KR102504273B1 - DRT request information clustering method for allocating DRT cars and system thereof - Google Patents

Abstract

An embodiment of the present invention discloses a demand responsive transit (DRT) request information clustering method for allocating DRT vehicles, comprising the steps of: receiving a request for a demand responsive transit service from a plurality of user terminals; recognizing a request time and a request position included in the received request for each user, and searching to find whether demand prediction information corresponding to the recognized request time and request position exists; if the demand prediction information is retrieved; classifying a plurality of users corresponding to the user terminals into one or more clusters depending on the retrieved demand prediction information; and calculating an optimal moving route for each of the clusters and transmitting the calculated moving route to a demand responsive transit high-occupancy vehicle. Therefore, provided are a method for maximizing the efficiency of an existing demand responsive transit service and a system for realizing the method.

Description

DRT request information clustering method and system for allocating DRT cars and system thereof}

The present invention relates to a method and system for dispatching DRT request information clustering, and more particularly, to a method and system for effectively dispatching multi-passenger vehicles for DRT service through clustering in order to effectively operate the DRT system.

Demand Responsive Transport (DRT) service is a future public transportation service that operates in areas with poor means of transportation. Means a means of transportation that helps

Demand-responsive transportation services that flexibly operate vehicles, get on and off, and travel routes according to passenger requests (calls) raise the level of transportation welfare in rural and rural areas where mobility has deteriorated due to population decline, and It is a next-generation public transportation service that can resolve the imbalance with public transportation welfare benefits in the metropolitan area while reducing the necessary cost.

In particular, the existing line-type public transportation service in areas with poor transportation is inconvenient to wait for 1 hour at a minimum and 3 to 4 hours at a maximum for each region as it is an operation method that moves only to a designated stop (route) at a set time. There is a problem in that the time and cost efficiency is low as it has to operate even if there are no passengers on board.

1 is a diagram schematically illustrating an existing line-type public transportation means.

As shown in FIG. 1, the existing route-type public transportation service is operated in a region with low population density and not many passengers due to the nature of providing transportation services to passengers while repeatedly circulating a set route with multi-passenger vehicles. There is an unavoidable problem of operating a transportation operator in the red.

On the other hand, the DRT service is cheaper to operate than existing route-type public transportation, aims to improve transportation service satisfaction, and aims to provide transportation service that satisfies both operators and passengers (users), minimizing the above-mentioned problems. There is a need to introduce technology to do this.

Republic of Korea Patent No. 10-1297073 (Announced on August 14, 2013)

The problem to be solved by the present invention is to provide a method capable of maximizing the efficiency of an existing demand-responsive transportation service and a system for implementing the method.

Method according to an embodiment of the present invention for solving the above technical problem, the step of receiving a demand response transport (Demand Responsive Transport) service request from a plurality of user terminals; identifying a request time and request location included in the received request for each user, and searching for demand forecast information corresponding to the identified request time and request location; classifying a plurality of users corresponding to the user terminal into at least one cluster according to the searched demand forecast information when the demand forecast information is retrieved; and calculating an optimal movement route for each cluster, and transmitting the calculated movement route to a waiting multi-occupant vehicle for demand response type transportation.

In the method, in the step of transmitting the transmission to the multi-occupant vehicle, in the process of calculating the optimal movement route for each cluster, a user located closest to the on-demand transport multi-occupant vehicle is designated as a first-priority occupant. can be set

In the method, in the step of classifying into clusters, if the demand forecast information is not searched, calculating the expected arrival time for each user terminal and transmitting the calculated expected arrival time to the multi-occupant vehicle includes: Accordingly, a movement route for moving the waiting multi-occupant vehicle to a location corresponding to each user terminal may be calculated, and the calculated movement route may be transmitted to the multi-occupant vehicle.

A system according to another embodiment of the present invention for solving the above technical problem includes a communication unit for receiving a demand response traffic service request from a plurality of user terminals; and a processor for processing a request received by the communication unit, wherein the processor identifies a request time and request location included in the received request for each user, and predicts demand corresponding to the identified request time and request location. Information is searched, and if the demand forecast information is found, a plurality of users corresponding to the user terminal are classified into at least one cluster according to the searched demand forecast information, and an optimal movement route is calculated for each cluster. After that, the communication unit controls the calculated movement route to be transmitted to the demand response type transport multi-occupant vehicle.

An embodiment of the present invention may provide a computer readable recording medium storing a program for executing the method.

According to the present invention, the efficiency of the demand response type transportation service can be improved, and the mobility of passengers residing in rural areas can be maximized.

According to the present invention, it is not necessary to operate a vehicle unnecessarily or to have a driver driving the vehicle, so that the profits of business operators operating demand-responsive transportation services can be greatly improved.

1 is a diagram schematically illustrating an existing line-type public transportation means.
2 is a diagram schematically illustrating a demand-responsive transportation service in which a method according to the present invention is implemented.
Figure 3 is a diagrammatic representation of the entire system implementing the method according to the invention.
4 is a block diagram showing an example of the management server described in FIG. 3 .
5 is a flowchart illustrating an example of a method according to the present invention.

Since the present invention can apply various transformations and have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. Effects and features of the present invention, and a method of achieving them will become clear with reference to the embodiments described later in detail together with the drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various forms.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, and when describing with reference to the drawings, the same or corresponding components are given the same reference numerals, and overlapping descriptions thereof will be omitted. .

In the following embodiments, terms such as first and second are used for the purpose of distinguishing one component from another component without limiting meaning.

In the following embodiments, singular expressions include plural expressions unless the context clearly indicates otherwise.

In the following embodiments, terms such as include or have mean that features or elements described in the specification exist, and do not preclude the possibility that one or more other features or elements may be added.

When an embodiment is otherwise embodied, a specific process sequence may be performed differently from the described sequence. For example, two processes described in succession may be performed substantially simultaneously, or may be performed in an order reverse to the order described.

2 is a diagram schematically illustrating a demand-responsive transportation service in which a method according to the present invention is implemented.

A route indicated by a dotted line in FIG. 2 means a route through which service providing vehicles repeatedly circulate when an existing route circulation type transportation service is provided to users, and has already been described in FIG. 1 .

A route indicated by a solid line in FIG. 2 means a route visited by a service providing vehicle on a one-time basis when an existing demand-responsive transportation service is provided to users. That is, the route indicated by the solid line in FIG. 2 is an irregular route established according to the user's request. Hereinafter, for convenience of description, a path in which service providing vehicles circulate once while providing demand response type transportation service will be abbreviated as DRT service path.

Although the DRT service path of FIG. 2 is one-time in view of the nature of the service provided to the user, in the present invention, by including the past DRT service path in information constituting "demand forecast information", demand response type transportation service is considered. to maximize the efficiency of

Figure 3 is a diagrammatic representation of the entire system implementing the method according to the invention.

Referring to FIG. 3 , the system 1 according to the present invention has a structure in which a user terminal group 110 and a vehicle group 120 are connected to a management server 200 through a communication network 130 .

The user terminal group 110 of FIG. 3 may include at least one or more user terminals. For example, the number of user terminals included in the user terminal group 110 may be one or n as shown in FIG. 3 . In FIG. 3, n may be an integer greater than 1. In the user terminal of the present invention, a user is a person who intends to move to a destination through a demand-responsive transportation service, and means a reserve passenger of a DRT service vehicle. Each user terminal included in the user terminal group 110 refers to an electronic device equipped with a communication module capable of communicating with the management server 200 .

A user terminal refers to a smart device including an input device for receiving a user's input, an output device (display) for visually outputting an input of a user terminal or a processing result of a terminal, and a communication module capable of communicating with an external device, As long as it includes only the aforementioned input device, output device and communication module, it is not limited in size or type. For example, although the user terminal is shown in the form of a smart phone in FIG. 3, the user terminal may be a PC, laptop, netbook, etc. capable of communicating with the management server 200.

The vehicle group 120 may include at least one or more DRT service vehicles. For example, the number of DRT service vehicles included in the vehicle group 120 may be one or m as shown in FIG. 3 . In FIG. 3, m may be an integer greater than or equal to 1. In FIG. 3 , the DRT service vehicle refers to a vehicle operated by an operator providing a demand response type transportation service. A vehicle terminal is built into the DRT service vehicle, and the vehicle terminal can receive specific information from the management server 200 and visually or audibly output the received information to a driver driving the DRT service vehicle. Each vehicle terminal included in the DRT service vehicle is also an electronic device equipped with a communication module capable of communicating with the management server 200 like a user terminal, and is omitted from FIG. 3 for intuitive description.

The management server 200 is a server on which an integrated management program is installed, and refers to a server that manages and controls data flow while communicating with elements included in the user terminal group 110 and the vehicle group 120 . An integrated management program (integrated management app) is installed in the management server 200, and according to an embodiment, a part of the integrated management program is implemented in the form of a client that runs in a user terminal and a vehicle terminal, respectively, so that the user terminal and It can be installed in a vehicle terminal.

The communication network 130 performs a function of connecting the user terminal group 110 and the vehicle group 120 with the management server 200, and may include various wired and wireless communication networks such as data networks, mobile communication networks, and the Internet.

4 is a block diagram showing an example of the management server described in FIG. 3 .

Referring to Figure 4, it can be seen that the management server 200 according to an embodiment of the present invention includes a database 210, a communication unit 230, a processing unit 250 and an output unit 270.

The management server 200 according to an embodiment of the present invention corresponds to at least one or more processors, or may include at least one or more processors. Accordingly, the communication unit 230, the processing unit 250, and the output unit 270 included in the management server 200 and the management server 200 are driven in a form included in a hardware device such as a microprocessor or a general-purpose computer system. It can be.

The name of each module included in the management server 200 shown in FIG. 2 is arbitrarily named to intuitively describe the representative function performed by each module, and when the management server 200 is actually implemented, each module may be given a name different from that described in FIG. 2 .

In addition, the number of modules included in the management server 200 of FIG. 2 may vary depending on the embodiment. More specifically, the management server 200 of FIG. 2 includes a total of three modules, but according to an embodiment, at least two or more modules are integrated into one module, or at least one or more modules are separated into two or more modules. It may be implemented in the form of

The database 210 stores various data necessary for the management server 200 to operate. As an example, the database 210 stores an integrated management program for controlling the operation of the management server 200, and the database 210 stores demand forecast information collected and cleaned in various ways. . Demand forecasting information will be described later.

The communication unit 230 communicates with the user terminal and the vehicle terminal, transmits information received from the user terminal and the vehicle terminal to the processing unit 250, or transmits information processed by the processing unit 250 to the user terminal and the vehicle terminal. function can be performed.

The processing unit 250 processes data received by the communication unit 230 and data to be transmitted. As another example, the processing unit 250 receives the demand response type transportation service request received from the user terminal, determines whether there is demand prediction information, and accordingly determines the optimal movement route for providing the DRT service to the vehicle terminal. It can handle the operation to provide.

The output unit 270 receives a command from the processing unit 250 and performs a function of calculating and outputting various data. As an example, the output unit 270 may output the result data processed by the processing unit 250 and transmit it to the communication unit 230, and the result data transmitted to the user terminal and the vehicle terminal via the communication unit 230 is the user A screen output from a display device of a terminal and a vehicle terminal may be controlled.

Hereinafter, each module of the management server 200 described in FIG. 4 will be described in detail through which process the efficiency of the DRT service can be maximized, and will be described with reference to FIG. 3 .

First, the communication unit 230 receives a demand response type transportation service request from a plurality of user terminals. The communication unit 230 transmits the received demand response traffic service request to the processing unit 250 .

The processing unit 250 identifies the request time and request location included in the request received by the communication unit 230 for each user terminal, and searches whether there is demand forecast information corresponding to the identified request time and request location. Here, the request time may include at least one of a time when the user intends to board the DRT service vehicle and a time when the user arrives at a destination via the DRT service vehicle. In addition, the requested location may include at least one of a departure location (originating point) where the user intends to board the DRT service vehicle and a destination location where the user intends to move, and may be in the form of GPS coordinates or land numbers.

When the demand forecast information is retrieved, the processing unit 250 may classify a plurality of users corresponding to the user terminal into one or more clusters based on the searched demand forecast information.

The demand forecast information is information stored in the database 210 or the processing unit 250, and may be a condition for the processing unit 250 to classify a plurality of users corresponding to user terminals into at least one cluster. Demand forecast information can be various types of information. As an example, the demand forecasting information may be a demand forecasting scenario composed of information about a radius centered on a user and a maximum waiting time.

Table 1 is a table showing demand forecast information stored in the database 210 or the processing unit 250 by way of example. Scenario 1 in Table 1 is a scenario in which users located within a radius of 500 m from the initial requester and whose waiting time is less than 10 minutes are classified into the same cluster. Scenario 2 in Table 1 is a scenario in which users located within a radius of 1000 m from the initial requester and whose waiting time is less than 15 minutes are classified into the same cluster. Although only two scenarios are included in Table 1, this is for convenience of description, and when the present invention is actually implemented, numerous demand forecasting scenarios may be included in the database 210 or the processing unit 250.

In addition, in Table 1 described above, only the time and location of the user boarding the DRT service vehicle were described based on the standard, but in the present invention, the requested time includes not only the time the user boards the DRT service vehicle, but also the expected time the user arrives at the destination. Time may be included and the requested location may further include coordinate information on the destination location, so the demand forecasting scenario may be longer and more complex than those described in Table 1.

The processing unit 250 classifies a plurality of users into one or more clusters, and dispatches DRT service vehicles for each cluster. For example, if 10 users are classified into 2 clusters (clusters each including 4 users and 6 users), a total of two DRT service vehicles may be allocated.

The processing unit 250 calculates the optimal travel path for each cluster, and controls the communication unit 230 to transmit the calculated travel path to the waiting DRT service vehicle (demand response type traffic multi-seater vehicle). A driver driving a DRT service vehicle can efficiently transport a plurality of users by checking the optimal movement route output from the vehicle terminal and moving. According to the present invention, a driver driving a DRT service vehicle does not arbitrarily determine a route, but as an optimized movement route is determined by the processing unit 250, many users can be transported in the shortest time.

The processing unit 250 may calculate the distance between the stop position of the DRT service vehicle and the position of a plurality of user terminals, and determine a user located at the shortest distance as a first passenger (first priority passenger). After the first occupant is determined, the processing unit 250 may determine a user who is located closest to the first occupant or whose departure time is earlier as the second-priority occupant according to the demand prediction information, and the second-priority occupants and subsequent occupants For , an optimal movement path can be calculated through the same process.

As an optional embodiment, the information referred to by the processing unit 250 as demand forecast information may include not only fixed information but also updated DRT service information. For example, the processing unit 250 may physically or logically include a learning model that performs a machine learning algorithm, and the learning model included in the processing unit 250 provides past DRT services at regular intervals. By analyzing and learning the history (one of 3 days, 1 week, 2 weeks, and a month), more effective demand forecasting scenarios can be added or the contents of the previously set demand forecasting scenarios can be partially changed. According to this optional embodiment, the longer the DRT service is operated, the more quality data is accumulated, and by supplementing the demand forecasting scenario with the accumulated data, it is more efficient than the initially set demand forecasting scenario and minimizes customer complaints. Demand forecasting scenarios can be built automatically.

Table 2 is a table for explaining a typical DRT service. As shown in Table 2, when 4 users request DRT service at different request times from 7:00 am to 7:20 am, a total of 4 DRT service vehicles arrive at Stop A in response to each request time. Therefore, since the users P ₁ to P ₄ must be moved to the destination, high operating costs including labor costs for four drivers and fuel costs for four vehicles are generated.

Table 3 is a table for explaining DRT allocation states optimized by the system according to the present invention. As shown in Table 3, users P ₁ , P ₂ , and P ₃ are classified into cluster 1, and all three of them board vehicle 1, and user P ₄ is classified into cluster 2 and board vehicle 2. or wait until additional users are recruited. In order to calculate the above result, the processing unit 250 may refer to a scenario in which the maximum waiting time is less than 15 minutes as a demand prediction scenario, and in order to board all users P ₁ to P ₃ , the first vehicle is called A stop. , together with a message to the effect of arriving by 7:15 AM, an optimal travel route to reach A stop may be transmitted.

In Table 3, for intuitive explanation, the starting point is described as A stop, but if the starting points of users _P1 to _P4 are A, B, and D, respectively, and B is closest to the DRT service vehicle, , The processing unit 250 may construct an optimal movement path to visit B, A, and C in order, and transmit the information to the vehicle terminal. As an extended embodiment, the processing unit 250 may additionally consider the arrival time of the destination in Table 3 to assign DRT service vehicles and build an optimal movement route, and the process for this may be the same as described above. It can be.

As another example, the processing unit 250 may coordinate the arrival time of the DRT service vehicle at each departure point. For example, in Table 3, when the DRT service vehicle first moves to stop B, the processing unit 250 may transmit a guide message about delayed arrival to the user terminal of user P ₁ located at stop A with an earlier request time. . In this case, user P ₁ may stop using the service after receiving the guide message, and the processing unit 250 may reconstruct an optimal moving route excluding stop A and transmit the reconstructed route to the vehicle terminal.

As another optional embodiment different from the above-described embodiment, based on the occurrence probability information, demand forecast information may be set, and the arrival time of the DRT service vehicle to each departure point may be adjusted. In order to implement this optional embodiment, processing unit 250 may refer to database information such as Table 4.

Table 4 tabulates the statistics of occupants who boarded the vehicle from 8:00 AM to 9:00 AM for ten days. Specifically, the horizontal axis of Table 4 represents passengers boarding every 5 minutes from 8:00 am to 9:00 am, and the vertical axis of Table 4 represents boarding statistics of passengers by day. In addition, referring to Table 4, it can be seen that passenger information is aggregated in units of 5 minutes, 10 minutes, and 15 minutes.

For example, referring to Table 4, assuming that passenger information is aggregated in units of 5 minutes, the value of 3.1 for passengers who boarded between 8:30 and 8:35 am is 10% for 3 confirmed passengers. The probability of occurrence of can be interpreted by the processing unit 250 as a value added. In other words, as shown in Table 4, if it is recorded that more than 3.1 people ride on average during a specific time period, this can predict demand that one or more passengers will occur with a 310% riding probability during that time period.

Demand forecasting scenarios set in the processing unit 250 may be defined not only for 5-minute probabilities, but also for 10-minute probabilities, 15-minute probabilities, 20-minute probabilities, and 30-minute probabilities, respectively. If it is greater than the value, the processing unit 250 may adopt a demand prediction scenario for the corresponding time probability. For example, if the preset value is "2 confirmed people", referring to Table 4, 5-minute probabilities and 10-minute probabilities with fewer than 2 confirmed people in a specific section are excluded, and for the 15-minute probability Only the demand forecasting scenario can be adopted. As another example, if the predetermined value is "3 confirmed persons", demand prediction scenarios for 20-minute probability and 30-minute probability omitted from Table 4 may be adopted. Preset values of the processing unit 250 may be continuously updated for the floating population, population density, and information on the number of passengers that are collected and updated.

As described above, the processing unit 250 may reasonably determine an appropriate driving interval of the DRT service vehicle based on the occurrence probability information.

5 is a flowchart illustrating an example of a method according to the present invention.

Since Figure 5 can be implemented by the management server 200 according to Figure 4, hereinafter, it will be described with reference to Figures 3 and 4.

The management server 200 receives a DRT service request from user terminals included in the user terminal group 110 (S510).

The management server 200 identifies the request time and request location for each user terminal, and searches whether there is demand prediction information corresponding to the identified request time and request location (S520).

The management server 200 determines whether demand forecast information is retrieved (S530), and when the demand forecast information is retrieved, it can classify users into one or more clusters according to the searched demand forecast information (S540). Users classified in the same cluster in step S540 can board the same DRT service vehicle.

If the demand prediction information is not retrieved, the management server 200 calculates the expected arrival time for each user terminal (S550).

The management server 200 calculates the boarding location of each user and the travel route to the destination, and transmits the calculated travel route to the DRT service vehicle (demand response type transportation multi-seater vehicle), thereby providing efficient movement of the DRT service vehicle. can assist

According to the present invention, the efficiency of the demand response type transportation service can be improved, and the mobility of passengers residing in rural areas can be maximized.

In addition, according to the present invention, it is not necessary to operate a vehicle unnecessarily or to have a driver driving the vehicle, so that the profits of business operators operating demand-responsive transportation services can be greatly improved.

Embodiments according to the present invention described above may be implemented in the form of a computer program that can be executed through various components on a computer, and such a computer program may be recorded on a computer-readable medium. At this time, the medium is a magnetic medium such as a hard disk, a floppy disk and a magnetic tape, an optical recording medium such as a CD-ROM and a DVD, a magneto-optical medium such as a floptical disk, and a ROM hardware devices specially configured to store and execute program instructions, such as RAM, flash memory, and the like.

Meanwhile, the computer program may be specially designed and configured for the present invention, or may be known and usable to those skilled in the art of computer software. An example of a computer program may include not only machine language code generated by a compiler but also high-level language code that can be executed by a computer using an interpreter or the like.

Specific implementations described in the present invention are examples and do not limit the scope of the present invention in any way. For brevity of the specification, description of conventional electronic components, control systems, software, and other functional aspects of the systems may be omitted. In addition, the connection of lines or connecting members between the components shown in the drawings are examples of functional connections and / or physical or circuit connections, which can be replaced in actual devices or additional various functional connections, physical connection, or circuit connections. In addition, if there is no specific reference such as “essential” or “important”, it may not be a component necessarily required for the application of the present invention.

In the specification of the present invention (especially in the claims), the use of the term “above” and similar indicating terms may correspond to both singular and plural. In addition, when a range is described in the present invention, it includes an invention in which individual values belonging to the range are applied (unless there is a description to the contrary), and each individual value constituting the range is described in the detailed description of the invention Same as Finally, unless an order is explicitly stated or stated to the contrary for the steps constituting the method according to the present invention, the steps may be performed in any suitable order. The present invention is not necessarily limited according to the order of description of the steps. The use of all examples or exemplary terms (eg, etc.) in the present invention is simply to explain the present invention in detail, and the scope of the present invention is limited due to the examples or exemplary terms unless limited by the claims. it is not going to be In addition, those skilled in the art can appreciate that various modifications, combinations and changes can be made according to design conditions and factors within the scope of the appended claims or equivalents thereof.

110: user terminal group
120: vehicle group
130: communication network
200: management server
210: database
230: communication department
250: processing unit
270: output unit

Claims (5)

Receiving a Demand Responsive Transport service request from a plurality of user terminals;
identifying a request time and request location included in the received request for each user terminal, and searching for demand forecast information corresponding to the identified request time and request location;
classifying a plurality of users corresponding to the user terminal into at least one cluster according to the searched demand forecast information when the demand forecast information is retrieved; and
Calculating an optimal travel route for each cluster and transmitting the calculated travel route to a demand-responsive transportation multi-occupant vehicle in standby;
The step of transmitting to the demand response type traffic multi-occupant vehicle,
The DRT request information clustering dispatch method, wherein the demand response type transport multi-occupant vehicle calculates an optimal movement route for transporting a plurality of users belonging to the cluster to each destination included in the requested location through one operation. According to claim 1,
The step of transmitting to the multi-occupant vehicle,
In the process of calculating the optimal movement route for each cluster, the DRT request information clustering allocation method of setting a user located closest to the waiting multi-occupant vehicle as a first-priority passenger. According to claim 1,
The step of classifying into the clusters,
If the demand prediction information is not retrieved, an estimated arrival time is calculated for each user terminal,
The step of transmitting to the multi-occupant vehicle,
A DRT request for calculating a movement route for moving a waiting demand-responsive transportation multi-seater vehicle to a location corresponding to each user terminal according to the calculated estimated arrival time, and transmitting the calculated movement route to the multi-seater vehicle. Information clustering allocation method. A computer-readable recording medium storing a program for executing the method according to any one of claims 1 to 3. a communication unit for receiving a demand response type traffic service request from a plurality of user terminals; and
Including a processor for processing the request received by the communication unit,
the processor,
Identifying the request time and request location included in the received request for each user terminal, and searching for demand forecast information corresponding to the identified request time and request location;
When the demand forecast information is retrieved, a plurality of users corresponding to the user terminal are classified into at least one cluster according to the searched demand forecast information;
After calculating the optimal travel route for each cluster, the communication unit controls the calculated travel route to be transmitted to a waiting demand response type transport multi-occupant vehicle;
the processor,
The DRT request information clustering dispatch system, wherein the demand response type transport multi-passenger vehicle calculates an optimal movement route for transporting a plurality of users belonging to the cluster to each destination included in the requested location through one operation.

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