Search device, search method, and search program

Abstract

A search device (20) collects location information about a plurality of vehicles, and stores, in a spatiotemporal database (10), spatiotemporal data including the collected location information as well as time information including times at which the foregoing were traveling. The search device (20) then predicts traffic densities of respective time slots for respective roads on the basis of the location information stored in the spatiotemporal database (10), and stores the predicted traffic densities in a traffic flow database (23). Next, if a search request has been received, the search device (20) assesses whether each of the roads, during a planned travel time, will be congested on the basis of the traffic densities stored in the traffic flow database (23), searches for route information including a road assessed not to be congested, uses the retrieved route information and a planned time during which to travel on the road of that route information to generate spatiotemporal data including location information of any vehicles presumed to travel the route in the future on the basis of the route information as well as time information including a planned time at which to travel, and stores same in the spatiotemporal database (10).

Description

[Technical Field]

[0001] The present invention relates to a search device, a search method, and a search program. [Background technology]

[0002] As a conventional technology, a method for avoiding traffic congestion in car navigation systems or route search apps is known in which route search information is shared from multiple vehicles and a route is selected for each vehicle to avoid concentrating on the same route.

[0003] There is also technology that shares route information for each vehicle, calculates the passing weight of each route from the current location to the destination, and calculates the expected congestion level based on that, thereby selecting the route that minimizes the total congestion level to the destination. [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Michio Yamashita et al., "Proposal of a cooperative car navigation system for smooth traffic flow", Transactions of Information Processing Society of Japan, Vol. 49, No. 1, 2008 Summary of the Invention [Problem to be solved by the invention]

[0005] However, conventional technologies have the problem of being unable to select an appropriate route while avoiding congested sections. For example, while conventional technologies predict the congestion level of each road, they do not go so far as to compare the predicted congestion level with the critical density of each road to determine whether it is congested or not, which can result in an inability to select an appropriate route.

[0006] Generally, congestion phenomena react sharply when the traffic volume on a road approaches a critical density, and once that point is reached, traffic flow suddenly becomes unstable. Therefore, when selecting a route, it is important not only to predict the degree of congestion, but also to avoid congestion. However, while conventional technologies attempt to predict the degree of road congestion to some extent, they do not distinguish between congestion and non-congestion.

[0007] The present invention has been made in view of the above, and aims to provide a search device, a search method, and a search program that are capable of selecting an appropriate route while avoiding congested sections. [Means for solving the problem]

[0008] In order to solve the above-mentioned problems and achieve the object, the search device of the present invention comprises: a spatio-temporal data collection unit that collects position information of a plurality of vehicles and stores spatio-temporal data including the collected position information and time information including the time of traveling in a first memory unit; a traffic condition prediction unit that predicts traffic density for each road and for each time period based on the position information stored in the first memory unit and stores the predicted traffic density in a second memory unit; a route search unit that, when a search request is received, determines whether each road is congested at a planned traveling time based on the traffic density stored in the second memory unit and searches for route information including roads that are determined not to be congested; and a spatio-temporal data generation unit that uses the route information searched by the route search unit and the planned time to travel on the roads in the route information to generate spatio-temporal data including position information of a vehicle that is assumed to travel a future route based on the route information and time information including the planned time of traveling, and stores the data in the first memory unit. [Effects of the Invention]

[0009] According to the present invention, it is possible to select an appropriate route while avoiding congested sections. [Brief explanation of the drawings]

[0010] [Figure 1]FIG. 1 is a block diagram illustrating the configuration of a search device according to this embodiment. [Figure 2] FIG. 2 is a diagram showing an example of data stored in the traffic flow DB. [Figure 3] FIG. 3 is a diagram showing an outline of the process performed by the search device. [Figure 4] FIG. 4 is a flowchart illustrating an example of a processing procedure performed by the route search unit of the search device. [Figure 5] FIG. 5 is a diagram showing an example of data stored in the traffic flow DB. [Figure 6] FIG. 6 is a flowchart illustrating an example of a processing procedure performed by the spatiotemporal data generating unit of the search device. [Figure 7] FIG. 7 is a flowchart illustrating an example of a processing procedure performed by the spatiotemporal data collection unit of the search device. [Figure 8] FIG. 8 is a flowchart illustrating an example of a processing procedure performed by the traffic condition prediction unit of the search device. [Figure 9] FIG. 9 is a diagram illustrating the polygon counting process. [Figure 10] FIG. 10 is a diagram illustrating a computer that executes a program. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of a search device, a search method, and a search program according to the present application will be described in detail with reference to the accompanying drawings. However, the present invention is not limited to the embodiments described below.

[0012] [Configuration of search device] Fig. 1 is a block diagram illustrating the configuration of a search device according to this embodiment. As illustrated in Fig. 1, search devices 20 according to this embodiment are connected via a counting device 1. Regarding the network configuration shown in Fig. 1, each device may communicate via any communication network, whether wired or wireless, such as the Internet, a LAN, or a VPN (Virtual Private Network). Furthermore, the configuration shown in Fig. 1 is merely an example, and the specific configuration and the number of devices are not particularly limited.

[0013] The counting device 1 also has a spatiotemporal DB 10. The spatiotemporal DB 10 stores a group of data associated with both spatial positions (spatial information) such as latitude and longitude, and temporal information such as time and period. For example, the counting device 1 stores information transmitted by a plurality of vehicles (objects). When the counting device 1 receives a search request from the search device 20, it searches in real time from the stored dynamic objects for dynamic objects that are in a specific area at a certain time, and responds to the search device 20. The counting device 1 may apply, for example, technology from Axispot (registered trademark).

[0014] The search device 20 counts the number of vehicles currently traveling on the road, and also predicts and counts the congestion situation in the near future based on route search information from each vehicle, thereby searching for a route that avoids congestion for each vehicle.

[0015] The search device 20 has a spatiotemporal data collection unit 21, a traffic situation prediction unit 22, a traffic flow DB 23, a route search unit 24, and a spatiotemporal data generation unit 25. Each unit will be described below.

[0016] The spatio-temporal data collector 21 collects position information of a plurality of vehicles and stores spatio-temporal data including the collected position information and time information including the time of travel in the spatio-temporal DB 10. For example, every time the spatio-temporal data collector 21 receives position information periodically transmitted from each vehicle at predetermined intervals, it generates spatio-temporal data representing the vehicle's position information at each time using the position information and time information. Then, the spatio-temporal data collector 21 registers the generated spatio-temporal data in the spatio-temporal DB 10 of the counting device 1.

[0017] The traffic condition prediction unit 22 predicts the traffic density for each time period for each road based on the position information stored in the time-space DB 10, and stores the predicted traffic density in the traffic flow DB 23. For example, the traffic condition prediction unit 22 counts the future number of vehicles for each road based on the position information of vehicles assumed to travel on a future route, and predicts the traffic density based on the counted number of vehicles, the section length of the road, and the number of lanes, and stores the predicted traffic density in the traffic flow DB 23. Here, the traffic condition prediction unit 22 calculates, as the traffic density, for example, the number of vehicles for a certain road at a certain time divided by the value obtained by multiplying the section length of the road and the number of lanes (counted value / (section length·number of lanes)).

[0018] The traffic flow DB 23 stores the traffic density predicted for each road and for each time period. For example, as shown in FIG. 2, the traffic flow DB 23 stores a "road ID" that uniquely identifies the road, a "time" that indicates the time, a "number" that indicates the number of vehicles, a "traffic density" predicted by the traffic condition prediction unit 22, the "distance" of the road, the "number of lanes" of the road, and a "critical density" of the road, in association with each other. Here, the "critical density" is a value that indicates a predetermined vehicle density for each road and is a value that determines whether or not there is congestion. The critical density is, for example, a value obtained by dividing the number of vehicles allowed on each road by a value obtained by multiplying the road section length by the number of lanes.

[0019] When receiving a search request, the route search unit 24 determines whether each road is congested at the scheduled travel time based on the traffic density stored in the traffic flow DB 23, and searches for route information including roads determined not to be congested. For example, the route search unit 24 compares the traffic density of each road stored in the traffic flow DB 23 with a preset critical density for each road, determines that a road with a traffic density less than the critical density is not congested, and searches for route information including roads determined not to be congested.

[0020] The spatio-temporal data generation unit 25 uses the route information searched by the route search unit 24 and the planned time for traveling on the roads in the route information to generate spatio-temporal data including position information of a vehicle assumed to travel on a future route based on the route information and time information including the planned time for traveling, and stores the generated spatio-temporal data in the spatio-temporal DB 10. For example, the spatio-temporal data generation unit 25 uses the route information searched by the route search unit 24 and the planned time for traveling on the roads in the route information to extract position information of a vehicle assumed to travel on a future route based on the route information, generates spatio-temporal data including the extracted position information and time information including the planned time for traveling, and stores the generated spatio-temporal data in the spatio-temporal DB 10.

[0021] Here, an overview of the processing by the search device 20 will be explained using Fig. 3. Fig. 3 is a diagram showing an overview of the processing by the search device. When the search device 20 receives a route search request from a vehicle or the like, it refers to the traffic conditions in the traffic flow DB 23 and determines a route that avoids congestion (see (1) in Fig. 3). Then, the search device 20 generates spatio-temporal data by assuming the travel of vehicles on the route, and registers this together with the current time in the spatio-temporal DB 10 (see (2) in Fig. 3). Next, the search device 20 tallies the number of vehicles for each road and each time, calculates the traffic density, and registers it in the traffic flow DB 23 (see (3) in Fig. 3).

[0022] In this way, the search device 20 aggregates the position information of each vehicle currently traveling as spatiotemporal data, and also samples the position information that each vehicle is expected to pass through while traveling from the route information obtained by the search, and aggregates it together as spatiotemporal data for the near future.The search device 20 then aggregates the spatiotemporal data to estimate the congestion situation in the near future, and selects a route by determining whether there is congestion or not, and avoiding congested sections.

[0023] [Search device processing procedure] Next, an example of the processing procedure of the processing executed by the search device 20 will be described with reference to Fig. 4 to Fig. 9. First, the processing of the route search unit 24 will be described with reference to Fig. 4. Fig. 4 is a flowchart showing an example of the processing procedure by the route search unit of the search device.

[0024] 4, the route search unit 24 of the search device 20 receives a route search request from an in-vehicle device of a vehicle or the like (step S101). For example, the route search unit 24 receives the search request along with the location information of the departure point and the destination point, and the desired time of departure or arrival from the in-vehicle device.

[0025] Then, the route search unit 24 tentatively selects a route that allows travel from the departure point to the destination in the shortest time (step S102). Here, the route search unit 24 does not take into account congestion, and calculates travel at a constant speed that is arbitrarily set in advance for each type of road, such as an expressway or an ordinary road.

[0026] Next, the route search unit 24 assumes travel along the provisionally selected route and tracks the roads on which the vehicle is expected to travel at regular time intervals P (step S103). Then, the route search unit 24 refers to the traffic flow DB for the road on which the vehicle is traveling at time T, and compares the traffic density in that time period with the critical density (step S104).

[0027] If the route search unit 24 determines that the traffic density value is equal to or greater than the critical density value (No at step S105), it determines that the road is congested, avoids that road, and selects a subsequent route (step S106), and returns to step S104. For example, in the example of Fig. 5, the route search unit 24 determines that the road ID "002-01" is a congested section and excludes it because the traffic density "16" for the time "09:10" on the road ID "002-01" exceeds the critical density "15". Fig. 5 is a diagram showing an example of data stored in the traffic flow DB.

[0028] Furthermore, if the route search unit 24 determines that the traffic density value is less than the critical density value (Yes in step S105), it selects the route, adds P to T (if the desired departure time was specified when the search request was accepted in step S101) or subtracts P (if the desired arrival time was specified when the search request was accepted in step S101), and sets the resulting time as T (step S107), and determines whether the road along which the route is planned to be traveled exists on the route at time T (step S108). As a result, if the road exists on the route (Yes in step S108), the route search unit 24 returns to the processing of step S104.

[0029] Furthermore, if the road along which the vehicle is to travel does not exist on the route at time T (No at step S108), the route search unit 24 returns the searched route to the in-vehicle device that issued the search request (step S109), and passes the searched search information and time information to the spatiotemporal data generation unit 25 (step S110). In this way, in response to the search request, the route search unit 24 refers to the traffic flow DB 23, calculates a route taking into account the traffic conditions, and returns the result while passing it to the spatiotemporal data generation unit 25.

[0030] Next, the processing of the spatio-temporal data generation unit 25 will be described with reference to Fig. 6. Fig. 6 is a flowchart showing an example of a processing procedure by the spatio-temporal data generation unit of the search device. As illustrated in Fig. 6, the spatio-temporal data generation unit 25 receives route information and time information from the route search unit 24 (step S201), extracts position information of points that the vehicle is expected to pass through at time T while traveling along the route, and generates spatio-temporal data (step S202).

[0031] The spatio-temporal data generation unit 25 then registers the generated spatio-temporal data in the spatio-temporal DB 10 (step S203), sets the time T obtained by adding P to the time T (step S204), and determines whether the destination has been reached (step S205). As a result, if the spatio-temporal data generation unit 25 determines that the destination has not been reached (No in step S205), the process returns to step S202. On the other hand, if the spatio-temporal data generation unit 25 determines that the destination has been reached (Yes in step S205), the process ends.

[0032] In this way, the spatiotemporal data generation unit 25 assumes that the vehicle will travel along the searched route information, samples the position information of points that the vehicle is expected to pass through at regular time intervals, generates spatiotemporal data, and registers the data in the spatiotemporal DB 10. Note that the vehicle travels at a constant speed that is arbitrarily set in advance for each type of road, such as an expressway or an ordinary road, provided that the road is not congested.

[0033] Next, the processing of the spatio-temporal data collection unit 21 will be described with reference to Fig. 7. Fig. 7 is a flowchart showing an example of a processing procedure by the spatio-temporal data collection unit of the search device. As illustrated in Fig. 7, when a regular data collection time arrives (step S301), the spatio-temporal data collection unit 21 receives position information transmitted from each vehicle (step S302).

[0034] Then, the spatio-temporal data collector 21 generates spatio-temporal data representing the position information of the vehicle at each time, based on the position information and time information (step S303). Then, the spatio-temporal data collector 21 registers the generated spatio-temporal data in the spatio-temporal DB 10 of the counting device 1 (step S304). In this way, the spatio-temporal data collector 21 collects current position information of each vehicle at regular time intervals, generates spatio-temporal data, and registers it in the spatio-temporal DB 10. It is assumed that each vehicle is permitted to transmit its position information to the search device 20.

[0035] Next, the processing of the traffic condition prediction unit 22 will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of a processing procedure by the traffic condition prediction unit of the search device. As illustrated in Fig. 8, the traffic condition prediction unit 22 simultaneously performs polygon aggregation for the search target area at search times every fixed time interval P within a future aggregation time range (step S401). For example, as illustrated in Fig. 9, for the search target area (x0, y0) to (x1, y1), polygon aggregation is simultaneously performed at search times t0 to t3 every fixed time interval P within a future aggregation time range R, and aggregation for four time periods is performed in parallel.

[0036] Then, the traffic condition prediction unit 22 outputs the aggregated value for each road (polygon) and each time period (step S402). Note that the aggregated value at t0 is the actual aggregated value, and the aggregated values ​​from t1 onwards are future times and are therefore treated as provisional aggregated values.

[0037] Then, the traffic condition prediction unit 22 corrects the total sum of the counted values ​​at t0, i.e., the total number of vehicles currently traveling in the entire search area, while maintaining the overall proportion (step S403). Subsequently, the traffic condition prediction unit 22 calculates the total sum / (section length x number of lanes) for each road (polygon) and each time period to obtain the traffic density (step S404).

[0038] Then, the traffic condition prediction unit 22 registers and updates the aggregated value and traffic density for each road (polygon) and each time period in the traffic flow DB 23 (step S405), advances time by P (step S406), and returns to step S401. In this way, the traffic condition prediction unit 22 periodically repeats polygon aggregation for the data from the present to the future in the time-space DB 10, calculates the traffic density for each road (polygon), and updates the traffic flow DB 23.

[0039] [Effects of the embodiment] As described above, the search device 20 according to the embodiment collects position information of a plurality of vehicles and stores spatio-temporal data including the collected position information and time information including the time of travel in the spatio-temporal DB 10. Then, the search device 20 predicts the traffic density for each road and for each time period based on the position information stored in the spatio-temporal DB 10 and stores the predicted traffic density in the traffic flow DB 23. Next, upon receiving a search request, the search device 20 determines whether each road is congested at the planned travel time based on the traffic density stored in the traffic flow DB 23, searches for route information including roads determined not to be congested, and generates spatio-temporal data including the position information of a vehicle assumed to travel a future route based on the route information and time information including the planned time of travel, and stores the data in the spatio-temporal DB 10. This enables the search device 20 to select an appropriate route while avoiding congested sections.

[0040] For example, the search device 20 predicts and samples information on "what time and where a vehicle will pass" from route information collected from vehicles currently in motion at regular time intervals, and aggregates and compiles the data as spatiotemporal data to determine whether there will be congestion or no congestion in the near future. Therefore, the search device 20 specifically estimates the traffic volume in the near future based on route information from vehicles currently in motion, determines whether there will be congestion or no congestion, and detects a route that can be traveled in the shortest time while avoiding congested sections, thereby increasing the reliability of actually being able to avoid congestion on that route compared to conventional methods.

[0041] [System configuration, etc.] The components of each device shown in the drawings according to the above embodiments are conceptual functional units and do not necessarily have to be physically configured as shown. In other words, the specific form of distribution and integration of each device is not limited to that shown, and all or part of each device can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. Furthermore, all or any part of the processing functions performed by each device can be realized by a CPU and a program analyzed and executed by the CPU, or can be realized as hardware using wired logic.

[0042] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using a known method.In addition, the information including the processing procedures, control procedures, specific names, various data and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified.

[0043] 〔program〕 It is also possible to create a program written in a computer-executable language that executes the processes executed by the search device 20 described in the above embodiment. In this case, the same effects as those of the above embodiment can be obtained by having a computer execute the program. Furthermore, such a program may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read and executed by a computer to realize processes similar to those of the above embodiment.

[0044] 10 is a diagram showing a computer that executes a program. As shown in the example of FIG. 10, a computer 1000 includes, for example, a memory 1010, a CPU 1020, a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070, and these components are connected by a bus 1080.

[0045] As shown in FIG. 10, the memory 1010 includes a ROM (Read Only Memory) 1011 and a RAM 1012. The ROM 1011 stores a boot program such as a BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to a hard disk drive 1090 as shown in FIG. 10. The disk drive interface 1040 is connected to a disk drive 1100 as shown in FIG. 10. A removable storage medium such as a magnetic disk or optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to a mouse 1110 and a keyboard 1120 as shown in FIG. 10. The video adapter 1060 is connected to a display 1130 as shown in FIG. 10.

[0046] 10, the hard disk drive 1090 stores, for example, an OS 1091, an application program 1092, a program module 1093, and program data 1094. That is, the above programs are stored, for example, on the hard disk drive 1090 as program modules in which instructions to be executed by the computer 1000 are written.

[0047] The various data described in the above embodiment are stored as program data, for example, in the memory 1010 or the hard disk drive 1090. The CPU 1020 then reads the program module 1093 and the program data 1094 stored in the memory 1010 or the hard disk drive 1090 into the RAM 1012 as needed, and executes various processing procedures.

[0048] Note that the program module 1093 and program data 1094 related to the program are not limited to being stored in the hard disk drive 1090, and may be stored in, for example, a removable storage medium and read by the CPU 1020 via a disk drive or the like. Alternatively, the program module 1093 and program data 1094 related to the program may be stored in another computer connected via a network (such as a LAN (Local Area Network) or WAN (Wide Area Network)) and read by the CPU 1020 via the network interface 1070.

[0049] Although the present invention has been described above as an embodiment, the present invention is not limited to the description and drawings that form part of the disclosure of the present invention. In other words, other embodiments, examples, and operational techniques that can be made by those skilled in the art based on the present invention are all included in the scope of the present invention. [Explanation of symbols]

[0050] 1. Counting device 10 Space-time DB 20 Search Device 21 Spatiotemporal Data Collection Unit 22 Traffic Condition Prediction Department 23 Traffic flow DB 24 Route search section 25 Spatiotemporal Data Generation Unit

Claims

1. a spatio-temporal data collection unit that collects position information of a plurality of vehicles and stores spatio-temporal data including the collected position information and time information including the time of travel in a first storage unit; a traffic situation prediction unit that predicts traffic density for each road and for each time period based on the location information stored in the first storage unit, and stores the predicted traffic density in a second storage unit in association with a critical density that is a value indicating a preset allowable vehicle density for each road; a route search unit that, when receiving a search request, determines whether or not each road is congested at the scheduled travel time based on the traffic density stored in the second storage unit, and searches for route information including roads that are determined not to be congested; a spatio-temporal data generating unit that generates spatio-temporal data including position information of a vehicle that is assumed to travel a future route based on the route information and time information including a scheduled time of travel, using the route information searched by the route search unit and a scheduled time for traveling on the roads of the route information, and stores the generated spatio-temporal data in the first storage unit; and The route search unit compares the traffic density of each road stored in the second memory unit with the critical density of each road stored in the second memory unit, and determines that a road whose traffic density is less than the critical density is not congested.

2. The search device according to claim 1, characterized in that the traffic condition prediction unit counts the number of future vehicles for each road based on position information of vehicles assumed to travel on a future route, and predicts the traffic density based on the counted number of vehicles, the section length of the road, and the number of lanes.

3. 2. The search device according to claim 1, wherein the spatio-temporal data generation unit extracts position information of a vehicle that is assumed to travel a future route based on the route information, using the route information searched by the route search unit and a planned time for traveling on the roads in the route information, generates spatio-temporal data including the extracted position information and time information including a planned time for traveling, and stores the generated data in the first storage unit.

4. A search method executed by a search device, comprising: a collection step of collecting position information of a plurality of vehicles and storing spatiotemporal data including the collected position information and time information including the time of travel in a first storage unit; a prediction step of predicting traffic density for each time period for each road based on the location information stored in the first storage unit, and storing the predicted traffic density in a second storage unit in association with a critical density, which is a value indicating a preset allowable vehicle density for each road; a search step of, when receiving a search request, determining whether or not each road is congested at the scheduled travel time based on the traffic density stored in the second storage unit, and searching for route information including roads determined not to be congested; a generation step of generating spatiotemporal data including position information of a vehicle assumed to travel a future route based on the route information and time information including a scheduled time of travel, using the route information searched in the search step and a scheduled time for travel along the roads of the route information, and storing the data in the first storage unit; Including, The route search unit compares the traffic density of each road stored in the second memory unit with the critical density of each road stored in the second memory unit, and determines that a road whose traffic density is less than the critical density is not congested.

5. A search program for causing the search device according to any one of claims 1 to 3 to function.

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