicle type m at time t + 1, vm,n(t) represents the speed of the nth vehicle of the vehicle type m at time t, vmaxWhich represents the maximum travel speed of a vehicle of type m on a road segment.
2) Deceleration law
vm,n(t+1)＝min[vm,n(t),dm,n(t)]
Wherein d ism,n(t) represents the forward vehicle distance.
3) Rules of lane change
1. Random lane change
Position condition
xm,n(t)＜LT or xm,n(t)≥LT+Lt
Wherein x ism,n(t) represents the current position of the nth vehicle of the vehicle type m.
Track changing machine
Safety conditions
When the vehicle satisfies the above condition, pchangeAnd (6) changing the channel. Wherein the front distance is dm,n(t)＝xm,n(t)-xm,n+1(t)-lvehThe distance between the rear wheels is
Distance of front of adjacent lane, dsafeFor a safe distance, xm,n-1(t) represents the current position of the (n-1) th vehicle of the vehicle type m,lvehindicating the length of the vehicle.
2. Forced lane change
Suppose LcAt the merging position of the ramp, with the vehicle at LcThe position must be reversed.
Position condition
xm,n(t)＝Lc
Track changing machine
Safety conditions
When the vehicle satisfies the above condition, pchangeLane changes are made as 1.
4) Stochastic moderation
The vehicle can be randomly slowed down with probability p on any road section:
if p＜pslow,vm,n(t+1)＝max(vm,n(t)-1,vmin)
wherein p is a generated random number, pslowIs the random slowing-down probability, vminIs the minimum travel speed of the vehicle.
5) Location update
xm,n(t+1)＝xm,n(t)+vm,n(t)
In the formula, xm,n(t+1)、xm,n(t) is the position of the nth vehicle of the mth type at time t + 1, t.
Step 5, judging whether T < T is established or not, if so, making T equal to T +1, updating the intersection signal lamp, and returning to the step 3; if not, ending the simulation.
And simultaneously, adding new arriving vehicle information to the vehicle information array every m step lengths to form a periodic boundary condition.
In another embodiment, a method for predicting an abnormal traffic event is further disclosed, wherein a traffic flow model simulation method based on the cellular automata is used for simulating a traffic evolution rule, and an evolution result is compared with a traffic flow state parameter in a pre-stored abnormal traffic event scene to determine whether the abnormal traffic event occurs currently.
Optionally, the abnormal traffic event includes a fire, an interruption, a jam, and a disturbance.
Optionally, the traffic flow state parameters include lane change probability, tunnel interior slowing-down probability, tunnel exterior slowing-down probability, relative density, number of closed lanes, and duration.
In another embodiment, the present invention further provides a traffic flow model simulation system based on cellular automata, referring to fig. 2, including:
the model building module is used for building a traffic flow model;
the model initialization module is used for initializing the traffic flow model, setting the initial time T to be 1 and setting the simulation termination time to be T;
the model traversal module is used for traversing the vehicle information array by using a traffic flow model and determining the attribute of the currently occupied cellular vehicle;
the vehicle state updating module is used for updating the vehicle state based on the position of the current cellular in the road network according to the attribute of the vehicle occupying the cellular currently;
the loop judgment module is used for judging whether T < T is true, if so, making T equal to T +1, and returning to the model traversal module; if not, ending the simulation.
The traffic flow model proposed by the present invention is verified as follows:
the model verification takes the small traffic flow and the average speed data of the Shandong expressway Gangtang section as the reference, and verifies the simulation effect of the cellular automaton. The cross-sectional data are the flow rate per hour and the average velocity, and the average density can be determined.
The simulation sets 100 average densities with the interval of 0.01 between 0 and 1, then each density is used for simulation to obtain the flow, and further a basic flow-density diagram can be drawn to be compared with a real value. The error calculation was performed as follows:
wherein n is the data amount,
to predict data, yiIs the actual data.
Through calculation, the MAPE of the flow is about 7.58%, the prediction accuracy is about 92.42%, the checking effect of the representative model by using real data is good, and the change of the traffic flow can be truly simulated. The basic flow-density diagram of the simulation and the real value is shown in fig. 3, and it can be seen from the diagram that the simulated flow is closer to the real flow, and the simulation effect is better. When the standardized density is less than 0.2, the traffic flow is in a free flow state, synchronous flow appears when the standardized density is about 0.2 along with the increase of the density, and when the density is more than 0.2, the traffic flow gradually forms congestion and is more consistent with the change trend of free flow-synchronous flow-large-area congestion in the three-phase traffic flow theory, which shows that the simulation can more truly simulate the traffic flow state.
The traffic flow evolution law is introduced as follows:
based on a traffic flow simulation model based on cellular automata, the evolution rule of the traffic flow under a typical traffic event is simulated, and microscopic traffic flow parameters under the influence of the event are obtained.
In order to more truly depict the main characteristics of various types of events, the input parameters of the cellular automaton simulation model are adjusted, and then the change rule of the traffic flow is observed. The simulation logic of the typical event is set as shown in table 1, and the simulation result is shown in table 2, so that the simulation result has high precision:
TABLE 1 simulation logic parameters for events
260 times of simulation is carried out, and 260 times of simulation results are finally obtained. On the basis of results, counting traffic flow parameters:
queuing length: and extracting all the cell space positions with the speed of 0 at the upstream of the accident occurrence point, and calculating the queuing length according to the positions.
Influence scope: and extracting all the cellular space positions with the highest speed limit of 50% at the upstream of the accident occurrence point, and calculating the space influence range according to the positions.
Influence time: and extracting the time t1 when the first speed is 50% of the highest speed limit after the accident occurs and the time t2 when all the cell speeds are higher than the free flow speed, wherein the influence time is t2-t 1.
Table 2 simulation output results example
Therefore, the traffic flow evolution mechanism of the expressway tunnel and the upstream and downstream road sections is analyzed, the traffic state evolution rule in the tunnel is simulated by using the traffic flow model, the running states of vehicles upstream and downstream of the tunnel are evaluated, and a sufficient reference basis is provided for predicting the influence range of the traffic incident.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A traffic flow model simulation method based on cellular automata is characterized by comprising the following steps:
step 1, constructing a traffic flow model based on a cellular automata theory;
step 2, initializing a traffic flow model, setting an initial time T to be 1, and setting a simulation termination time to be T;
step 3, traversing the vehicle information array by using a traffic flow model, and determining the attribute of the vehicle occupying the cellular at present;
step 4, updating the vehicle state based on the position of the current cellular in the road network according to the attribute of the vehicle occupying the cellular currently;
step 5, judging whether T < T is true, if so, making T equal to T +1, and returning to the step 3; if not, ending the simulation.
2. The traffic flow model simulation method based on cellular automata according to claim 1, wherein the road section cells and the multi-lane cells are arranged such that the total length of the expressway is L, the width is W, each direction has N lanes, and the length of the expressway tunnel region is LtL on a highwayTAnd obtaining a traffic flow model based on the cellular automaton.
3. The method for simulating a traffic flow model based on a cellular automaton according to claim 1, wherein the initializing in step 2 comprises: setting initial values for the speed limit, the random slowing probability and the lane changing probability of each road section, initially generating a vehicle information array on the road section, setting an initial time T to be 1, and setting a simulation termination time to be T.
4. The method for simulating a traffic flow model based on cellular automata according to claim 1, wherein in step 3, the attributes of the currently occupied cellular vehicles include: vehicle length, vehicle maximum travel speed.
5. The traffic flow model simulation method based on cellular automata according to claim 1, wherein the specific step of updating the vehicle state in step 4 is as follows:
step 4.1, determining the position of the current cellular in a road network, wherein the position comprises a ramp, a main road and a tunnel;
step 4.2, according to the position, different updating strategies are executed on the vehicle state; if the current cellular is positioned on the ramp, executing an updating strategy A; if the current cell is located in the main channel, executing an updating strategy B; and if the current cell is positioned in the tunnel, executing an updating strategy C.
6. The traffic flow model simulation method based on cellular automata according to claim 5, wherein the update strategy A is as follows: carrying out vehicle acceleration calculation, vehicle deceleration calculation and vehicle random slowing calculation on the occupied cellular vehicles, judging whether the positions of the occupied cellular vehicles are positioned at the ends of ramps or not, if so, merging into a main road and carrying out vehicle position updating, and if not, directly carrying out vehicle position updating;
the updating strategy B is as follows: determining a random slowing probability and a lane changing probability for the vehicles occupying the cellular cells, if the lane changing probability and the lane changing probability are in accordance with a lane changing condition, carrying out vehicle lane changing and vehicle position updating, and if the lane changing probability and the lane changing condition are not in accordance with the lane changing condition, carrying out vehicle acceleration calculation, vehicle deceleration calculation, vehicle random slowing calculation and vehicle position updating;
the updating strategy C is as follows: and forbidding lane change when the occupied cellular vehicle is in the tunnel, carrying out vehicle acceleration calculation, vehicle deceleration calculation and vehicle random slowing calculation on the occupied cellular vehicle, and updating the position of the vehicle.
7. A method for predicting abnormal traffic events, which is characterized in that the method for simulating the traffic flow model based on the cellular automata according to any one of claims 1 to 6 is used for simulating traffic evolution rules, and the evolution results are compared with the traffic flow state parameters in the abnormal traffic event scene stored in advance to determine whether the abnormal traffic events happen currently.
8. The method of claim 7, wherein the abnormal traffic event comprises a fire, an interruption, a jam, or an interference.
9. The method of claim 7, wherein the traffic flow state parameters comprise lane change probability, intra-tunnel slowdown probability, extra-tunnel slowdown probability, relative density, number of closed lanes, and duration.
10. A traffic flow model simulation system based on cellular automata is characterized by comprising:
the model building module is used for building a traffic flow model;
the model initialization module is used for initializing the traffic flow model, setting the initial time T to be 1 and setting the simulation termination time to be T;
the model traversal module is used for traversing the vehicle information array by using a traffic flow model and determining the attribute of the currently occupied cellular vehicle;
the vehicle state updating module is used for updating the vehicle state based on the position of the current cellular in the road network according to the attribute of the vehicle occupying the cellular currently;
the loop judgment module is used for judging whether T < T is true, if so, making T equal to T +1, and returning to the model traversal module; if not, ending the simulation.
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| CN115019508A (en) * | 2022-06-13 | 2022-09-06 | 华南理工大学 | Road monitoring traffic flow simulation method, device, equipment and medium based on machine learning |
| CN115019508B (en) * | 2022-06-13 | 2023-09-29 | 华南理工大学 | Road monitoring traffic flow simulation method, device, equipment and medium based on machine learning |
| CN115393969A (en) * | 2022-07-20 | 2022-11-25 | 招商新智科技有限公司 | Lane management and control method for expressway multi-section traffic state feedback ramp toll station |
| CN115393969B (en) * | 2022-07-20 | 2023-09-19 | 招商新智科技有限公司 | Lane control method for ramp toll station for feeding back multi-section traffic state of expressway |
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