KR101538154B1 - System and Method for Determining Optimal Departure time - Google Patents

Abstract

The present invention relates to an optimal departure time determination system and method. The optimal departure time determination system of the present invention includes an input unit for inputting utilization period, departure point, waypoint, and destination of traffic history data, A large-scale path generating unit for generating a large-scale path by determining the order of passing through the waypoint between the place of departure and the destination on the basis of the distance data on the processed data, A small path generating unit for generating a small path between the departure point, the waypoint, and the destination according to the duel order, and an optimal path is created by connecting the small path and the large path, The smallest total distance of the route Wherein the departure time determination unit extracts a candidate departure time zone of each of the small-scale routes using the processed data to determine a start time zone of the first small-scale route as a guarantee time zone And a second determiner for determining an optimal departure time for minimizing the total required time within the guaranteed start time zone.

Description

[0001] SYSTEM AND METHOD FOR DETERMINING OPTIMAL DEAN TIME [0002]

The present invention relates to an optimal departure time determination system and method.

Big data refers to a vast amount of data that is difficult to collect, store, search, and analyze in the conventional way, because the amount, period, and format of the data are too large for existing data. Big data appeared due to the development of various sensors and internet. As computers and processing technologies develop, it becomes possible to observe and predict phenomena based on the big data generated in the digital environment.

It is also possible to use such big data to search the optimal route of the vehicle when driving on the road. If the starting point and the destination point are determined, not only the optimal route for reducing the time required by using the existing huge traffic history data but also the big data can be used for determining which waypoint passes first even in the case of passing through various way points .

On the other hand, it is important to select the route when driving, but it will have a significant effect on the time required to depart. If travel time is included in the rush hour of traffic congestion, the time required can be greatly increased. Thus, there is a growing need for a system and method for selecting an optimal path and determining an optimal starting time for that path.

Korean Patent Publication No. 10-2011-0052386

SUMMARY OF THE INVENTION It is an object of the present invention to provide an optimum departure time determination system for generating an optimal path having a plurality of intermediate points by using big data and determining an optimal start time of an optimal path.

Another problem to be solved by the present invention is to provide an optimal start time determination method for generating an optimal path having a plurality of intermediate points by using big data and determining an optimal start time of an optimal path.

The problems to be solved by the present invention are not limited to the above-mentioned problems, and other matters not mentioned can be clearly understood by those skilled in the art from the following description.

According to an aspect of the present invention, there is provided an optimal departure time determination system including an input unit for inputting utilization period, departure point, transit point, and destination of traffic history data, traffic history data within the utilization period, A large-scale route generating unit for generating a large-scale route between the departure place and the destination place based on the distance data on the processing data based on the order of passing the stopping place, A small-scale route generating unit for generating a small-scale route between the departure place, the stopover destination and the destination according to a route order, and an operation unit for generating an optimal route by connecting the small- Having a small overall turnaround time Wherein the departure time determination unit extracts a candidate departure time zone of each of the small-scale routes using the processed data to determine a start guarantee time period of the first small-scale route, 1 judging unit and a second judging unit for judging an optimum departure time for minimizing the total required time within the guaranteed start time zone.

The traffic history data may be collected by a Dedicated Short Range Communication (DSRC) system.

Wherein the second determination unit sets the candidate departure time that is the minimum total time required for each of the candidate departure times of the start guarantee time zone as the optimal departure time, When the first partial required time which is the partial required time of the small path increases in accordance with the flow of time in the candidate progress time added with the partial required time of the previous small path by the candidate departure time in the small path, Wherein the first partial required time is the first partial required time, and the first partial required time, which is the partial required time of the corresponding small-scale path at the candidate progress time plus the partial required time of the previous small path by the candidate departure time, The partial required time is set such that the partial required time of the corresponding small- And a third part time required for the second partial time required for the second partial time required to increase again according to the difference between the time of the minimum point and the time of the candidate in the first partial time required, It may be the minimum value of the required time.

The large-scale route generating unit may include a grouping unit that connects the departure place and each of the waypoints with a first line segment on a map, forms a circle having the first line segment as a diameter, A group path generating unit for determining the order of the intermediate points in the group and generating a group path according to the order, and a group path generating unit for completing a large-scale path in the order of closest distance between the last intermediate points in each group path And a path completion unit.

Wherein the group path generation unit generates a route by setting a routing order in the order of the closest distance between the stopping place and the departure place in the group when there are two stopping places in the group and if there are three or more stopping places in the group, A second line segment connecting the departure point and the most distant route within the group is formed and the route segment is preferentially selected in a manner that the route segment is preferentially selected with respect to the second segment, And a route can be created by setting a route route which is farthest from the departure place as a next way route and setting a route order in the order of the nearest route to the departure place and the remaining route way.

Wherein the small-scale path generation unit includes a speed input unit for inputting a measurement speed of a moving vehicle of a link corresponding to a part of an actual road on the processed data, a score aggregation unit for generating a congestion score of each link based on the speed, A multipath generating unit for generating a multipath from a departure point to a waypoint or a destination on a map in which a node corresponding to an intersection of the link and the actual road is displayed; And a path selection unit for selecting the path having the smallest congestion score as the small-scale path. The path selection unit may further include a path selection unit for selecting a path having the smallest congestion score as the small-scale path.

The path score calculating unit may calculate a congestion score by adding a lane score proportional to the number of lanes and a speed score proportional to a difference between the reference speed and the measurement speed.

The reference speed may be a speed limit defined in the link.

According to another aspect of the present invention, there is provided a method of determining an optimal departure time, the method comprising: receiving a usage period, a departure point, a stopover destination, and a destination of traffic history data and classifying traffic history data in the utilization period by time and space And generating a large-scale route by determining the order of passage of the intermediate route between the departure place and the destination according to the distance data on the processing data, and using the processing data, A route generating unit for generating a plurality of small-scale routes between the destination and the destination, generating an optimal route by connecting the small-scale route and the large-scale route, extracting a candidate departure time zone of each small- The guarantee time of the start-up time of the vehicle, It involves determining the optimal time for starting the whole time with a minimum guaranteed within the period starting time.

The extracting of the candidate departure time zone may include extracting a time zone having a partial required time that is equal to or shorter than a reference partial time required to averaged at a predetermined time interval from the partial time required for the small-scale path to the candidate departure time zone.

The optimal start time may be determined as an optimal start time for each candidate start time of the start guarantee time zone, the candidate start time having the minimum total time taken to sum the partial time required for each of the small- When the first partial required time which is the partial required time of the small route increases in accordance with the passage of time in the candidate proceeding time added with the partial required time of the previous small route by the candidate departure time, Wherein the partial required time is the first partial required time and the first partial required time that is the partial required time of the corresponding small path at the candidate progress time plus the partial required time of the previous small path by the candidate departure time is The partial time required is a part of the corresponding small-scale route A second partial required time which is a partial required time at a minimum point where the time increases again with the passage of time and a second partial required time which is a time corresponding to the difference between the time of the minimum point and the candidate progress time Of the third part of the time required for the addition.

The creation of the large-scale route may include forming a circle having the starting point and each of the intermediate points on the map as a first line segment and having the first line segment as a diameter on the map, , Creating a group path by determining the order of each intermediate point in the group, and completing the large-scale path in the order of closest distance between the last intermediate points in each group path .

The generating of the group path may include generating a route by determining a passing order in the order of the closest distance between the stopping point and the departure point in the group when the number of the stopping points is two within the group, A second line segment connecting the departure point and the most distant point in the group on the map, and selecting a place having the same or a larger number of waypoints based on the second line segment, The route can be created by setting the order of passing through the stop route and setting the stop route that is farthest from the stop route as the next stop route and setting the route order in the order of closest to the stop route and the remaining stop route.

Generating the small-scale route includes receiving measurement speed data of a moving vehicle of a link corresponding to a partial section of an actual road on the processed data and displaying the node corresponding to the intersection of the link and the actual road on a map Generating a multi-path from the source to the destination or the destination, generating a congestion score of each link based on the measurement rate, and generating a congestion score for each route in the multi- Calculating a congestion score, and selecting the smallest path as the smallest path.

The details of other embodiments are included in the detailed description and drawings.

The embodiments of the present invention have at least the following effects.

In other words, the system and method for determining the optimal departure time according to an embodiment of the present invention can accurately calculate the required time using the big data, and thus can more accurately determine the optimal departure time.

In addition, the optimal start time determination system and method according to an embodiment of the present invention can obtain a more optimized start time because the time required is calculated in consideration of the optimal route according to the order of passage even when there are a plurality of way points.

The effects according to the present invention are not limited by the contents exemplified above, and more various effects are included in the specification.

1 is a block diagram for explaining an optimal departure time determination system according to an embodiment of the present invention.
FIG. 2 is a block diagram for explaining the large-scale path generation unit of FIG. 1 in detail.
3 to 6 are diagrams for explaining a large-scale path generation method of the large-scale path generation unit of FIG.
FIG. 7 is a block diagram for explaining the small-scale path generation unit of FIG. 1 in detail.
8 to 10 are diagrams for explaining a small-scale path generation method of the small-scale path generation unit of FIG.
FIG. 11 is a block diagram for explaining the departure time determination unit of FIG. 1 in detail.
FIGS. 12 to 20 are diagrams for explaining a departure time determination method of the departure time determination unit of FIG.
FIG. 21 is a flowchart illustrating an optimal start time determination method according to an embodiment of the present invention.
FIG. 22 is a flowchart for explaining the large-scale path generation step of FIG. 21 in detail.
FIG. 23 is a flowchart for describing the small-scale path generation step of FIG. 21 in detail.
FIG. 24 is a flowchart for explaining the optimal departure time determination step of FIG. 21 in detail.

BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention and the manner of achieving them will become apparent with reference to the embodiments described in detail below with reference to the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

Although the first, second, etc. are used to describe various elements, components and / or sections, it is needless to say that these elements, components and / or sections are not limited by these terms. These terms are only used to distinguish one element, element or section from another element, element or section. Therefore, it goes without saying that the first element, the first element or the first section mentioned below may be the second element, the second element or the second section within the technical spirit of the present invention.

It is to be understood that when an element or layer is referred to as being "on" or " on "of another element or layer, All included. On the other hand, a device being referred to as "directly on" or "directly above " indicates that no other device or layer is interposed in between.

The terms spatially relative, "below", "beneath", "lower", "above", "upper" May be used to readily describe a device or a relationship of components to other devices or components. Spatially relative terms should be understood to include, in addition to the orientation shown in the drawings, terms that include different orientations of the device during use or operation. For example, when inverting an element shown in the figure, an element described as " below or beneath "of another element may be placed" above "another element. Thus, the exemplary term "below" can include both downward and upward directions. The elements can also be oriented in different directions, in which case spatially relative terms can be interpreted according to orientation.

The terminology used herein is for the purpose of illustrating embodiments and is not intended to be limiting of the present invention. In the present specification, the singular form includes plural forms unless otherwise specified in the specification. It is noted that the terms "comprises" and / or "comprising" used in the specification are intended to be inclusive in a manner similar to the components, steps, operations, and / Or additions.

Unless defined otherwise, all terms (including technical and scientific terms) used herein may be used in a sense commonly understood by one of ordinary skill in the art to which this invention belongs. Also, commonly used predefined terms are not ideally or excessively interpreted unless explicitly defined otherwise.

1 to 20, an optimal departure time determination system according to an embodiment of the present invention will be described.

FIG. 1 is a block diagram for explaining an optimum departure time determination system according to an embodiment of the present invention, and FIG. 2 is a block diagram for explaining a large-scale path generation unit of FIG. 1 in detail. FIGS. 3 to 6 are diagrams for explaining a large scale path generation method of the large scale path generation unit of FIG. 2, and FIG. 7 is a block diagram for explaining the small scale path generation unit of FIG. 1 in detail. FIGS. 8 to 10 are diagrams for explaining a small-scale route generation method of the small-scale route generation unit of FIG. 7, and FIG. 11 is a block diagram for explaining the departure-time determination unit of FIG. 1 in detail. FIGS. 12 to 20 are diagrams for explaining a departure time determination method of the departure time determination unit of FIG.

Referring to FIG. 1, an optimal departure time determination system according to an embodiment of the present invention includes an input unit 100, a data processing unit 200, a large scale path generation unit 300, a small scale path generation unit 400, And a determination unit 500.

The input unit 100 may receive the utilization period of the traffic history data. The utilization period may be a specific range from the past. The unit of time range may include year, month, day, hour, minute, and second. However, it may be limited by units of minimum time unit of traffic history information. That is, if traffic history data is measured in 5-minute increments, 5 minutes may be the minimum unit of utilization period.

The traffic history information is not particularly limited, but may be the speed information of the moving vehicle of the link at the past time. That is, a value representative of the speed of the vehicle located on the link. The representative value may be, for example, an average value. Also, the traffic history information may be the time information of the link.

The traffic history information can be tabulated at a speed according to the link at the past time. A link is a part of an actual road. The intersection of a road on a map is a node, and a line connecting the node is a link. A road is composed of a plurality of links, and a point where a road intersects is a node. The traffic history information may be collected by a tollgate or a DSRC (Dedicated Short Range Communication) system. However, the present invention is not limited thereto. It may be collected by public transportation, a police car, or navigation.

The input unit 100 can receive the start point, the intermediate point, and the destination. If the waypoint does not exist, it may not be input. That is, the input unit 100 may receive only the source and destination. The waypoint may be singular or plural.

The data processing unit 200 may receive traffic history information. The traffic history information may be collected by a tollgate or a DSRC (Dedicated Short Range Communication) system. However, the present invention is not limited thereto. It may be collected by public transportation, a police car, or navigation.

The data processing unit 200 may process the utilization period of the traffic history data received by the input unit 100. [ The utilization period may be a continuous one range, or a continuous range may be intermittently plural. That is, the range of the target time may be, for example, the entire day of December 1, 2013, or between 6 pm and 7 pm during November 2013.

The data processing unit 200 can use a big data analysis tool. Although not particularly limited, the data processing section 200 can use, for example, a distributed file system.

The large-scale route generating unit 300 can receive the distance information from the data processor 200 to the start point, the destination, and the intermediate point. The large-scale route generating unit 300 may generate a large-scale route by determining the route order between the start point, the destination, and the intermediate point. The function of the large-scale path generation unit 300 may not be needed if there is one or more stopping points. However, if there are a plurality of waypoints, a large-scale path generating unit 300 may be required to determine the way of passing through the waypoints.

Referring to FIG. 2, the large-scale path generation unit 300 includes a grouping unit 310, a group path generation unit 320, and a path completion unit 330.

The grouping unit 310 may determine a group of a plurality of waypoints.

The grouping method will be described with reference to FIG. For example, it starts from Dongnae department store in Busan. In Fig. 3, the Geumjeong Police Station, the Banan Agricultural Products Market, Pusan National University, Asad Stadium, Yeonje-gu Office and Jin-gu Office, which are shown as red circles,

On the map as shown in FIG. 3, the first line segment connecting the departure point, Dongnae Department Store, and each stopover point can be obtained. Then, a circle having the diameter of the first line segment is formed on the map. If there are other waypoints in the formed circle, they can be assigned to the same group. In FIG. 3, Dongnae Department Store, Pusan National University and Geumjung Police Station are one group, and Donghae Department Store, Asad Stadium, Yeonje-gu Office and Jingu-gu Office can be one group.

The group path generation unit 320 may generate the group path by determining the order of the intermediate points in the group.

The group path generation method will be described with reference to FIGS. 4 and 5. FIG. If there is only one stopover point in the group, the group route may be a route connecting the departure point and the stopover point.

Referring to FIG. 4, when there are two intermediate grounds in the group, it is possible to use the intermediate ground E1 as the last intermediate ground and the other intermediate ground E2 as the final intermediate ground. That is, as shown, a group path can be generated in the order of S (departure) -> E2 (waypoint) -> E1 (waypoint).

Referring to FIG. 5, if there are three or more staples in the group, the first segment D1 and the second segment D1 connecting the starting point S and the most distant route E1 can be obtained. Then, the portions with the more intermediate points or the same portions (E3 and E4) are preferentially selected based on the second line segment D1. At this time, when the numbers of the intermediate points are the same, two paths can be selected. The selected parts can be docked in the order of closest distance. That is, the order of S (departure) -> E3 (waypoint) -> E4 (waypoint).

 The starting point (S) and the furthest intermediate point (E1) may be the next order. That is, the order may be S (origin) -> E3 (intermediate) -> E4 (intermediate) -> E1 (intermediate). The non-selected waypoint E2 can be determined in the order of the closest distance to the departure place S and the farthest way way E1. For example, the order may be S (origin) -> E3 (intermediate) -> E4 (intermediate) -> E1 (intermediate) -> E2 (intermediate).

The path completion unit 330 can complete the path by continuing to the last waypoint in each group path. The route completion unit 330 may route the route in the order of the closest distance between the last stopping points.

Referring to FIG. 6, the Geumjung Police Station and the Halla Agricultural Products Market are the final stopping points, and the Asiad Stadium and the Yeonje-Gu District Office can be the final stopping places according to the number of the two cases. The path completion unit 330 may follow the paths in the closest order according to the distances of the four last way points. Specifically, a large-scale route is completed after the group including the last stopping point closest to the closest distance, and the last stopping point nearest to the last stopping point in the remaining group.

The small-scale route generating unit 400 may generate a small-scale route between the departure points and the intermediate points according to the order of the large-scale route generated by the large-scale route generating unit 300.

7, the small-scale path generation unit 400 includes a speed input unit 410, a multipath generation unit 420, a score aggregation unit 430, a path score calculation unit 440, and a path selection unit 450 do.

The speed input unit 410 can receive the reference speed, measurement speed, link and node information on the machining data. The measurement speed may mean the measurement speed of the moving vehicle of the link. The speed input unit 410 may receive information on links included in the small-scale route in accordance with the route order on the large-scale route. Referring to FIG. 8, the speed input unit 410 may receive input of links and node information within a square on a map including a starting node (Dongnae department store) and a destination node (Geumjeong police station) on a small scale route.

The multi-path generating unit 420 may generate a multi-path from the starting node on the small-scale path to the destination node. Referring to FIG. 9, a route can be connected to all the nodes connected at the origin node, but only in the direction of the destination node. There is no circumvention or retreat.

The score aggregation unit 430 may aggregate the congestion points of links included in the multipath.

The congestion score of each link can be a number of lanes, a reference speed and a measurement speed. Referring to FIG. 10, the number of lanes according to the number of lanes can be given to each link. This is because the more the number of cars, the more congestion is because the more cars are accommodated. Here, a speed score proportional to the difference between the reference score and the measurement speed can be given. For example, in FIG. 10, one point is given per 10 km / h. Since the speed score is subtracting the reference speed from the measurement speed, a negative number may be possible. The final score can be calculated by summing the speed score and the number of lanes. The higher the final score, the less crowded, and the lower the final score, the more crowded.

The path score computing unit 440 may add the final score of the link to each path to compute the final score of each path. That is, the final score of each of the multipaths from the origin node to the destination node can be aggregated.

The path selecting unit 450 may sort the final scores of the paths in descending order. Therefore, the route with the highest score can be selected as a small-scale route. This is because it is possible to minimize the time required as the least congested route.

The departure time determination unit 500 may generate the final path by connecting the small path and the large path. The start time determination unit 500 may determine the optimal start time of the final path.

Referring to FIG. 11, the departure time determination unit 500 includes a first determination unit 510 and a second determination unit 520.

The first determination unit 510 can use the processed data. The processed data may be a time-required time of the small-scale path. The first determination unit 510 can determine the start guarantee time zone of the first small route of the large-scale route. The start guarantee time zone may refer to a time zone including an optimal departure time.

A method of determining the start guaranteed time zone will be described with reference to FIGS. 12 to 17. FIG.

Referring to FIG. 12, it is assumed that the route from Dongnae Department Store to Pusan National University is the first small route of a large-scale route. The x-axis is the departure time from 0:00 to 24:00, and the y-axis is the required time. That is, FIG. 12 shows the time required for the first small route according to each departure time.

Then, the average value can be calculated at predetermined time intervals. The constant time interval may be, for example, 3 hours. However, the present invention is not limited thereto. This can be represented by a line, as shown.

Referring to FIG. 13, a Primary Recommended Candidate Box (PRCB), an Unknown Recommended Candidate Box (unRCB), a Secondary Recommended Candidate Box (SRCB), and a FOB (Filtering Out Box) can be designated.

division Explanation The coordinate value at the lower left of PRCB (X, Y), where X is the starting time at which less than the average value starts, and Y is the smallest of the times below the average value. The coordinate value of the upper right corner of PRCB (X, Y), where X is the start time at which less than the average value ends, and Y is the maximum time required, whichever is less than the average value. Width of PRCB Departure time at which less than average value ends - Departure time at which less than average value starts Height of PRCB Largest turnaround time in PRCB - Smallest turnaround time in PRCB

Generation criteria Explanation Rule ₁
(Existence) There must be departure times with a time period that is less than the average value. Rule ₂
(Minimum width limit) If there are N total small-scale paths, then the minimum width limit of the PRCB is N / 2 or more of the length from the starting time at which the average value is less than the average value to the starting time at which the average value is less than the average. Rule ₃
(Minimum vertical length limit) There is no minimum length limit for PRCB. Rule ₄
(Overlapping) PRCBs in one path can not overlap.

The PRCB can be defined by the definition of Table 1 and the generation criterion of Table 2. PRCB may be the candidate departure time zone. The optimum departure time can be determined in the time zone belonging to PRCB. The portion excluding the PRCB may be designated as unRCB first. PRCB and unRCB can be tabulated and classified.

PRCB
Sequence Left Coordinate Bottom Coordinate Right Coordinate Up Coordinate L ength H eight One 01:35 200 02:35 225 13 25 2 03:05 180 04:35 225 19 45 ... ... ... ... ... ... ...

unRCB
Sequence Left Coordinate Bottom Coordinate Right Coordinate Up Coordinate L ength H eight One 00:00 220 01:30 280 19 60 2 02:30 225 03:00 255 7 30 ... ... ... ... ... ... ...

Table 3 is the PRCB table and Table 4 is the unRCB table.

The unRCB group having the largest value among the PRCBs sorted according to Table 3 can be classified as FOB and all remaining unRCB groups can be designated as the SRCB group.

Referring to FIG. 14, it is possible to overlap both the PRCB and the FOB for each small-scale path, and to eliminate the time zone of the FOB part. This means eliminating time zones that can not be the optimal departure time.

Referring to FIG. 15, it is possible to calculate the time required for each small route. That is, the x-axis of the existing PRCB is fixed, while the y-axis continues to be added.

Path ₁ = (X ₁ , Y ₁ ), (X ₂ , Y ₂ ), (Travel Time ₁ , Travel Time ₂ )

Path ₂ = (X ₃ , Y ₃ ), (X ₄ , Y ₄ ), (Travel Time ₃ , Travel Time ₄ )

_{= (X 3, Y 1 +} Travel Time 1), (X 4, Y 2 + (Travel Time 2 -Travel Time 1), (Travel Time 3, Travel Time 4)

...

_{Path N = (X 2N -1,} Y 2N -1), (X 2N, Y 2N), (Travel Time 2N -1, Travel Time 2N)

= _{(2N -1} X, Y _2N + _{-3 -3} Travel Time _2N), (X _2N, Y _2N + _-2 (Travel Time _{2N -2} - Travel Time _{-3 2N),} (Travel Time _{2N -1,} Travel Time _2N ) (except when N = 1)

In the above equation, Path _i = (lower left coordinate), (upper left coordinate), (time required in lower coordinate, time required in upper coordinate). In other words, it is a process of calculating the total time taken by summing the partial time required for each small route.

Referring to FIG. 16, in consideration of the time taken in the previous small-scale route, the left-side value in the next small-scale route is removed.

Path ₁ = (X ₁ , Y ₁ ), (X ₂ , Y ₂ ), (Travel Time ₁ , Travel Time ₂ )

Path ₂ = (X ₃ , Y ₃ ), (X ₄ , Y ₄ ), (Travel Time ₃ , Travel Time ₄ )

_{= (X 3 + (Travel Time} 2 -Travel Time 1), Y 1 + Travel Time 1), (X 4, Y 2 + (Travel Time 2 -Travel Time 1), (Travel Time 3, Travel Time 4)

...

_{Path N = (X 2N -1,} Y 2N -1), (X 2N, Y 2N), (Travel Time 2N -1, Travel Time 2N)

= (X _{2N -1} + (Travel Time _{2N 2N -3 -2} -Travel Time), Y _2N + _{-3 -3} Travel Time _2N), (X _2N, Y _2N + _-2 (Travel Time _{2N -2} - Travel Time _{2N -3} ), (Travel Time _{2N -1} , Travel Time _2N ) (except when N = 1)

That is, the first value of PRCB of Path ₂ is delayed by the time required for the first small path than the first value of PRCB of Path ₁ . Likewise, the first values of the third, fourth, and last PRCBs can be adjusted as well.

Referring to Fig. 17, the y axis can be repositioned by adding the required time up to the last small path (Path _{6 in} Fig. 17) in the above manner. The start guarantee time zone is from the start time of the first small route to the time when the last travel time of the last route is subtracted from the total travel time. That is, when starting in this time zone, the route can be traveled with a small required time.

Referring again to FIG. 11, the second determination unit 520 may determine an optimal departure time that minimizes the total required time within the guaranteed start time zone. The second determination unit 520 may obtain the partial time required for the small-scale route according to the candidate start time, when each time of the start-guaranteed time zone is referred to as a candidate start time. The candidate departure time, which is the minimum total travel time of all of the partial travel times in the respective small-scale routes, may be the optimal departure time.

Referring to Figure 18, the first small path travel time (T _{travel _ R1)} to the second in the proceeding candidate small path time (T _{start _ R2)} takes the two parts of the second smaller channel time plus the time of a candidate start time for increasing with the passage, it is possible to determine the total time required to accept that part time (T _{travel _ R2).} In this case, the required time can be calculated by a linear function formula (triangle in Fig. 18).

Referring to Figure 19, the first small path travel time (T _{travel _ R1)} to the second in the proceeding candidate small path time (T _{start _ R2)} takes the two parts of the second smaller channel time plus the time of a candidate start time The time required for the part may not be used as it is. Minimum point to a portion duration is decreased rise up with the passage of time may be a time piece used in _{(T? Tart _ R2, T} ? Ravel _ R2).

Specifically, D _{wating time} (= T ' _{start _ R2} - T _{start _ R2} ) is D _{travel time} (= T _{travel _ R2} - T ' _{travel _ R2} ), D _{travel time,} it takes less time to depart, so T ' _{travel _ R2} to D _{travel You} can use the value of _time plus partial time required.

In contrast, D _{wating time} (= T ' _{start _ R2} - T _{start _ R2} ) is D _{travel time} - if greater than _{(= T travel _ R2 T '} travel _ R2) , or the same, so it kind of takes less time to depart immediately, without waiting time, T _{travel _ R2} to be used as part of the time spent. In this case, the required time can be calculated by a linear function formula.

The second determination unit 520 may calculate each partial time required and determine the candidate start time when the total time is minimum to be the optimal start time.

Referring to FIG. 20, if there is no PRCB according to each small path, an interval may exist. In this case, the SRCB can be used instead to calculate the partial time required.

The optimal departure time determination system according to an embodiment of the present invention analyzes big data using PRCB, SRCB, etc., unlike the existing technology, .

Hereinafter, a method of determining an optimal departure time according to an embodiment of the present invention will be described with reference to FIGS. 21 to 24. FIG. Parts similar to the above-described optimal departure time determination system will be briefly described or omitted.

FIG. 21 is a flow chart for explaining a method of determining an optimal departure time according to an embodiment of the present invention, and FIG. 22 is a flowchart for explaining in detail the step of generating a large-scale path in FIG. FIG. 23 is a flowchart for explaining the small-scale route generation step of FIG. 21 in detail, and FIG. 24 is a flowchart for explaining the optimal departure time determination step of FIG.

21, the utilization period, the start point, the intermediate point and the destination are inputted (S2100).

The intermediate point can be input only when it exists. The waypoint is not particularly limited and may be plural or singular. If there are plural waypoints, the last waypoint can be the destination soon. The utilization period can be one continuous period or several intermittent periods. That is, there is no particular limitation.

Subsequently, processing data is generated (S2110).

The processed data may be data obtained by classifying traffic history data by time intervals. The traffic history data and the processed data may be data tabulated by time intervals.

Subsequently, a large-scale path is generated (S2120).

A large-scale route can be created only when there are plural waypoints. The large-scale route refers to the route along the route from the departure point to the stopover point.

Subsequently, a small-scale path is generated (S2130).

A small-scale route may mean a route between each stopping point and a starting point according to a large-scale route.

Subsequently, an optimum path is generated (S2140).

The optimal path can be created by connecting each small path in the order of passing through the large path.

Subsequently, a start guarantee time is calculated (S2150).

The guaranteed start time zone can be calculated by extracting the candidate departure time zone. The candidate departure time zone may be extracted in the form of a PRCB (see FIG. 13).

Then, the optimum departure time is determined (S2160).

The optimal departure time means a time when the total time required within the guaranteed start time zone is small. That is, when starting at the optimum departure time, it is possible to travel the final route most quickly.

Referring to FIG. 22, in order to generate a large-scale path, a group is designated first (S2121).

It is possible to form a first line segment connecting the starting point and the intermediate point on the map, and forming a circle having the first line segment as the diameter. In the case where other intermediate points are included in the circle, the same group can be used.

Then, a group path is generated (S2123).

The group path can be generated in such a way that the group path is shortened when there are two waypoints and when the group pathway is three. Specifically, when there are three waypoints, a method may be used in which the waypoints farthest from the departure point are connected by the second line segment, and the waypoints are selected by the second line segment more or less.

Subsequently, a large-scale path is completed (S2125).

When the group path is completed, a large-scale path can be completed in the nearest order by using the distance between last end points of each group path. The closest case is completed first, and the distance is compared again in the remaining case.

Referring to FIG. 23, in order to generate the small-scale route, first, the measurement speed of the moving vehicle of the link is inputted (S2131).

Depending on the large scale path, the start and destination nodes of the small scale path can be determined. Therefore, the speed information of the link and the node within the range of the rectangle including the start node and the arrival node can be inputted.

Then, a multipath is generated (S2133).

It is possible to connect the route from the source node to all connected nodes, but only in the direction of the destination node. There is no case of bypassing or retreating (see FIG. 9)

Then, a congestion score is generated (S2135).

The congestion score can be generated based on the moving speed of the link vehicle. The higher the congestion score, the less crowded.

Then, the congestion score of each route is calculated (S2137).

By adding all the congestion points of the links belonging to each path, the congestion score of each path can be calculated.

Then, a small-scale path is selected (S2139).

The path with the highest congestion score for each path can be selected as a small path. That is, the least congested path is selected as the small path.

Referring to FIG. 24, in determining the optimal departure time, it is first determined whether the first partial required time is increased (S2161).

The first part time required is the partial time required for the small path at the point in time past the partial time required for the previous small path at the candidate departure time of the previous small path.

Then, when the first part time required time increases, the partial time required is determined as the first part time required time (S2163).

In this case, since the shorter time does not need to be taken even when waiting, it is possible to determine the first part time required time as the partial time required.

When the first part time required time decreases, the partial required time may be the second one of the second partial required time and the third partial required time. The second partial time required is a partial required time at which the first partial required time decreases and then increases again, and the third partial required time is a time required for the first partial required time, It may be a time plus the time of the car. In other words, if the shorter time required for waiting for the difference between the time of the minimum point and the time of the candidate time can be obtained, the partial time required for waiting time can be calculated.

Then, the total required time is calculated (S2167).

The total travel time may refer to the sum of the fractional travel times in each small route.

Then, the optimum departure time is determined (S2169).

It is possible to determine the candidate departure time having the minimum total time as the optimum departure time.

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, You will understand. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive.

100: Input unit 200: Data processing unit
300: Large scale path generation unit 400: Small scale path generation unit
500: Departure time judging unit

Claims (14)

An input unit for inputting utilization period, departure point, transit point and destination of traffic history data;
A data processing unit for classifying traffic history data within the utilization period by time and space to generate processed data;
A large-scale path generation unit for generating a large-scale path between the departure place and the destination by determining the order of the passing way based on the distance data on the processing data;
A small-scale route generation unit for generating a small-scale route between the departure point, the waypoint and the destination according to the route order using the processed data; And
And a departure time determiner for generating an optimal route by connecting the small scale route and the large scale route and determining a departure time having the smallest total travel time of the optimum route using the processed data,
Wherein the departure time determination unit includes a first determination unit for extracting a candidate departure time zone of each of the small-scale routes using the processed data to obtain a guaranteed start time of a first small route among the small-scale routes,
And a second determiner for determining an optimal departure time that minimizes the total required time within the guaranteed start time zone. The method according to claim 1,
Wherein the traffic history data is collected by a DSRC (Dedicated Short Range Communication) system. The method according to claim 1,
The second determination unit may determine,
The candidate departure time, which is the minimum total time required for each of the candidate departure times of the start guarantee time zone, is calculated as an optimal departure time,
When the first partial required time which is the partial required time of the small route increases in accordance with the passage of time in the candidate proceeding time added with the partial required time of the previous small route by the candidate departure time,
The partial required time is the first partial required time,
When the first partial required time which is the partial required time of the corresponding small-scale route decreases along with the passage of time in the candidate progress time added with the partial required time of the previous small-scale route by the candidate departure time, A second partial required time that is a partial required time at a very small point where the partial required time of the corresponding small scale path increases again with the passage of time; The minimum departure time of the third part time required by the time difference. The method according to claim 1,
The large-scale route generating unit may include a grouping unit that connects the departure place and each of the waypoints with a first line segment on a map, forms a circle having the first line segment as a diameter, ,
A group path generating unit for determining the order of each intermediate point in the group and generating a group path according to the order;
And a path completion unit for completing a large-scale path following the path in order of closest distance between the last intermediate points in each group path. 5. The method of claim 4,
Wherein the group path generation unit comprises:
If the number of intermediate points in the group is two, generating a route by setting a passage order in the order of the intermediate point and the departure point in the group,
A second line segment connecting the departure point and the farthest distance from the departure point in the group is formed on the map on the map when the number of the stopping points is three or more in the group, And a route is set up as a next stopping point, and a route is formed by setting a stopping order in the order of the closest distance between the stopping point and the remaining stopping point farthest from the departure point, The optimal departure time determination system. The method according to claim 1,
The small-scale path generation unit includes a speed input unit that receives a measurement speed of a moving vehicle of a link corresponding to a section of an actual road on the processed data,
A score aggregation unit for generating a congestion score of each link based on the speed,
A multipath generating unit for generating a multipath from a start point to a stop point or a destination on a map on which a node corresponding to an intersection of the link and the actual road is displayed;
A path score calculator for calculating a congestion score of each path among the multiple paths by adding all the congestion points of the links included in each of the multiple paths;
And a route selection unit selecting the route having the smallest congestion score as the small route. The method according to claim 6,
Wherein the path score calculation unit calculates a congestion score by adding a lane score proportional to the number of lanes and a speed score proportional to a difference between the reference speed and the measurement rate. 8. The method of claim 7,
Wherein the reference speed is a speed limit defined in the link. The utilization period of the traffic history data, the starting point, the intermediate point and the destination,
Generating traffic data by classifying traffic history data within the utilization period by time and space,
Generating a large-scale route by determining the order of passage of the intermediate route between the departure place and the destination according to the distance data on the processing data,
Generating a small-scale route between the departure point, the waypoint, and the destination according to the route order using the processed data,
Generating an optimal path by connecting the small path and the large path,
Extracting a candidate departure time zone of each of the small-scale routes using the processed data, obtaining a guarantee time for starting a first small route among the small-scale routes,
And determining an optimal departure time that minimizes the total required time within the start guaranteed time period. 10. The method of claim 9,
To extract the candidate departure time zone,
And extracting, as the candidate departure time zone, a time zone having a partial required time that is equal to or shorter than a reference partial time required to averaged at a predetermined time interval from the partial time required for the small-scale path. 10. The method of claim 9,
Determining the optimal departure time comprises:
The candidate departure time, which is the minimum total time required for each of the candidate departure times of the start guarantee time zone, is calculated as an optimal departure time,
When the first partial required time which is the partial required time of the small route increases in accordance with the passage of time in the candidate proceeding time added with the partial required time of the previous small route by the candidate departure time,
The partial required time is the first partial required time,
When the first partial required time which is the partial required time of the corresponding small-scale route decreases along with the passage of time in the candidate progress time added with the partial required time of the previous small-scale route by the candidate departure time, A second partial required time that is a partial required time at a very small point where the partial required time of the corresponding small scale path increases again with the passage of time; And determining the optimal departure time, which is the minimum value among the third portion required time added by the difference (difference). 10. The method of claim 9,
Generating the large-scale path,
The start point and each stop point on the map as a first segment,
Forming a circle having a diameter on the first line segment on the map,
If there are other waypoints in the circle, they are assigned to the same group,
Generating a group path by determining the order of the intermediate points in the group,
And completing the large-scale path between the last stopping points in the respective group paths, each of the distances being closest in order. 13. The method of claim 12,
Generating the group path comprises:
If the number of the waypoints is two within the group, a route is created by setting a passing order in the order in which the waypoint and the departure point are close to each other in the group,
A second line segment connecting the departure point and the farthest distance from the departure point in the group is formed on the map when the number of the waypoints is three or more in the group, And determines the route order in the closest distance between the departure place and the stop route and sets the stop route farthest from the departure place as the next stop route and sets the turn route in the order of the closest stop route to the departure place and the remaining stop route, A method for determining an optimal departure time to be generated. 13. The method of claim 12,
The creation of the small-
And the measurement speed data of the moving vehicle of the link corresponding to a part of the actual road on the machining data,
Generating a multipath from the departure point to the waypoint or the destination on a map in which a node corresponding to an intersection of the link and the actual road is displayed,
Generating a congestion score of each link based on the measurement rate,
Calculating a congestion score of each of the multiple paths according to the congestion score of the link,
And selecting the route having the smallest congestion score as the small route.

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