Estimation device, estimation method, and program

Abstract

To provide an estimation apparatus, an estimation method, and a program for calculating doorstep time necessary for formulating a more accurate delivery plan.SOLUTION: An estimation apparatus 100 includes: a position information acquisition unit 110 which acquires the time series of position of a first deliverer; a moving speed estimation unit 120 which estimates the moving speed of the first deliverer at each position; a partial time series extraction unit 130 which extracts partial time series from the acquired time series, based on the estimated moving speed; a partial time series selection unit which selects a partial time series to be associated with a first delivery destination; and a doorstep time estimation unit 150 which specifies an alighting position and a boarding position, based on a preset rule, from the selected partial time series, estimates a first stopping / parking position based on the specified alighting position and boarding position, and estimates a doorstep time related to a combination of the first delivery destination and the first stopping / parking position, from the time at which the boarding position is detected and the time at which the alighting position is detected.SELECTED DRAWING: Figure 3

Description

[Technical Field]

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

[0002] There is a known technology for creating a delivery plan for multiple packages delivered by a delivery vehicle. For example, Patent Document 1 discloses a presentation device that calculates a user's interest level based on the difference between the time when delivery notification information was sent and the time when the delivery notification information was viewed, and sets the delivery order of multiple packages based on the calculated interest level. This presentation device determines the scheduled delivery time of each package by searching map information for a delivery route that minimizes delivery costs using the delivery destination address of each package. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2019 / 138776 Summary of the Invention [Problem to be solved by the invention]

[0004] However, typically, a delivery person temporarily parks and stops their vehicle, gets out, and then delivers the package to the delivery destination. Therefore, the time it takes from parking and delivering the package to getting back into the vehicle varies greatly depending on whether the delivery destination is, for example, a high-rise apartment building or a single-family home facing a public road. This time is called the doorstep time. Therefore, there is a possibility that the delivery may not be made according to a delivery plan created in advance. Therefore, by taking the doorstep time for each delivery destination into consideration, it is possible to create a more accurate delivery plan.

[0005] The present invention has been made in consideration of the above-described circumstances, and provides an estimation device, an estimation method, and a program for calculating a doorstep time required to create a more accurate delivery plan. [Means for solving the problem]

[0006] In order to solve the above problems, an estimation device according to the present invention includes: An estimation device that estimates a door step time, which is a time from when a delivery person gets off a vehicle, delivers a package to a delivery destination, and gets on the vehicle, Detected in the process of the first deliverer delivering the first package to the first delivery destination using the first vehicle 、 The first deliverer Delivery person terminal carried by a location information acquisition unit that acquires a time series of the location of the a movement speed estimation unit that estimates the movement speed of the first delivery person at each of the locations from the time at which each of the locations included in the time series acquired by the location information acquisition unit was detected; a partial time series extraction unit that extracts a partial time series in which it is estimated that the first delivery person is not riding in the first vehicle from the time series acquired by the location information acquisition unit based on the moving speed estimated by the moving speed estimation unit; and a partial time series selection unit that selects a partial time series to be associated with the first delivery destination from the partial time series extracted by the partial time series extraction unit; a door step time estimation unit that identifies, based on a predetermined rule, a drop-off location where the first delivery person dropped off the first vehicle and a boarding location where the first delivery person boarded the first vehicle from the partial time series selected by the partial time series selection unit, estimates a first parking location where the first delivery person parked the first vehicle to deliver the first package to the first delivery destination based on the identified drop-off location and the identified boarding location, and estimates a door step time for the combination of the first delivery destination and the first parking location from the time when the identified boarding location was detected and the time when the identified drop-off location was detected; Equipped with. [Effects of the Invention]

[0007] According to the present invention, it is possible to provide an estimation device, an estimation method, and a program for calculating the door step time required to create a more accurate delivery plan. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is an explanatory diagram showing cooperation between the estimation device and other devices. [Figure 2] FIG. 10 is a diagram showing an example of a delivery instruction list received by the deliverer terminal. [Figure 3] FIG. 2 is an explanatory diagram illustrating a functional configuration of the estimation device. [Figure 4] FIG. 10 is a diagram illustrating an example of time-series data acquired by a location information acquisition unit. [Figure 5] FIG. 10 is a diagram illustrating an example of time series data extracted by a partial time series extraction unit. [Figure 6] 10A and 10B are diagrams illustrating an example of processing in which a partial time series extraction unit links a partial time series with a delivery destination. [Figure 7] FIG. 1 is a diagram showing an example of time-series data plotted on a map. [Figure 8] 8 is a diagram showing the beginning and end of a partial time series of the time series data illustrated in FIG. 7. FIG. [Figure 9] FIG. 10 is a diagram showing an example of a doorstep time table generated by a doorstep time estimation unit. [Figure 10] FIG. 10 is a diagram showing an example of a delivery schedule list received by a delivery destination information acquisition unit. [Figure 11] FIG. 10 is a diagram illustrating an example of a prediction result table generated by a prediction unit. [Figure 12] FIG. 2 is an explanatory diagram illustrating a physical configuration of the estimation device. [Figure 13] 10 is a flowchart of a doorstep time estimation process. [Figure 14] 10 is a flowchart of a doorstep time prediction process. [Figure 15] FIG. 10 is a diagram illustrating an example of processing by a prediction unit. [Figure 16] FIG. 10 is a diagram illustrating another example of processing by the prediction unit. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Note that these embodiments are for illustrative purposes only and do not limit the scope of the present invention. Therefore, those skilled in the art may adopt embodiments in which each or all of the elements are replaced with equivalents, and these embodiments are also within the scope of the present invention.

[0010] (Relationship between the estimation device, the delivery person's terminal, and the program) The estimation device according to this embodiment estimates the door step time, which is the time it takes for a deliverer driving a vehicle to deliver a package to a delivery destination to stop the vehicle at a parking location, get off the vehicle, deliver the package, return to the parking location, and get back into the vehicle. The estimation device is configured with one or more server devices. The estimation device collects a time series of the location of a deliverer terminal, which is a computer operated by the deliverer, and calculates the door step time for a combination of a delivery destination and a parking location based on the collected time series.

[0011] The estimation device of this embodiment is generally realized by having a computer execute a program, but it is also possible to have a dedicated electronic circuit execute the processing. In addition, as an intermediate form between a computer and a dedicated electronic circuit, it is also possible to configure the estimation device of this embodiment by applying a technology such as FPGA (Field Programmable Gate Array), which compiles a program into a design script for an electronic circuit and dynamically configures the electronic circuit based on the design script.

[0012] The estimation device according to this embodiment is realized by one or more server computers that communicate with the deliverer terminal executing the functions realized by one or more server programs.

[0013] The deliverer terminal according to this embodiment is a terminal computer that realizes a smartphone, tablet computer, wearable terminal, etc., and can be realized by executing a terminal program that is provided in advance. The terminal program can be equivalent to a so-called "app."

[0014] In addition, a general browser can be used as the terminal program, or a script program that runs on a browser can be used as the terminal program.

[0015] Generally, programs executed on server computers and terminal computers can be recorded on computer-readable non-transitory information recording media such as compact discs, flexible disks, hard disks, magneto-optical disks, digital video disks, magnetic tapes, ROMs (Read Only Memory), EEPROMs (Electrically Erasable Programmable ROM), flash memories, semiconductor memories, etc. These information recording media can also be distributed or sold independently of the server computers.

[0016] In server computers and terminal computers, programs stored on non-transitory information recording media such as flash memory or hard disks are read into random access memory (RAM), a temporary storage device, and then the instructions contained in the read program are executed by a central processing unit (CPU). However, in architectures that allow ROM and RAM to be mapped and executed in a single memory space, the instructions contained in the program stored in ROM are directly read and executed by the CPU.

[0017] Furthermore, server programs and terminal programs can be distributed and sold to server computers and terminal computers from distribution servers managed by operators via temporary transmission media such as computer communications networks, independently of the computers on which the programs are executed.

[0018] In addition, when the estimation device is configured with multiple computers, the programs running on each computer are multiple different server programs that have different functions but work together, and the combination of these multiple programs can be considered as a system program for realizing the prediction device.

[0019] (Overall composition) 1 is an explanatory diagram showing the cooperation between the estimation device and other devices. The following description will be made with reference to this diagram.

[0020] The estimation device 100 is connected to a delivery person terminal 200 via a computer communication network 300 such as the Internet.

[0021] The estimation device 100 is operated, for example, by a parcel delivery service provider, an online supermarket, or the like. Based on past delivery records, the estimation device 100 estimates the door step time for each delivery destination, which is the time it takes for the deliverer to park their vehicle at a parking location, get off the vehicle, deliver the package, return to the parking location, and get back on the vehicle. The estimation device 100 acquires time series data indicating the location of the deliverer terminal 200 and calculates the travel speed at each location. Based on the travel speed, the estimation device 100 extracts a partial time series indicating a section where the deliverer is estimated to have disembarked from the vehicle, and uses the extracted partial time series to identify an estimated parking location for each delivery destination and calculate the door step time. The estimation device 100 stores the calculation results in a database. When delivering a package to a new delivery destination not registered in the database, the estimation device 100 refers to the database and calculates a predicted door step time for the new delivery destination based on information about delivery destinations with matching or similar addresses. Note that a delivery destination refers to a combination of an address and a name, and the estimation device 100 identifies delivery destinations with the same address but different names as different delivery destinations.

[0022] The deliverer terminal 200 is a smartphone, tablet, or other dedicated delivery terminal carried by a deliverer who delivers packages while working. The deliverer terminal 200 includes a location detection means for detecting the location of the deliverer terminal 200 and a display means for displaying information.

[0023] The position detection means includes a receiver that receives signals from a satellite. The receiver is, for example, a Global Positioning System (GPS), a system for measuring the current location. The position detection means acquires the latitude and longitude, which are position information of the deliverer terminal 200, based on signals received by the GPS at preset intervals. The deliverer terminal 200 associates the position information acquired by the position detection means with the time of acquisition and transmits the information to the estimation device 100. The position detection means may acquire the position information of the deliverer terminal 200 based on a mobile phone base station communicating with the deliverer terminal 200. Alternatively, the position detection means may acquire the position information of the deliverer terminal 200 based on a wireless LAN base station communicating with the deliverer terminal 200.

[0024] The display means may be, for example, a liquid crystal display or an organic electroluminescence (EL) display. For example, the delivery person displays a delivery instruction list showing a list of delivery destinations by day, half-day, etc. on the display means, and checks the addresses of the delivery destinations, the delivery order, etc., before delivering the parcels. This delivery instruction list is created by a delivery plan planning system that creates a delivery plan, and is transmitted to the estimation device 100 and the deliverer terminal 200. As illustrated in FIG. 2, the delivery instruction list includes information such as a "delivery ID" for uniquely identifying delivery information, a "delivery destination ID" that is identification information for identifying the delivery destinations, a "name" indicating the name of the delivery destination, an "address" indicating the address of the delivery destination, and a "target delivery time" indicating the target time of delivery for each delivery destination. The delivery instruction list may also include information such as the delivery time slot specified by the customer when the order was accepted and a parcel ID for identifying the parcel to be delivered.

[0025] (Functional configuration of the estimation device) Next, the functional configuration of the estimation device 100 will be described with reference to Fig. 3. The estimation device 100 includes a location information acquisition unit 110, a travel speed estimation unit 120, a partial time series extraction unit 130, a disembarking / boarding position identification unit 140, a doorstep time estimation unit 150, a doorstep time storage unit 160, and a doorstep time prediction unit 170.

[0026] The location information acquisition unit 110 acquires time series data transmitted from the deliverer terminal 200, including the location information of the deliverer terminal 200 and the time the location information was acquired. The time series data is stored in the memory of the estimation device 100. As illustrated in FIG. 4, the time series data includes information such as a "date" indicating the date on which the deliverer terminal 200 acquired the location information, a "time t" indicating the time of acquisition, and "location information" indicating the acquired location coordinates. The time series data may also include information such as a terminal ID that identifies the deliverer terminal 200 that collected the location information.

[0027] 3, the movement speed estimation unit 120 estimates the movement speed of the delivery person terminal 200. Specifically, the movement speed estimation unit 120 calculates the movement distance and movement time of the delivery person terminal 200 from the time information and location information included in the time series data illustrated in FIG. 4 stored in the memory of the estimation device 100, and calculates the movement speed of the delivery person terminal 200 from the calculated movement distance and movement time.

[0028] Returning to FIG. 3 , the partial time series extraction unit 130 extracts, for each delivery destination, a partial time series, which is time series data for a section where it is estimated that a delivery person is not in a vehicle, from the time series data accumulated in the memory of the estimation device 100. Specifically, the partial time series extraction unit 130 extracts a partial time series for each delivery destination based on a predetermined rule. For example, as illustrated in FIG. 5 , the partial time series extraction unit 130 extracts data from the time series data shown in FIG. 4 acquired by the location information acquisition unit 110, where the travel speed calculated by the travel speed estimation unit 120 is equal to or less than a predetermined threshold, for example, 6 km / h. Note that the threshold for the travel speed is not limited to 6 km / h, and any speed at which it is estimated that a delivery person is not traveling in a vehicle may be set.

[0029] Next, the partial time series extraction unit 130 divides the time series data whose moving speed is equal to or less than a threshold into multiple temporally consecutive partial time series. For example, time series data from delivering packages to multiple delivery destinations includes at least multiple partial time series for each delivery destination in which it is estimated that a delivery person is not in the vehicle. The partial time series extraction unit 130 divides the extracted time series data into multiple partial time series based on an arbitrary rule, such as classifying two pieces of time series data into the same partial time series if the time difference from the previous piece of time series data is within one minute, and classifying two pieces of time series data into different partial time series if the time difference from the previous piece of time series data is one minute or more.

[0030] Next, the partial time series extraction unit 130 links the partial time series with the delivery destinations. Specifically, the partial time series extraction unit 130 calculates the distance between the position of each time series included in each partial time series and the position of each delivery destination included in the delivery instruction list shown in Fig. 2, and links the partial time series with the closest distance to the delivery destination, as shown in Fig. 6. The partial time series extraction unit 130 is an example of a partial time series selection unit.

[0031] Returning to FIG. 3, the drop-off / boarding position identifying unit 140 identifies the drop-off and boarding positions of the vehicle when it delivers to the delivery destination, based on the partial time series linked to the delivery destination by the partial time series extraction unit 130. Specifically, the drop-off / boarding position identifying unit 140 extracts, from each partial time series linked to the delivery destination, a leading portion, which is position data acquired at an earlier time, and a trailing portion, which is position data acquired at a later time, based on a number of predetermined rules. FIG. 8 is a diagram showing the leading portion and the trailing portion of the partial time series of the time series data exemplified in FIG. 7. As shown in the figure, based on the rule of extracting five leading portions and five trailing portions, for example, the drop-off / boarding position identifying unit 140 extracts the first to fifth earliest acquired position data A1 to A5 as leading portions, and the first to fifth latest acquired position data B1 to B5 as trailing portions. Next, the disembarking and boarding position specifying unit 140 calculates the distance between each of the position data A1 to A5 of the front portion and each of the position data B1 to B5 of the rear portion, and specifies the position data of the front portion with the shortest distance as the disembarking position, and specifies the position data of the rear portion with the shortest distance as the boarding position. For example, if the distance between A5 and B2 is shorter than the distances between any other combinations, the disembarking and boarding position specifying unit 140 specifies A5 as the disembarking position and B2 as the boarding position.

[0032] Returning to FIG. 3, the door step time estimation unit 150 calculates a door step time associated with a combination of the vehicle's parking / stopping position and the delivery destination. Specifically, the door step time estimation unit 150 identifies the center position of the coordinates of the drop-off position and the boarding position identified by the drop-off / boarding position identification unit 140 as the parking / stopping position. Next, the door step time estimation unit 150 calculates the door step time by subtracting the time at which the identified drop-off position was detected from the time at which the identified boarding position was detected. The door step time estimation unit 150 associates the calculated door step time with the identified parking / stopping position and the delivery destination and stores it in the door step time estimation unit 150.

[0033] The door-step time storage unit 160 stores a door-step time table showing the door-step time for each delivery destination calculated by the door-step time estimation unit 150. As illustrated in FIG. 9, the door-step time table includes information such as a "delivery ID" for uniquely identifying delivery information, a "delivery destination ID" which is identification information for identifying the delivery destination, a "name" indicating the name of the delivery destination, an "address" indicating the address of the delivery destination, a "parking location" indicating location information of the parking location for each delivery destination, and a "door-step time" indicating the door-step time for each delivery destination. As illustrated, the door-step time estimation unit 150 may calculate the average of the time when the boarding location and the time when the disembarking location are detected as an estimated time for delivering the package, and include the calculated "estimated delivery completion time" in the door-step time table.

[0034] Returning to Figure 3, the doorstep time prediction unit 170 includes a delivery destination information acquisition unit 171, a doorstep time information extraction unit 172, a prediction unit 173, and an error estimation unit 174, and calculates a predicted value of the doorstep time of the scheduled delivery destination by referring to the doorstep time table stored in the doorstep time memory unit 160.

[0035] The delivery destination information acquisition unit 171 accepts input of a scheduled delivery list that lists delivery destinations with delivery schedules by day, half-day, etc., and acquires delivery information for each delivery destination. As illustrated in FIG. 10, the scheduled delivery list includes information such as a "delivery ID" for uniquely identifying delivery information, a "delivery destination ID" that is identification information for identifying the delivery destination, a "name" indicating the name of the delivery destination, an "address" indicating the address of the delivery destination, and a "designated delivery time" indicating the designated delivery time for each delivery destination. Note that if the user of the delivery destination does not specify a delivery time, the designated delivery time does not need to include time information. If only morning or afternoon is specified, it is sufficient to include information specifying whether it is morning or afternoon. The delivery destination information acquisition unit 171 acquires information for each delivery destination from the input scheduled delivery list and, by referring to the doorstep time table shown in FIG. 9, determines whether each delivery destination is a new delivery destination for which the doorstep time has not been calculated. Specifically, whether a delivery destination is a new delivery destination is determined by checking whether the delivery destination ID of each delivery destination included in the scheduled delivery list is included in the doorstep timetable, or whether the doorstep timetable contains a combination of name and address that matches the combination of each delivery destination included in the scheduled delivery list.

[0036] Returning to Fig. 3, the doorstep time information extraction unit 172 extracts doorstep time information including the doorstep time and parking / stopping position, which is data used to predict the doorstep time of each delivery destination, from the doorstep time table shown in Fig. 9 stored in the doorstep time storage unit 160. Specifically, if the delivery destination information acquisition unit 171 determines that the doorstep time of the delivery destination to be calculated has already been calculated and that it is not a new delivery destination, the doorstep time and parking / stopping position of the same delivery destination as this delivery destination are extracted from the doorstep time table.

[0037] Furthermore, if the delivery destination information acquisition unit 171 determines that the delivery destination to be calculated is a new delivery destination for which the door step time has not yet been calculated, the door step time information extraction unit 172 extracts the door step times and parking locations of multiple delivery destinations that have the same or similar addresses as the delivery destination from the door step time table. Details of this process will be described later.

[0038] The prediction unit 173 calculates predictions of the door step times and parking positions of delivery destinations included in the delivery schedule list illustrated in Fig. 10 based on the door step times and parking positions extracted by the door step time information extraction unit 172. Specifically, in the case of a prediction of a delivery destination that is determined to be the same as a delivery destination included in the door step time table, the prediction unit 173 identifies the door step times and parking positions stored in the door step time table as the door step times and parking positions of the delivery to be calculated.

[0039] Furthermore, when predicting a delivery destination that has been determined to be a new delivery destination, the prediction unit 173 calculates each prediction based on the door step times and parking positions of multiple delivery destinations that have addresses that are the same as or similar to the address of the delivery destination to be calculated, which have been extracted by the door step time information extraction unit 172. Specifically, the prediction unit 173 obtains the average door step times of the multiple extracted delivery destinations and the central position of the parking positions, and calculates the obtained average door step time and the central position of the parking positions as the door step time and parking position of the delivery destination to be calculated.

[0040] The error estimation unit 174 calculates the error between the door step time and the parking stop position of each delivery destination calculated by the prediction unit 173, and creates a prediction result table showing the predicted door step time for each delivery destination based on the prediction result by the prediction unit 173 and the calculated error. As shown in FIG. 11, the prediction result table includes information such as a "delivery destination ID" which is identification information for identifying the delivery destination, a "name" which indicates the name of the delivery destination, an "address" which indicates the address of the delivery destination, a "door step time" which indicates the door step time for each delivery destination, a "door step time error" which indicates the error in the door step time, "parking position 1," "parking position 2," and "parking position 3" which indicate the position information of the parking position for each delivery destination, and a "parking position error" which indicates the error in the parking position. Details of the processing by the error estimation unit 174 will be described later.

[0041] (Physical configuration of the estimation device) The estimation device 100 having the functional configuration described above physically comprises, as shown in FIG. 12, a CPU 11 that executes processing according to a program, a RAM 12 which is a volatile memory, a ROM (Read Only Memory) 13 which is a non-volatile memory, a storage unit 14 that stores data, an input unit 15 that accepts input of information, a display unit 16 that visualizes and displays information, and a communication unit 17 that transmits and receives information, all of which are connected via an internal bus 99.

[0042] The CPU 11 executes various processes by reading out the programs stored in the storage unit 14 into the RAM 12 and executing them. The CPU 11 executes the processes of a travel speed estimation unit 120, a partial time series extraction unit 130, a disembarking / boarding position identification unit 140, a doorstep time estimation unit 150, and a doorstep time prediction unit 170, which are main functions provided by the programs.

[0043] The RAM 12 is used as a work area for the CPU 11. The ROM 13 stores a control program executed by the CPU 11 for the basic operation of the estimation device 100, a BIOS (Basic Input Output System), and the like.

[0044] The storage unit 14 includes a hard disk drive, and stores the programs executed by the CPU 11 and various data used when the programs are executed. The storage unit 14 functions as a doorstep time storage unit 160.

[0045] The input unit 15 is a user interface including a keyboard, a mouse, a communication device, etc. The display unit 16 is a display device such as a liquid crystal display or an organic EL (Electro Luminescence) display that visualizes and displays information.

[0046] The communication unit 17 is a network termination device or a wireless communication device that connects to a network, and a serial interface or a LAN (Local Area Network) interface that connects to them.

[0047] (Doorstep time estimation process) Next, a description will be given of the operation of the estimation device 100. First, a description will be given of a door step time estimation process for estimating a door step time for each delivery destination based on past delivery records, with reference to FIG.

[0048] As a preliminary preparation, the time series data illustrated in Fig. 4 is stored in advance in the storage unit 14 of the estimation device 100. The location information acquisition unit 110 of the estimation device 100 receives the location information transmitted from the delivery person terminal 200 and the time when the location information was acquired, generates the time series data shown in the figure, and stores it in the storage unit 14.

[0049] When the user operates the input unit 15 of the estimation device 100 to set the period for which the user wishes to refer to time-series data and instructs the estimation device 100 to start the doorstep time estimation process, the estimation device 100 starts the process.

[0050] The location information acquisition unit 110 of the estimation device 100 acquires time-series data to be processed (step S101). Specifically, the location information acquisition unit 110 acquires time-series data for a set period from the storage unit 14 of the estimation device 100.

[0051] Next, the movement speed estimation unit 120 estimates the movement speed of the deliverer terminal 200 (step S102). Specifically, the movement speed estimation unit 120 calculates the movement distance and movement time of the deliverer terminal 200 from the time information and location information included in the time series data illustrated in FIG. 4 acquired in step S101, and calculates the movement speed of the deliverer terminal 200 from the calculated movement distance and movement time. For example, when calculating the movement speed at the illustrated location information (a2, b2), the movement speed estimation unit 120 calculates the movement distance, which is the distance between the location information (a1, b1) and (a2, b2) of the immediately previous time series data, and the movement time, which is the difference between the acquisition time t2 of the location information (a2, b2) = 10:13:22 and the acquisition time t1 of the location information (a1, b1) = 10:13:10, and calculates the movement speed by dividing the movement distance by the movement time. Furthermore, for example, the movement speed estimation unit 120 may use previous and subsequent time series data to determine the movement distance, which is the sum of the distance between the location information (a1, b1) and (a2, b2) of the previous time series data and the distance between the location information (a3, b3) and (a2, b2) of the next time series data, and the movement time, which is the difference between the acquisition time t3 of the location information (a3, b3) = 10:13:30 and the acquisition time t1 of the location information (a1, b1) = 10:13:10, and calculate the movement speed of the location information (a2, b2) by a method such as dividing the movement distance by the movement time.

[0052] 13, next, based on the travel speed calculated in step S102, the partial time series extraction unit 130 extracts, for each delivery destination, a partial time series, which is time series data for a section where it is estimated that a delivery person is not in the vehicle, from the time series data (step S103). Specifically, the partial time series extraction unit 130 extracts data from the time series data where the travel speed calculated by the travel speed estimation unit 120 is equal to or less than a preset threshold, for example, 6 km / h or less.

[0053] Next, the partial-time-series extraction unit 130 divides the time series data whose moving speed is equal to or less than a threshold into a plurality of temporally consecutive partial-time-series. The partial-time-series extraction unit 130 divides the time series data whose moving speed is equal to or less than a threshold into a plurality of partial-time-series according to an arbitrary rule, such as classifying two pieces of time series data into the same partial-time-series if the time difference from the immediately preceding time series data is within one minute, and classifying two pieces of time series data into different partial-time-series if the time difference from the immediately preceding time series data is more than one minute.

[0054] Next, the partial time series extraction unit 130 links the divided partial time series to delivery destinations based on the delivery instruction list illustrated in FIG. 2. Specifically, the partial time series extraction unit 130 references the delivery instruction list for the same day as the acquisition date of the partial time series to identify the delivery destination closest to the location of each time series included in the partial time series, and links the delivery destination to the partial time series. The distance may be calculated using any method, such as calculating the average distance between each location included in the partial time series and the delivery destination, or calculating the distance between the first or last location of the partial time series and the delivery destination. As illustrated in FIG. 6, for example, if the partial time series extraction unit 130 determines that the distance between partial time series 1 and the location of delivery destination ID "A001" is closest, it links partial time series 1 to delivery destination ID "A001."

[0055] Returning to FIG. 13, next, the drop-off / boarding position specifying unit 140 selects a partial time series of a delivery destination for which the doorstep time is to be calculated (step S104). Specifically, the drop-off / boarding position specifying unit 140 identifies an unprocessed delivery destination from the delivery instruction list and selects a partial time series of the identified delivery destination. For example, if the delivery destination ID "A001" is identified as an unprocessed delivery destination, the drop-off / boarding position specifying unit 140 selects a partial time series linked to the delivery destination ID "A001" from the partial time series generated in step S104.

[0056] Next, the drop-off / boarding position identifying unit 140 identifies the drop-off and boarding positions of the vehicle when it delivers to the delivery destination based on the partial time series selected in step S104 (step S105). Specifically, the drop-off / boarding position identifying unit 140 extracts, from the partial time series, a leading portion of position data acquired at an earlier time and a trailing portion of position data acquired at a later time, the number of which is based on a preset rule. A set number of position data may be extracted from each of the leading portion and the trailing portion, or a number of pieces of data based on a set proportion of the total number of data included in the partial time series may be extracted from each of the leading portion and the trailing portion. As illustrated in FIG. 8, for example, based on the rule of extracting five leading portions and five trailing portions, the drop-off / boarding position identifying unit 140 extracts the first to fifth earliest acquired position data A1 to A5 as the leading portion and the first to fifth latest acquired position data B1 to B5 as the trailing portion. Next, the disembarking and boarding position specifying unit 140 calculates the distance between each of the position data A1 to A5 in the leading portion and each of the position data B1 to B5 in the trailing portion, and specifies the position data in the leading portion with the shortest distance as the disembarking position, and specifies the position data in the trailing portion with the shortest distance as the boarding position. For example, if the distance between the position data A5 and the position data B2 is shorter than the distance between any other combination, the disembarking and boarding position specifying unit 140 specifies the position data A5 as the disembarking position and the position data B2 as the boarding position.

[0057] Returning to Fig. 13, the door step time estimation unit 150 calculates the door step time associated with the combination of the vehicle's parking / stopping position and the delivery destination. Specifically, the door step time estimation unit 150 identifies the center position of the coordinates of the drop-off position and the boarding position identified by the drop-off / boarding position identification unit 140 as the parking / stopping position (step S106). In the example of Fig. 8, if the position data A5 is identified as the drop-off position and the position data B2 is identified as the boarding position, the door step time estimation unit 150 identifies the center position of the position data A5 and the position data B2 as the parking / stopping position.

[0058] Next, the door step time estimation unit 150 calculates the door step time by subtracting the time when the specified disembarking position was detected from the time when the specified boarding position was detected (step S107). For example, if the acquisition time t of position data A5, which is the disembarking position, is 10:13:53 and the acquisition time t of position data B2, which is the boarding position, is 10:14:55, the door step time estimation unit 150 calculates 62 seconds as the door step time. The door step time estimation unit 150 associates the calculated door step time with the specified parking position and delivery destination to create a door step time table shown in FIG. 9, stores this in the door step time storage unit 160 (step S108), and ends the processing.

[0059] (Doorstep time prediction processing) Next, the door-step time prediction process for calculating the predicted door-step time for each scheduled delivery destination based on the door-step time table created in the door-step time estimation process will be described with reference to Fig. 14. In the following explanation, an example will be given in which a delivery schedule list showing a list of scheduled delivery destinations, as shown in Fig. 10, is input to the estimation device 100, and the door-step time for each delivery destination included in the delivery schedule list is predicted.

[0060] The delivery destination information acquisition unit 171 acquires delivery destination information, which is information about the delivery destination to be processed (step S201). Specifically, the delivery destination information acquisition unit 171 acquires information about each delivery destination from the delivery schedule list illustrated in FIG. 10, and determines whether each delivery destination is a new delivery destination for which the doorstep time has not been calculated, by referring to the doorstep time table illustrated in FIG. 9. In the delivery schedule list illustrated in FIG. 10, the delivery destination IDs "A001," "A002," and "A003" are included in the doorstep time in FIG. 9, so the delivery destination information acquisition unit 171 determines that the doorstep times for these delivery destinations have been calculated. Furthermore, the delivery destination ID "B001" is not included in the doorstep time, so the delivery destination information acquisition unit 171 determines that the delivery destination ID "B001" is a new delivery destination for which the doorstep time has not been calculated. Note that the delivery destination information acquisition unit 171 may determine whether a delivery destination is included in the doorstep time table based on whether the combination of name and address is the same, regardless of the delivery destination ID.

[0061] Next, the door-step time information extraction unit 172 extracts door-step time information, including the door-step time and the parking / stop location, from the door-step time table shown in FIG. 9, which is data used to predict the door-step time of each delivery destination. Specifically, if it is determined in step S201 that the delivery destination to be processed is a delivery destination included in the door-step time table and is not a new delivery destination (step S202; No), the door-step time and the parking / stop location of the same delivery destination as this delivery destination are extracted from the door-step time table (step S203). For example, if the delivery destination ID to be processed is "A001," the door-step time information extraction unit 172 extracts the door-step time "300 seconds" and the parking / stop location "(X1, Y1)" of the delivery destination ID "A001" from the door-step time table shown in FIG. 9. Note that if there are multiple pieces of door-step time information for the same delivery destination in the door-step time table, the door-step time information extraction unit 172 extracts multiple pieces of door-step time information.

[0062] On the other hand, if it is determined in step S201 that the delivery destination to be processed is a new delivery destination not included in the doorstep time table (step S202; Yes), the doorstep time information extraction unit 172 extracts the doorstep times and parking positions of delivery destinations that have the same or similar address as this delivery destination (step S204). Specifically, the doorstep time information extraction unit 172 identifies delivery destinations that have the same address but different names, or delivery destinations that have highly similar addresses, from the doorstep time table, and extracts the doorstep times and parking positions of the identified multiple delivery destinations. A method of identifying highly similar addresses is, for example, to compare address 1 and address 2, scan the character strings of each address from a wide area to a narrow area to find a common character string, and use the number of characters a in address 1, the number of characters b in address 2, and the number of characters c in the common character string to find 2×c / (a+b), c 2 The similarity is calculated using a formula such as / (a×b), and if the similarity is equal to or greater than a threshold, it is determined that the address has a high similarity. If there are multiple delivery destinations with the same or similar addresses, the doorstep time information extraction unit 172 extracts doorstep time information for multiple delivery destinations that meet the conditions from the doorstep time table.

[0063] Next, the prediction unit 173 calculates a prediction of the door step time and parking position of the delivery destination included in the delivery schedule list illustrated in Fig. 10 based on the door step time information extracted by the door step time information extraction unit 172 (step S205). Specifically, in the case of a prediction of a delivery destination determined in step S202 to be the same as a delivery destination included in the door step time table, the prediction unit 173 calculates the door step time and parking position extracted in step S203 as the door step time and parking position of the delivery destination to be processed.

[0064] If multiple pieces of door step time information are extracted in step S202, the average of the multiple door step times and the center position of the multiple parking and stopping positions may be calculated as the door step time and parking and stopping positions of the target delivery destination, respectively. Alternatively, multiple parking and stopping positions may be calculated by grouping nearby parking and stopping positions into one parking and stopping position. Specifically, for example, an upper limit on the distance between parking and stopping positions may be set, and parking and stopping positions whose distances fall within the upper limit may be grouped together. As shown in FIG. 15, if parking and stopping positions C1 to C6 are extracted, the center positions C7 and C8 of parking and stopping positions C1 to C3 and parking and stopping positions C4 to C6 whose distances fall within the upper limit may be calculated as the target delivery destination parking and stopping positions, respectively. The average door step times of parking and stopping positions C1 to C3 and parking and stopping positions C4 to C6 may be calculated as the door step times of the respective parking and stopping positions.

[0065] On the other hand, when predicting a delivery destination determined to be a new delivery destination in step S202, the prediction unit 173 calculates the average of the door step times of the multiple delivery destinations extracted in step S204 and calculates the calculated average as the door step time of the delivery destination to be processed. Furthermore, the prediction unit 173 calculates the center position of the parking positions of the multiple delivery destinations extracted in step S204 as the parking position of the delivery destination to be processed. Specifically, as illustrated in FIG. 16 , for example, if the new delivery destination is the third floor of building D3 and buildings D1, D2, and D3 are identified as delivery destinations with similar addresses, the prediction unit 173 calculates the average of the door step times of buildings D1, D2, and D3 as the door step time of the third floor of building D3, which is the new delivery destination to be calculated. Furthermore, the prediction unit 173 calculates the central position C12 of the parking position C9 of building D1, the parking position C10 of building D2, and the parking position C11 on the first floor of building D3 as the parking position on the third floor of building D3, which is the new delivery destination to be calculated. Note that the average is not limited to a simple average, and a weight may be set for each delivery destination according to the similarity of the addresses to calculate a weighted average, which may then be used as the door step time for the new delivery destination. For example, the weighted average may be calculated by setting a larger weight than that of building D1 or building D2 for the door step time on the first floor of building D3, which is the new delivery destination to be calculated and differs only in the number of floors from the third floor of building D3.

[0066] Next, the error estimation unit 174 calculates the error between the door step time of the delivery destination calculated by the prediction unit 173 and each of the parking stop positions (step S206). Specifically, if a delivery destination determined in step S202 to be the same as a delivery destination included in the door step time table has multiple parking stop positions identified, the error estimation unit 174 calculates the error between the door step time and each of the parking stop positions based on the door step time and position information of each parking stop position. For example, if two parking stop positions have been identified, the error estimation unit 174 calculates the error in the door step time by adding the average or maximum value of the door step times of parking stop positions 1 and 2 to the standard deviation between the door step time of parking stop position 1 and the door step time of parking stop position 2. The error estimation unit 174 also calculates the distance between parking stop position 1 and parking stop position 2, and calculates the calculated distance as the error of the parking stop positions. If there are three or more parking positions, the longest distance between the parking positions may be used as the error of the parking positions, or the standard deviation of the distances between the parking positions may be used as the error of the parking positions.

[0067] Furthermore, when calculating the error for a delivery destination determined to be a new delivery destination in step S202, the error estimation unit 174 calculates each error based on the door step times and parking / stopping positions of multiple delivery destinations with the same or similar addresses, using a method similar to that described above. Specifically, when calculating the error in door step time, the error is calculated by adding the average or maximum door step time of multiple delivery destinations with the same or similar address as the new delivery destination to the standard difference in door step times of the multiple delivery destinations. Furthermore, when calculating the error in parking / stopping position, the error estimation unit 174 calculates the longest distance between parking / stopping positions among the parking / stopping positions of multiple delivery destinations with the same or similar address as the new delivery destination, or the standard deviation of the distances between parking / stopping positions.

[0068] The error estimation unit 174 generates a prediction result table as shown in Fig. 11 based on the prediction result by the prediction unit 173 and the errors between the door step time and the parking position calculated in step S206 (step S207). The generated prediction result table is sent to a delivery plan creation system that creates a delivery plan including the delivery route, delivery order, number of deliveries, etc., and the delivery plan creation system creates a delivery plan taking into account the door step time and parking position of each delivery destination included in the prediction result table.

[0069] Specifically, for example, a delivery plan creation system uses a route search means installed in a conventional car navigation system to search for a vehicle's travel route from a departure location to a delivery destination. The delivery plan creation system identifies a parking location in the search results obtained, and obtains the doorstep time and error from the parking location, thereby calculating the time required to travel from the departure location to a delivery destination, complete delivery to the delivery destination, and use the parking location of that delivery destination as a new departure location for the next delivery destination, as well as the error. When creating delivery plans for multiple delivery destinations, the delivery plan creation system not only shortens the required time, but also creates a delivery plan in which the sum of the errors in the required times for each delivery destination is within the error range allowed for a delivery slot, for example, from when the package is loaded at an office and delivery begins until the delivery returns to the office.

[0070] As described above, the estimating device 100 identifies the parking and stopping location for each delivery destination from time-series data of the location of the delivery person terminal 200 detected during the delivery process, and calculates the door step time. The estimating device 100 associates the calculated door step time and parking and stopping location with the delivery destination and stores them in a database. The estimating device 100 acquires the door step time and parking and stopping location of the scheduled delivery destination from the accumulated database. When a new delivery destination not registered in the database is included in the delivery schedule list, the estimating device 100 calculates a predicted door step time and parking and stopping location of the new delivery destination from the door step times and parking and stopping locations of multiple delivery destinations with the same or similar addresses. This makes it possible to calculate the door step time required to create a more accurate delivery plan.

[0071] (Variation) In the above embodiment, the partial-timeseries extraction unit 130 has been described as linking a partial timeseries to a delivery destination that is closest to each position included in the partial timeseries, but this is not limited to this. For example, in an operation in which a delivery person registers information indicating that delivery has been completed each time he or she delivers a package to a delivery destination, it is sufficient to link a partial timeseries to the delivery destination whose delivery completion time is closest to the acquisition time of each position included in the partial timeseries.

[0072] Furthermore, the programs executed by the estimation device 100 and the like can be stored and distributed on computer-readable recording media such as CD-ROMs (Compact Disc Read Only Memory), DVDs (Digital Versatile Discs), MOs (Magneto-Optical Disks), USB memories, memory cards, etc. By installing such programs on a specific or general-purpose computer, the computer can be made to function as the estimation device 100 in the above-described embodiment.

[0073] The above program may also be stored on a disk device of a server device on a communication network such as the Internet, and then downloaded to a computer, for example, by superimposing the program on a carrier wave. The above process can also be achieved by launching and executing the program while transferring it via the communication network. Furthermore, the above process can also be achieved by running all or part of the program on a server device, and having a computer execute the program while transmitting and receiving information related to the process via the communication network.

[0074] In addition, when the above-mentioned functions are shared and realized by the OS (Operating System) or by the OS working together with an application, only the parts other than the OS may be stored on the above-mentioned recording medium and distributed, or may be downloaded to a computer.

[0075] Various aspects of the present disclosure are summarized below as appendices.

[0076] (Appendix 1) An estimation device that estimates a door step time, which is a time from when a delivery person gets off a vehicle, delivers a package to a delivery destination, and gets on the vehicle, a location information acquisition unit that acquires a time series of locations of a first delivery person detected in the process of the first delivery person delivering a first package to a first delivery destination using a first vehicle; a movement speed estimation unit that estimates the movement speed of the first delivery person at each of the locations from the time at which each of the locations included in the time series acquired by the location information acquisition unit was detected; a partial time series extraction unit that extracts a partial time series in which it is estimated that the first delivery person is not riding in the first vehicle from the time series acquired by the location information acquisition unit based on the moving speed estimated by the moving speed estimation unit; and a partial time series selection unit that selects a partial time series to be associated with the first delivery destination from the partial time series extracted by the partial time series extraction unit; a door step time estimation unit that identifies, based on a predetermined rule, a drop-off location where the first delivery person dropped off the first vehicle and a boarding location where the first delivery person boarded the first vehicle from the partial time series selected by the partial time series selection unit, estimates a first parking location where the first delivery person parked the first vehicle to deliver the first package to the first delivery destination based on the identified drop-off location and the identified boarding location, and estimates a door step time for the combination of the first delivery destination and the first parking location from the time when the identified boarding location was detected and the time when the identified drop-off location was detected; An estimation device comprising:

[0077] (Appendix 2) the doorstep time estimation unit identifies the disembarking position based on a leading portion of the partial time series selected by the partial time series selection unit, which is position data with an earlier detection time, and identifies the boarding position based on a trailing portion of the partial time series, which is position data with a later detection time. 10. The estimation apparatus of claim 1.

[0078] (Appendix 3) the doorstep time estimation unit extracts, from the selected partial time series, a set number of pieces of position data of the beginning portion and a set number of pieces of position data of the end portion, the number of pieces being based on a set ratio of the total number of data included in the partial time series, and specifies one of the extracted position data of the beginning portion as the disembarking position, and specifies one of the extracted position data of the end portion as the boarding position; 10. The estimation device of claim 2.

[0079] (Appendix 4) the doorstep time estimation unit identifies a combination of the position data of the front portion and the position data of the rear portion that are closest in distance from the extracted position data of the front portion and the position data of the rear portion, and identifies the identified position data of the front portion and the identified position data of the rear portion as the disembarking position and the boarding position, respectively. 10. The estimation device of claim 3.

[0080] (Appendix 5) The door step time estimation unit specifies a center position between the specified disembarking position and the getting on position as the first parking / stopping position. 5. The estimation apparatus of any one of claims 1 to 4.

[0081] (Appendix 6) the partial time series selection unit selects the partial time series closest to the address of the first delivery destination and associates it with the first delivery destination; 6. The estimation apparatus of any one of appendixes 1 to 5.

[0082] (Appendix 7) A computer that estimates a door step time, which is the time it takes for a delivery person to get off a vehicle, deliver a package to a delivery destination, and get on the vehicle, A step of acquiring a time series of positions of a first deliverer detected in the process of the first deliverer delivering a first package to a first delivery destination using a first vehicle; Estimating the moving speed of the first delivery person at each of the locations from the time at which each of the locations included in the acquired time series was detected; extracting a partial time series in which it is estimated that the first delivery person is not riding in the first vehicle from the acquired time series based on the estimated moving speed; selecting a sub-time series to be associated with the first delivery destination from the extracted sub-time series; from the selected partial time series, based on a predetermined rule, identifying a drop-off location where the first delivery person dropped off the first vehicle and a boarding location where the first delivery person boarded the first vehicle; estimating a first parking location where the first delivery person parked the first vehicle to deliver the first package to the first delivery destination based on the identified drop-off location and the identified boarding location; and estimating a doorstep time for the combination of the first delivery destination and the first parking location based on the time when the identified boarding location was detected and the time when the identified drop-off location was detected; Estimation methods including:

[0083] (Appendix 8) A computer that estimates a doorstep time, which is the time it takes for a delivery person to get off a vehicle, deliver a package to a delivery destination, and get on the vehicle, A process of acquiring a time series of the location of a first deliverer detected in the process of the first deliverer delivering a first package to a first delivery destination using a first vehicle; A process of estimating a moving speed of the first delivery person at each of the locations from the time at which each of the locations included in the acquired time series was detected; extracting a partial time series in which it is estimated that the first delivery person is not riding in the first vehicle from the acquired time series based on the estimated moving speed; selecting a partial time series to be associated with the first delivery destination from the extracted partial time series; a process of identifying, from the selected partial time series based on a preset rule, a drop-off location where the first delivery person dropped off the first vehicle and a boarding location where the first delivery person boarded the first vehicle, estimating a first parking location where the first delivery person parked the first vehicle to deliver the first package to the first delivery destination based on the identified drop-off location and the identified boarding location, and estimating a doorstep time for the combination of the first delivery destination and the first parking location based on the time when the identified boarding location was detected and the time when the identified drop-off location was detected; A program that executes the following.

[0084] The present disclosure allows various embodiments and modifications without departing from the broad spirit and scope of the present disclosure. Furthermore, the above-described embodiments are intended to illustrate the present disclosure and do not limit the scope of the present disclosure. That is, the scope of the present disclosure is defined by the claims, not the embodiments. Various modifications made within the scope of the claims and the meaning of equivalent disclosures are considered to be within the scope of the present disclosure. [Industrial Applicability]

[0085] The present invention can be suitably adopted in an estimation device, estimation method, and program that estimates the doorstep time of a delivery destination based on time-series data indicating the location of a delivery person's terminal. [Explanation of symbols]

[0086] 100 Estimation device, 200 Delivery person terminal, 300 Computer communication network, 110 Location information acquisition unit, 120 Travel speed estimation unit, 130 Partial time series extraction unit, 140 Drop-off and pick-up location identification unit, 150 Door step time estimation unit, 160 Door step time memory unit, 170 Door step time prediction unit, 171 Delivery destination information acquisition unit, 172 Door step time information extraction unit, 173 Prediction unit, 174 Error estimation unit, 11 CPU, 12 RAM, 13 ROM, 14 Memory unit, 15 Input unit, 16 Display unit, 17 Communication unit, 99 Internal bus, C1, C2, C3, C4, C5, C6, C9, C10, C11 Parking and stopping position, C7, C8, C12 Center position, D1, D2, D3 Building.

Claims

1. An estimation device that estimates a door step time, which is a time from when a delivery person gets off a vehicle, delivers a package to a delivery destination, and gets on the vehicle, a location information acquisition unit that acquires a time series of locations of a deliverer terminal carried by a first deliverer, the locations being detected during the process of the first deliverer delivering a first package to a first delivery destination using a first vehicle; a movement speed estimation unit that estimates the movement speed of the first delivery person at each of the locations from the time at which each of the locations included in the time series acquired by the location information acquisition unit was detected; a partial time series extraction unit that extracts a partial time series in which it is estimated that the first delivery person is not riding in the first vehicle from the time series acquired by the location information acquisition unit based on the moving speed estimated by the moving speed estimation unit; and a partial time series selection unit that selects a partial time series to be associated with the first delivery destination from the partial time series extracted by the partial time series extraction unit; a door step time estimation unit that identifies, based on a predetermined rule, a drop-off location where the first delivery person dropped off the first vehicle and a boarding location where the first delivery person boarded the first vehicle from the partial time series selected by the partial time series selection unit, estimates a first parking location where the first delivery person parked the first vehicle to deliver the first package to the first delivery destination based on the identified drop-off location and the identified boarding location, and estimates a door step time for the combination of the first delivery destination and the first parking location from the time when the identified boarding location was detected and the time when the identified drop-off location was detected; An estimation device comprising:

2. the doorstep time estimation unit identifies the disembarking position based on a leading portion of the partial time series selected by the partial time series selection unit, which is position data with an earlier detection time, and identifies the boarding position based on a trailing portion of the partial time series, which is position data with a later detection time. The estimation device according to claim 1 .

3. the doorstep time estimation unit extracts, from the selected partial time series, a set number of pieces of position data of the beginning portion and a set number of pieces of position data of the end portion, the number of pieces being based on a set ratio of the total number of data included in the partial time series, and specifies one of the extracted position data of the beginning portion as the disembarking position, and specifies one of the extracted position data of the end portion as the boarding position; The estimation device according to claim 2 .

4. the doorstep time estimation unit identifies a combination of the position data of the front portion and the position data of the rear portion that are closest in distance from the extracted position data of the front portion and the position data of the rear portion, and identifies the identified position data of the front portion and the identified position data of the rear portion as the disembarking position and the boarding position, respectively. The estimation device according to claim 3 .

5. The door step time estimation unit specifies a center position between the specified disembarking position and the getting on position as the first parking / stopping position. The estimation device according to any one of claims 1 to 4.

6. the partial time series selection unit selects the partial time series closest to the address of the first delivery destination and associates it with the first delivery destination; The estimation device according to any one of claims 1 to 4.

7. A computer that estimates a door step time, which is the time it takes for a delivery person to get off a vehicle, deliver a package to a delivery destination, and get on the vehicle, A step of acquiring a time series of locations of a deliverer terminal carried by a first deliverer, which are detected during a process in which the first deliverer delivers a first package to a first delivery destination using a first vehicle; estimating a moving speed of the first delivery person at each of the locations from the time at which each of the locations included in the acquired time series was detected; extracting a partial time series in which it is estimated that the first delivery person is not riding in the first vehicle from the acquired time series based on the estimated moving speed; selecting a sub-time series to be associated with the first delivery destination from the extracted sub-time series; from the selected partial time series, based on a predetermined rule, identifying a drop-off location where the first delivery person dropped off the first vehicle and a boarding location where the first delivery person boarded the first vehicle; estimating a first parking location where the first delivery person parked the first vehicle to deliver the first package to the first delivery destination based on the identified drop-off location and boarding location; and estimating a doorstep time for the combination of the first delivery destination and the first parking location based on the time when the identified boarding location was detected and the time when the identified drop-off location was detected; Estimation methods including:

8. A computer that estimates a doorstep time, which is the time it takes for a delivery person to get off a vehicle, deliver a package to a delivery destination, and get on the vehicle, A process of acquiring a time series of locations of a deliverer terminal carried by a first deliverer, which are detected during a process in which the first deliverer delivers a first package to a first delivery destination using a first vehicle; A process of estimating a moving speed of the first delivery person at each of the locations from the time at which each of the locations included in the acquired time series was detected; extracting a partial time series in which it is estimated that the first delivery person is not riding in the first vehicle from the acquired time series based on the estimated moving speed; selecting a partial time series to be associated with the first delivery destination from the extracted partial time series; a process of identifying, from the selected partial time series based on a preset rule, a drop-off location where the first delivery person dropped off the first vehicle and a boarding location where the first delivery person boarded the first vehicle, estimating a first parking location where the first delivery person parked the first vehicle to deliver the first package to the first delivery destination based on the identified drop-off location and boarding location, and estimating a doorstep time for the combination of the first delivery destination and the first parking location based on the time when the identified boarding location was detected and the time when the identified drop-off location was detected; A program that executes the following.

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