KR102199369B1 - Method for predicting and simulating congestion of road flow - Google Patents

Abstract

The present invention relates to a disaster response training system, and more particularly, to a road traffic congestion prediction and simulation method performed by a disaster response training system computer (e.g., desktop, laptop, tablet PC, etc.), traffic congestion level guide terminal At (100), by accessing the real-time traffic situation information guide server 200, the average moving speed of the vehicle actually moving in the simulated traffic section, which is the road section of the area to be simulated according to the disaster response training, (VC _j ) Obtaining real-time road information including a (S100) and; As information included in the real-time road information secured by the traffic congestion level guidance terminal 100, the average distance of vehicles moving in the simulated traffic section based on the average moving speed (VC _j ) of the vehicle actually moving in the simulated traffic section Predicting the distance I _jreal (S200); The traffic congestion level information terminal 100 predicts road traffic congestion that considers both the actual moving vehicle and the simulated vehicle in the simulated traffic section based on the _predicted average inter-vehicle distance (I _jreal ) of the vehicle moving in the simulated traffic section. , The simulation step (S300); consists of.

Description

Road traffic congestion prediction and simulation method {Method for predicting and simulating congestion of road flow}

The present invention relates to a disaster response training system, and more particularly, to a road traffic congestion prediction and simulation method performed by a computer for disaster response training system.

In order to simulate a vehicle's road movement in a disaster response training system using M&S (Modeling & Simulation) techniques, it is necessary to predict and simulate the traffic congestion of the road.

For reference, the M&S (Modeling & Simulation) technique is an abbreviation of modeling and simulation, and is a technique to design a model instead of an actual experiment, and to predict and supplement problems through a virtual experiment. For example, in the field of defense science and technology, the above-described M&S (Modeling & Simulation) technique is a scientific technique that models characteristics and functions similar to the battlefield, and predicts and verifies experimental results through simulation in this environment. Is being used.

Estimating the degree of congestion by road section to be simulated in such a disaster response training system is the degree of congestion by real-world general vehicles that can be secured from real-time road information, as well as a virtual congestion degree by the simulated vehicle mobilized for training performed by simulation techniques. It is necessary to consider at the same time, so a method of predicting and simulating road congestion is necessary.

Meanwhile, the traffic congestion index calculation method and apparatus according to Patent Document 1 calculates the traffic congestion index by measuring the number of vehicles passing through the road during a unit time using a roof sensor installed on the road. It is to calculate the congestion index.

In addition, the traffic density-based road congestion estimation system and its method according to Patent Document 2 collects the traffic density using a probe vehicle that detects surrounding vehicles while driving on the road, and estimates the road congestion level by reflecting it.

The traffic congestion calculation system and method according to Patent Document 3 predicts the current traffic congestion by using the previous traffic history data such as the movement speed of vehicles by road in the past.

As in Patent Documents 1 to 3, technologies for calculating road traffic congestion using actual road situation information have been developed, but vehicles running on actual roads in a specific area to be simulated in the disaster response training system There is a need for a technology that simultaneously considers the traffic congestion caused by the simulated vehicle and the traffic congestion caused by the simulated vehicle.

KR 10-1671245 B1 KR 10-1817127 B1 KR 10-1545494 B1

The present invention is to predict and simulate road traffic congestion in a disaster response training system, and an object of the present invention is to calculate the inter-vehicle distance of vehicles running on the road according to the vehicle speed, and apply this to the prediction and simulation of traffic congestion. It is to provide a method of predicting and simulating traffic congestion.

Another object of the present invention is to add traffic congestion by real vehicles running on a real road section in a specific area to be simulated in a disaster response training system using a M&S (Modeling & Simulation) technique. It is to provide a method for predicting and simulating road traffic congestion that simultaneously considers traffic congestion caused by the problem.

In order to achieve the object of the present invention as described above, the road traffic congestion prediction and simulation method according to the present invention is, in the traffic congestion level guidance terminal, accesses a real-time traffic situation information guide server, and conducts simulation training according to disaster response training. Obtaining real-time road information including an average moving speed (VC _j ) of a vehicle actually moving in a simulated traffic section, which is a road section of a desired area; In the traffic congestion level information terminal, as information included in the secured real-time road information, the average inter-vehicle distance (I) of the vehicle moving in the simulated traffic section based on the average moving speed (VC _j ) of the vehicle actually moving in the simulated traffic section _jreal ) predicting; The traffic congestion level information terminal predicts and simulates road traffic congestion in consideration of both the actual moving vehicle and the simulated vehicle in the predicted simulated traffic section.

The information included in the real-time road information secured by the traffic congestion level information terminal, based on the average moving speed (VC _j ) of the vehicle actually moving in the simulated traffic section, the average inter-vehicle distance of the vehicle actually moving in the simulated traffic section In the step of calculating (I _jreal ), the average inter-vehicle distance (I _jreal ) of a vehicle actually moving in the simulated traffic section is characterized by a value obtained by the following equation.

I _jreal = (21/5000)(VC _j ) ² + 0.07(VC _j ) + 1

The traffic congestion level information terminal predicts road traffic congestion in consideration of both the actual moving vehicle and the simulated vehicle in the simulated traffic section based on the average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the predicted simulated traffic section, In the simulation step, based on the average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the corresponding simulated traffic section calculated at the traffic congestion level guide terminal, the actual number of vehicles (DC _jreal ) per unit length of the corresponding simulated transportation section is _calculated . It includes; predicting.

In the step of calculating the actual number of vehicles (DC _jreal ) in motion per unit length of the corresponding simulated transportation section based on the _calculated average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the corresponding simulated transportation section in the traffic congestion level guidance terminal. In this _regard , the number of vehicles actually moving per unit length of the simulated transportation section (DC _jreal ) is characterized in that it is a value obtained by the following equation.

n _j is the actual number of lanes in the simulated traffic section.

S is the vehicle's overall length.

In the traffic congestion level guide terminal, based on the calculated average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the corresponding simulated traffic section, the road traffic congestion degree is predicted in consideration of both the actual moving vehicle and the simulated vehicle in the simulated traffic section, The step of simulating may further include a step of predicting the number of simulated vehicles entering the simulated traffic section and affecting the road traffic congestion (N _veh ).

In the step of predicting the number of simulated entry vehicles (N _veh ) entering the corresponding simulated traffic section at the traffic congestion level guidance terminal, the number of simulated entry vehicles (N _veh ) is a value obtained by the following equation. .

∵ T _jreal = 3600 × (1/VC _j )

∴

T _arrival is the arrival time interval (unit: seconds) at which simulated vehicles entering the simulated transportation section arrive at the access road of the simulated transportation section during disaster response training.

T _jreal is the time (unit: second) it takes for the vehicle to pass through the unit length (1,000m) of the simulated traffic section at the average moving speed (VC _j ) of the vehicle in motion.

In the traffic congestion level guide terminal, based on the calculated average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the corresponding simulated traffic section, the road traffic congestion degree is predicted in consideration of both the actual moving vehicle and the simulated vehicle in the simulated traffic section, The simulating step is a step of simulating a simulated average inter-vehicle distance (I _jvirtual ) of all vehicles in which the number of vehicles actually moving in the corresponding simulated traffic section and the number of vehicles entering the simulated vehicle (N _veh ) are reflected in the traffic congestion level guidance terminal. Include.

In the step of simulating a simulated average inter-vehicle distance (I _jvirtual ) of all vehicles in which the number of actual moving vehicles and the number of simulated entering vehicles (N _veh ) are reflected in the traffic congestion level guidance terminal, the simulated average The inter-vehicle distance I _jvirtual is characterized by being a value obtained by the following equation.

In the traffic congestion level guide terminal, based on the calculated average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the corresponding simulated traffic section, the road traffic congestion degree is predicted in consideration of both the actual moving vehicle and the simulated vehicle in the simulated traffic section, The simulating step includes simulating a simulated average moving speed (VC _jvirtual ) of all vehicles in which the simulated number of vehicles entering the simulated vehicle (N _veh ) is reflected in the traffic congestion level guidance terminal in the corresponding simulated traffic section.

In the step of simulating a simulated average moving speed (VC _jvirtual ) of all vehicles in which the number of simulated entering vehicles (N _veh ) is reflected in a corresponding simulated traffic section at the traffic congestion level guidance terminal, the simulated number of entering vehicles (N _veh ) the total reflected simulated average moving speed (VC _jvirtual) of the vehicle, using the formula for the root of the quadratic equation to calculate the average movement simulated speed (VC _jvirtual).

I _jvirtual = (21/5000) (VC _jvirtual ) ² + 0.07 (VC _jvirtual ) + 1

In the traffic congestion level guide terminal, based on the calculated average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the corresponding simulated traffic section, the road traffic congestion degree is predicted in consideration of both the actual moving vehicle and the simulated vehicle in the simulated traffic section, The simulating step may include determining a traffic congestion level based on a simulated average moving speed (VC _jvirtual ) of all vehicles in which the simulated number of vehicles entering the simulated vehicle (N _veh ) is reflected in the traffic congestion level guide terminal, and outputting this through an output unit; It includes more.

The present invention calculates the inter-vehicle distances of vehicles traveling on a road according to the vehicle speed, and applies it to prediction and simulation of traffic congestion, thereby exhibiting an effect of predicting more accurate traffic congestion for each vehicle speed.

In addition, the present invention uses a disaster response training system using M&S (Modeling & Simulation) techniques to simulate traffic congestion by actual vehicles running on the road in a real road section of a specific area to be simulated. There is also an effect that it is possible to predict and simulate traffic congestion by simultaneously considering traffic congestion.

1 is an overall configuration diagram showing a configuration required to perform a road traffic congestion prediction and simulation method according to the present invention.
2 is a block diagram showing in detail a traffic congestion level guide terminal among the configurations required to perform a road traffic congestion prediction and simulation method according to the present invention.
3 and 4 are simulation training based on the moving speeds of vehicles actually running on the road in the simulated traffic section in which the simulation training is to be conducted according to the disaster response training using the road traffic congestion prediction and simulation method according to the present invention. It is a flow chart showing the process of predicting and simulating traffic congestion when entering the simulated vehicle mobilized according to the scenario.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the accompanying drawings.

The method for predicting and simulating road traffic congestion according to the present invention described below is not limited to the above-described embodiment, and anyone with ordinary knowledge in the relevant technical field without departing from the gist of the technology claimed in the claims It has its technical spirit to the extent that it can be implemented with various changes.

1 is an overall configuration diagram showing a configuration necessary to perform the road traffic congestion prediction and simulation method according to the present invention, and FIG. 2 is a traffic congestion diagram among the configurations required to perform the road traffic congestion prediction and simulation method according to the present invention. It is a block diagram showing in detail the information terminal.

As shown, the configuration necessary to perform the road traffic congestion prediction and simulation method according to the present invention largely includes a traffic congestion guidance terminal 100 and a real-time traffic condition information guide server 200 used in a disaster response training system. Done.

The real-time traffic situation information guide server 200 is a member that provides real-time guidance on the vehicle movement speed of each road divided by type, and the roads in the nationwide area divided by administrative districts are divided by type.

The real-time traffic condition information guide server 200 that performs such a function is a server operated by the National Traffic Information Center (http://www.its.go.kr/), and is a real-time road according to the type of road in the country. Provides a service that guides information to be viewed publicly through standard node/link data (nodelink.its.go.kr).

Here, a node is a definition of a place where a change in speed occurs when a vehicle travels on a road. Intersections, bridge start and end points, overpass start and end points, road start and end points, underpass road start and end points, tunnel start and end points, administrative boundaries, IC /JC, etc. are included.

In addition, a link is a line connecting a node, which is a point of speed change, and a road in the real world, and includes roads, bridges, overpasses, underpasses, and tunnels.

In addition, the real-time road information provides the average moving speed of the actual vehicles on the link connecting the nodes of the road in km/h.

For reference, Table 1 below shows a guide table for classifying the degree of congestion by road type according to the average moving speed of vehicles guided by the National Traffic Information Center.

Congestion Speed(km/h) City District Urban highway highway Circle bow 25 or more 50 or more 50 or more 80 or more Standing row 15-25 30 to 50 30 to 50 40 to 80 Identity Less than 15 Less than 30 Less than 30 Less than 40

The traffic congestion level guidance terminal 100 is a terminal member that performs a road traffic congestion prediction and simulation method, and a desktop, a laptop computer, a smart phone, a tablet PC, or the like may be used.

The detailed configuration of the traffic congestion guidance terminal 100 performing this function includes an input processing unit 110, an output unit 120, and an operation unit 130.

The input processing unit 110, the output unit 120, and the operation unit 130 are preferably installed in the form of an application program on the traffic congestion level guide terminal 100.

The input processing unit 110 is a member that processes the actual vehicle movement speed of a corresponding road on which disaster response training is performed from the real-time traffic condition information guide server 200.

The output unit 120 is a member that outputs and processes the simulated average moving speed and road traffic congestion information processed by predicting and simulating road traffic congestion.

The calculation unit 130 calculates the inter-vehicle distance based on the moving speeds of vehicles actually running on the road in the simulated traffic section, which is a road section (corresponding to the link mentioned above) in the area to be simulated according to the disaster response training. And, to control various information necessary to simulate traffic congestion by predicting the vehicle movement speed that changes when a virtual vehicle (passenger, van, midsize bus, large bus, freight car, etc.) mobilized according to the simulation training scenario enters. Is absent.

That is, within the unit length of 1km, which is the aforementioned simulated transportation section, the calculation unit 130 considers the number of lanes in the simulated transportation section, and the total number of vehicles running is calculated as the unit length of 1km. It can be calculated by dividing by the sum. Here, the type of vehicle to be simulated is assumed to be based on a passenger vehicle having a total length of 5 m, but for a large bus (or freight vehicle), one can be considered as two passenger cars.

In an embodiment of the present invention, the inter-vehicle distance for each moving speed of the vehicle is defined as follows.

When the vehicle's moving speed is 0km/h, that is, when the vehicle is stationary, the inter-vehicle distance is 1m, when the vehicle's moving speed is 50km/h, the inter-vehicle distance is 15m, and the vehicle's moving speed is 100km/h per hour. Assuming that the inter-vehicle distance at this time is 50m, the equation related to the vehicle movement speed and the inter-vehicle distance is defined as the quadratic equation "y = ax ² + bx + c".

Here, the main reason for defining the inter-vehicle distance according to the vehicle's moving speed as a quadratic equation is that if the vehicle speed increases linearly, the inter-vehicle distance must increase in a quadratic function so that the maximum safety distance can be secured in case of sudden stop according to the vehicle speed. Because.

In addition, y is the inter-vehicle distance for each moving speed of the vehicle, x is the moving speed of the vehicle, and a, b, c are coefficients of each term.

Accordingly, when the vehicle movement speed is 0km/h, the inter-vehicle distance is 1m, the inter-vehicle distance is 15m at 50km/h, and 50m at 100km/h, respectively, to obtain the following system of equations.

c = 1

2500a + 50b + c = 15

10000a + 100g + c = 50

Solving the above system of equations to find the coefficients a, b, c, a = 21/5000, b=0.07, c=1. Therefore, the quadratic equation is expressed as Equation 1.

In addition, when the vehicle movement speed is substituted into the quadratic equation expressed in Equation 1, a distance value for each movement speed as shown in the graph below is obtained.

[graph]

Based on this, the calculation unit 130 calculates the number of vehicles (DC _jreal ) per unit length (Unit Distance, 1,000m) of the simulated traffic section by Equation 2 below.

In Mathematics 2, I _j is the average inter-vehicle distance (unit is meter) between moving vehicles in the unit length (1,000m) of the simulated traffic section, S is the total length of the vehicle (5m based on the car), and n is The number of lanes in the simulated traffic section.

For example, if the number of vehicles (DC _jreal ) per unit length (defined as 1,000m) of the simulated transportation section (defined as 1,000m) is predicted by the calculation unit 130 based on a passenger car on a road with a speed limit of 60km/h, it is as follows.

"으로서, I_j의 값인 15는 수학식 1의 연산으로 구해진 값이고, 결과값이 50n이 나오는데, n은 차로 수이기 때문에, 1차로로 가정할 경우 50대가 연산된다. First, when communication is smooth, that is, when the vehicle's moving speed is 50km/h, the number of vehicles (DC _jreal ) per unit length (defined as 1,000m) of the simulated traffic section based on the passenger car is "

As ", 15, which is the value of I _j, is a value obtained by the operation of Equation 1, and the result is 50n, and since n is a number of lanes, 50 units are calculated assuming a first lane.

"으로서, I_j의 값인 6.88은 수학식 1의 연산으로 구해진 값이고, 결과값이 84.2n이 나오는데, n은 차로 수이기 때문에, 1차로로 가정할 경우 대략 84대 정도가 연산된다. In addition, when traffic is slow, that is, when the moving speed of the section is 30km/h, the number of vehicles (DC _jreal ) per unit length (defined as 1,000m) of the simulated traffic section based on the passenger car is "

As ", 6.88, which is the value of I _j, is a value obtained by the operation of Equation 1, and the result is 84.2n. Since n is the number of lanes, approximately 84 units are calculated assuming a first lane.

"으로서, I_j의 값인 2.12는 수학식 1의 연산으로 구해진 값이고, 결과값이 140.4n이 나오는데, n은 차로 수이기 때문에, 1차로로 가정할 경우 대략 140대 정도가 연산된다. Further, if the communication is static In other words, the logarithmic (DC _jreal) of the vehicle per interval when moving speed is 10km / h day (defined as 1,000m) unit length of the passenger traffic simulation interval based on the "

As ", 2.12, which is the value of I _j, is a value obtained by the operation of Equation 1, and the result is 140.4n. Since n is the number of lanes, approximately 140 units are calculated assuming a first lane.

As described above, the traffic congestion information terminal 100 of the present invention controls the operation unit 130 to provide a disaster response training scenario in a simulated traffic section, which is a road in a specific communication state by a general vehicle in a situation before starting the disaster response training. Accordingly, when the training simulation vehicle enters, the changed communication state is simulated and output through the output unit 120 so that the manager can recognize the traffic congestion level according to the simulation training.

Meanwhile, in an embodiment of the present invention, the arrival time interval of the simulated vehicles entering the simulated transportation section according to the simulated disaster response training scenario is the time it takes for the vehicle to pass through the unit length (1,000m) of the simulated transportation section. If it is greater than, it is assumed that the degree of congestion is not affected by the simulated vehicle.

Here, the arrival time interval of the simulated vehicles entering the simulated traffic section is a value calculated according to the simulation logic in the simulated training system.

According to the configuration of the present invention, if the moving speed of the vehicle is known, the inter-vehicle distance for each moving speed of the vehicle can be predicted, and conversely, when the inter-vehicle distance for each moving speed of the vehicle is known, the moving speed of the vehicle can be predicted.

In addition, in the present invention, in addition to disaster response training using the M&S technique, it is applicable to predicting the actual road congestion.

To this end, in the present invention, the arrival time interval of the vehicle entering link j can be measured based on the current vehicle movement speed in a simulated traffic section in which the average moving speed of the actual moving vehicle can be measured. It can also be used as a method of predicting the degree of congestion caused or intensified by vehicles entering newly on roads with sensors installed. As the above-described sensor, a detector that measures the number of vehicles entering the simulated traffic section, for example, an optical sensor may be applied.

In this way, using the traffic congestion level guidance terminal 100 and the real-time traffic condition information guide server 200 of the present invention, the movement speed of vehicles running on the actual road in the simulated traffic section to be simulated according to the disaster response training On the basis of, the process of predicting and simulating traffic congestion when entering a simulated vehicle mobilized according to a simulated training scenario will be described with reference to FIGS. 3 and 4 as follows.

First, the manager manipulates the input processing unit 110 of the traffic congestion level guidance terminal 100, accesses the real-time traffic condition information guide server 200, and then performs simulation training according to the disaster response training among road sections divided by administrative districts. Real-time road information including the average moving speed (VC _j ) of vehicles actually moving in the simulated traffic section, which is a road section of the region to be implemented, is secured (S100).

Here, the real-time traffic condition information guide server 200 is viewed or viewed from the standard node/link data (nodelink.its.go.kr) provided by the National Transportation Information Center (http://www.its.go.kr/). It can be obtained through download.

Thereafter, the operation unit 130 of the traffic congestion level guidance terminal 100 is moving in the simulated traffic section based on the average moving speed (VC _j ) of the vehicle actually moving in the simulated traffic section among the information included in the secured real-time road information. The average inter-vehicle distance (I _jreal ) of the vehicle is predicted (S200).

At this time, the calculation unit 130 predicts the average inter-vehicle distance (I _jreal ) of the vehicle moving in the corresponding simulated traffic section is obtained through Equation 3 below.

Equation 3 is substantially the same as Equation 1.

That is, in Equation 1, y is the average inter-vehicle distance of the vehicle, and in Equation 3 corresponding thereto, I _jreal is the average inter-vehicle distance of the vehicle actually moving in the simulated traffic section, which is an actual road to perform a simulation test, Only the name is specific to the actual driving situation.

This is an average moving speed of a moving vehicle in both x in Equation 1 and VC _j in Equation 3, and only the name of the equation according to whether or not the vehicle is actually traveling is specified according to the situation.

Thereafter, the traffic congestion level guidance terminal 100 predicts road traffic congestion in consideration of both the actual moving vehicle and the simulated vehicle in the simulated traffic section based on the _predicted average inter-vehicle distance (I _jreal ) of the vehicle moving in the simulated traffic section. And, a simulation process (S300) is performed, and the process (S300) is performed in detail as follows.

First, the calculation unit 130 of the traffic congestion level guidance terminal 100 is based on the average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the predicted simulated transportation section, the actual number of vehicles moving per unit length of the simulated transportation section ( DC _jreal ) is predicted (S310).

Here, the prediction of the number of vehicles actually moving (DC _jreal ) per unit length of the corresponding simulated transportation section is obtained through Equation 4 below.

In Equation 4, n _j is the actual number of lanes in the simulated traffic section, and S is the total length of the vehicle.

Here, Equation 4 is substantially the same as Equation 2.

That is, I _{j in} Equation 2 and I _jreal in Equation 4 are as mentioned above, only the name of the equation according to whether or not the vehicle is actually driven is specific to the situation.

In addition, n in Equation 2 and n _{j in} Equation 4 are also specific to only the name of the equation according to whether or not the vehicle is actually driven.

Thereafter, the operation unit 130 of the traffic congestion level guidance terminal 100 enters the corresponding simulated traffic section according to the scenario of the disaster response training system and predicts the number of simulated entering vehicles (N _veh ) affecting the road traffic congestion (S320). ) Calculate.

Here, the number of simulated entry vehicles (N _veh ) is obtained through Equation 5 below.

Looking at Equation 5, during disaster response training, the arrival time interval at which vehicles entering the simulated transportation section arrive at the access road of the simulated transportation section (unit seconds) After obtaining T _arrival , the arrival time interval of the entering vehicle T _arrival If the value is less than the unit distance transit time (T _jreal = 3600 × VC _j ) (unit seconds) of the vehicle's simulated traffic section, it is assumed that the number of vehicles per unit length, that is, the number of vehicles affecting the vehicle density.

In addition, the value of the number of simulated vehicles entering the vehicle (N _veh ) is rounded to an integer.

In Equation 5, T _arrival is the arrival time interval (unit: seconds) at which vehicles entering the simulated transportation section during disaster response training arrive at the access road of the simulated transportation section (unit: second), and T _jreal is the vehicle simulated It is the time (unit: second) it takes to pass the unit length (1,000m) of the traffic section at the average moving speed (VC _j ) of the vehicle actually moving.

Thereafter, the calculation unit 130 of the traffic congestion level guidance terminal 100 simulates the simulated average inter-vehicle distance (I _jvirtual ) of all vehicles in which the number of vehicles actually moving and the number of simulated entering vehicles (N _veh ) are reflected in the corresponding simulated traffic section ( S330) calculate.

Here, the simulated average inter-vehicle distance (I _jvirtual ) increases traffic congestion by the simulated vehicle entering according to the disaster response training system, and is obtained through Equation 6 below.

In Equation 6, DC _jreal is the number of vehicles actually moving per unit length of the corresponding simulated transportation section, n _j is the actual number of lanes in the simulated transportation section, and S is the total length of the vehicle.

Thereafter, the calculation unit 130 of the traffic congestion level guidance terminal 100 simulates (S340) a simulated average moving speed (VC _jvirtual ) of all vehicles in which the number of simulated entering vehicles (N _veh ) is reflected in the corresponding simulated traffic section.

Here, the simulated average moving speed (VC _jvirtual ) of all vehicles in which the simulated number of vehicles entering the vehicle (N _veh ) is reflected is obtained by Equation 7 below.

Looking at Equation 7, since the value of the simulated average inter-vehicle distance (I _jvirtual ) of the vehicle is derived from the calculated value, the root of the quadratic equation for VC _jvirtual is the number of vehicles moving and the number of simulated vehicles entering the road. It becomes the calculated value (positive number) of the simulated average moving speed (VC _jvirtual ) of all vehicles in which (N _veh ) is reflected.

Equation 7 is substantially the same as Equation 1.

That is, y in Equation 1 and I _jvirtual in Equation 7 are average inter-vehicle distances of the vehicle, and only the name of the equation according to whether or not the disaster response training is simulated is specific to the situation.

In addition, x in Equation 1 and VC _jvirtual in Equation 7 are the average moving speed of the moving vehicle, and only the name of the equation according to whether or not the disaster response training is simulated is specific to the situation.

Thereafter, the calculation unit 130 of the traffic congestion level guidance terminal 100 determines the traffic congestion level based on the simulated average moving speed (VC _jvirtual ) of all vehicles in which the simulated number of vehicles entering the vehicle (N _veh ) is reflected, and the output unit 120 ) Through the output (S350).

At this time, the criterion for determining the degree of traffic congestion is the speed range consisting of smooth, slow or congested sections in which the simulated average moving speed (VC _jvirtual ) of the simulated vehicle is calculated according to the type of road to which the simulated traffic section belongs through Table 1 mentioned above. By guiding the manager to recognize which section it belongs to, it completes the prediction and simulation process of traffic congestion on the road according to the simulated vehicle expected to enter during the disaster response training.

100: computer 110: input processing unit
120: output unit 130: operation unit
200: Real-time traffic information guide server

Claims (11)

From the traffic congestion information terminal 100, the average moving speed of the vehicle actually moving in the simulated traffic section, which is the road section of the area to be simulated according to the disaster response training by accessing the real-time traffic situation information guide server 200 (S100) obtaining real-time road information including (VC _j );
As information included in the real-time road information secured by the traffic congestion level guidance terminal 100, the average distance of vehicles moving in the simulated traffic section based on the average moving speed (VC _j ) of the vehicle actually moving in the simulated traffic section Predicting the distance I _jreal (S200);
The traffic congestion level information terminal 100 predicts road traffic congestion that considers both the actual moving vehicle and the simulated vehicle in the simulated traffic section based on the _predicted average inter-vehicle distance (I _jreal ) of the vehicle moving in the simulated traffic section It consists of, the step of simulating (S300);
In the traffic congestion information terminal 100, as information included in the secured real-time road information, based on the average moving speed (VC _j ) of the vehicle actually moving in the simulated traffic section, In step S200 of calculating the average inter-vehicle distance (I _jreal ),
Road traffic congestion prediction and simulation method, characterized in that the average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the simulated traffic section is a value obtained by the following equation:
I _jreal = (21/5000)(VC _j ) ² + 0.07(VC _j ) + 1 delete The method of claim 1,
In the traffic congestion level guidance terminal 100, a road traffic congestion level in consideration of both the actual moving vehicle and the simulated vehicle in the simulated traffic section is _calculated based on the average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the predicted simulated traffic section. The S300 step of predicting and simulating,
The traffic congestion information terminal 100 _{predicts the} actual number of vehicles (DC _jreal ) in motion per unit length of the corresponding simulated traffic section based on the _calculated average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the corresponding simulated traffic section. Step (S310); road traffic congestion prediction and simulation method comprising a. The method of claim 3,
The traffic congestion level information terminal 100 calculates the actual number of vehicles (DC _jreal ) in motion per unit length of the corresponding simulated transportation section based on the _calculated average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the corresponding simulated transportation section. In step S310,
Road traffic congestion prediction and simulation method, characterized in that the number of vehicles actually moving per unit length of the simulated traffic section (DC _jreal ) is a value obtained by the following equation.

n _j is the actual number of lanes in the simulated traffic section.
S is the vehicle's overall length. The method of claim 3,
In the traffic congestion level guidance terminal 100, based on the calculated average inter-vehicle distance (I _jreal ) of the vehicle actually moving in the corresponding simulated traffic section, a road traffic congestion degree taking into account both the vehicle actually moving in the simulated traffic section and the simulated vehicle is calculated. The S300 step of predicting and simulating,
In the traffic congestion level guidance terminal 100, the step of predicting the number of simulated vehicles (N _veh ) affecting the road traffic congestion by entering the corresponding simulated traffic section (S320); road traffic congestion, characterized in that it further comprises Method of prediction and simulation. The method of claim 5,
In step S320 of predicting the number of simulated vehicles entering the traffic congestion level (N _veh ), which has an effect on the traffic congestion of the road by entering the corresponding simulated traffic section,
The simulated number of vehicles entering the vehicle (N _veh ) is a road traffic congestion prediction and simulation method, characterized in that the value obtained by the following equation.

∵ T _jreal = 3600 × (1/VC _j )
∴

T _arrival is the arrival time interval (unit: seconds) at which simulated vehicles entering the simulated transportation section arrive at the access road of the simulated transportation section during disaster response training.
T _jreal is the time (unit: second) it takes for the vehicle to pass through the unit length (1,000m) of the simulated traffic section at the average moving speed (VC _j ) of the vehicle in motion. delete delete delete delete delete

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