Congestion prediction system, terminal, congestion prediction method, and congestion prediction program

Abstract

To predict congestion at an arbitrary location by collecting images of mobile objects on a road.SOLUTION: A congestion prediction device 100 collects images of mobile objects on a road captured by a plurality of terminals 110 and position information indicating positions at which the images were captured. An image processing unit 101 performs image processing on the collected images, and extracts traffic information, which is information relating to the mobile objects existing in the images. A traffic information processing unit 103 calculates information relating to congestion prediction for the road corresponding to the captured position using the traffic information extracted by the image processing unit 101, and generates congestion prediction information, which is then transmitted to the terminals 110.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a traffic congestion prediction system, a terminal device, a traffic congestion prediction method, and a traffic congestion prediction program that predict traffic congestion using images captured by a moving object. However, the use of the present invention is not limited to the traffic congestion prediction system, the terminal device, the traffic congestion prediction method, and the traffic congestion prediction program. [Background technology]

[0002] Conventionally, a server that judges the congestion situation at a vehicle's position based on the speed from the vehicle and the average speed at the vehicle's position has been disclosed as a technology for predicting road congestion (see, for example, Patent Document 1 below). This technology also describes the use of image analysis of images captured by the vehicle to judge congestion.

[0003] In addition, there is a system that judges congestion using images captured by cameras fixedly installed on the road. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2011-134195 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the above-mentioned conventional technology cannot predict congestion at any location. In addition, the configuration for judging congestion using images has problems such as high installation costs and the limitation that congestion judgment can only be performed at locations where cameras are fixedly installed.

[0006] In addition, the system determines whether the section of the road on which the vehicle is traveling is congested based on information such as the speed received from the vehicle, and there is a problem that it cannot predict future congestion. These include: [Means for solving the problem]

[0007] In order to solve the above-mentioned problems and achieve the objective, the traffic congestion prediction system of the invention of claim 1 is characterized by having an acquisition unit that acquires an image of a moving object on a road and location information indicating the location where the image was taken, an image processing unit that extracts traffic information, which is information about the moving object present in the image, and a traffic information processing unit that uses the traffic information extracted by the image processing unit to calculate information related to traffic congestion prediction on the road corresponding to the location where the image was taken.

[0008] In addition, the terminal device of the invention of claim 6 is a terminal device that transmits an image of a moving object on a road to a traffic congestion prediction device that predicts traffic congestion for the moving object on the road based on the image, and is characterized in having an imaging unit that captures an image of the moving object on the road, and a processing unit that adds location information of the image captured by the imaging unit and transmits the image to the traffic congestion prediction device.

[0009] The terminal device according to the invention of claim 7 is a terminal device connected to a traffic congestion prediction device that predicts traffic congestion of a moving object on a road based on an image of the moving object on the road, and has a processing unit that requests the traffic congestion prediction device to provide a traffic congestion prediction including a desired position and time, and outputs information related to the traffic congestion prediction received from the traffic congestion prediction device. It is a sign.

[0010] Furthermore, the traffic congestion prediction method according to the invention of claim 8 is a traffic congestion prediction method implemented by a traffic congestion prediction device, and includes an acquisition step of acquiring an image of a moving object on a road and location information indicating the location where the image was taken, an image processing step of extracting traffic information which is information about the moving object present in the image, and a traffic information processing step of calculating information related to traffic congestion prediction on the road corresponding to the location where the image was taken, using the traffic information extracted by the image processing step.

[0011] A traffic congestion prediction program according to the invention of claim 9 causes a computer to execute the traffic congestion prediction method described above. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram illustrating a configuration example of a traffic congestion prediction system according to an embodiment. [Diagram 2] FIG. 2 is a flowchart of an example of a processing procedure of the traffic congestion prediction system according to the embodiment. [Diagram 3] FIG. 3 is a block diagram showing an example of a hardware configuration of the navigation device. [Figure 4] FIG. 4 is a block diagram illustrating an example of a hardware configuration of the server. [Diagram 5] FIG. 5 is a flowchart showing the image transmission process performed by the navigation device. [Figure 6] FIG. 6 is a flowchart showing the processing steps of generating traffic information by the server. [Figure 7] FIG. 7 is a chart showing an example of the contents of traffic information. [Figure 8] FIG. 8 is a flowchart showing the processing of traffic congestion prediction using traffic information. [Figure 9] FIG. 9 is a flowchart showing the process from a traffic congestion prediction request to transmission of traffic congestion prediction information. [Figure 10] FIG. 10 is an explanatory diagram showing a state in which spontaneous congestion occurs. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] (Embodiment) DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of a traffic congestion prediction system, a terminal device, a traffic congestion prediction method, and a traffic congestion prediction program according to the present invention will be described in detail below with reference to the accompanying drawings.

[0014] 1 is a block diagram showing a configuration example of a traffic congestion prediction system according to an embodiment. The traffic congestion prediction system according to the embodiment can be configured by a traffic congestion prediction device 100 and a terminal device 110 that are communicatively connected to each other.

[0015] The traffic congestion prediction device 100 includes an image processing unit 101, an information combination unit 102, a traffic information processing unit 103, a storage unit 104, and a traffic congestion prediction information management unit 105. The terminal device 110 includes an imaging unit 111, a positioning unit 112, and a processing unit 113.

[0016] The traffic congestion prediction device 100 can be configured, for example, using a single server, but is not limited to this. The traffic congestion prediction device 100 may also be configured using multiple servers distributed according to the functions of the image processing unit 101 and the traffic information processing unit 103.

[0017] The terminal device 110 captures an image of the area ahead while moving using the imaging unit 111, and transmits the image to the traffic congestion prediction device 100. At this time, the processing unit 113 transmits the image captured by the imaging unit 111 together with the image capture ID. , and location information at the time of shooting measured by the positioning unit 112. In addition, the speed during movement and time information may be transmitted. Furthermore, the processing unit 113 requests the traffic congestion prediction device 100 to predict traffic congestion in response to a user operation or the like, processes the traffic congestion prediction information transmitted from the traffic congestion prediction device 100, and outputs the information to a display unit (not shown) or the like.

[0018] The terminal device 110 can be configured using a navigation device installed in a mobile object, a smartphone or mobile phone carried by a user, a portable personal computer, etc., and can transmit images used for traffic congestion prediction to the traffic congestion prediction device 100 and also request a traffic congestion prediction from the traffic congestion prediction device 100.

[0019] Without being limited to these, the terminal device 110 may be configured using a personal computer or the like fixedly installed at home, a facility (store), or the like. In this case, the terminal device 110 omits the function of transmitting an image used for traffic congestion prediction to the traffic congestion prediction device 100, and requests a traffic congestion prediction from the traffic congestion prediction device 100. In the case of such a terminal device 110, the functions of the imaging unit 111 and the positioning unit 112 can be omitted, and the processing unit 113 of the terminal device 110 requests a traffic congestion prediction by a user operation or the like, and outputs the information on the traffic congestion prediction transmitted from the traffic congestion prediction device 100 to a display unit or the like.

[0020] The image processing unit 101 of the traffic congestion prediction device 100 processes the captured image sent from the terminal device 110. In this image processing, the road and vehicle shown in the captured image are extracted (detected). Then, the lane in which the terminal device 110 (mobile body) is traveling (own lane), the number of lanes, the number of vehicles per lane, and the distance between vehicles are detected. In addition, the type of vehicle of each vehicle included in the captured image can also be detected.

[0021] The information combination unit 102 associates the shooting ID, shooting time, and shooting position of the captured image transmitted from the terminal device 110 and stores them in the storage unit 104. When using the time of the traffic congestion prediction device 100, the time when the captured image was received can be used, and the terminal device 110 does not need to transmit time information. In addition, when the traffic congestion prediction device 100 knows the speed of the mobile object on which the terminal device 110 is installed, the traffic congestion prediction device 100 can obtain the speed of the mobile object by calculation based on, for example, the amount of movement of the terminal device 110 in latitude and longitude and the required time for movement.

[0022] Then, the information combination unit 102 associates (combines) the own lane information, which is the result of image processing by the image processing unit 101, with the same photography ID, and stores it as traffic information in the storage unit 104. The information combination unit 102 also stores vehicle information (number of vehicles) by lane for each photography ID as vehicle information by lane in the storage unit 104. The traffic information is stored in the storage unit 104 in a state of being classified by road, position, and time period.

[0023] The traffic information processing unit 103 calculates the probability of congestion occurrence by location and time period using a statistical function for each of the traffic information classified by road location and time period stored in the storage unit 104, and stores the calculated probability as congestion prediction information in the storage unit 104. The storage unit 104 stores traffic information based on changes in multiple captured images of the same road, and the traffic information processing unit 103 predicts the occurrence of congestion by comparing the traffic information at the time of a request from the terminal device 110 with past traffic information (for example, the same time and place).

[0024] The traffic information is not limited to information about the same road, but may be information about a predetermined range (other roads).

[0025] At this time, traffic information for each lane is obtained based on the captured images, so the degree of congestion can be determined based on the number of vehicles in each lane (equivalent to the distance between vehicles and vehicle density), and congestion predictions can be made for each lane.

[0026] The traffic congestion forecast information includes future traffic congestion forecast near the current location of the terminal device 110, traffic congestion forecast on the driving route to the destination of the terminal device 110, traffic congestion forecast near the destination, etc. The calculation process of this traffic congestion forecast information may be calculated in advance for each road (location), or may be calculated for the relevant road (location) when requested by the terminal device 110.

[0027] In addition, the traffic congestion prediction information is not limited to traffic congestion prediction for the road corresponding to the position where the image was taken, but may be, for example, a road closest to the position information where the image was taken, or a road on map information that is map-matched at the time when the image was taken.

[0028] The traffic congestion prediction information management unit 105 manages traffic congestion prediction information. The traffic congestion prediction information management unit 105 transmits to the terminal device 110 traffic congestion prediction information for a position or area corresponding to a traffic congestion prediction request from the terminal device 110.

[0029] The terminal device 110 requests, for example, traffic congestion prediction information for the travel route of the mobile body when it moves or within a predetermined area near the destination from the traffic congestion prediction device 100. In addition, when searching for a route by the mobile body, the traffic congestion prediction device 100 similarly requests traffic congestion prediction information for the travel route of the terminal device 110 (mobile body) or within a predetermined area near the destination. In response to this, the traffic congestion prediction device 100 can extract traffic congestion prediction information for the travel route of the terminal device 110 (mobile body) or within a predetermined area near the destination and transmit it to the terminal device 110.

[0030] 2 is a flowchart showing an example of a processing procedure of the traffic congestion prediction system according to the embodiment. The processing contents of traffic congestion prediction performed by the traffic congestion prediction device 100 are shown. First, the traffic congestion prediction device 100 acquires a captured image, and position information, speed, and time at the time of capture from the terminal device 110 (step S201). As described above, the speed and time can be acquired without receiving them from the terminal device 110.

[0031] Next, the traffic congestion prediction device 100 processes the captured image (step S202). This image processing detects the lane in which the terminal device 110 (mobile body) is traveling (the vehicle's own lane), the number of lanes, the number of vehicles per lane, the type of each vehicle, and the distance between vehicles. The detected information is associated (combined) with the information acquired in step S201 (position information, speed, time), and traffic information is generated and stored in the storage unit 104.

[0032] The same or different terminal devices 110 transmit a plurality of captured images for different locations and times to the traffic congestion prediction device 100. As a result, the traffic congestion prediction device 100 repeatedly executes steps S201 and S202, and stores a large amount of traffic information by road location and time in a database. The traffic congestion prediction device 100 uses this traffic information to perform the following traffic congestion prediction process.

[0033] Next, the traffic congestion prediction device 100 waits to receive a traffic congestion prediction request from the terminal device 110 (step S203: No loop). When the traffic congestion prediction request is received (step S203: Yes), traffic congestion prediction processing is performed (step S204). In this traffic congestion prediction processing, the traffic information generated in step S202 is referenced, and traffic congestion occurrence at the location (range) and time included in the request is predicted based on past traffic congestion occurrence at the same location and time as the location (range) and time.

[0034] The location where the congestion is predicted does not have to be exactly the same as the location included in the request, and may be predicted based on past traffic information and congestion occurrences on the same road or within a specified range as the location included in the request. The same is true for the time, and the time including the time included in the request may be predicted. It may be a predetermined time period.

[0035] Thereafter, the traffic congestion prediction information is transmitted to the terminal device 110 (step S205), and the series of traffic congestion prediction processes is terminated.

[0036] According to the above embodiment, traffic congestion prediction is performed using images taken at multiple locations and times on the road by a camera possessed by the terminal device, so that traffic congestion prediction can be performed not only at the installation location using fixedly installed equipment such as cameras and sensors on the road, but also at any location on the road.

[0037] In addition, since a large amount of traffic information for determining the congestion state of roads can be obtained from images transmitted from a large number of terminal devices, the accuracy of congestion prediction at any location can be improved.

[0038] As terminal devices can be general-purpose devices such as mobile phones, smartphones, and car navigation systems equipped with cameras that can take images and transmit location information, the congestion prediction device can easily obtain a large number of images and reduce system costs.

[0039] In addition, when generating congestion prediction information, the distance between vehicles and the number of vehicles are used, but other data that may be congestion factors, such as the weather, day of the week, traffic information on other roads connected to the road of the requested location, and the presence or absence of events near the requested location, may be added as parameters for congestion prediction. This will further improve the accuracy of congestion prediction.

[0040] Traffic congestion was judged based on the number of vehicles (distance between vehicles), but by performing image processing to determine the similarity between an image of a location on the road before the traffic congestion and a current image sent from a terminal device, traffic congestion can also be predicted by comparing the images.

[0041] Furthermore, the images captured by the terminal device are not limited to still images, but may be video images. Video images can also be used to determine the speed of moving objects in other lanes, providing more detailed traffic information.

[0042] In addition, statistics such as multiple regression analysis may be applied to generate traffic congestion prediction data. In this case, an appropriate optimal algorithm can be reconfigured for each road. EXAMPLES

[0043] An embodiment of the present invention will be described below. In this embodiment, a configuration will be described in which a navigation device 300 is mounted on a moving body (vehicle), and each user's navigation device 300 accesses a server serving as the above-mentioned traffic congestion prediction device 100. Here, the navigation device 300 has the function of the above-mentioned terminal device 110, and takes pictures of roads while the moving body is moving and transmits them to the traffic congestion prediction device 100. In addition, this navigation device 300 requests traffic congestion prediction information for a desired location from the traffic congestion prediction device 100 (server).

[0044] (Hardware configuration of the navigation device 300) Fig. 3 is a block diagram showing an example of the hardware configuration of a navigation device. In Fig. 3, the navigation device 300 includes a CPU 301, a ROM 302, a RAM 303, a magnetic disk drive 304, a magnetic disk 305, an optical disk drive 306, an optical disk 307, an audio I / F (interface) 308, a microphone 309, a speaker 310, an input device 311, a video I / F 312, a display 313, a communication I / F 314, a GPS unit 315, various sensors 316, and a camera 317. The components 301 to 317 are connected to each other via a bus 320.

[0045] The CPU 301 is responsible for the overall control of the navigation device 300. The ROM 302 stores a boot program, a traffic congestion prediction program, etc. The RAM 303 is used as a work area for the CPU 301. That is, the CPU 301 is responsible for the overall control of the navigation device 300 by executing various programs stored in the ROM 302 while using the RAM 303 as a work area.

[0046] The magnetic disk drive 304 controls reading / writing of data from / to the magnetic disk 305 under the control of the CPU 301. The magnetic disk 305 records the data written under the control of the magnetic disk drive 304. As the magnetic disk 305, for example, a HD (hard disk) or a FD (flexible disk) can be used.

[0047] Further, optical disk drive 306 controls reading / writing of data from / to optical disk 307 under the control of CPU 301. Optical disk 307 is a removable recording medium from which data is read under the control of optical disk drive 306. A writable recording medium can also be used as optical disk 307. In addition to optical disk 307, MO, memory card, etc. can be used as the removable recording medium.

[0048] Examples of information recorded on the magnetic disk 305 and the optical disk 307 include map data, vehicle information, road information, driving history, etc. The map data is used when searching for a route in a car navigation system, and is vector data including background data representing features such as buildings, rivers, the ground surface, and energy supply facilities, and road shape data representing the shape of roads with links, nodes, etc.

[0049] The audio I / F 308 is connected to a microphone 309 for audio input and a speaker 310 for audio output. The audio received by the microphone 309 is A / D converted within the audio I / F 308. The microphone 309 is installed, for example, on the dashboard of the vehicle, and the number of microphones may be one or more. Audio obtained by D / A converting a predetermined audio signal within the audio I / F 308 is output from the speaker 310.

[0050] Examples of the input device 311 include a remote control, a keyboard, a touch panel, etc., which are provided with a plurality of keys for inputting characters, numbers, various instructions, etc. The input device 311 may be realized in any one form of a remote control, a keyboard, or a touch panel, but may also be realized in a plurality of forms.

[0051] The video I / F 312 is connected to the display 313. Specifically, the video I / F 312 is composed of, for example, a graphic controller that controls the entire display 313, a buffer memory such as a VRAM (Video RAM) that temporarily records image information that can be displayed immediately, and a control IC that controls the display 313 based on image data output from the graphic controller.

[0052] Various data such as icons, cursors, menus, windows, characters, images, etc. are displayed on the display 313. As the display 313, for example, a TFT liquid crystal display, an organic EL display, etc. can be used.

[0053] The camera 317 captures an image of the road outside the vehicle. The image may be either a still image or a video. By capturing an image of the road outside the vehicle with the camera 317, the road and other vehicles traveling on it are captured. This captured image is processed by the CPU 301, and then transmitted to the congestion prediction device 100 (the server 4 described later) together with the position information of the GPS unit 315. 00) will be sent.

[0054] The communication I / F 314 is wirelessly connected to a network and functions as an interface between the navigation device 300 and the CPU 301. Communication networks that function as networks include in-vehicle communication networks such as CAN and LIN (Local Interconnect Network), public line networks, mobile phone networks, DSRC (Dedicated Short Range Communication), LAN, WAN, etc. The communication I / F 314 is, for example, a public line connection module, an ETC (non-stop automatic toll payment system) unit, an FM tuner, a VICS (Vehicle Information and Communication System: registered trademark) / beacon receiver, etc.

[0055] The GPS unit 315 receives radio waves from GPS satellites and outputs information indicating the current position of the vehicle. The output information of the GPS unit 315, together with output values ​​of various sensors 316 described later, is used when the CPU 301 calculates the current position of the vehicle. The information indicating the current position is, for example, information that identifies a point on map data, such as latitude, longitude, and altitude.

[0056] Various sensors 316 output information for determining the position and behavior of the vehicle, such as a vehicle speed sensor, an acceleration sensor, an angular velocity sensor, and an inclination sensor. The output values ​​of the various sensors 316 are used by the CPU 301 to calculate the current position of the vehicle and the amount of change in speed and direction.

[0057] A CPU 301 shown in Fig. 3 executes a program stored in a ROM 302 or the like to realize the functions of the processing unit 113 of the terminal device 110 shown in Fig. 1. A camera 317 in Fig. 3 realizes the functions of the imaging unit 111 in Fig. 1, and a GPS unit 315 in Fig. 3 realizes the functions of the positioning unit 112 in Fig. 1.

[0058] (Server configuration example) Fig. 4 is a block diagram showing an example of a hardware configuration of a server. The server 400 constituting the traffic congestion prediction device 100 has a configuration similar to that of the navigation device 300 shown in Fig. 3. Note that the server 400 does not require the GPS unit 315, various sensors 316, camera 317, etc. shown in Fig. 3.

[0059] 1 realizes the function of traffic congestion prediction by having a CPU 401 execute a predetermined program using programs and data recorded in a ROM 402 or the like provided in a server 400. The above traffic information and traffic congestion prediction information are stored in a magnetic disk 405 or the like of the server 400. In addition, the server 400 communicates with a navigation device 300 via a communication I / F 414, and outputs traffic congestion prediction information to the navigation device 300 in response to a request from the navigation device 300.

[0060] The server 400 may also have a function of calculating the transmission time of an image transmitted from each navigation device 300 and the speed of a moving object equipped with the navigation device 300. The server 400 can calculate the speed of the moving object based on the amount of movement of the navigation device 300 in latitude and longitude and the required time for the movement.

[0061] A CPU 401 shown in Fig. 4 executes a program stored in a ROM 402 or the like to realize the functions of the image processing unit 101, the information combination unit 102, the traffic information processing unit 103, and the traffic congestion prediction information management unit 105 of the traffic congestion prediction device 100 shown in Fig. 1. The magnetic disk 305, the optical disk 307, etc. in Fig. 4 realize the function of the storage unit 104 shown in Fig. 1.

[0062] (Image transmission process by navigation device) 5 is a flow chart showing the process of image transmission by the navigation device. The navigation device 300 constantly measures the position of the moving object using the GPS unit 315 or the like when the moving object is moving (step S501). Then, for example, the camera 317 captures an image of the road at predetermined intervals (for example, every minute) (step S502). For example, by capturing an image of the road ahead, the moving object on the road is captured together with the road.

[0063] Furthermore, the trigger (timing) for photographing the road by the camera 317 can be a request output from the server 400 to the navigation device 300, an instruction from a user of the navigation device 300, or the like.

[0064] Then, each time an image is captured, the navigation device 300 transmits the captured image together with location information at the time of capture to the server 400 (step S503). Note that the navigation device 300 may also assign a time stamp to each captured image and transmit multiple images to the server 400 at predetermined intervals.

[0065] The navigation device 300 repeatedly captures images as described above and transmits them to the server 400. In this way, the server 400 can collect images of a plurality of roads captured by a plurality of navigation devices 300.

[0066] (Generation of traffic information by server) 6 is a flowchart showing the process of generating traffic information by the server. First, the server 400 receives the image and the position information (photographing position) transmitted from the navigation device 300 (step S601). Next, the server 400 obtains the speed of the moving object (navigation device 300) and the time when the image was received (step S602).

[0067] The server 400 may obtain the speed and time of the moving object transmitted by the navigation device 300, or may use those generated within the server 400. If generated by the server 400, the time used is the time within the server 400 at the time of receiving the image. The movement of the moving object is calculated based on the amount of movement of the moving object (navigation device 300) in latitude and longitude and the time required for the movement.

[0068] Next, the server 400 detects the vehicle, the driving lane, and the number of lanes from the acquired image by image processing of the image processing unit 101 (step S603). At this time, for example, the captured image is converted into an ortho-image, so that the position and distance of the vehicle can be specified and the number of vehicles in each lane can be detected. In addition, the width of the own lane is derived from the ortho-image, and the number of lanes and the distance between vehicles on the road to be driven on are determined.

[0069] Next, the server 400 causes the information combination unit 102 to associate each piece of information, such as location information, speed, time, number of vehicles per lane, and the own lane, with each acquired image (ID), and stores the information as traffic information in the storage unit 104 (step S604). The above-mentioned processes of steps S601 to S604 are performed every time an image is received from the navigation device 300.

[0070] (Example of traffic information) Fig. 7 is a chart showing an example of the contents of traffic information. Fig. 7(a) shows captured image processing information 701 obtained by image processing one image sent from one user. This captured image processing information 701 consists of lane information (number of lanes), own lane information, and lane-specific vehicle information ID. The illustrated example shows that the own vehicle is the first from the left (own lane information) on a three-lane road (lane information).

[0071] The details of the lane-specific vehicle information ID in (a) of FIG. 7 are shown in 702 in (b) of FIG. The lane-specific vehicle information ID 702 is an ID that is assigned to each image and is obtained by image processing. The image data includes information on the position of each lane (lane position, i.e., the lane position of the moving object present in the image) and the vehicle distance for each lane (the distance between the moving object present in the image and the own vehicle). In the illustrated example, when ID=1, it indicates that a moving object is present in a position (lane 1) ahead of the own vehicle position with a vehicle distance of 45 m, and that there are moving objects present in a position (lane 3) ahead of the own vehicle position with a vehicle distance of 30 m and 60 m. In the illustrated example, of the three lanes, the position of the left lane is indicated as "1", the position of the center lane as "2", and the position of the right lane as "3".

[0072] The shooting condition information 703 shown in (c) of Figure 7 is information that associates the shooting image processing information 701 in (a) of Figure 7 with the shooting position (latitude and longitude in the illustrated example), speed, and time, which are the conditions at the time of shooting, by a shooting ID.

[0073] (d) of Fig. 7 is traffic information 704. This is information in which the shooting condition information 703 shown in (c) of Fig. 7 is accumulated for the same user at different locations and times, and for different users. By increasing the number of rows of this traffic information 704, it is possible to build a database of current lane information, lane information, and lane-specific vehicle information ID (lane position, vehicle distance) by time for a desired shooting position.

[0074] (Traffic congestion prediction processing using traffic information) 8 is a flow chart showing the process of traffic congestion prediction using traffic information. First, the traffic information processing unit 103 of the server 400 classifies the traffic information 704 by location, date, and time period (step S801), and creates a statistical function for each classified data (step S802). In this state, the traffic information processing unit 103 waits for input of the request conditions included in the traffic congestion prediction request from the navigation device 300 (step S803: No loop).

[0075] If a required condition is input (step S803: Yes), the traffic information processing unit 103 calculates a congestion occurrence value using a statistical function corresponding to the required condition (step S804).Then, the traffic information processing unit 103 judges whether the congestion occurrence value is high (positive value) (step S805), and if the congestion occurrence value is higher than a predetermined value (step S805: Yes), generates this congestion occurrence value as congestion prediction information and stores it in the storage unit 104 (step S806).If the congestion occurrence value is low (step S805: No), it judges that no congestion will occur, and ends the current process.

[0076] A specific example of the above process will be described. In the above example, the congestion occurrence value is compared with a predetermined threshold value as a reliability to determine the occurrence of congestion. For example, a case will be described in which the required conditions for congestion prediction transmitted (input) from the navigation device 300 specify the left lane traffic volume, right lane traffic volume, date (day of the week), and time period.

[0077] The navigation device 300 transmits the captured image, and the server 400 analyzes the image to determine the left lane traffic volume, right lane traffic volume, date (day of the week), and time period. The date and time are determined based on the time the server receives data, etc. An example of traffic volume is the number of cars per unit time.

[0078] First, the traffic information processing unit 103 refers to the traffic information 704 corresponding to the specified date (day of the week) and time period from the storage unit 104. For example, the traffic information 704 is referenced when the requested conditions are x road, Tuesday, and 12:00 to 13:00.

[0079] Then, the traffic information processing unit 103 receives as input the statistical function fn relating to the occurrence of congestion and the required conditions X and Y, and calculates the congestion probability using the following equation. fn(X,Y)=an(X)+bn(Y) fn(xn,yn)=an(xn)+bn(yn) Congestion occurrence value = max(1 / fn(|X-xn|,|Y-yn|))-tn

[0080] where fn is a statistical function (n = 0, 1, ...). The statistical function fn is a function for comparing past congestion prediction data with the current traffic volume to calculate the congestion occurrence value. X: left lane input traffic volume, Y: right lane input traffic volume, xn: left lane statistical traffic volume, yn: right lane statistical traffic volume, an: left lane prediction coefficient, bn: right lane prediction coefficient, tn: threshold value. The traffic volume is obtained from the speed of the moving object when the image was taken and the number of moving objects photographed.

[0081] When it is determined that congestion will occur on a certain road, traffic congestion forecast data is generated by going back a certain time from the time that congestion was determined on that road (the time going back differs for each road) and collecting traffic information (e.g., traffic volume, speed, and time for each lane) up until the occurrence of the congestion. If this statistical data is significantly different from the usual traffic information, it becomes congestion forecast data for that road. However, there are many forecast data for one road according to parameters such as date and time. The above-mentioned congestion occurrence may be determined by the traffic congestion forecast device 100 of the system, or if the information is obtained from another external system, it is determined that congestion has occurred when the distance between vehicles is narrow or the speed is low.

[0082] In addition, regarding the above statistical function fn, in the above example (two-lane road), if the road has two characteristics, such as "when the right lane becomes extremely congested, traffic jams will occur afterwards" or "when both the right and left lanes become congested to some extent, traffic jams will occur afterwards", there are two patterns of traffic jam occurrence, so fn will be n=2. This characteristic is calculated from traffic jam forecast data.

[0083] The right lane input traffic volume is the traffic volume in the right lane calculated from the image captured by the navigation device 300, and is indicated by the number of lines at position "3" in lane-specific vehicle information ID 702 "1" in Fig. 7(b) (the road with lane-specific capture ID "1" is a three-lane road) (i.e., the number of vehicles in the right lane of the three-lane road). The left lane input traffic volume is indicated by the number of lines at position "1" in lane-specific vehicle information ID 702 "1" in Fig. 7(b) (i.e., the number of vehicles in the left lane of the three-lane road).

[0084] The right (left) lane statistical traffic volume is a statistical value of the average or most frequent value of the traffic volume at position "3" in the corresponding pattern in the congestion prediction data. The right (left) lane prediction coefficient is a coefficient that weights the difference between the right (left) lane statistical traffic volume and the right lane input traffic volume. For example, if there is a pattern on a road where "when the right lane becomes extremely congested, congestion occurs afterwards," the right lane prediction coefficient is increased. This allows the difference in the right lane to be significantly reflected in the final congestion occurrence value.

[0085] A specific example of the above traffic congestion prediction will be described. The traffic congestion prediction device 100 assumes that at time t on a certain road, statistical data (data in the storage unit 104) for 30 minutes before traffic congestion occurs has two patterns: "the right lane is extremely congested" and "both right and left lanes are slightly congested." It is found that on this road, signs of traffic congestion appear 30 minutes beforehand. These two patterns are stored in the storage unit 104 as traffic congestion forecast data for this road at time t.

[0086] It is also assumed that an image is transmitted from the user of the navigation device 300 at time t. It is assumed that the traffic congestion prediction device 100 determines that the right lane is very congested by image analysis of this image. It is assumed that the traffic congestion prediction device 100 calculates the similarity of the image analysis result with traffic congestion prediction data using a statistical function, and finds that the result is very similar to a pattern in which "the right lane is extremely congested." In this case, the traffic information processing unit 103 of the traffic congestion prediction device 100 determines that traffic congestion will occur 30 minutes after the image is transmitted, and outputs to the traffic congestion prediction information management unit 105 that this road will be congested in 30 minutes.

[0087] A specific example of calculation using the statistical function of the traffic congestion prediction device 100 will be described. This road has the following two patterns of traffic congestion prediction, Pattern 1: "Traffic jams occur when the right lane becomes extremely congested." Pattern 2: "Traffic jams occur when both lanes are slightly congested." Therefore, two statistical functions fn are prepared. f1(X,Y)=a1(x1)+b1(y1) f2(X,Y)=a2(x2)+b1(y2)

[0088] x1: Left lane traffic volume (average or most frequent) of predicted data for pattern 1 → statistical traffic volume of the left lane y1: Right lane traffic volume (average or most frequent) of predicted data for pattern 1 x2: Left lane traffic volume (average or most frequent) of predicted data for pattern 2 y2: Right lane traffic volume (average or most frequent) of predicted data for pattern 2 a1: Weight of the left lane in pattern 1, b1: Weight of the right lane in pattern 1 (in pattern 1, we want to focus only on the right lane, so we make the value of a1 larger and the value of b1 smaller) a2: weight of the left lane in pattern 2, b2: weight of the right lane in pattern 2 (in pattern 2, we want to focus on both lanes, so a2=b2 is fine)

[0089] Then, when the traffic volumes X and Y of each lane are input, the congestion prediction information management unit 105 performs the following: Calculate 1 / f1(|x1-X|,|y1-Y|) and 1 / f2(|x2-X|,|y2-Y|). The larger of these values ​​is adopted and compared with the threshold value tn. If it is larger than tn, congestion forecast information is generated.

[0090] The traffic congestion prediction information management unit 105 stores and manages the traffic congestion prediction information in the storage unit 104, and transmits the traffic congestion prediction information to the navigation device 300 when a traffic congestion prediction request is received from the navigation device 300.

[0091] If the navigation device 300 specifies required conditions, such as weather, when requesting a congestion prediction, the traffic information processing unit 103 refers to the traffic information 704 including these parameters. As the required conditions increase, it becomes possible to perform a highly accurate congestion prediction in response to the request.

[0092] In the above processing example, the traffic congestion prediction information is calculated after a traffic congestion prediction request is received from the navigation device 300, but the present invention is not limited to this. For example, the traffic information processing unit 103 may be configured to generate traffic congestion prediction information by location and time in advance based on the traffic information 704. In this case, when a traffic congestion prediction request is received from the navigation device 300, the traffic congestion prediction information for the corresponding location and time can be immediately transmitted to the navigation device 300.

[0093] (Regarding traffic congestion forecast requests and transmission of traffic congestion forecast information) 9 is a flowchart showing the process from a traffic congestion prediction request to transmission of traffic congestion prediction information. First, the navigation device 300 transmits a traffic congestion prediction request for a specified position or a specified range to the server 400 (step S901).

[0094] The traffic congestion forecast information management unit 105 of the server 400 receives this traffic congestion forecast request (step S902), and transmits (distributes) the traffic congestion forecast for the corresponding specified position or specified range to the navigation device 300 (step S903).

[0095] The navigation device 300 receives the traffic congestion prediction information transmitted from the server 400 (step S904), and displays the traffic congestion prediction information on the display unit (step S905).

[0096] After step S903, the traffic congestion forecast information management unit 105 of the server 400 may be configured to monitor whether or not a change has occurred in the traffic congestion forecast based on the possibility that the traffic congestion state may change over time (step S906). If there is no change in the traffic congestion forecast (step S906: No), the process ends. However, if there is a change in the traffic congestion forecast (step S906: If the result of step S906 is Yes, the congestion prediction information is calculated again and transmitted in step S903.

[0097] (Regarding traffic congestion) Fig. 10 is an explanatory diagram showing the occurrence of spontaneous congestion. As shown in Fig. 10(a), as the number of moving objects traveling increases, the distance between the moving objects becomes narrower. Next, as shown in Fig. 10(b), when the speed of a moving object 1001 slows down, the following moving objects 1011, 1012 apply the brakes one after another to reduce their speed so as not to collide with each other. As a result, as shown in Fig. 10(c), a moving object 1021 comes to a complete halt, and the following moving objects 1031, 1032, 1033, 1034 also stop one after another, causing spontaneous congestion.

[0098] (Regarding traffic congestion predictions) In the embodiment, the traffic congestion prediction predicts that the greater the number of moving objects traveling, the narrower the distance between the moving objects, by referring to images captured by the navigation device 300 and a past database (traffic information 704). By referring to the past traffic information 704 corresponding to the requested conditions, the server 400 determines which of the conditions (a) to (c) the request corresponds to in the example of natural congestion shown in Fig. 10, and transmits to the navigation device 300 traffic congestion prediction information corresponding to the requested date and time, etc., included in the traffic congestion prediction request.

[0099] In addition, a prediction of how the traffic congestion will change after the requested time (for example, when the request is for a time corresponding to FIG. 9(a), for example, a natural traffic congestion as shown in FIG. 9(c) will occur 10 minutes later can also be transmitted to the navigation device 300 as traffic congestion prediction information.

[0100] (Applying traffic congestion forecast information to route planning) When the above-mentioned traffic congestion forecast information indicates that the possibility of traffic congestion is high (traffic congestion occurrence value is high), the navigation device 300 can provide route guidance by adding traffic congestion forecast information to roads on the corresponding traffic congestion candidate route.

[0101] The navigation device 300 according to the embodiment performs route search taking into consideration the above-mentioned congestion prediction information. As a result, since the average travel time of roads (links) where congestion is predicted is long, a route that avoids these congestion points is searched for.

[0102] However, in such a route search using traffic congestion forecast information, the presence or absence of actual traffic congestion at the relevant location becomes clear at the predicted time. Therefore, it is possible that the predicted traffic congestion does not occur (no traffic congestion occurs) at the predicted time. In such a case, when the predicted time arrives, the navigation device 300 searches for a route again and does not use the previously used traffic congestion forecast information.

[0103] The route search may be performed not only by the navigation device 300 but also by the server 400. The server 400 may acquire location information such as the current location of the navigation device 300 and the destination, and perform a route search including other search conditions such as time.

[0104] In the above embodiment, the traffic volume is detected by image processing the number of lanes, the number of vehicles, the distance between vehicles, etc. from the captured image, but other parameters can also be obtained by image processing. For example, if there is a guide sign indicating that construction is underway on a lane of a road in the captured image, there is a possibility that a sudden traffic jam will occur due to the construction. In addition, vehicle types (buses, police cars), pedestrian crossings, etc. that may cause traffic jams can also be used as parameters.

[0105] Therefore, by statistically processing the captured images themselves for similarities and differences between images before and after the occurrence of a traffic jam, the accuracy of the traffic information 704 can be improved.

[0106] In addition, in the above embodiment, the traffic congestion prediction is performed by a traffic congestion prediction device 100 such as a server, but it is also possible to have a configuration in which a terminal device 110 such as a car navigation device or a personal computer predicts traffic congestion and transmits the traffic congestion prediction to other terminal devices 110 that have requested the traffic congestion prediction.

[0107] The method for traffic congestion prediction described in this embodiment can be realized by executing a prepared program on a computer such as a personal computer or a workstation. This program is recorded on a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM, an MO, or a DVD, and is executed by being read from the recording medium by the computer. This program may also be a transmission medium that can be distributed via a network such as the Internet. [Explanation of symbols]

[0108] 100 Traffic jam prediction device 101 Image processing section 102 Information connection part 103 Traffic information processing unit 104 Storage area 105 Traffic Congestion Forecast Information Management Department 110 Terminal Equipment 111 Imaging unit 112 Positioning unit 113 Processing section

Claims

[Claim 1] an acquisition unit that acquires an image of a moving object on a road and location information indicating the location where the image was captured; an image processing unit that extracts traffic information, which is information about moving objects present in the image; a traffic information processing unit that calculates information regarding a congestion forecast on the road corresponding to the photographed position by using the traffic information extracted by the image processing unit; and A traffic congestion prediction system comprising:

Images