KR20230169630A - Traffic big data construction system and method that can build traffic big data for signal intersections and links in the city center - Google Patents

Abstract

A traffic big data construction system and method is disclosed that can build reliable traffic big data by collecting traffic information about signalized intersections in the city and links between signalized intersections and signalized intersections. For this purpose, an intersection vehicle detector is installed at a signalized intersection and collects intersection information by detecting vehicles passing through the signalized intersection, and is installed on a link connecting the signalized intersection and a signalized intersection located behind the signalized intersection and vehicles passing the link. A vehicle detection unit including a link vehicle detector that detects and collects link information, an external server that stores external information about the area where the intersection vehicle detector and the link vehicle detector are installed, and is installed at the signalized intersection or link, and the vehicle Transportation that includes an edge server that creates traffic data processed in the form of statistical data by linking intersection information and link information provided from the detection unit with external information collected from the external server, and builds traffic big data by accumulating the traffic data. Provides a big data construction system. According to the present invention, vehicle traffic data of vehicles passing through signalized intersections and links can be collected and processed to generate traffic-related data that can be used to analyze or predict traffic conditions, and traffic-related data can be continuously accumulated to improve traffic flow. Big data can be built.

Description

Traffic big data construction system and method that can build traffic big data about signal intersections and links in the city {TRAFFIC BIG DATA CONSTRUCTION SYSTEM AND METHOD THAT CAN BUILD TRAFFIC BIG DATA FOR SIGNAL INTERSECTIONS AND LINKS IN THE CITY CENTER}

The present invention relates to a traffic big data construction system and method that can build traffic big data used to analyze and predict traffic congestion in the city, and more specifically, to signal intersections in the city center and links between signal intersections and signal intersections. This relates to a transportation big data construction system and method that can collect traffic information and build reliable transportation big data.

In general, road congestion level is an indicator of the degree of road traffic congestion, which refers to the phenomenon of continuous delays and operating costs due to interactions between vehicles that occur when the traffic volume on the road approaches the road capacity. It refers to the degree, and in the past, road congestion was mostly observed and estimated using point observation technology using loop detectors and image information detectors, and section observation technology using AVI (Automatic Vehicle Identification) or DSRC (Dedicated Short Range Communication).

Road congestion is used as information on smooth traffic sections and congested sections of the road in devices that provide transportation services, such as navigation systems.

Meanwhile, conventional observation technology is usually a method of estimating road congestion by observing travel time according to vehicle speed on the road, so there is a problem in that it cannot reflect traffic volume-density, which is an indicator of the number of vehicles on the road.

For example, in the case of the conventional point observation technology, only the traffic volume, average speed, and average movement within a unit time of vehicles passing the installation location could be observed, while the conventional section observation technology could only observe the travel time for the corresponding section. there was.

However, even if the travel time on a specific road is short, if the density of vehicles is high, that is, if the traffic volume-density is high, there is a very high possibility that congestion will occur in a short period of time due to an unexpected situation or sudden braking of some vehicles. .

In particular, in the case of speed control in the city, the traffic volume-density between each signalized intersection is affected by various factors such as physical road conditions, traffic conditions, environmental (climate) conditions, and driver habits that make up the signalized intersection. Correlation analysis and prediction of traffic volume and density are recognized as difficult fields.

Therefore, there is an urgent need for the development of technology to build analyzable and reliable traffic big data to solve the above-mentioned problems.

Republic of Korea Patent No. 10-2359902 (announced on February 9, 2022) Republic of Korea Patent No. 10-1833359 (announced on February 28, 2018) Republic of Korea Patent Publication No. 10-2010-0130055 (published on April 7, 2021) Republic of Korea Patent No. 10-2145217 (announced on August 18, 2020)

Therefore, the first purpose of the present invention is to provide a traffic big data construction system that builds traffic big data by collecting, processing, and storing traffic information on vehicles passing through signalized intersections and links in the city.

In addition, the second object of the present invention is to collect vehicle traffic data using an intersection vehicle detector installed at a signalized intersection and a link vehicle detector installed in a link connecting signalized intersections, and collect vehicle traffic data at a preset collection period or The goal is to generate traffic-related data that can be used to analyze or predict traffic situations by classifying them based on the TOD of intersection traffic lights, and to provide a method of constructing traffic big data by accumulating this data.

In order to achieve the first object of the present invention described above, in one embodiment of the present invention, an intersection vehicle detector is installed at a signalized intersection and collects intersection information by detecting vehicles passing through the signalized intersection, and A vehicle detection unit installed at a link connecting a signalized intersection located at the rear and including a link vehicle detector that detects vehicles passing through the link and collects link information, and an external vehicle detection unit for the area where the intersection vehicle detector and the link vehicle detector are installed. An external server where information is stored, and installed at the signalized intersection or link, links intersection information and link information provided from the vehicle detection unit with external information collected from the external server to generate processed traffic data in the form of statistical data, A transportation big data construction system including an edge server that accumulates the traffic data and builds transportation big data is provided.

In addition, in order to achieve the first object of the present invention, in another embodiment of the present invention, an intersection vehicle detector is installed at a signalized intersection and collects intersection information by detecting vehicles passing through the signalized intersection, and A vehicle detection unit installed at a link connecting a signalized intersection located at the rear and including a link vehicle detector that detects vehicles passing through the link and collects link information, and an external vehicle detection unit for the area where the intersection vehicle detector and the link vehicle detector are installed. An external server where information is stored, and the vehicle detection unit and the external server are connected through a communication network to link the intersection information and link information provided from the vehicle detection unit with external information collected from the external server to produce traffic data processed in the form of statistical data. It provides a transportation big data construction system that includes a central server that accumulates the traffic data and builds transportation big data.

In addition, in order to achieve the second object of the present invention, in one embodiment of the present invention, the vehicle detection unit generates intersection information by detecting a vehicle passing through a signalized intersection, and connects the signalized intersection with the signalized intersection located behind the signalized intersection. A vehicle information detection step in which link information is generated by detecting a vehicle passing through a link, a detection information collection step in which an edge server receives intersection information and link information provided from the vehicle detection unit, and the edge server generates intersection information and link information. Verify information based on pre-set data format, data size, and data type to determine if it deviates from the above standards. A data purification step of deleting intersection information and link information, an external information collection step in which the edge server collects external information related to the area where the vehicle detection unit is installed from an external server, and the edge server statistics the intersection information and link information. Pattern information that generates traffic statistical information by accumulating it in the form of data, and creates traffic pattern information by classifying the traffic statistical information by intersection direction, time series, and lane based on the preset collection cycle or signal time plan of intersection traffic lights. A creation step, a linked data generation step in which the edge server generates traffic-linked data by linking it with external information of the time at which the traffic pattern information was collected, and the edge server accumulates the traffic-linked data to build traffic big data. Provides a transportation big data construction method that includes the big data construction steps.

The present invention collects and processes vehicle traffic data of vehicles passing not only at signalized intersections, but also the link connecting signalized intersections, and generates traffic-related data that can be used to analyze or predict traffic situations. , Traffic-related data can be continuously accumulated to build transportation big data.

In addition, the present invention can highly train artificial intelligence to predict traffic conditions using traffic big data constructed by classifying and storing each intersection, direction, and time series.

In addition, the present invention can generate numerical data that can be used for pattern analysis by intersection direction and analysis of congestion causes in intersection sections, thereby generating highly reliable raw data of traffic information for optimal signal operation. You can.

In addition, the present invention builds traffic big data that can be used to improve traffic congestion problems near signalized intersections, so it can help reduce social congestion costs caused by traffic congestion.

In addition, the present invention can be used for various services such as resolving traffic congestion problems, traffic safety, optimal route guidance, traffic congestion information display, and detour guidance by linking the constructed traffic big data with surrounding infrastructure.

1 is a configuration diagram illustrating a traffic big data construction system according to an embodiment of the present invention.
Figure 2 is a perspective view schematically showing a link vehicle detector and edge server according to the present invention.
Figure 3 is a block diagram showing a traffic big data construction system according to the present invention.
Figure 4 is a flowchart explaining the method of constructing traffic big data according to the present invention.

Hereinafter, a transportation big data construction system capable of constructing traffic big data on signal intersections and links in the city according to preferred embodiments of the present invention with reference to the attached drawings (hereinafter abbreviated as 'transportation big data construction system') ) is explained in detail.

Figure 1 is a configuration diagram for explaining a traffic big data construction system according to an embodiment of the present invention, and Figure 2 is a perspective view schematically showing a link vehicle detector and an edge server according to the present invention.

Referring to Figures 1 and 2, the traffic big data construction system according to the present invention includes a vehicle detection unit that generates intersection information, which is information about vehicles passing through a signalized intersection, and link information, which is information about vehicles passing the link between signalized intersections. (100), an external server (not shown) storing external information about the area where the vehicle detection unit 100 is installed, and traffic data processed in the form of statistical data by linking the intersection information and link information with the external information. It includes an edge server 200 that generates and builds transportation big data.

At this time, the vehicle detection unit 100 detects the vehicle passing through the intersection vehicle detector 120, which generates intersection information by detecting vehicles passing through the signalized intersection, and the link connecting the signalized intersection and the signalized intersection located behind the signalized intersection. It may be configured to include a link vehicle detector 110 that collects link information. Here, the intersection vehicle detector 120 may be installed in each direction of the signalized intersection.

These intersection vehicle detectors 120 and link vehicle detectors 110 can track the movement trajectory of moving traffic, and through this, traffic information such as occupied lanes, average movement time, vehicle type, and detection area departure time is obtained. The direction (vehicle movement within the occupied lane or signal intersection area) can be determined (turn left, go straight, turn right).

Hereinafter, each component will be described in more detail with reference to the drawings.

Referring to Figures 1 and 2, the traffic big data construction system according to the present invention includes a vehicle detection unit 100.

The vehicle detection unit 100 generates vehicle information by detecting vehicles passing between an arbitrary first signalized intersection and a second signalized intersection located behind the first signalized intersection, and is an intersection vehicle detector installed at the signalized intersection. It includes a link vehicle detector 110 installed on the link between 120 and the signalized intersection.

The intersection vehicle detector 120 is installed in each direction at a signalized intersection located behind the target link, and detects moving traffic traveling in a detection area set for each direction of the rear signalized intersection according to the display cycle of the traffic light, and displays one display cycle. Intersection information including the number of vehicles flowing in for each link is generated, and the intersection information is transmitted to the edge server 200.

The intersection vehicle detector 120 collects intersection information by detecting vehicles passing through a signalized intersection set as a detection area, and is installed in each direction of the signalized intersection to monitor vehicles moving in each direction, and detects vehicles passing through a signalized intersection set as a detection area. Intersection information is generated by detecting vehicles that are stopping or moving according to traffic lights. At this time, the traffic light may include at least two or more signals, for example, red, green, yellow, and left turn signals, but is not limited thereto.

For example, when the signalized intersection has four lanes, the intersection vehicle detector 120 includes a first intersection vehicle detector 121, a second intersection vehicle detector 122, a third intersection vehicle detector 123, and a fourth intersection vehicle detector. It may consist of an intersection vehicle detector 124. At this time, the first intersection vehicle detector 121 detects the first direction detection area 126 of the intersection, the second intersection vehicle detector 122 detects the second direction detection area 127 of the intersection, and the third intersection vehicle detector 122 detects the first direction detection area 126 of the intersection. The intersection vehicle detector 123 detects the third direction detection area 128 of the intersection, and the fourth intersection vehicle detector 124 detects the fourth direction detection area 129 of the intersection.

Additionally, the intersection vehicle detector 120 generates intersection information by detecting vehicles in real time or at intervals when traffic lights are turned on.

The intersection information includes the number of vehicles flowing into each link, which is generated by counting the number of vehicles passing in each direction of the signalized intersection when the green traffic light is on and the number of vehicles turning right when the red signal is on.

In this way, since the link detection area of the link vehicle detector 110 targets a part of the link rather than the entire link, it reflects the inflow of moving flows outside the link detection area, and the inflow vehicle is from the first link located on the same line of the link. The targets include vehicles going straight in the same direction, right-turning vehicles in the second link located on the right side of the link, left-turning vehicles in the third link located on the left side of the link, and U-turn vehicles within the link.

If necessary, the intersection information may include the moving vehicle information for which the cumulative number of moving vehicles in each lane has been detected and the device number of the link vehicle detector 110. Specifically, moving vehicle information can generate driving information that includes the vehicle's movement trace, detection date and time, driving speed, number of passing vehicles, occupied lane, vehicle ID, and vehicle type information.

This intersection vehicle detector 120 can record traffic volume by time period.

In addition, the intersection vehicle detector 120 is installed on a traffic light or a pole placed on the roadside and detects traffic information about vehicles entering the detection area.

In addition, the intersection vehicle detector 120 may transmit link information including the number of incoming vehicles for each link to the edge server 200 in charge of the signalized intersection where the intersection vehicle detector 120 is installed.

The intersection vehicle detector 120 can use adhesive vehicle detectors such as roofs and contact pads, or non-adhesive vehicle detectors such as video cameras, infrared cameras, ultrasonic sensors, and heat sensors, and other sensors that can detect vehicles. More can be included, and a Vehicle Detection System (VDS) can be used.

For example, the intersection vehicle detector 120 is installed at a traffic light in each direction of a four-way signalized intersection, and collects driving information on vehicles in each direction (going straight, turning left, turning right).

The intersection vehicle detector 120 counts the direction of vehicles moving continuously in each lane within the signalized intersection detection area by cycle, and records the number of vehicles passing in the left turn and straight directions at the time of the green signal and the moving speed within the detection area. In addition, when a red signal occurs, the number of vehicles passing in the right turn direction can be calculated.

The intersection vehicle detector 120 calculates distributed traffic statistics according to the display period of the signalized intersection only for the movement that has completed passing after the operation of the traffic light, and provides intersection information generated for each effective green time to the edge server 200. You can.

Meanwhile, the link vehicle detector 110 is installed in the link connecting the signalized intersection and the signalized intersection located behind it, and detects in real time the moving flow passing through the preset link detection area to detect the moving flow. Link information including speed, vehicle type, and number of vehicles is generated, and the link information is transmitted to the edge server.

The link information includes passing vehicle information generated by counting the number of vehicles passing through the link detection area, and optionally, the link detection area, which is the detection area of the link vehicle detector 110, along the queue created from the stop line of the signal intersection ahead. Queue generation information generated by detecting stopped vehicles and then detecting the number of vehicles forming a queue within the link detection area, vehicle type, distance between vehicles, etc. may be further included.

This link vehicle detector 110 can directly determine the length of the congestion queue within the link detection area by analyzing the image generated by photographing the link detection area, and can also determine the length of the congestion queue by analyzing the number of vehicles, vehicle type, and distance between vehicles. . At this time, the vehicle type is determined by searching the vehicle name matched with the appearance information collected from the image through the vehicle model information database where the appearance information and vehicle name are stored. The vehicle type information database may be provided in the link vehicle detector 110 or the edge server 200.

If necessary, the link information may include moving vehicle information whose travel distance for each lane during the effective green time has been detected and the device number of the link vehicle detector 110. Specifically, the moving vehicle information can generate driving information that includes the vehicle movement trace and movement distance during the effective green time, detection date and time, driving speed, number of passing vehicles, occupied lane, vehicle ID, and vehicle type information.

This link vehicle detector 110 can record traffic volume by time period.

Additionally, the link vehicle detector 110 is installed on a pole placed on the roadside and can generate link information about a vehicle that has entered the link detection area.

The link vehicle detector 110 can be used as an adhesive vehicle detector such as a roof or contact pad, or a non-adhesive vehicle detector such as a video camera, an infrared camera, an ultrasonic sensor, or a heat sensor, and other sensors capable of detecting a vehicle. More can be included, and a Vehicle Detection System (VDS) can be used.

This link vehicle detector 110 can generate link information classified by time series using data collected/accumulated in the link detection area.

In addition, the link vehicle detector 110 counts the number of vehicles passing within the link detection area by display period, and when calculating the queue, information on vehicles waiting for a signal is provided based on the moving speed of the vehicle finally detected within the link detection area. It can be collected.

The link vehicle detector 110 acquires individual (by vehicle type, lane) traffic information for the moving flow flowing into the link detection area, and queue occurrence information (measurement time, whether a congestion queue is created, congestion queue) within the link detection area. Length, congestion queue length creation speed, congestion queue movement speed when moving from a standstill, congestion queue information by lane, congestion queue density) can be generated.

Referring to Figures 1 and 2, the traffic big data construction system according to the present invention includes an external server.

The external server stores external information about the area where the intersection vehicle detector 120 and the link vehicle detector 110 are installed, a signal controller that stores signal time plan (TOD: Time Of Day) information, and a Korea Meteorological Administration server that stores weather information. , it may include one or more of a traffic data exchange server where public data is stored, and a government office server where traffic information of the area where the intersection vehicle detector 120 and the link vehicle detector 110 are installed is stored.

If necessary, the external server may further include a local government server where road condition information of T-GIS (traffic safety facilities) is stored. For example, road condition information includes any of the following: link length, number of lanes, average lane, width, slope, presence of dedicated left and right turn lanes, left turn curve radius, right turn flow rate, bus stop location, on-street parking lot, and entrance/exit road length. Includes more.

Meanwhile, the signal controller is configured to control the operation of signal intersection traffic lights in constant cycle TOD mode according to a preset signal operation table. Here, the regular TOD mode is an operation mode that controls the signal operation of traffic lights according to the signal operation table for each time period and date.

Additionally, the signal controller is connected wired or wirelessly to the traffic lights at the intersection, and is connected to the edge server 200 through a communication network.

In addition, the signal controller is placed near the signalized intersection, and, if necessary, may be installed on a pole near the signalized intersection along with the intersection vehicle detector 120, the edge server 200, or both.

Referring to Figures 1 and 2, the traffic big data construction system according to the present invention includes an edge server 200.

The edge server 200 is connected to the vehicle detection unit 100 and an external server via a wired, wireless, or communication network, and is installed at a signal intersection or link. Here, Ethernet can be used as a wired method, and short-distance wireless communication such as Wi-Fi, Bluetooth, or ZigBee can be used wirelessly.

In addition, communication networks include CDMA (Code Division Multiple Access), WCDMA (wideband code division multiple access), HSDPA (High Speed Downlink Packet Access), LTE (Long Term Evolution), and IMT-2020 (International) to enable long-distance signal transmission. Mobile Telecommunications-2020), ADSL (Asymmetric Digital Subscriber Line), VDSL (Very high-data rate Digital Subscriber Line), HDSL (High-bit-rate Digital Subscriber Line), and optical LAN (optic LAN) can be used. You can.

This edge server 200 can be installed together with the intersection vehicle detector 120 at a pole near where the signal controller of each signalized intersection is installed to provide convenience in operating and managing the signal lights at the signalized intersection.

The edge server 200 uses the data collected and accumulated at the corresponding signalized intersection to generate big data, classifies and stores it by signalized intersection, direction, and time series to create rows of intersection information that can be used for big data analysis. Data (raw data) can be generated.

The edge server 200 provides meta data in the form of statistical data for big data analysis and learning through purification functions (error, integrity, direction, time series information collection, etc.) on raw data of intersection information and link information. Data can be generated.

More specifically, the raw data purification function of the edge server 200 includes an error check function for the raw data provision format (defined data type and length), field format and value range, and vehicle purification according to the collection cycle of the collected raw data. Includes functions to generate statistical data. At this time, statistical data includes the total number of vehicles during the set time, the number of vehicles by direction, and the average vehicle speed by lane.

The collection cycle of the raw data is a set time for generating statistical data in the edge server 200, for example, the effective green signal time, and can be changed to 10 seconds, 30 seconds, 60 seconds, etc. depending on the purpose.

This edge server 200 can use an edge system equipped with edge computing technology to provide raw data purification functions, etc. Here, edge computing is a computing method that supports acceleration of data flow by processing data generated from various terminal devices in real time at the site or nearby where the data is generated rather than sending it to a centralized data center such as the cloud.

The edge server 200 creates traffic data processed in the form of statistical data by linking the intersection information and link information provided from the vehicle detection unit with external information collected from an external server, and builds traffic big data by accumulating the traffic data. do.

As necessary, the edge server 200 analyzes the road condition information of the link, the link information provided from the link vehicle detectors 112 and 114, and the intersection information provided from the intersection vehicle detector to determine the target link connecting the signalized intersection and the signalized intersection. Traffic volume-density can be calculated. At this time, the traffic volume-density is calculated by calculating the traffic volume by link, distributed traffic volume (traffic distribution pattern), and saturation by lane of the link as the vehicle occupancy ratio to the total link length, and calculated by the arithmetic average of the occupancy ratio of all lanes constituting the link, and TOD Accumulate according to the cycle.

In addition, the edge server 200 may be connected to a government office server where road condition information is stored through a communication network in order to search for road condition information of traffic safety facilities and extract link information for the target link. In this way, the edge server 200 searches road condition information of T-GIS (traffic safety facilities) stored in an external server, such as a government office server, and extracts and uses link information for the link where the link vehicle detector 110 is installed. , a personal identifier is added to each link, so link information previously stored in the edge server 200 can be used.

The edge server 200 searches road condition information of T-GIS (traffic safety facilities) stored in an external server, such as a government office server, and detects signal intersections for signalized intersections arranged in front and behind the link where the link vehicle detector 110 is installed. The information can be extracted and used, or the signalized intersection information pre-stored in the edge server 200 with a personal identification symbol added to each signalized intersection can be used.

The edge server 200 uses the correlation for each signalized intersection to assign relationships to the signalized intersections through upstream and downstream links for each school district, and then provides signal condition information according to the fixed cycle traffic light operation time information (TOD DB), which is the display period for each school district. It can be mapped to a relationship key and stored in the traffic information DB of the storage unit.

The edge server 200 records traffic data stored in the traffic information DB by date, time, and signal cycle (minimum traffic volume in each direction, maximum traffic volume in each direction, congestion duration, morning and afternoon peak hours, congestion). It is classified and stored by average number of traffic by vehicle type over time.

The edge server 200 is directly connected to a signal controller placed at a signalized intersection where the intersection vehicle detector 120 is installed, extracts signal condition information stored in the signal controller, and stores it in a traffic information DB, or a traffic controller that manages each signal controller. After accessing the central control system, the signal condition information of the signalized intersection where the intersection vehicle detector 120 is installed can be searched and extracted and stored in the traffic information DB. Here, the signal condition information includes one or more of the signal period, minimum green time, maximum green time, pedestrian green time, yellow time, signal intersection offset, and left turn type.

The edge server 200 includes forward link information transmitted from the first link vehicle detector 112, front intersection information transmitted from the front intersection vehicle detector 120, and rear link information transmitted from the second link vehicle detector 114. Information and rear intersection information transmitted from the rear intersection vehicle detector 120 can be stored in the traffic information DB.

The edge server 200 may store environmental condition information including items such as rainfall, snowfall, and fine dust concentration in the traffic information DB. To this end, the edge server 200 connects to the Korea Meteorological Administration server through a communication network, searches and extracts weather information for the link where the link vehicle detector 110 is installed, and stores the weather information in the traffic information DB.

The edge server 200 stores intersection information by signal cycle in the traffic information DB, and calculates congestion information such as traffic volume and density based on the intersection information and link information. Here, the effective green time for each signal intersection direction is slightly different depending on the signal cycle, that is, the time zone, and traffic volume information based on the effective green time directly affects traffic volume-density. It is measured by time and cycle, and the effective green time is based on that standard.

The traffic volume-density is calculated by adding the number of incoming vehicles of the target link to the maximum number of vehicles occupying space in the initial queue preset by the edge server 200, subtracting the number of outgoing vehicles of the target link, and dividing by the maximum number of vehicles occupying space of the link. can do. At this time, the edge server 200 can read the occurrence of a congestion queue that has reached the link detection area 113 of the link vehicle detector 110 to determine the time of calculation of traffic volume-density.

In order to ensure the reliability of the traffic big data construction system, the edge server 200 extracts the moving distance of the queue moving within the link detection area during the effective green time from the link information and detects the forward signal during the effective green time from the front intersection information. The number of vehicles that passed through the intersection can be extracted and repeatedly compared to see if they match. At this time, the link vehicle detector 110 converts the moving distance of the queue moving within the link detection area during the effective green time into the number of vehicles, and then compares whether it matches the number of vehicles that passed the signal intersection.

Referring to FIG. 1, the first intersection vehicle detector 121 installed in the direction of the first link (RD-1) passes through the first direction detection area 126 in the direction of the third link (RD-3) on the opposite side. It is possible to detect and accumulate the distributed traffic volume by direction (vehicles going straight and left-turning in the effective green time by current time and right-turning vehicles by current time) for all vehicles, and the queue length of vehicles waiting at the signal for each lane of the road (RD-3). And the number of waiting vehicles can be calculated and accumulated. In addition, information such as average vehicle passing speed, number of vehicles, and vehicle type by lane for all vehicles passing through the intersection is accumulated as statistical data by display period or user-defined collection period, and accumulated traffic data by intersection direction and time series. can be created.

In addition, the first link vehicle detector 112 installed in the first link calculates the average vehicle speed for each lane, number of vehicles, and vehicle type information for all vehicles detected in the link detection area 116, and calculates information by display cycle or user-defined Section traffic information is generated by accumulating data in the form of statistical data for each collection cycle. In addition, the queue length and number of queued vehicles for each lane of vehicles waiting at a signal within the link detection area 116 can be calculated and accumulated together when generating data for each display cycle to provide information. And based on the link information about the dedicated vehicle detected through the first link vehicle detector 112, the vehicle's space occupancy, time occupancy, queue length, saturation, and traffic volume for each link at the intersection are calculated. It can be calculated and accumulated into one record along with traffic data.

The edge server 200 can calculate the space occupancy rate according to [Equation 1] below. At this time, space occupancy refers to the distance occupied by m vehicles in section S.

[Equation 1]

At this time, S is the unit distance, and l _j means the length of the car.

The edge server 200 can calculate the time occupancy rate according to [Equation 2] below. At this time, the time occupancy rate means the time that n vehicles existed at point S.

[Equation 2]

At this time, T is the unit time, l _i is the length of the car, and V _i means the speed.

The edge server 200 can calculate the queue length according to [Equation 3] below. At this time, the queue length refers to the queue length for n vehicles in intersection lane j.

[Equation 3]

At this time, L _j is the queue length in lane j, l _i is the length of the car, G is the gap between cars, and n is the number of cars. And the link information includes information about the vehicle type, the length of the vehicle is calculated as the vehicle length (constant value) defined for each vehicle type, and the distance between vehicles uses a constant value defined in traffic engineering rather than an actual value.

The edge server 200 can calculate the traffic volume according to [Equation 4] below. At this time, traffic volume refers to the traffic volume for each link by car.

[Equation 4]

At this time, V _t is the number of vehicles during unit time t, i is the number of lanes, and C _ti means the number of vehicles per lane during unit time t.

The edge server 200 can calculate the degree of saturation according to [Equation 5] below. At this time, saturation refers to the traffic saturation of each lane group.

[Equation 5]

_{At this time ,} is the traffic flow rate (vph), g _i is the effective green time (seconds) of lane group i, and C is the intersection signal period (seconds).

Meanwhile, the edge server 200 according to the present invention extracts the distributed traffic volume for each signalized intersection direction according to the presentation period from the intersection information, and determines the environmental conditions of the environmental condition information with the same period as the distributed distributed traffic volume for each signalized intersection direction. can be set.

Additionally, the edge server 200 can classify distributed traffic flow for each direction of a signalized intersection by environmental conditions and then accumulate them to generate a statistical-based distribution pattern.

Figure 3 is a block diagram showing a traffic big data construction system according to the present invention.

Referring to FIG. 3, the edge server 200 according to the present invention includes a detector communication unit 210 connected to the intersection vehicle detector 120 and the link vehicle detector 110 through a communication network, and the detector communication unit 210. A detection information collection unit 220 that receives intersection information provided from the intersection vehicle detector 120 and link information provided by the link vehicle detector 110, and a public data collection unit 230 that collects the external information from the external server. ), and the intersection information and link information provided from the detection information collection unit 220 are linked with external information provided from the public data collection unit 230 to generate traffic data processed in the form of statistical data, and this is accumulated to create traffic data. It may be configured to include a big data generation unit 240 that builds big data.

The public data collection unit 230 searches for road condition information of traffic safety facilities stored in a government office server through a communication network and extracts link information including the length of the link and the number of lanes on which the link vehicle detector 110 is installed. Perform.

The big data generator 240 verifies the collected intersection information and link information based on the preset data format, data size, and data type, and detects any deviation from the above standards. A data purification unit 242 that deletes intersection information and link information, and a data accumulation unit 244 that generates traffic statistical information by accumulating the intersection information and link information that have passed through the data purification unit 242 in the form of statistical data. A data processing unit 246 that generates transportation-related data by reprocessing the traffic statistical information to be linked to external information, and a data storage unit 248 that accumulates the traffic-related data to build transportation big data. It can be configured.

The data processing unit 246 generates traffic pattern information by classifying traffic statistical information by intersection direction, time series, and lane based on a preset collection cycle or TOD of intersection traffic lights, and the time at which the traffic pattern information was collected. Transportation-related data can be created by linking with external information.

The data storage unit 248 is connected to the detection information collection unit 220, the public data collection unit 230, and the data linking unit 250, and includes link information , initial queue length , number of vehicles occupying the maximum space in the queue, and inflow vehicles. Stores the number, number of spilled vehicles, and traffic volume-density. Additionally, the data storage unit 248 may be equipped with a traffic information DB. In addition, the data storage unit 248 can acquire road condition information and signal condition information in advance and build a database that can identify each.

If necessary, the edge server 200 is connected to the external terminal 300 through a communication network and provides an external communication unit 254 that receives terminal identification information and a sharing request signal, and data corresponding to the terminal identification information. A data linkage unit ( 250) may be further included.

The protocol may use any one of Vehicle to Everything communication (V2X), REpresentational State Transfer (REST), and JavaScript Object Notation (JSON).

The traffic big data construction system according to the present invention may further include a central server (not shown) or a cloud server (not shown).

The central server stores and manages traffic data provided from each edge server 200, and is connected to each edge server 200 through a communication network.

Additionally, the central server can provide each edge server 200 with a connection authentication code granting permission to use traffic big data. In addition, when the central server receives an approval request signal for applying to use traffic big data from the external terminal 300, it provides the external terminal 300 with a connection authentication code granting permission to use traffic big data.

The cloud server stores and manages traffic data provided from each edge server 200, and is connected to each edge server 200 through a communication network.

This central server or cloud server integrates and manages traffic big data provided from each edge server 200 that manages a preset detection area.

Referring to FIG. 3, the traffic big data construction system according to the present invention may further include an external connection terminal 300.

The external terminal 300 connects to the edge server 200 and requests sharing of terminal identification information that can confirm the data format, encryption method, and protocol used, and traffic big data built in the edge server 200. By providing a sharing request signal, when traffic big data is provided from the edge server 200, a third party service is provided based on this.

In this way, the external terminal 300 shares traffic big data with the edge server 200 through standard communication protocols such as V2X, 4G, 5G, WIFI, Bluetooth, REST, and JSON.

In addition, the edge server 200 or the central server provides an application programming interface (API) to the external terminal 300 to share accumulated information and real-time traffic information in order to share traffic big data for each intersection and link. can do.

In addition, the external connection terminal 300 transmits an approval request signal to apply for the use of transportation big data to the central server, receives an access authentication code authorized to use transportation big data from the central server, and receives the access authentication code from the central server. It is provided to an edge server located nearby. In this case, the edge server 200 checks whether the connection authentication code provided from the external connection terminal 300 matches the connection authentication code provided from the central server, and then provides a connection authentication code that matches the connection authentication code provided from the central server. Traffic big data is shared with the external connection terminal (300).

Figure 4 is a flowchart explaining the method of constructing traffic big data according to the present invention.

Referring to FIG. 4, the traffic big data construction method (hereinafter abbreviated as 'traffic big data construction method') that can build traffic big data about signalized intersections and links in the city according to the present invention is a vehicle detector that detects signals. A vehicle information detection step (S100) of generating intersection information by detecting vehicles passing through the intersection and generating link information by detecting vehicles passing through a link connecting the signalized intersection and the signalized intersection located behind the signalized intersection, and edge A detection information collection step (S200) in which the server 200 receives intersection information and link information provided from the vehicle detection unit 100, and the edge server 200 receives preset data format and data for intersection information and link information. Verify based on size and data type to determine if it deviates from the above standards. A data purification step (S300) of deleting intersection information and link information, an external information collection step (S410, S420) in which the edge server 200 collects external information related to the area where the vehicle detection unit is installed from an external server, The edge server 200 generates traffic statistical information by accumulating intersection information and link information in the form of statistical data, and collects the traffic statistical information by intersection direction, time series, and traffic based on a preset collection cycle or the TOD of intersection traffic lights. A pattern information generation step (S500) in which traffic pattern information is generated by classifying by lane, and a linkage data generation step in which traffic connection data is generated by linking with external information at the time when the edge server 200 collected the traffic pattern information. (S600), and a big data construction step (S700) in which the edge server 200 builds transportation big data by accumulating the transportation-related data.

In the detection information collection step (S200), the edge server 200 collects link information from the link vehicle detector 110 and stores it in the traffic information DB, and collects intersection information from the intersection vehicle detector 120 and stores it in the traffic information DB. You can save it.

In the external information collection steps (S410 and S420), the edge server 200 searches for road condition information and extracts link information including the link length and number of lanes where the link vehicle detector 110 is installed. And in the external information collection steps (S410 and S420), the edge server 200 collects the initial queue length formed between the link detection area, which is the detection area of the link vehicle detector 110, and the stop line at the signal intersection.

If necessary, in the external information collection steps (S410, S420), the edge server 200 collects road condition information, signal condition information, and link information for each signalized intersection through an upload method through an input means or a download method through an external server. Accumulated traffic volume history information and environmental condition information can be stored in the traffic information DB.

In the pattern information generation step (S500), the edge server 200 can calculate the vehicle's space occupancy, time occupancy, queue length, saturation, and traffic volume for each link at the intersection based on the link information.

Meanwhile, the present invention may further include a big data sharing step after the big data construction step (S700). This big data sharing step is a step in which the external terminal 300 connects to the edge server 200 and shares traffic big data.

In addition, the present invention may further include a sharing approval step between the big data construction step (S700) and the big data sharing step. This sharing approval step is a step in which the external connection terminal 300 connects to the central server that manages each edge server 200 and receives approval for use of traffic big data.

More specifically, the sharing approval step is an API installation process in which the external terminal 300 connects to the central server and installs the API provided by the central server, and the data format that the external terminal 300 is using through the API. and an identification information registration process of registering terminal identification information including information on encryption method and protocol and data items desired to be shared, and the external linked terminal 300 transmits an approval request signal to the central server and connects from the central server. It can be configured to include a shared approval process in which an authentication code is provided and stored in the API.

In addition, the big data sharing step is a connection process in which the external terminal 300 searches for and connects to an edge server 200 located nearby through an API, and the edge server 200 to which the external terminal 300 is connected. ) may be configured to include a data sharing process of providing a connection authentication code and downloading traffic data matched with pre-designated data items from the edge server 200.

This link information vehicle information detection step (S100), detection information collection step (S200), data purification step (S300), external information collection step (S410, S420), and pattern information generation step (S500) are linked. In the data creation step (S600), the big data construction step (S700), the big data sharing step, and the sharing approval step, the detailed configurations included in the transportation big data construction system described above are used, so redundant information is omitted.

Although the present invention has been described above with reference to preferred embodiments, those skilled in the art can make various modifications and changes to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below. You will understand that it is possible.

100: vehicle detection unit 110: link vehicle detector
112: first link vehicle detector 114: second link vehicle detector
116: Link detection area 120: Intersection vehicle detector
121: 1st intersection vehicle detector 122: 2nd intersection vehicle detector
123: 3rd intersection vehicle detector 124: 4th intersection vehicle detector
126: first direction detection area 127: second direction detection area
128: Third direction detection area 129: Fourth direction detection area
200: Edge server 210: Detector communication unit
220: Detection information collection unit 230: Public data collection unit
240: Big data generation unit 242: Data purification unit
244: data accumulation unit 246: data processing unit
248: data storage unit 250: data linkage unit
252: Data provision department 254: External link communication department
300: External connection terminal

Claims (18)

An intersection vehicle detector is installed at a signalized intersection and collects intersection information by detecting vehicles passing through the signalized intersection, and is installed at a link connecting the signalized intersection and a signalized intersection located behind the signalized intersection and detects vehicles passing the link. a vehicle detection unit including a link vehicle detector that collects link information;
an external server storing external information about the area where the intersection vehicle detector and the link vehicle detector are installed; and
It is installed at the signalized intersection or link, and connects the intersection information and link information provided from the vehicle detection unit with external information collected from the external server to generate processed traffic data in the form of statistical data, and accumulates the traffic data to generate traffic data. A transportation big data construction system that includes an edge server that builds big data. The method of claim 1, wherein the external server is
It includes one or more of the following: a signal controller storing signal time plan information, a Korea Meteorological Administration server storing weather information, a traffic data exchange server storing public data, and a government office server storing traffic information for areas where intersection vehicle detectors and link vehicle detectors are installed. A transportation big data construction system characterized by: The method of claim 2, wherein the external server is
A transportation big data construction system characterized by further including a local government server in which road condition information of traffic safety facilities is stored. The method of claim 2, wherein the weather information is
A transportation big data construction system characterized by including one or more of temperature, humidity, precipitation, snowfall, and illuminance. According to claim 1,
A central server that provides an access authentication code granting permission to use traffic big data to each edge server; and
An approval request signal to apply for the use of transportation big data is transmitted to the central server, an access authentication code authorized to use the transportation big data is provided, and the access authentication code is provided to an edge server located nearby. A transportation big data construction system characterized by further including an externally linked terminal that shares transportation big data built on an edge server. The method of claim 1, wherein the edge server
A detector communication unit connected to the intersection vehicle detector and the link vehicle detector through a communication network,
A detection information collection unit that receives intersection information provided from an intersection vehicle detector and link information provided by a link vehicle detector through the detector communication unit;
A public data collection unit that collects the external information from the external server, and
A big data generator that creates traffic data processed in the form of statistical data by linking the intersection information and link information provided from the detection information collection unit with external information provided by the public data collection unit, and accumulates this to build traffic big data. A transportation big data construction system comprising: The method of claim 6, wherein the big data generator
The collected intersection information and link information is verified based on the preset data format, data size, and data type to detect deviations from the above standards. A data purification unit that deletes intersection information and link information,
a data accumulation unit that accumulates the intersection information and link information that have passed the data purification unit in the form of statistical data to generate traffic statistical information;
A data processing unit that generates traffic-related data by reprocessing the traffic statistical information to link it with external information, and
A transportation big data construction system comprising a data storage unit that accumulates the transportation-related data to construct transportation big data. The method of claim 7, wherein the data processing unit
Traffic pattern information is generated by classifying the traffic statistical information by intersection direction, time series, and lane based on the preset collection cycle or signal time plan of intersection traffic lights, and linking the traffic pattern information with external information at the time of collection. A transportation big data construction system characterized by generating transportation-related data. According to clause 6,
Traffic, characterized in that it further comprises an external connection terminal that connects to the edge server and provides terminal identification information that can check the data format, encryption method, and protocol used, and a sharing request signal to request sharing of the transportation big data. Big data construction system. The method of claim 9, wherein the edge server
An externally connected communication unit that is connected to the externally connected terminal through a communication network and receives terminal identification information and a sharing request signal, and supports the protocol after compressing the traffic related data using a data format and encryption method corresponding to the terminal identification information. A transportation big data construction system characterized by further comprising a data linkage unit including a data provider that transmits to an external link terminal using a communication module of an external link communication unit. The method of claim 10, wherein the protocol
A transportation big data construction system characterized by one of vehicle-to-machine communication, REST (REpresentational State Transfer), and JSON (JavaScript Object Notation). The method of claim 1, wherein the edge server
A traffic big data construction system characterized by calculating space occupancy, time occupancy, queue length, saturation, and traffic volume for each link at intersections based on the link information and accumulating them into one record along with traffic data. An intersection vehicle detector is installed at a signalized intersection and collects intersection information by detecting vehicles passing through the signalized intersection, and is installed at a link connecting the signalized intersection and a signalized intersection located behind the signalized intersection and detects vehicles passing the link. a vehicle detection unit including a link vehicle detector that collects link information;
an external server storing external information about the area where the intersection vehicle detector and the link vehicle detector are installed; and
It is connected to the vehicle detection unit and an external server through a communication network to link the intersection information and link information provided by the vehicle detection unit with external information collected from the external server to generate traffic data processed in the form of statistical data, and the traffic data A transportation big data construction system that includes a central server that accumulates and builds transportation big data. A vehicle information detection step in which a vehicle detection unit generates intersection information by detecting a vehicle passing a signalized intersection, and generates link information by detecting a vehicle passing a link connecting the signalized intersection and a signalized intersection located behind it;
A detection information collection step in which an edge server receives intersection information and link information provided from the vehicle detection unit;
The edge server verifies the intersection information and link information based on the preset data format, data size, and data type, and detects any deviation from the above standards. A data purification step of deleting intersection information and link information;
An external information collection step in which the edge server collects external information related to an area where the vehicle detection unit is installed from an external server;
The edge server generates traffic statistical information by accumulating intersection information and link information in the form of statistical data, and distributes the traffic statistical information by intersection direction, time series, and lane based on a preset collection cycle or signal time plan of intersection traffic lights. A pattern information generation step of generating traffic pattern information by categorizing each category;
A linkage data generation step in which the edge server generates traffic linkage data by linking it with external information of the time at which the traffic pattern information was collected; and
A transportation big data construction method including a big data construction step in which the edge server accumulates the transportation-related data to construct transportation big data. According to claim 14, after the big data construction step,
A transportation big data construction method further comprising a big data sharing step in which an externally connected terminal connects to the edge server and shares the transportation big data. The method of claim 15, between the big data construction step and the big data sharing step.
A transportation big data construction method further comprising a sharing approval step in which the external link terminal accesses a central server that manages each edge server and receives approval for use of the transportation big data. The method of claim 16, wherein the sharing approval step is
An API installation process in which the external terminal connects to the central server and installs an application programming interface provided by the central server;
An identification information registration process in which the externally connected terminal registers terminal identification information including information on the data format, encryption method, and protocol being used through an application programming interface, and data items desired to be shared, and
A transportation big data construction method comprising a shared approval process in which the external link terminal transmits an approval request signal to the central server, receives a connection authentication code from the central server, and stores it in an application programming interface. The method of claim 17, wherein the big data sharing step is
A connection process in which the externally connected terminal searches for and connects to an edge server located nearby through an application programming interface, and
A traffic big data construction method comprising a data sharing process of providing a connection authentication code to an edge server to which the external connection terminal is connected and downloading traffic data matched to the data item from the edge server.