KR20120007844A - Apparatus and method of search for vehicle route using predicted traffic volume - Google Patents

Abstract

PURPOSE: A vehicle route searching device, using predicted passage quantity of vehicles and a method thereof are provided to prevent global warming by reducing fuel consumption. CONSTITUTION: A vehicle route searching device, using predicted passage quantity of vehicles comprises a receiver(410), a traffic amount predicting part(420), a correlation coefficient computation part(430), a distance detection part(440), and a storage(460). The receiver receives traffic information and geographic information. The traffic information comprises the route information to the destination, a real-time average traffic amount and average velocity. The traffic amount predicting part predicts traffic per sections from the routing information. The correlation coefficient computation part computes correlation coefficient per sections from the traffic information. The distance detection part extracts the distance per sections from the geographic information. The storage stores at least one of the predicted traffic amount, the calculated correlation coefficient, and the extracted distance.

Description

Apparatus and method of search for vehicle route using predicted traffic volume}

The present invention relates to a vehicle route search apparatus using the predicted traffic volume of the vehicle and a method thereof, and more particularly, a vehicle using the predicted traffic volume to search for an optimal vehicle route by predicting traffic flow using future data. A route searching device and a method thereof are provided.

Conventional route information using a traffic information system used a modeling technique using current traffic flow or historical data, a predictive route using a pattern, and the like. As a method of predicting traffic congestion using real-time traffic information, there is a system that predicts future traffic congestion using modeling technique while continuously updating real-time traffic information. That is, traffic congestion was predicted by inputting the average speed of a specific vehicle in a certain time unit, analyzing a cumulative speed pattern by time and day of the week, or using a Kalman filter method.

The traffic congestion prediction method using a pattern includes a system for predicting future traffic congestion by patterning past traffic flows. For example, a traffic congestion table was prepared for each time zone by patterning the north bank of the Han River.

As such, the prior art has difficulty in providing an optimized route by using only current and historical data and not using a predicted traffic volume in predicting future traffic information. In particular, when there is a big difference from the past traffic flow patterns (for example, holidays, weekends, special circumstances due to bad weather, etc.), traffic information prediction has a problem that shows a great difference from reality.

The present invention provides a vehicle route search apparatus using the predicted traffic volume of a vehicle and a method thereof so as to search an optimal vehicle route by predicting traffic flow using future data.

The vehicle route search server using the predicted traffic volume for achieving the above object comprises: a receiving unit for receiving traffic information and geographic information including route information, real-time average traffic volume and average speed from one or more vehicles to a destination; A traffic amount prediction unit for predicting a traffic volume for each road section from the route information; A correlation coefficient calculation unit for calculating a correlation coefficient for each road section from the traffic information; A distance extracting unit which extracts a distance for each road section from the geographic information; And a storage unit which stores at least one of the estimated traffic volume for each road section, the calculated correlation coefficient for each road section, and the extracted distance for each road section. It includes.

Preferably, the operation time estimating unit for estimating the driving time required for each road section using the estimated traffic volume for each road section, the calculated correlation coefficient for each road section and the extracted distance for each road section; It further includes.

The receiving unit may further include a candidate path selecting unit configured to further receive a path search request and to select one or more candidate paths corresponding to the path search request; A route search unit for calculating the total travel time of each candidate route by summing the estimated travel time for each of the road segments for at least one road segment included in each candidate route; And a transmitter for transmitting each candidate path and the calculated total travel time of each candidate path. It further includes.

On the other hand, the vehicle route search terminal using the predicted traffic volume according to the present invention, the input unit for receiving a route search request from the user; A route selection unit for selecting one or more candidate routes according to the route search request; A wireless communication unit for retrieving and receiving a request from a server for predicted traffic volume, correlation coefficient, and distance for each road section for at least one road section included in each candidate path; A time estimating unit estimating driving time required for each road section using the received traffic volume, correlation coefficient, and distance for each road section; And a search unit for calculating the total travel time of each candidate path by summing the estimated travel time for each road section for one or more road sections included in each candidate path. It includes.

Preferably, the route selecting unit selects the least one of the candidate road sections among the candidate routes.

In addition, the display unit for displaying the respective candidate paths and the calculated total travel time of each candidate path; It further includes.

On the other hand, the vehicle route search method using the predicted traffic according to the present invention, the server for searching the route of the vehicle using the predicted traffic, the step of receiving route information from one or more vehicles to the destination; Receiving traffic information including real time average traffic volume and average speed; Receiving geographic information; Predicting traffic volume for each road section from the route information; Calculating a correlation coefficient for each road section from the traffic information; Extracting a distance for each road section from the geographic information; And storing at least one of the estimated traffic volume for each road section, the calculated correlation coefficient for each road section, and the extracted distance for each road section; It includes.

Preferably, estimating the driving time required for each road section using the estimated traffic volume for each road section, the calculated correlation coefficient for each road section, and the extracted distance for each road section; It further includes.

In addition, receiving a route search request; Selecting one or more candidate paths corresponding to the path search request; Calculating total travel time for each candidate path by summing the estimated travel time for each road section for one or more road sections included in each candidate path; And transmitting each candidate path and the calculated total travel time of each candidate path. It further includes.

On the other hand, the vehicle route search method using the predicted traffic according to the present invention, the terminal for searching the route of the vehicle using the predicted traffic, the step of receiving a route search request from the user; Selecting one or more candidate paths according to the path search request; Retrieving and receiving a request from a server for traffic volume, correlation coefficient, and distance for each road section for at least one road section included in each candidate path; Estimating travel time for each road section using the received traffic volume, correlation coefficient, and distance; Calculating total travel time of each candidate route by summing the estimated travel time for each road segment for at least one road segment included in each candidate route; It includes.

Preferably, the step of selecting the candidate path, characterized in that the priority is selected to the minimum of the road section of each candidate path.

The method may further include displaying each candidate path and the calculated total travel time of each candidate path; It further includes.

According to the present invention, it is possible to increase the user's convenience by predicting future traffic volume and searching for a vehicle route using the predicted traffic volume to provide an optimal route to the user.

In addition, by effectively utilizing the limited resources of the road, it is possible to expect the effects of saving fuel costs, time savings, etc., as well as preventing global warming due to fuel savings.

1 is a diagram showing a departure point, an arrival point, a waypoint, a road section, a time required for each road section, and the like.
2 is a block diagram of a vehicle route search server using a predicted traffic volume of a vehicle.
3 is a block diagram of a vehicle route search terminal using a predicted traffic volume of a vehicle.
4 is a flowchart of a method of a server searching a path of a vehicle by using a predicted traffic volume.
5 is a flowchart illustrating a method in which a terminal searches a route of a vehicle using a predicted traffic volume.

Advantages and features of the present invention and methods for achieving them will be apparent with reference to the embodiments described below in detail with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but will be implemented in various forms, and only the present embodiments are intended to complete the disclosure of the present invention, and the general knowledge in the art to which the present invention pertains. It is provided to fully convey the scope of the invention to those skilled in the art, and the present invention is defined only by the scope of the claims. It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification. As used herein, “comprises” and / or “comprising” refers to the presence of one or more other components, steps, operations and / or elements. Or does not exclude additions. In addition, detailed descriptions of well-known functions and configurations that may unnecessarily obscure the subject matter of the present invention will be omitted.

1 is a diagram showing a departure point, an arrival point, a waypoint, a road segment, and a time required for each road segment; FIG. 2 is a block diagram of a vehicle route search server using a predicted traffic volume of the vehicle; and FIG. 4 is a flowchart illustrating a method of a server searching for a route of a vehicle using a predicted traffic volume, and FIG. 5 is a diagram of a method of searching for a route of a vehicle using a predicted traffic volume. It is a flow chart.

The term of FIG. 1 is defined as follows for explanation of the present invention. n1 (node 1) is the starting point, n4 (node 4) is the arrival point, and n2 (node 2) and n3 (node 3) are the waypoints between the starting point and the arrival point. S1 (Street 1) represents the road segment between starting point n1 and waypoint n2, S2 (Street 2) means waypoint n1 and waypoint n2, S3 (Street 3) means waypoint n2 and n3, and S4 (Street 4) waypoint n2 and destination points n4 and S5 (Street 5) indicate a road section between waypoint n3 and destination point n4. t1 means n1, that is, time at the starting point, t2 means time at waypoint n2, t3 means time at waypoint n3, and t4 means time at destination point n4.

The terms used in the present invention are defined as follows.

Average traffic volume: Average Traffic Volume (ATV)

Predicted Traffic Volume (PTV)

Vehicle average passage speed: AV (Average Velocity)

Predicted Velocity (PV): Predicted Velocity

Average time taken to cross the road segment: AT (Average Time)

Estimated time to pass the road segment: PT (Predicted Time)

Referring to FIG. 2, a server searching a route of a vehicle using predicted traffic according to the present invention includes a receiver 410, a traffic predictor 420, a correlation coefficient calculator 430, a distance extractor 440, The driving time estimator 450, the storage unit 460, the candidate route selecting unit 470, the path search unit 480, and the transmitter 490 are included.

The receiver 410 receives route information from one or more vehicles from a starting point to a destination point. In addition, the receiver 410 receives traffic information including a real-time traffic volume and an average speed. Such traffic information may be received from a probe vehicle or a Transport Protocol Experts Group (TPEG). In addition, the receiving unit 410 receives geographic information. Such geographic information may be obtained from a GPS (Global Positioning System).

The traffic estimator 420 predicts the predicted traffic amount for each road section from the route information received by the receiver 410. In this case, a statistical technique may be used. Since the starting point, waypoint and destination point can be grasped from the route information received by the receiving unit 410, it is possible to predict the traffic volume for each road section. For example, as a result of collecting route information from 100 vehicles having a starting point A, it is assumed that there are 80 vehicles having a destination B, 10 vehicles having a C, and 10 vehicles having a D. In this case, when there are 1000 vehicles starting from the actual starting point A, 800 vehicles having the arrival point B, 100 vehicles having C, and 100 vehicles having D can be predicted. That is, since the route information cannot be collected from all the vehicles, the traffic volume is predicted using statistical techniques together.

The correlation coefficient calculation unit 430 calculates a correlation coefficient for each road segment from the real-time average traffic volume and average speed. First, it can be defined that the following correlation coefficient between the average traffic volume (ATV) of the vehicle and the average passage speed (AV) of the vehicle.

t: time

AV _Sn (t): Vehicle average travel speed at road section Sn

α _{Sn :} Correlation coefficient at road section Sn

ATV _Sn (t): Average traffic on road section Sn

For example, the correlation coefficient α _S5 in the road section S5 can be calculated by the following equation.

The distance extractor 440 extracts a distance for each road section from the geographic information received by the receiver 410. That is, distance information for each road section between the starting point, each waypoint and the arrival point is extracted from the geographic information.

The running time estimator 450 estimates the driving time required for each road section by using the estimated traffic volume for each road section, the calculated correlation coefficient for each road section, and the extracted distance for each road section.

Referring to FIG. 1, the traffic volume flowing into the road section S5 may be the sum of the traffic volume of S2 and the traffic volume of S3, and may be expressed as follows.

S25: Traffic volume flowing from road segment S2 to road segment S5

S35: Traffic volume flowing from road segment S3 to road segment S5

Based on the above equation, the estimated traffic volume at road segment S5 can be obtained as follows.

Referring to Equation 1, the following equation is established.

Developing Equation 4 using this can be arranged as follows.

The average time to pass through the road section Sn is equal to the distance of the road section Sn divided by the average speed of the vehicle. This is expressed as follows.

D _Sn : Distance of road section Sn

Therefore, the travel time required to pass through the road section Sn can be estimated as follows.

The candidate path selecting unit 470 selects one or more candidate paths Rn from the destination point and the starting point information. Referring to FIG. 1, when the starting point is n1 and the destination point is n4, the candidate path R that can go from the starting point to the destination point is as follows. Candidate Route R1 (Routes S1 and S4), Candidate R2 (Routes S1, S3, and S5), Candidate R3 (Routes S2, S5), and Candidate R4 (Road Segments) Paths through S2, S3, and S4). Each candidate path includes one or more road segments. In this case, it is preferable to first select the one of the one or more candidate paths with the minimum road section to search the path. That is, in the above-described example, it is preferable to first select R1 and R3 having a minimum road section among candidate paths R1, R2, R3, and R4.

The storage unit 460 stores at least one of the estimated traffic volume for each road section, the calculated correlation coefficient for each road section, and the extracted distance for each road section. In addition, the estimated driving time for each road segment can be stored.

Next, the route search unit 480 calculates a travel time of each candidate route Rn selected by the candidate route selector 470. Since the candidate route Rn includes one or more road segments, it is calculated by summing the time required for each road segment. For example, when the prediction time of the entire path is calculated for the candidate paths R1 and R3, the following equation is obtained.

Here, the arrival prediction time t2 of the road segment S1 may be expressed as follows.

The general formula for calculating the total travel time of each candidate route Ri is as follows.

Where t _Sk is the time from Sk. For example, t _Sk = 0 when starting Sk now, and t _Sk = 10 when starting Sk after 10 seconds.

In addition, it is preferable to select as the optimal candidate path the minimum total travel time among one or more candidate paths. If this is expressed as an expression, it is as follows.

The transmitter 490 transmits one or more candidate paths and the calculated total travel time of each candidate path. At this time, it is preferable to transmit the information on the total running time of the optimal candidate path and the optimal candidate path described above.

Referring to FIG. 3, a terminal for searching a vehicle route using predicted traffic according to the present invention includes an input unit 510, a route selecting unit 520, a wireless communication unit 530, a time estimation unit 540, and a searching unit. 550, and a display unit 560.

The input unit 510 receives a path search request from a user. The user enters a starting point and a destination point and requests a route search.

The route selecting unit 520 selects one or more candidate paths from the starting point and destination point information received by the input unit 510. Each candidate path includes one or more road segments. In this case, it is preferable to first select the minimum of the road section among each candidate path. The selection of the candidate path by the path selecting unit 520 is the same as selecting the candidate path by the candidate path selecting unit 470.

The wireless communication unit 530 requests the server for information on the predicted traffic volume, correlation coefficient, and distance for each road section for one or more road sections included in each candidate path, and receives the information.

The time estimator 540 estimates the travel time for each road section by using the estimated traffic volume, correlation coefficient, and distance information for each road section received by the wireless communication unit 530. Estimation of the driving time required by the road section of the time estimation unit 540 is the same as estimating the driving time required by the road section by the driving time estimator 450.

The search unit 550 calculates the total travel time of each candidate path by summing operation time for each road section estimated by the time estimation unit 540 for one or more road sections included in each candidate path. Computation of the total travel time of each candidate path of the search unit 550 is the same as that of calculating the total travel time of each candidate path of the path search unit 480.

The display unit 560 shows each candidate route and the calculated total travel time of each candidate route. In this case, it is preferable to display the optimal candidate path and the optimal candidate path total operation time with the minimum total travel time.

Referring to FIG. 4, in the vehicle route search method using the predicted traffic volume according to the present invention, a server searching for a route of the vehicle using the predicted traffic volume first receives route information from one or more vehicles to a destination (S205). .

Next, the server receives the traffic information including the real-time average traffic volume and the average speed (S210). Real-time traffic information can be received from a vehicle or the like.

Next, the server receives geographic information (215). Geographic information can be received from a GPS or the like.

Next, the server receives a route search request from the terminal (S220).

Next, the server predicts the traffic volume for each road section from the route information (S225). At this time, the traffic volume prediction for each road section is the same as predicting the traffic volume in the traffic prediction unit 420.

Next, the server calculates a correlation coefficient for each road segment from the real-time average traffic volume and average speed (S230). In this case, the correlation coefficient calculation for each road section is the same as calculating the correlation coefficient for each road section in the correlation coefficient calculation unit 430.

Next, the server extracts the distance for each road section from the geographic information (S235).

Next, the server estimates the driving time required for each road section using the estimated traffic volume for each road section, the calculated correlation coefficient for each road section, and the extracted distance for each road section (S240). In this case, the estimation of the driving time required for each road section is the same as estimating the driving time for each road section by the driving time estimator 450.

Next, the server stores at least one of the estimated traffic volume for each road section, the calculated correlation coefficient for each road section, and the extracted distance for each road section (S245). In addition, the estimated travel time for each road segment can be stored.

Next, the server selects one or more candidate paths according to the path search request (S250). Each candidate path includes one or more road segments. In this case, it is preferable to first select the one of the one or more candidate paths with the minimum road section to search the path. In this case, selecting the candidate path is the same as selecting the candidate path by the candidate path selecting unit 470.

Next, the server calculates the total travel time required for each candidate path by summing the estimated travel time for each road section for one or more road sections included in each candidate path (S255). In this case, the total travel time calculation of each candidate path is the same as that of the path search unit 480 to calculate the total travel time of each candidate path.

Next, the server selects one of the one or more candidate paths having the minimum total travel time as the optimal candidate path (S260).

Next, the server transmits each candidate path and the calculated total running time of each candidate path (S265). In this case, it is preferable to transmit the total candidate travel time of the optimal candidate path and the optimal candidate path.

Referring to FIG. 5, in the vehicle route search method using the predicted traffic volume according to the present invention, a terminal searching for a route of the vehicle using the predicted traffic volume first receives a route search request from a user (S310). The user enters a starting point and a destination point and requests a route search.

Next, the terminal selects one or more candidate paths from the starting point and destination point information input from the user (S320). Each candidate path includes one or more road segments. In this case, it is preferable to first select the minimum of the road section among each candidate path. At this time, the selection of the candidate path is the same as selecting the candidate path in the path selection unit 520.

Next, the terminal requests the server for information on the estimated traffic volume, correlation coefficient, and distance for each road section, for one or more road sections included in each candidate path, and receives the information (S330).

Next, the terminal estimates the driving time required for each road section using the received traffic volume, correlation coefficient, and distance information for each road section (S340). In this case, the estimation of the driving time required for each road section is the same as estimating the driving time for each road section in the time estimation unit 540.

Next, the terminal calculates the total travel time of each candidate path by summing the estimated travel time for each road section for one or more road sections included in each candidate path (S350). At this time, the total travel time calculation of each candidate path is the same as that of the search unit 550 to calculate the total travel time of each candidate path.

Next, the terminal selects as the optimal candidate path the minimum total travel time among one or more candidate paths (S360).

Next, the terminal displays each candidate route and the calculated total travel time of each candidate route (S370). In this case, it is preferable to display the optimal candidate path and the total candidate travel time together.

According to an embodiment of the present invention, the server may estimate the travel time for each road segment, select a candidate route, and calculate a total travel time of each candidate route. In addition, in another embodiment according to the present invention, the terminal may estimate the driving time for each road segment, select a candidate route, and calculate a total running time of each candidate route.

While the above has been shown and described with respect to preferred embodiments and applications of the present invention, the present invention is not limited to the specific embodiments and applications described above, the invention without departing from the gist of the invention claimed in the claims Various modifications can be made by those skilled in the art, and these modifications should not be individually understood from the technical spirit or the prospect of the present invention.

410: receiver 420: traffic prediction unit
430: correlation coefficient calculation unit 440: distance extraction unit
450: running time estimation unit 460: storage unit
470: candidate path selection unit 480: path search unit
490: transmitting unit
510: input unit 520: path selection
530: wireless communication unit 540: time estimation
550: search unit 560: display unit

Claims (12)

Receiving unit for receiving traffic information and geographic information, including route information, real-time average traffic volume and average speed from one or more vehicles to the destination;
A traffic amount prediction unit for predicting a traffic volume for each road section from the route information;
A correlation coefficient calculation unit for calculating a correlation coefficient for each road section from the traffic information;
A distance extracting unit which extracts a distance for each road section from the geographic information; And
A storage unit which stores at least one of the estimated traffic volume for each road section, the calculated correlation coefficient for each road section, and the extracted distance for each road section; Vehicle route search server using a predicted traffic including a.
The method of claim 1,
A driving time estimating unit estimating a driving time required for each road section by using the estimated traffic volume for each road section, the calculated correlation coefficient for each road section, and the extracted distance for each road section; Vehicle path search server using the predicted traffic, characterized in that it further comprises.
The method of claim 1,
The receiving unit further receives a route search request,
A candidate path selecting unit selecting one or more candidate paths corresponding to the path search request;
A route search unit for calculating total travel time of each candidate route by summing the estimated travel time for each of the estimated road sections with respect to at least one road section included in each candidate route; And
A transmitter for transmitting each candidate path and the calculated total travel time of each candidate path; Vehicle path search server using the predicted traffic, characterized in that it further comprises.
An input unit for receiving a route search request from a user;
A route selection unit for selecting one or more candidate routes according to the route search request;
A wireless communication unit for retrieving and receiving a request from a server for predicted traffic volume, correlation coefficient, and distance for each road section for at least one road section included in each candidate path;
A time estimating unit estimating driving time required for each road section using the received traffic volume, correlation coefficient, and distance for each road section; And
A search unit configured to calculate the total travel time of each candidate path by summing the estimated travel time for each road section for at least one road section included in each candidate path; Vehicle path search terminal using the predicted traffic, characterized in that it comprises a.
The method of claim 4, wherein
The path selection,
The vehicle path search terminal using the predicted traffic amount is selected in priority to the road section among the candidate paths.
The method of claim 4, wherein
A display unit for displaying the candidate paths and the calculated total travel time of each candidate path; Vehicle path search terminal using the predicted traffic volume, characterized in that it further comprises.
The server that searches for the route of the vehicle using the predicted traffic volume,
Receiving route information from one or more vehicles to a destination;
Receiving traffic information including real time average traffic volume and average speed;
Receiving geographic information;
Predicting traffic volume for each road section from the route information;
Calculating a correlation coefficient for each road section from the traffic information;
Extracting a distance for each road section from the geographic information; And
Storing at least one of the estimated traffic volume for each road section, the calculated correlation coefficient for each road section, and the extracted distance for each road section; Vehicle route search method using a predicted traffic including a.
The method of claim 7, wherein
Estimating driving time required for each road section using the estimated traffic volume for each road section, the calculated correlation coefficient for each road section, and the extracted distance for each road section; Vehicle route search method using a predicted traffic, characterized in that it further comprises.
The method of claim 7, wherein
Receiving a route search request;
Selecting one or more candidate paths corresponding to the path search request;
Calculating total travel time for each candidate path by summing the estimated travel time for each road section for one or more road sections included in each candidate path; And
Transmitting the candidate paths and the calculated total travel time of each candidate path; Vehicle route search method using a predicted traffic, characterized in that it further comprises.
The terminal for searching the route of the vehicle using the predicted traffic volume,
Receiving a route search request from a user;
Selecting one or more candidate paths according to the path search request;
Retrieving and receiving a request from a server for traffic volume, correlation coefficient, and distance for each road section for at least one road section included in each candidate path;
Estimating travel time for each road section using the received traffic volume, correlation coefficient, and distance; And
Calculating total travel time for each candidate path by summing the estimated travel time for each road section for one or more road sections included in each candidate path; Vehicle route search method using a predicted traffic, characterized in that it comprises a.
The method of claim 10,
Selecting the candidate path,
Vehicle path search method using the predicted traffic volume is selected to give priority to the minimum road section of each candidate route.
The method of claim 10,
Displaying the candidate paths and the calculated total travel time of each candidate path; Vehicle route search method using a predicted traffic volume, characterized in that it further comprises.

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