KR102328592B1 - Traffic impact assessment analysis method and system - Google Patents

Abstract

The present invention relates to a traffic impact assessment analysis method to provide artificial intelligence-based traffic impact assessment analysis information according to a building construction and a system thereof. According to one embodiment of the present invention, the traffic impact assessment analysis method comprises the following steps: receiving a request for the size of a building according to a building development and at least one surrounding facility from a client terminal through a network; searching for at least one map data in which the building corresponding to the size of the building and the at least one surrounding facility are located from each other within a predetermined distance from a map database; requesting local traffic information corresponding to the at least one map data to an external traffic control server to receive a response and selecting reference map data from the at least one map data on the basis of the local traffic information; recognizing separation coordinate information and a plurality of pieces of road design information for each surrounding facility from the reference map data; generating building modeling information on the basis of the separation coordinate information and the plurality of pieces of road design information; and providing an average value of traffic volume information within a predetermined distance for each surrounding facility detected from the at least one map data as traffic impact assessment analysis information for the building modeling information.

Description

TRAFFIC IMPACT ASSESSMENT ANALYSIS METHOD AND SYSTEM

The present invention relates to a method and system for analyzing traffic impact assessment, and more particularly, to a method and system for analyzing traffic impact assessment that can analyze traffic impact assessment according to a building area and provide optimal road design information.

Traffic impact assessment is an assessment that analyzes and evaluates the overall impact on the transportation system in the project area or surrounding areas in advance when implementing a project that induces or is likely to cause a large amount of traffic demand or installs a facility, and takes countermeasures accordingly.

The purpose of the transport impact assessment is to first examine whether the size and nature of the project is appropriate in light of the current status of the surrounding land use and transportation system at the regional level before implementation, and secondly to examine the various impacts on the transportation system in the surrounding area. and to find ways to minimize it and take it into account in the planning process. Third, if the implementation of the project has a ripple effect on the transportation system in the surrounding area or the need for public investment is requested, the cause and beneficiary of the degree are identified and This is to determine the principle of cost burden.

However, as there is currently a lack of a support system that allows traffic impact assessment reviewers to make accurate decisions, even if a traffic impact assessment is performed before the development project is implemented, it will cause serious traffic problems on the surrounding roads after completion and become a social problem. In order to solve this problem, there are cases in which additional measures must be established with citizens' taxes.

Therefore, in order to increase the effectiveness of the traffic impact assessment, it is necessary to accurately diagnose traffic problems caused by the development project and to effectively provide extensive analysis results for the deliberation members to easily understand. In addition, there is a need for a system that can establish an integrated management system by computerizing the traffic impact assessment results analyzed by a number of traffic assessment companies.

Republic of Korea Patent Publication KR 10-1745321 B1 Republic of Korea Patent Publication KR 10-1587878 B1 Republic of Korea Patent Publication KR 10-1954394 B1

An object to be solved by the present invention is to provide a traffic impact assessment analysis method and system that provides artificial intelligence-based traffic impact assessment analysis information according to building construction and provides optimal road design information.

The traffic impact assessment analysis method according to an embodiment of the present invention comprises the steps of receiving a request for a building size and at least one surrounding facility according to building development through a network from a client terminal, a building corresponding to the building scale and at least one surrounding facility Searching for at least one map data located within a certain distance from each other from a map DB, receiving local traffic information corresponding to the at least one map data from an external traffic control server and receiving a response, based on the local traffic information to select reference map data from among the at least one map data, recognizing separation coordinate information and a plurality of road design information for each surrounding facility from the reference map data, the separation coordinate information and the plurality of roads Based on the design information, generating building modeling information and providing an average value of traffic volume information within a certain distance for each surrounding facility detected from the at least one map data as traffic impact assessment analysis information for building modeling information includes

According to an embodiment, the separation coordinate information includes horizontal distance information spaced apart in the X-axis direction and vertical distance information spaced apart in the Y-axis direction from any building in the reference map data, and the plurality of road design information is It is design information for road traffic elements that mean sidewalks, traffic lights, entry and exit roads, intersections, and bumps.

According to an embodiment, the providing of the analysis information includes learning a plurality of road design information and daily vehicle average speed located within a predetermined distance of each surrounding facility recognized in the at least one map data to an artificial neural network, and the and providing an output value output by inputting virtual road design information arranged in the architectural modeling information into the artificial neural network as traffic impact assessment analysis information for the architectural modeling information.

According to an embodiment, the selecting may include: extracting reference traffic information in which a difference in daily traffic volume at a time before and after construction among the local traffic information corresponds to a reference section and a daily vehicle speed change is a minimum value; and determining map data corresponding to the reference traffic information from the map data as the reference map data.

A communication unit that receives a request for a building scale and at least one surrounding facility according to a building development through a network from a client terminal according to an embodiment of the present invention, a building corresponding to the building scale and at least one surrounding facility are located within a certain distance from each other A search unit that searches for at least one map data from a map DB, receives a response by requesting an external traffic control server for local traffic information corresponding to the at least one map data, and based on the local traffic information, the at least one a selection unit that selects reference map data from among the map data of a recognition unit that recognizes separation coordinate information and a plurality of road design information for each surrounding facility from the reference map data, the separation coordinate information and the plurality of road design information Based on the analysis information that provides the average value of the traffic volume information within a certain distance for each surrounding facility detected from the at least one map data and the generator generating the building modeling information as traffic impact assessment analysis information for the building modeling information based on the analysis information provided includes wealth.

According to an embodiment of the present invention, it is possible to provide artificial intelligence-based traffic impact assessment analysis information according to building construction, and to provide architectural modeling information for optimal road design.

1 is a diagram illustrating a traffic impact assessment analysis system according to an embodiment of the present invention.
2 is an embodiment of architectural modeling information.
FIG. 3 is a diagram for explaining a learning operation according to another embodiment of the analysis information providing unit 160 of FIG. 1 .
FIG. 4 is a diagram for explaining a learning operation of the analysis information providing unit 160 of FIG. 1 according to another embodiment.
5 is a flowchart illustrating a traffic impact assessment analysis method according to an embodiment.
FIG. 6 is an operation process for the analysis information providing unit of FIG. 1 .
FIG. 7 is an operation process for the selection unit of FIG. 1 .
FIG. 8 is a diagram for explaining learning of an artificial neural network for the analysis information providing unit of FIG. 1 .
9 is an exemplary diagram of the configuration of a traffic impact assessment analysis system according to an embodiment.

Hereinafter, with reference to the accompanying drawings, the embodiments of the present invention will be described in detail so that those of ordinary skill in the art can easily implement them. However, the accompanying drawings and the following description relate to a preferred form among various forms for effectively explaining the characteristics of the present invention, and the present invention can be changed to several different forms other than the embodiments described herein. It is not limited. It is to be understood that all modifications, equivalents or substitutions of the embodiments shall fall within the scope of the rights.

Specific structural or functional descriptions of the embodiments are disclosed for purposes of illustration only, and may be changed and implemented in various forms. Accordingly, the embodiments are not limited to the specific disclosure form, and the scope of the present specification includes all modifications, equivalents or replacements included in the technical spirit.

Terms such as 'first' or 'second' may be used to describe various components, but these terms should be interpreted only for the purpose of distinguishing one component from other components. For example, a first component may be referred to as a second component, and similarly, a second component may also be referred to as a first component.

When it is said that a certain element is 'connected' to another element, it may be directly connected or connected to the other element, but it should be understood that another element may exist in between.

Terms used in the examples are used for the purpose of description only, and should not be construed as limiting. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present specification, terms such as 'comprise' or 'have' are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, and one or more other features or It is to be understood that the existence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded in advance.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present specification. does not

In addition, in the description with reference to the accompanying drawings, the same components are given the same reference numerals regardless of the reference numerals, and the overlapping description thereof will be omitted. In the description of the embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description thereof will be omitted.

The embodiments may also be implemented in various types of products, such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent cars, kiosks, wearable devices, and the like.

1 is a diagram illustrating a traffic impact assessment analysis system 100 according to an embodiment of the present invention, and FIG. 2 is an embodiment of building modeling information.

1 and 2, the traffic impact assessment analysis system 100 includes a communication unit 110, a search unit 120, a selection unit 130, a recognition unit 140, a generation unit 150, and an analysis information system. Study 160 may be included.

First, the communication unit 110 may receive a request for the size of the building and at least one surrounding facility for traffic impact assessment through the network from the client terminal 10 . Here, the building scale means at least one of the building height, the use of the building, and the building area, and the surrounding facilities may mean at least one of a parking lot, an access road, a bicycle storage, a sidewalk, a rest room, a security room, a walking path, a playground, and a park. .

Next, the search unit 120 may search for at least one map data in which the building corresponding to the size of the building and at least one surrounding facility are located within a predetermined distance from each other from the map DB 101 . For example, when the building scale is a 10-story building and at least one nearby facility is a bicycle storage, the search unit 120 searches for map data located within a predetermined distance between the 10-story building and the bicycle storage from the map DB 101 . can do.

Here, the map DB 101 may collect and store map data including information on the size of the building and surrounding facilities in advance. The map DB 101 is shown in an independent form, but is not limited thereto, and may be implemented in a form coupled to the traffic impact assessment analysis system 100 .

Next, the selection unit 130 receives a response by requesting the traffic control server 103 for local traffic information of at least one map data searched through the search unit 120 , and based on the local traffic information, the at least one Reference map data may be selected from one map data.

Specifically, the selection unit 130 extracts the reference traffic information in which the difference in daily traffic volume at the time before and after construction of the local traffic information received from the external traffic control server 103 corresponds to the reference section and the daily vehicle speed change is the minimum value. can do. Then, the selector 130 may determine the map data corresponding to the reference traffic information in the at least one map data as the reference map data.

Next, the recognition unit 140 may recognize the separation coordinate information for each surrounding facility and a plurality of road design information from the reference map data selected by the selection unit 130 .

Here, the separation coordinate information may include horizontal distance information spaced apart in the X-axis direction and vertical distance information spaced apart in the Y-axis direction from any building in the reference map data.

In addition, the plurality of road design information is design information on road traffic elements meaning crosswalks, traffic lights, entry and exit roads, intersections and bumps, for example, the location, length and number of crosswalks, and the location of traffic lights. and the duration, the number of round-trip lanes for entry and exit roads, the location and number of intersections, and the location and number of bumps.

Next, the generation unit 150 may generate architectural modeling information according to the building development based on the separation coordinate information and the plurality of road design information recognized through the recognition unit 140 .

Here, the building modeling information is, as shown in FIG. 2 , on the building development map, the virtual building 151 and the virtual surrounding facilities 152_1 to 152_N are arranged according to the separation coordinate information, and a plurality of road design It may be a 3D graphic image in which virtual road design elements 153_1 to 153_N) are arranged according to information.

For example, the generator 150 may arrange a virtual building and virtual surrounding facilities according to street coordinate information on the map data according to the building development, and arrange road traffic elements according to road design information.

Next, the analysis information providing unit 160 receives the traffic volume information within a certain distance for each nearby facility detected from at least one map data, and provides the average value of the traffic volume information as traffic impact assessment analysis information for the building modeling information. can do.

According to an embodiment, the analysis information providing unit 160 receives a plurality of road design information located within a certain distance of each surrounding facility recognized from at least one map data, and outputs the average daily vehicle speed to the artificial neural network. can be learned in In this case, the analysis information providing unit 160 may provide an output value output as the virtual road design information arranged in the architectural modeling information is input to the artificial neural network as traffic impact evaluation analysis information for the architectural modeling information.

Hereinafter, a learning operation according to another embodiment of the analysis information providing unit 160 will be described in more detail with reference to FIG. 3 .

FIG. 3 is a diagram for explaining a learning operation according to another embodiment of the analysis information providing unit 160 of FIG. 1 .

Referring to FIG. 3 , the analysis information providing unit 160 may use the convolutional neural network 200 .

First, the analysis information providing unit 160 may input a plurality of road design information located within a certain distance of each surrounding facility and the average daily vehicle speed within a certain distance of each surrounding facility as input to the convolutional neural network 200. have. In this case, the analysis information providing unit 160 may output the average values for the traffic information and input them to the convolutional neural network 200 .

Here, the convolutional neural network 200 may be a convolutional neural network including a feature extraction neural network 210 and a classification neural network 220 .

Specifically, the feature extraction neural network 210 may separate the plurality of road design information and the average daily vehicle speed according to the size of the building.

The feature extraction neural network 210 stacks an input signal with a convolutional layer and a pooling layer sequentially. The convolution layer contains convolution operations, convolution filters, and activation functions. The calculation of the convolution filter is adjusted according to the matrix size of the target input, but a 9X9 matrix is generally used. The activation function may generally use a ReLU function, a sigmoid function, a tanh function, and the like, but is not limited thereto. The pooling layer is a layer that reduces the size of the input matrix, and uses a method of extracting representative values by tying pixels in a specific area. In general, the average value or the maximum value is often used for calculation of the pooling layer, but is not limited thereto. The operation is performed using a square matrix, which is usually a 9x9 matrix. The convolutional layer and the pooling layer are repeated alternately until the corresponding input becomes small enough while maintaining the difference.

In this case, the classification neural network 220 may classify a plurality of road design information for each building size and an average daily vehicle speed separated through the feature extraction neural network 210 , and estimate average values for the traffic information. The classification neural network 220 may determine the size of a building in estimating average values for traffic information, but is not limited thereto.

Here, the classification neural network 220 has a hidden layer and an output layer. The convolutional neural network 200 of the present invention generally includes five or more hidden layers, and 80 nodes of each hidden layer can be designated, but it is also possible to set more than that in some cases. The activation function of the hidden layer uses a ReLU function, a sigmoid function, a tanh function, and the like, but is not limited thereto.

According to an embodiment, the number of output layer nodes of the convolutional neural network 200 may be 50 in total.

Specifically, the output of the convolutional neural network 200 is that the upper 25 output layer nodes among 50 output layer nodes may indicate the first learning pattern signal, and the lower 25 output layer nodes have the upper 25 output layer nodes the second learning pattern. You can direct the signal.

At this time, the method of matching the top 25 output layer nodes and the bottom 25 output layer nodes is a method of matching the upper nth output layer node and the lower nth output layer node, and the nth output layer node is the 25+nth output layer node in total. may proceed in a manner corresponding to , where n is an integer of any one of 1 to 25.

For example, a first output layer node may correspond to a 26th output layer node, a second output layer node may correspond to a 27th output layer node, a 25th output layer node may correspond to a 50th output layer node, and so on. The first and second learning pattern signals may be output as signal information corresponding to their types, but are not limited thereto. An output layer node having no output value among 50 output layer nodes of the convolutional neural network 200 may output the number '0' as its output value. Nodes including this number '0' among the top 25 output layer nodes are considered to have no corresponding problem and may be excluded from calculations in future neural network calculations. If there are 25 types of classified problems, the remaining problems may be processed automatically after all output values generated in advance are processed.

According to an embodiment, the convolutional neural network 200 is a user manual input value generated by the user's manual input when the user finds that the average values for the traffic information according to the convolutional neural network 200 are incorrectly estimated. can learn through

These user manual input values are created based on the error between the average values for traffic information and preset reference values, and in some cases, SGD using delta, a layout method, or a method following a backpropagation algorithm may be used. According to the user's manual input values, the convolutional neural network 200 performs learning by modifying the existing weights, and may use momentum in some cases. A cost function may be used to calculate the error, and a cross entropy function may be used as the cost function.

That is, the analysis information providing unit 160 outputs the traffic impact assessment analysis information by estimating the average value of the traffic volume information using the convolutional neural network 200 that is learned for a plurality of road design information and the daily average speed of the vehicle. can do.

FIG. 4 is a diagram for explaining a learning operation of the analysis information providing unit 160 of FIG. 1 according to another embodiment.

Referring to FIG. 4 , the analysis information providing unit 160 may use the neural network 300 .

Specifically, the analysis information providing unit 160 may input a plurality of road design information, daily vehicle average speed, and auxiliary estimated values previously stored in the database 330 as input values to the neural network 300 .

Here, the auxiliary estimated value may be stored in the database 330 and may be user evaluation information obtained by evaluating the average value of the traffic information. In this case, the output values may be average values of traffic volume information estimated for each building size.

More specifically, the input of the neural network 300 may consist of a matrix of one row and 550 columns in which a matrix of one row and 50 columns including a plurality of road design information and the average daily vehicle speed and a matrix of one row and 500 columns of auxiliary estimated values are combined. However, the present invention is not limited thereto. As an example, a total of 550 input layer nodes as inputs to the neural network 300 may be formed, and an output layer node may include 5 nodes.

That is, the analysis information providing unit 160 estimates the average value of the traffic volume information by using the neural network 300 that learns about a plurality of road design information, the daily vehicle average speed, and the auxiliary estimated value, thereby analyzing the traffic impact assessment information. can be printed out.

5 is a flowchart illustrating a traffic impact assessment analysis method according to an embodiment.

1, 2 and 5, in step S110, the traffic impact assessment analysis system 100 receives a request for a building scale and at least one surrounding facility for traffic impact assessment from the client terminal 10 through the network. can

Then, in step S120, the traffic impact assessment analysis system 100 may search the map DB 101 for at least one map data in which the building corresponding to the building scale and at least one surrounding facility are located within a predetermined distance from each other. .

Then, in step S130, the traffic impact assessment analysis system 100 receives a response by requesting the traffic control server 103 for local traffic information of at least one map data, and based on the local traffic information, the at least one Reference map data can be selected from among the map data.

Then, in step S140, the traffic impact assessment analysis system 100 may recognize the separation coordinate information for each surrounding facility and a plurality of road design information from the reference map data.

In this case, in step S150 , the traffic impact assessment analysis system 100 may generate architectural modeling information based on the separation coordinate information and a plurality of road design information.

Thereafter, in step S150, the traffic impact assessment analysis system 100 receives traffic volume information within a certain distance for each surrounding facility detected from at least one map data, and calculates the average value of the traffic volume information for traffic impact assessment for building modeling information It can be provided as analysis information.

FIG. 6 is an operation process for the analysis information providing unit 160 of FIG. 1 .

1, 2, and 6 , in step S210 , the analysis information providing unit 160 may recognize each surrounding facility from at least one map data searched for through the search unit 120 .

In this case, in step S220 , the analysis information providing unit 160 may teach the artificial neural network a plurality of road design information located within a predetermined distance of each surrounding facility and an average daily vehicle speed.

Then, in step S230, the analysis information providing unit 160 provides an output value output as the virtual road design information arranged in the architectural modeling information is input to the artificial neural network as traffic impact assessment analysis information for the architectural modeling information. can

FIG. 7 is an operation process for the selection unit 130 of FIG. 1 .

1 to 2 and 7 , in step S310, the selector 130 determines that the difference in daily traffic volume at the time before and after construction of the at least one map data of each region corresponds to the reference section, and the daily vehicle speed It is possible to extract the reference traffic information in which the change is the minimum value.

Then, in step S320 , the selector 130 may determine one map data corresponding to the reference traffic information in the at least one map data as the reference map data.

FIG. 8 is a diagram for explaining artificial neural network learning for the analysis information providing unit 160 of FIG. 1 .

In the present invention, the artificial neural network may be a component included in the traffic impact assessment analysis system 100 and may be learned through the traffic impact assessment analysis system 100 or a separate learning device.

The artificial neural network may receive the virtual road design information arranged in the architectural modeling information, and may output the traffic impact assessment analysis information indicating the expected average speed of the daily vehicle.

Hereinafter, a process in which an artificial neural network is learned through a separate learning device will be described in detail.

First, in step S410, the learning device may obtain a plurality of road design information located within a certain distance of each surrounding facility as training data, and obtain a building scale as a label.

Then, in step S420 , the learning device may obtain an average daily vehicle speed within a predetermined distance of each surrounding facility as an output value.

In this case, in step S430, the learning apparatus may classify and apply the training data and the output value for each label of the input/output layer of the artificial neural network. For example, the artificial neural network may be an artificial neural network trained according to supervised learning. In addition, the artificial neural network may be a convolutional neural network (CNN) or recurrent neural network (RNN) structure suitable for learning through supervised learning.

In this case, in step S440 , the learning apparatus may compare the output value through the artificial neural network with a preset reference value according to the label. The process of comparing the output value of the artificial neural network corresponding to the inference and the label corresponding to the correct answer may be performed by calculating a loss function. Here, as the loss function, a known mean squared error (MSE), cross entropy error (CEE), or the like may be used. However, the present invention is not limited thereto, and loss functions used in various artificial neural network models may be used if the deviation, error, or difference between the output of the artificial neural network and the label can be measured.

Thereafter, in step S450 , the learning apparatus may optimize the artificial neural network based on the comparison value. For example, by updating the weights of nodes of the artificial neural network so that the learning device comparison value becomes smaller and smaller, the output of the artificial neural network corresponding to inference and the label corresponding to the correct answer can be gradually matched, and this Through this, the artificial neural network can be optimized to output an inference close to the correct answer. In this case, the learning apparatus may optimize the artificial neural network by repeating the process of resetting the weight of the artificial neural network so that the loss function corresponding to the comparison value approaches the estimated value of the minimum value. A known backpropagation algorithm, stochastic gradient descent, or the like may be used for optimization of an artificial neural network. However, the present invention is not limited thereto, and a weight optimization algorithm used in various neural network models may be used.

9 is an exemplary diagram of the configuration of the traffic impact assessment analysis system 400 according to an embodiment.

The traffic impact assessment analysis system 400 according to an embodiment includes a processor 410 and a memory 420 . Specifically, the traffic impact assessment analysis system 400 may be a computer or a terminal that provides traffic impact assessment analysis information.

The processor 410 may include at least one of the devices described above with reference to FIGS. 1 to 5 , or perform at least one method described above with reference to FIGS. 1 to 5 . The memory 420 may store information related to the above-described method or a program in which the above-described method may be implemented, for example, an operation signal algorithm. The memory 420 may be a volatile memory or a non-volatile memory.

According to an embodiment, the processor 410 may execute a program and control the traffic impact assessment analysis system 400 . The code of the program executed by the processor 410 may be stored in the memory 420 . The traffic impact assessment analysis system 400 may be connected to an external device (eg, a personal computer or a network) through the interface 101 and exchange data.

The embodiments described above may be implemented by a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the apparatus, method, and component described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). Array), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions, may be implemented using one or more general purpose computers or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Software may comprise a computer program, code, instructions, or a combination of one or more of these, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or device, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems, and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

As described above, although the embodiments have been described with reference to the limited drawings, those skilled in the art may apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

100: Traffic Impact Assessment Analysis System
110: communication department
120: search unit
130: selection unit
140: recognition unit
150: generator
160: analysis information providing unit

Claims (5)

Receiving a request for the size of the building and at least one surrounding facility through a network from the client terminal;
searching for at least one map data in which a building corresponding to the scale of the building and at least one surrounding facility are located within a predetermined distance from each other from a map DB;
receiving a response to a request for local traffic information of the at least one map data from a traffic control server, and selecting reference map data from among the at least one map data based on the local traffic information;
recognizing separation coordinate information and a plurality of road design information for each surrounding facility from the reference map data;
generating architectural modeling information by using the separation coordinate information and the plurality of road design information; and
Providing an average value of traffic volume information within a certain distance for each surrounding facility detected from the at least one map data as traffic impact assessment analysis information for building modeling information,
The step of providing the analysis information may include: learning a plurality of road design information located within a predetermined distance of each surrounding facility recognized from the at least one map data and an average daily vehicle speed in an artificial neural network; and
and providing an output value output as the virtual road design information arranged in the building modeling information is input to the artificial neural network as traffic impact assessment analysis information for the building modeling information,
In the step of providing the analysis information, the average value of the traffic volume information is calculated for each building scale by using a neural network that learns about the plurality of road design information, the daily vehicle average speed, and the auxiliary estimate stored in advance in the database. Further comprising the step of estimating,
The auxiliary estimated value is stored in the database and is user evaluation information obtained by evaluating an average value of the traffic information. According to claim 1,
The separation coordinate information includes horizontal distance information spaced apart in the X-axis direction and vertical distance information spaced apart in the Y-axis direction from any building in the reference map data,
The plurality of road design information is design information on road traffic elements that mean crosswalks, traffic lights, entry and exit roads, intersections and bumps, traffic impact assessment analysis method. delete According to claim 1,
The selecting may include: extracting reference traffic information in which a difference in daily traffic volume at a time before and after construction from among the local traffic information corresponds to a reference section and a daily vehicle speed change is a minimum value; and
and determining, as the reference map data, map data corresponding to the reference traffic information in the at least one map data.




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