KR102532112B1 - Device and method for forecasting travel demand of personal mobility - Google Patents

Abstract

According to various embodiments of the present invention, a method for predicting demand for new use of a personal mobile device performed by a device for predicting demand for a personal mobile device includes the population residing in a counting district in which the device needs to survey the demand for a personal mobile device. and estimating the resident population of buildings located in the counting district by using the total floor area of the buildings. calculating, by the device, a usage rate of a personal mobility device in a building by using a ratio of age groups for each building; calculating, by the device, the number of trips of the personal mobile device of the building using the resident population of the building and the usage rate of the personal mobile device of the building; calculating, by the device, a plurality of central building indices and a plurality of central station indices by using the number of personal mobile devices in the building, the distance between the building and the bus stop, and the usage rate according to the distance; calculating, by the device, at least one central building and at least one central station using the plurality of central building indices and the plurality of central station indices; setting, by the device, a personal mobility device area using the at least one central building and the at least one central station; and determining, by the device, the number of operating personal mobility devices using a travel time difference between walking and personal mobility devices and an area of the personal mobility devices. Other embodiments may also be possible.

Description

Method and device for predicting new demand for personal mobile device {DEVICE AND METHOD FOR FORECASTING TRAVEL DEMAND OF PERSONAL MOBILITY}

The present invention relates to a method and device for predicting new usage demand for a personal mobile device, different from the existing invention on a methodology for predicting future demand based on current demand. It is difficult to predict with aggregated data. Therefore, a method and device for predicting new use demand of personal mobile devices for the purpose of first-mile and last-mile improvement by utilizing GIS open source data including data and building data in a county unit as more subdivided data will be. In addition, the present invention relates to a method for setting a service area of personal mobility devices based on traffic and deriving a reasonable number of operating personal mobility devices within the area.

Various studies have been conducted on the improvement of public transportation accessibility. Most conventional technologies for improving public transportation accessibility seek to improve First_Mile and Last-Mile through gait improvement. However, because the walking speed is limited, it is difficult to expect public transportation accessibility improvement beyond a certain standard only by improving walking. Since personal mobility devices (e.g., electric kickboards) can achieve higher speeds than walking speed and have fewer restrictions on areas of use, personal mobility device sharing services can be introduced to improve public transportation accessibility.

In order to develop public transportation accessibility improvement technology, it is important to set potential demand for public transportation. Conventional techniques for this include a buffer-method and a network-ratio method. The buffer-method is a technique that sets a buffer by setting a certain radius from the center assuming that the population is uniformly distributed within the range. This is an unrealistic assumption, and the network-ratio method was devised to compensate for this. It was assumed that the population was distributed in proportion to the length of the path. However, from the viewpoint of first-mile and last-mile improvement, since traffic occurs from buildings and transit stops, it is more appropriate to analyze traffic generated from each building rather than analyzing the entire population. Since passengers who get off at a public transport stop have a specific building as their destination, the number of passengers who get off at a public transport stop is allocated in proportion to the number of passages in each building. Conversely, the number of people who want to go from a building to a public transit stop is proportional to the number of trips that occur in the building. Therefore, there is a need for a method for predicting the demand for new use of personal mobile devices to improve public transportation accessibility by analyzing the number of traffic generated in buildings and traffic generated at stops.

KR Patent Publication 10-2020-0062526

The present invention has been made to solve the above problems, and its purpose is to provide a method for determining the area and number of operating personal mobility devices in order to provide short-distance transportation using personal mobility devices. there is. In addition, the present invention can utilize short-distance transportation to suit the conditions of the region where introduction is required, and provides a method and device for predicting new demand for use to improve the efficiency of providing various short-distance transportation services as well as personal mobile devices. There is a purpose.

According to various embodiments of the present invention, a method for predicting demand for new use of a personal mobile device performed by a device for predicting demand for a personal mobile device includes the population residing in a counting district in which the device needs to survey the demand for a personal mobile device. and estimating the resident population of buildings located in the counting district by using the total floor area of the buildings. calculating, by the device, a usage rate of a personal mobility device in a building by using a ratio of age groups for each building; calculating, by the device, the number of trips of the personal mobile device of the building using the resident population of the building and the usage rate of the personal mobile device of the building; calculating, by the device, a plurality of central building indices and a plurality of central station indices by using the number of personal mobile devices in the building, the distance between the building and the bus stop, and the usage rate according to the distance; calculating, by the device, at least one central building and at least one central station using the plurality of central building indices and the plurality of central station indices; setting, by the device, a personal mobility device area using the at least one central building and the at least one central station; and determining, by the device, the number of operating personal mobility devices using a travel time difference between walking and personal mobility devices and an area of the personal mobility devices.

In one embodiment of the present invention, estimating the resident population of the building may include estimating the resident population of the building using Equation 1 below.

[Equation 1]

는 집계구 k 내에 있는 건물 b의 거주 인구이고,

는 집계구 k에 있는 건물 b의 연면적을 나타내고,

는 집계구 k의 인구수이다.here

is the resident population of building b in county k,

represents the gross floor area of building b in county k,

is the population of population k.

In one embodiment of the present invention, the step of calculating the personal mobility device utilization rate of the building may include calculating the personal mobility device utilization rate of the building by using Equation 2 below.

[Equation 2]

는 빌딩 b에 있는 연령대 a의 비율이고,

는 연령대 a에 대한 전동킥보드 이용률이고, P^b _age는 연령대 비율을 고려한 건물 b의 개인형 이동장치 이용률이다.where a is the age group,

is the proportion of age group a in building b,

is the electric kickboard use rate for age group a, and P ^b _age is the use rate of personal mobility devices in building b considering the age group ratio.

In one embodiment of the present invention, the step of calculating the number of personal mobile device traffic in a building is performed by using the following Equation 3 to calculate the number of personal mobile device traffic in a residential building and the personal mobile device in a commercial building. Calculating the number of trips and calculating the number of trips of the personal mobile device of the building by simultaneously considering the number of trips of the personal mobile device of the residential building and the number of trips of the personal mobile device of the commercial building. there is.

[Equation 3]

및

는 각각 거주용 건물 b의 개인형 이동장치 통행수 및 상업용 건물 b의 개인형 이동장치 통행수이고,

는 건물 b의 거주 인구이고,

는 건물 b의 연면적이고, u는 통행원단위이고, 이용률이고, P^b _age는 연령대 비율을 고려한 건물 b의 개인형 이동장치 이용률이다.here

and

are the number of personal mobile device trips in residential building b and the number of personal mobile device trips in commercial building b, respectively,

is the resident population of building b,

is the total floor area of building b, u is the unit of traffic and usage rate, and P ^b _age is the usage rate of personal mobile devices in building b considering the age ratio.

In one embodiment of the present invention, the calculating of the plurality of center building indicators and the plurality of center stop indicators may include the plurality of center building indicators and the plurality of center stop indicators using Equation 4 below. It may include calculating the .

[Equation 4]

는 정류장과 건물 사이의 거리이고,

는 거리에 따른 이용률이다.where I ^b and I ^s are the central building index and the central station index, respectively, s is a subscript indicating a stop, b is a subscript indicating a building,

is the distance between the station and the building,

is the utilization rate according to the distance.

In an embodiment of the present invention, the step of calculating the at least one central building and the at least one central station includes the device having a centroid corresponding to a central building index having a value greater than a median value of the plurality of central building indices. determining a building as the at least one central building, and determining a central stop corresponding to a central stop indicator having a value greater than a median value of the plurality of central stop indicators as the at least one central stop.

In one embodiment of the present invention, the setting of the personal mobile device area may include calculating minimum distances that the device can reach from the at least one central building to all routes related to the station, respectively; Setting a central building area having an average value of the minimum distances as a radius and having the at least one central building as a center, generating a plurality of Voronoi polygons respectively corresponding to all stops in the aggregation district, and Setting a Voronoi polygon corresponding to the at least one central station among the Voronoi polygons as a central station area, and setting both the central building area and the central station area as a personal mobile device area, or setting the central building area and the central station area A step of setting only an overlapping area among the stop areas as a personal mobile device area may be included.

In one embodiment of the present invention, the step of determining the number of operating personal mobility devices includes generating a plurality of points where the personal mobility devices are arbitrarily arranged in the personal mobility device area, the plurality of Calculating the difference in travel time between the walking and personal mobile devices using points and Equation 5 below; Calculating the number of points satisfying the differential value of the difference in travel time to be 0; The method may include determining the number of points as the number of operating personal mobile devices.

[Equation 5]

는 보행만으로 이동할 때와 전동킥보드를 이용할 때의 통행시간의 차이이고,

는 걸어서 건물b에서 정류장s 사이를 이동하는 데 걸리는 통행시간이고,

는 걸어서 건물 b에서 전동킥보드 P까지 이동하는 데에 걸린 통행시간이고,

은 전동킥보드를 이용하여 전동킥보드를 승차 후 정류장까지 이동하는 데에 걸린 통행시간이다here

is the difference in travel time between walking alone and using an electric kickboard,

is the travel time it takes to travel from building b to stop s on foot,

is the travel time taken to walk from building b to electric kickboard P,

is the travel time taken to move to the stop after riding the electric kickboard using the electric kickboard

According to various embodiments of the present invention, an apparatus for predicting demand for new use of personal mobile devices includes a control unit that calculates the number of operating personal mobile devices and a database for storing data for predicting new demand for use of personal mobile devices. The control unit estimates the resident population of buildings located in the aggregated district using the total floor area of the building and the population residing in the aggregated district for which a demand survey of the personal mobile device is required, and uses the age ratio for each building to personalize the mobility of the building. The device usage rate is calculated, and the number of personal mobility devices in the building is calculated using the resident population of the building and the personal mobility device usage rate in the building, and the number of personal mobility devices in the building and the distance between the building and the stop are calculated. and calculating a plurality of central building indices and a plurality of central stop indices using the utilization rate according to the distance, and calculating at least one central building and at least one central station indices using the plurality of central building indices and the plurality of central station indices. A central stop is calculated, a personal mobile device area is set using the at least one central building and the at least one central stop, and a travel time difference between walking and personal mobility devices and a personal mobility device area are used. Determine the number of operating personal mobility devices.

According to the present invention as described above, it has various effects as follows.

According to the present invention, while only a methodology for predicting new use demand in the future based on the current new use demand has been developed, the demand forecasting method of the present invention is differentiated from the existing ones in that it predicts new demand and is highly practical. can

In addition, according to the present invention, it is possible to set a personal mobile device service area based on traffic and derive a reasonable number of personal mobility devices operating within the area.

In addition, according to the present invention, by predicting demand based on traffic, it is possible to set the range in which the maximum number of users can use the service and determine the area where the maximum profit can be made, and at the same time, many users can use First-Mile and Last-Mile You can benefit from improvement.

In addition, according to the present invention, an operator can calculate an appropriate investment cost by selecting a reasonable number of operating personal mobile devices within a set area.

In addition, according to the present invention, since the personal mobile device service is integrated with Mobility-as-a-Service (MaaS), accessibility of other means can be increased by improving First_MIle and Last-Mile. Through this, user satisfaction of MaaS can be increased.

In addition, according to the present invention, it can be used to build a shared transportation service system, and can be an alternative that can be presented as a public transportation fostering strategy to alleviate traffic congestion.

In addition, according to the present invention, public transportation competitiveness can be improved by predicting the demand for personal mobility devices and setting the area.

1 is a block diagram showing an apparatus for predicting demand for new use of a personal mobile device and an external device according to an embodiment of the present invention.
2 is a flowchart illustrating a method for predicting new usage demand of a personal mobile device according to an embodiment of the present invention.
3 to 12 are exemplary diagrams for explaining a method for predicting new usage demand for a personal mobile device according to an embodiment of the present invention.

Hereinafter, various embodiments of the present invention will be described in detail with reference to the accompanying drawings. And, in describing the embodiments of the present invention, if it is determined that a detailed description of a related known function or configuration may unnecessarily obscure the subject matter of the present invention, the detailed description thereof will be omitted. In addition, terms to be described later are terms defined in consideration of functions of the present invention, which may vary according to the intention or custom of a user or operator. Therefore, the definition should be made based on the contents throughout this specification.

A preferred embodiment of the present invention will be described with reference to the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms, but only the present embodiments make the disclosure of the present invention complete and fully cover the scope of the invention to those skilled in the art. It is provided to inform you.

Various embodiments of the present document may be implemented as software (eg, a program) including instructions stored in a storage medium readable by a machine (eg, a computer). A device is a device capable of calling a stored command from a storage medium and operating according to the called command, and may include an electronic device (eg, a server) according to the disclosed embodiments. An instruction may include code generated or executed by a compiler or interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-temporary' only means that the storage medium does not contain a signal and is tangible, but does not distinguish whether data is stored semi-permanently or temporarily in the storage medium.

According to one embodiment, the method according to various embodiments disclosed in this document may be provided by being included in a computer program product. Computer program products may be traded between sellers and buyers as commodities. The computer program product may be distributed in the form of a device-readable storage medium (eg compact disc read only memory (CD-ROM)) or online through an application store (eg Play Store™). In the case of online distribution, at least part of the computer program product may be temporarily stored or temporarily created in a storage medium such as a manufacturer's server, an application store server, or a relay server's memory.

Each component (eg, module or program) according to various embodiments may be composed of a single object or a plurality of objects, and some sub-components among the aforementioned sub-components may be omitted, or other sub-components may be used. It may be further included in various embodiments. Alternatively or additionally, some components (eg, modules or programs) may be integrated into one entity and perform the same or similar functions performed by each corresponding component prior to integration. According to various embodiments, operations performed by modules, programs, or other components are executed sequentially, in parallel, iteratively, or heuristically, or at least some operations are executed in a different order, are omitted, or other operations are added. It can be.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used in a meaning commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

Terms used in this specification are for describing embodiments and are not intended to limit the present invention. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements.

1 is a block diagram showing an apparatus for predicting demand for new use of a personal mobile device and an external device according to an embodiment of the present invention.

Referring to FIG. 1 , the apparatus 100 for predicting demand for new use of a personal mobile device according to an embodiment of the present invention uses the number of passages of buildings in a counting district, the distance between a building and a stop, and the rate of use of a personal mobile device. It is possible to predict the area and the number of operating units. For example, the device for predicting new demand for use of personal mobile devices 100 stores the control unit 110 that calculates the area of personal mobile devices, the number of operating units, etc., and GIS Open Source data, which is aggregated unit data and building data. It may include a database 120 and a communication unit 130 that transmits and receives data from the external device 200. Here, the external device 200 may be an external server holding data necessary for the present invention.

For example, the device for predicting new demand for use of a personal mobile device 100 and the external device 200 may be connected to each other through a network. Networks may include wireless networks and wired networks. For example, the network may be a short-range communication network (eg, Bluetooth, WiFi direct, or infrared data association (IrDA)) or a long-distance communication network (eg, a cellular network, the Internet, or a computer network (eg, a LAN or WAN)). there is.

In one embodiment, the personal mobile device new use demand prediction device 100 is a server, a smartphone, a tablet personal computer (tablet PC), a mobile phone, a video phone, and an e-book reader. (e-book reader), netbook computer, personal digital assistant (PDA), portable multimedia player (PMP), MP3 player, mobile medical device, camera, or wearable device. can

2 is a flowchart illustrating a method for predicting new usage demand of a personal mobile device according to an embodiment of the present invention. The operations of FIG. 2 may be performed by the apparatus 100 for predicting new demand for use of a personal mobile device shown in FIG. 1 .

Referring to FIG. 2 , in one embodiment, in operation 21, the device for predicting new demand for use 100 is located in the counting district by using the population living in the counting district and the gross floor area of the building in which demand investigation of the personal mobile device is required. It is possible to estimate the occupied population of the building. Here, the counting district is a district constituting an administrative unit and may be a target area for investigating the demand for personal mobility devices. Therefore, for convenience of description, although it is named a county district, it may mean that it includes various areas in which demand for personal mobile devices can be investigated, not limited to district units. For example, new demand for use may be predicted on a nationwide basis.

On the other hand, in order to predict the demand for new use of personal mobile devices, analysis should be performed focusing on buildings and bus stops, and the number of passages in a building representing people entering and exiting the building is required. This is because the number of traffic in the building and the demand for personal mobility devices are proportional to each other. Therefore, in order to calculate the traffic generated by the building, we must have the resident population data. However, readily available GIS data often do not specify the resident population. On the other hand, the resident population within the county is provided as open source data. The resident population of a building can be estimated by assuming that the population residing in an aggregated district is distributed in proportion to the gross floor area of all buildings existing in the aggregated district.

For example, the new use demand prediction device 100 may estimate the resident population of a building using Equation 1 below.

[Equation 1]

는 집계구 k 내에 있는 건물 b의 거주 인구이고,

는 집계구 k에 있는 건물 b의 연면적을 나타내고,

는 집계구 k의 인구수이다.here

is the resident population of building b in county k,

represents the gross floor area of building b in county k,

is the population of population k.

On the other hand, for convenience of description, the building b and the counting district k are exemplarily marked and described. In the detailed description, building b may mean any one building located in the counting district k.

That is, the new use demand prediction apparatus 100 may calculate the resident population of building b by multiplying the ratio of the total floor area of building b to the total floor area of all buildings in aggregation area k from the easily available population number of aggregation area k.

In an embodiment, in operation 22, the apparatus 100 for predicting new demand for use may calculate the use rate of personal mobile devices in a building by using the age ratio for each building. In order to predict the demand for personal mobile devices, the number of traffic generated in each building is required as described in operation 21 above. This is because the demand for personal mobility devices is proportional to the number of trips. However, since the use rate of personal mobility devices can vary by age group, the use rate of personal mobility devices in each building is required to reflect this. In addition, the use rate of the personal mobile device may vary depending on the age group. It is assumed that the age distribution follows the age distribution of the administrative unit to which the counting district belongs.

For example, the device for predicting new usage demand 100 may calculate the usage rate of personal mobile devices in a building using Equation 2 below.

[Equation 2]

는 빌딩 b에 있는 연령대 a의 비율이고,

는 연령대 a에 대한 전동킥보드 이용률이고, P^b _age는 연령대 비율을 고려한 건물 b의 개인형 이동장치 이용률이다.where a is the age group,

is the proportion of age group a in building b,

is the electric kickboard use rate for age group a, and P ^b _age is the use rate of personal mobility devices in building b considering the age group ratio.

That is, the new use demand prediction apparatus 100 may calculate the use rate of the personal mobile device of the building b considering the rate of age of the building b by using the rate of use of the personal mobile device and the rate of age of the building b.

In an embodiment, in operation 23, the apparatus 100 for predicting new demand for use may calculate the number of personal mobile device traffic in the building using the building's resident population and the building's personal mobile device usage rate. For example, as described above, the demand for the personal mobility device may be the number of passengers using the personal mobility device in a building. If the number of trips occurring in each building is multiplied by the use rate of the personal mobility device of the corresponding building, the number of trips using the personal mobility device in the building may be derived. To do this, we need to find the number of traffic that occurs in each building. The number of traffic for the residence may be derived by multiplying the population residing in the residential building by the unit of traffic, and in the case of a commercial building, the number of traffic of the building may be derived by multiplying the gross floor area by the unit of traffic.

Therefore, the new usage demand prediction device 100 can calculate the number of personal mobile device traffic in residential buildings and the personal mobile device traffic in commercial buildings using Equation 3 below, and the personal mobile device traffic in residential buildings The number of personal mobile device trips in the building can be calculated by adding the mobile device trips and the personal mobile device trips in the commercial building.

[Equation 3]

및

는 각각 거주용 건물 b의 개인형 이동장치 통행수 및 상업용 건물 b의 개인형 이동장치 통행수이고,

는 건물 b의 거주 인구이고,

는 건물 b의 연면적이고, u는 통행원단위이고, P^b _age는 연령대 비율을 고려한 건물 b의 개인형 이동장치 이용률이다.here

and

are the number of personal mobile device trips in residential building b and the number of personal mobile device trips in commercial building b, respectively,

is the resident population of building b,

is the total floor area of building b, u is the traffic unit, and P ^b _age is the personal mobile device usage rate of building b considering the age ratio.

That is, the number of personal mobile device traffic of a building may be calculated by considering residential or commercial use and utilizing the resident population or total floor area. On the other hand, the number of passengers may be as shown in Table 1.

Place of residence (traffic/day/person)

·일)Non-residence (traffic/

·Day) building for leisure building for business Accommodation 2.68 275.7 2033.6 320.3

In an embodiment, in operation 24, the apparatus for predicting new usage demand 100 uses the number of personal mobile devices passing through the building, the distance between the building and the stop, and the usage rate according to the distance to determine the plurality of center building indicators and the plurality of center points. Station indicators can be calculated. for example,

For First-Mile improvement, analysis should be centered on buildings, and for Last-Mile improvement, analysis should be centered on stops. In addition, the utilization rate of the personal mobile device may vary depending on the distance between the building and the stop. That is, if the distance between the building and the stop is too close or too far, there is a high possibility that the personal mobility device will not be used, so the distance can also be an important factor for calculating the utilization rate. For example, the distance between a building and a station can be calculated by using road shape files and GIS data of the network, which are open source data, through a known QGIS program.

For example, the new usage demand prediction apparatus 100 may calculate a plurality of central building indicators and a plurality of central station indicators using Equation 4 below.

[Equation 4]

는 정류장과 건물 사이의 거리이고,

는 거리에 따른 이용률이다.where I ^b and I ^s are the central building index and the central station index, respectively, s is a subscript indicating a stop, b is a subscript indicating a building,

is the distance between the station and the building,

is the utilization rate according to the distance.

In other words, in order to predict the demand for personal mobility devices, it is necessary to determine the central building and central station that can serve as standards within the counting district. As shown in Equation 4, it can be calculated by multiplying the utilization rate according to the distance, the distance between the stop and the building, and the number of personal mobility devices in the building. Meanwhile, indices may be calculated for all buildings and stations in the counting district, and indices for all stations may be calculated for each building.

In an embodiment, in operation 25, the apparatus 100 for predicting new usage demand may calculate at least one central building and at least one central station by using the plurality of central building indicators and the plurality of central station indicators. For example, in order to extract a central building, index values for all stops existing in the aggregate district (or network) are calculated for each building. Next, the apparatus 100 for predicting new demand for use may determine a central building corresponding to a central building index having a value greater than a median value of a plurality of central building indices as at least one central building. In addition, in order to extract the central station, index values for all buildings in the network are calculated for each station. Subsequently, the apparatus 100 for predicting new usage demand may determine a central station corresponding to a central station index having a value greater than a median value of a plurality of central station indexes as at least one central station.

In an embodiment, in operation 26, the device for predicting new demand for use 100 may set a personal mobility device area by using at least one central building and the at least one central station. For example, the personal mobile device area may be set based on the extracted central building and central station. Meanwhile, a central building area centered on a building and a central stop area centered on a station may be set differently.

For example, the new use demand prediction device 100 may calculate minimum distances that can reach all routes related to the station from at least one central building, and have an average value of the calculated minimum distances as a radius, and at least It is possible to set a central building area in which one central building is the center.

In addition, the new usage demand forecasting device 100 generates a plurality of Voronoi polygons respectively corresponding to all stations in an aggregate district for which a demand survey is required, and Voronoi polygons corresponding to at least one central station among the plurality of Voronoi polygons. can be set as the central stop area. Accordingly, the center stop area may be set as many as the number of center stops.

Next, the new use demand prediction device 100 may set both the central building area and the central stop area as the personal mobile device area, or set only the overlapping area of the central building area and the central stop area as the personal mobile device area.

In an embodiment, in operation 27, the apparatus 100 for predicting new usage demand may determine the number of operating personal mobility devices by using the travel time difference between walking and personal mobility devices and the area of the personal mobility devices. For example, the present invention may determine the number of operating personal mobility devices based on the finally determined area of the personal mobility devices.

On the other hand, the difference in travel time between the bus stop and the building on foot and the travel time between the bus stop and the building using a personal mobility device was compared. When using an electric kickboard, walk from the starting point to the place where the electric kickboard is located, and then use the electric kickboard to move to a building or bus stop. The difference in travel time between walking alone and using an electric kickboard is calculated as follows.

For example, the new usage demand prediction device 100 may generate a plurality of points where personal mobility devices are arbitrarily disposed in the personal mobility device area, and use the plurality of points and Equation 5 below to determine walking and personal It is possible to calculate the difference in travel time between type mobile devices, and to calculate the number of points that satisfy the differential value of the difference in travel time to be 0. can be determined by logarithms.

[Equation 5]

는 보행만으로 이동할 때와 전동킥보드를 이용할 때의 통행시간의 차이이고,

는 걸어서 건물b에서 정류장s 사이를 이동하는 데 걸리는 통행시간이고,

는 걸어서 건물 b에서 전동킥보드 P까지 이동하는 데에 걸린 통행시간이고,

은 전동킥보드를 이용하여 전동킥보드를 승차 후 정류장까지 이동하는 데에 걸린 통행시간이다.here

is the difference in travel time between walking alone and using an electric kickboard,

is the travel time it takes to travel from building b to stop s on foot,

is the travel time taken to walk from building b to electric kickboard P,

is the travel time taken to move to the bus stop after riding the electric kickboard using the electric kickboard.

가 양수이면 개인형 이동장치를 이용하는 것이 보행보다 우세한 것이고 음수이면 그 반대일 수 있다. 개인형 이동장치는 지정된 대여소를 기반으로 하지 않기 때문에 개인형 이동장치 권역 내에 임의의 점을 생성하고 이를 개인형 이동장치의 위치라 가정할 수 있다. 임의의 점의 개수를 기준으로 민감도 분석을 하여

그래프의 미분값이 0이 되는 순간을 최적 공유전동킥보드 운영 대수로 결정할 수 있다. 민감도 분석에 따른 미분값이 0이 되는 순간은 그래프가 포화상태에 도달할 때의 임의의 점 개수일 수 있으며 여기서 포화 상태가 되는 순간의 임의의 점 개수가 공유전동킥보드 운행 대수가 될 수 있다.in other words,

If is a positive number, the use of a personal mobility device is preferred over walking, and if it is a negative number, the opposite may be true. Since the personal mobility device is not based on a designated rental place, a random point within the personal mobility device area can be created and assumed to be the location of the personal mobility device. Sensitivity analysis based on the number of random points

The moment when the differential value of the graph becomes 0 can be determined as the optimal number of operating shared electric kickboards. The moment when the differential value according to the sensitivity analysis becomes 0 may be an arbitrary number of points when the graph reaches saturation, where the random number of points at the moment of saturation can be the number of shared electric kickboards in operation.

Meanwhile, experimental examples confirmed by actually applying the operation of the present invention described above will be described according to FIGS. 3 to 12 below. Here, the personal mobile device will be described as an example of an electric kickboard.

3 to 12 are exemplary diagrams for explaining a method for predicting new usage demand for a personal mobile device according to an embodiment of the present invention.

3 to 12, the method of the present invention was applied to Yeongjong Island using GIS Open Source data and Quickboard usage data, which are aggregate district unit data and building data of Yeongjong Island. Figure 3 is the distribution of buildings in Yeongjong Island to estimate the resident population and building gross floor area data in Yeongjong Island. Administrative dongs in Yeongjong Island consist of four districts: Yeongjong-dong, Yeongjong 1-dong, Unseo-dong, and Yongyu-dong. Table 2 shows the average and standard deviation of the age of each administrative dong.

Yeongjong-dong Yeongjong 1-dong Unseo-dong Yongyu-dong average 42.86 years old 33.89 years old 35.84 years old 53.77 years old Standard Deviation 1.18 years old 0.84 years old 0.94 years old 2.12 years old

The age distribution of each administrative dong is aggregated at 5-year intervals. Using the data of the shared electric kickboard currently in operation, the distribution of usage by age for the shared electric kickboard was derived for the month of November 2019. The age distribution of administrative dongs is shown in Figure 4, and the distribution of usage rates by age is shown in Figure 5. In addition, the result of deriving the utilization rate distribution according to the distance using the shared electric quick board data is shown in FIG. 6.

Upload the road shapefile and building shapefile in Yeongjongdo to QGIS to calculate the distance to the building and the stop. The center point of the building is derived and the shortest distance from the center point to the bus stop is calculated using the road. The building to the road and the road to the stop were set to move the shortest distance. 7 is an example for calculating the shortest distance between a building and a stop. The polygon in the figure represents the building, the point within the polygon represents the center point of the building, and the red triangle represents the station.

Using the results obtained above, the central building indicators and the central station indicators are calculated, and points larger than the median of the indicator values are selected. Using these selected points, the personal mobile device area in Yeongjong Island is determined. The results of this are disclosed in FIGS. 8 to 10 .

The radius of the central building area was set at 1,121 m. The central building area centered on buildings was determined to be near residences, lodging facilities, and airports. On the other hand, the central stop area centered on the stop was determined to be near the residence and the airport. As for the final personal mobility device area, which is a common part of the building and bus stop center areas, two areas are set as shown in FIG. 10. Area 1 (red area in FIG. 10) was determined around residences that were far from the bus stop and had a large gross floor area, and area 2 (blue area in FIG.

The number of operating electric kickboards was derived centering on the two final personal mobile device areas. In the case of the first personal mobility device area, sensitivity analysis was performed in units of 100 units, and in the case of the second personal mobility device area, sensitivity analysis was performed in units of 50 units. Graphs obtained by fitting polynomials using the sensitivity analysis result values are shown in FIGS. 11 and 12, and through this, the optimal number of operating shared kickboards was derived. The part where the differential value of the fitted graph becomes 0 is the point where the effect is not significant even if more electric kickboards are introduced, which means that the electric kickboard has reached saturation. As a result of the analysis, it was confirmed that the optimal number of shared electric kickboards operating in the personal mobility device area 1 was 1,256 units, and the optimal number of shared electric kickboards operating in the personal mobility device area 2 was 281 units.

According to various embodiments of the present invention, a method for predicting demand for new use of a personal mobile device performed by a device for predicting demand for a personal mobile device includes the population residing in a counting district in which the device needs to survey the demand for a personal mobile device. and estimating the resident population of buildings located in the counting district by using the total floor area of the buildings. calculating, by the device, a usage rate of a personal mobility device in a building by using a ratio of age groups for each building; calculating, by the device, the number of trips of the personal mobile device of the building using the resident population of the building and the usage rate of the personal mobile device of the building; calculating, by the device, a plurality of central building indices and a plurality of central station indices by using the number of personal mobile devices in the building, the distance between the building and the bus stop, and the usage rate according to the distance; calculating, by the device, at least one central building and at least one central station using the plurality of central building indices and the plurality of central station indices; setting, by the device, a personal mobility device area using the at least one central building and the at least one central station; and determining, by the device, the number of operating personal mobility devices using a travel time difference between walking and personal mobility devices and an area of the personal mobility devices.

According to various embodiments, estimating the resident population of the building may include estimating, by the device, the resident population of the building using Equation 1 below.

According to various embodiments of the present disclosure, calculating the personal mobility device utilization rate of the building may include calculating, by the device, the personal mobility device utilization rate of the building using Equation 2 below.

According to various embodiments of the present disclosure, the step of calculating the number of personal mobile device passages in a building may include the number of personal mobile device passages in residential buildings and commercial buildings by using Equation 3 below. Calculating; and calculating the number of personal mobility devices of the building by adding the number of personal mobility devices of the residential building and the number of personal mobility devices of the commercial building.

According to various embodiments, the calculating of the plurality of center building indicators and the plurality of center stop indicators includes the apparatus calculating the plurality of center building indicators and the plurality of center stop indicators using Equation 4 below. step; may be included.

According to various embodiments, the calculating of the at least one central building and the at least one central station may include the device determining a central building corresponding to a central building index having a value greater than a median value of the plurality of central building indices. determining at least one central building; and determining a center stop corresponding to a center stop index having a value greater than a median value of the plurality of center stop indicators as the at least one center stop.

According to various embodiments of the present disclosure, the setting of the personal mobile device area may include calculating minimum distances that the device can reach from the at least one central building to all routes related to the station, respectively; setting a central building area having an average value of the calculated minimum distances as a radius and having the at least one central building as a center; Generating a plurality of Voronoi polygons respectively corresponding to all stations in the aggregate district, and setting a Voronoi polygon corresponding to the at least one central station among the plurality of Voronoi polygons as a central station area; and setting both the central building area and the central stop area as the personal mobile device area or setting only an overlapping area between the central building area and the central stop area as the personal mobile device area.

According to various embodiments of the present disclosure, the determining of the number of operating personal mobility devices may include generating a plurality of points where the personal mobility devices are arbitrarily disposed in the personal mobility device area; Calculating a travel time difference between walking and personal mobile devices using the plurality of points and Equation 5 below; calculating the number of points satisfying the differential value of the travel time difference to be zero; and determining the calculated number of points as the number of operating personal mobile devices.

Therefore, the spirit of the present invention should not be limited to the above-described embodiments, and it should be said that not only the claims to be described later, but also all modifications equivalent or equivalent to these claims fall within the scope of the spirit of the present invention. will be.

100: Personal mobile device new use demand prediction device
200: external device

Claims (9)

A method for predicting new use demand for a personal mobile device performed by an apparatus for predicting demand for a personal mobile device,
estimating, by the device, the population residing in a building located in the aggregation district by using the total floor area of the building and the population residing in the aggregation district for which a demand survey of personal mobile devices is required;
calculating, by the device, a usage rate of a personal mobility device in a building by using a ratio of age groups for each building;
calculating, by the device, the number of trips of the personal mobile device of the building using the resident population of the building and the usage rate of the personal mobile device of the building;
calculating, by the device, a plurality of central building indices and a plurality of central station indices by using the number of personal mobile devices in the building, the distance between the building and the bus stop, and the usage rate according to the distance;
calculating, by the device, at least one central building and at least one central station using the plurality of central building indices and the plurality of central station indices;
setting, by the device, a personal mobility device area using the at least one central building and the at least one central station; and
determining, by the device, the number of operating personal mobility devices by using the travel time difference between the walking and personal mobility devices and the area of the personal mobility devices;
The step of calculating the number of personal mobility devices in the building,
Calculating, by the device, the number of personal mobile device passages in a residential building and the number of personal mobile device passages in a commercial building using Equation 3 below; and
Calculating the number of personal mobility devices of the building by adding the number of personal mobility devices of the residential building and the number of personal mobility devices of the commercial building;
[Equation 3]

here

and

are the number of personal mobile device trips in residential building b and the number of personal mobile device trips in commercial building b, respectively,

is the resident population of building b,

is the total floor area of building b, u is the traffic unit and the utilization rate, P ^b _age is the personal mobility device utilization rate of building b considering the age ratio,
Calculating a plurality of central building indicators and a plurality of central stop indicators includes:
Calculating, by the device, the plurality of central building indicators and the plurality of central station indicators using Equation 4 below,
[Equation 4]

where I ^b and I ^s are the central building index and the central station index, respectively, s is a subscript indicating a stop, b is a subscript indicating a building,

is the distance between the station and the building,

is the utilization rate according to the distance,
The calculating of the plurality of central building indices and the plurality of central station indices may include calculating index values for all stations existing in the aggregate district for each building for extracting the central building, and calculating each station for extracting the central station. Calculate index values for all buildings existing in the aggregate district for each,
Calculating the at least one central building and the at least one central station comprises:
determining, by the device, a central building corresponding to a central building indicator having a value greater than a median value of the plurality of central building indicators as at least one central building; and
determining a center stop corresponding to a center stop indicator having a value greater than a median value of the plurality of center stop indicators as at least one center stop;
The step of setting the personal mobile device area,
calculating, by the device, minimum distances that can respectively reach all routes associated with the station from the at least one central building;
setting a central building area having an average value of the calculated minimum distances as a radius and having the at least one central building as a center;
Generating a plurality of Voronoi polygons respectively corresponding to all stations in the aggregate district, and setting a Voronoi polygon corresponding to the at least one central station among the plurality of Voronoi polygons as a central station area; and
Setting both the central building area and the central stop area as a personal mobile device area or setting only an overlapping area of the central building area and the central stop area as a personal mobile device area. A method for predicting the demand for new use of mobile devices.
The method of claim 1, wherein the step of estimating the resident population of the building,
Estimating, by the device, the resident population of the building using Equation 1 below; a method for predicting new usage demand for a personal mobile device, comprising:
[Equation 1]

here

is the resident population of building b in county k,

represents the gross floor area of building b in county k,

is the population of population k.
The method of claim 1, wherein the step of calculating the personal mobility device usage rate of the building comprises:
A method for predicting new use demand for a personal mobility device, comprising: calculating, by the device, the usage rate of the personal mobility device of the building using Equation 2 below.
[Equation 2]

where a is the age group,

is the proportion of age group a in building b,

is the electric kickboard use rate for age group a, and P ^b _age is the use rate of personal mobility devices in building b considering the age group ratio.
delete delete delete delete The method of claim 1, wherein determining the number of operating personal mobile devices comprises:
generating, by the device, a plurality of points where the personal mobility device is arbitrarily placed in the area of the personal mobility device;
Calculating a travel time difference between walking and personal mobile devices using the plurality of points and Equation 5 below;
calculating the number of points satisfying the differential value of the travel time difference to be zero; and
Determining the calculated number of points as the number of operating personal mobile devices
[Equation 5]

here

is the difference in travel time between walking alone and using an electric kickboard,

is the travel time it takes to travel from building b to stop s on foot,

is the travel time taken to walk from building b to electric kickboard P,

is the travel time taken to move to the bus stop after riding the electric kickboard using the electric kickboard.
a control unit that calculates the number of operating personal mobile devices; and
A database for storing data for predicting demand for new use of a personal mobile device;
The control unit,
Estimating the resident population of the building located in the aggregation district by using the resident population and the total floor area of the building in the aggregation district requiring a demand survey of the personal mobile device;
Calculate the usage rate of the personal mobile device in the building using the age ratio for each building,
Calculating the number of personal mobility devices in the building using the resident population of the building and the personal mobility device usage rate of the building;
Calculate a plurality of central building indices and a plurality of central station indices using the number of personal mobile devices in the building, the distance between the building and the stop, and the usage rate according to the distance;
calculate at least one central building and at least one central station using the plurality of central building indicators and the plurality of central station indicators;
Establishing a personal mobile device area using the at least one central building and the at least one central station;
Determine the number of operating personal mobility devices by using the travel time difference between walking and personal mobility devices and the personal mobility device area,
The calculation of the number of personal mobile device traffic in the building is calculated by using the following Equation 3 to calculate the number of personal mobile device traffic in the residential building and the personal mobile device traffic in the commercial building, and Calculate the number of personal mobile device traffic of the building by adding the number of mobile device traffic and the number of personal mobile device traffic of the commercial building;
[Equation 3]

here

and

are the number of personal mobile device trips in residential building b and the number of personal mobile device trips in commercial building b, respectively,

is the resident population of building b,

is the total floor area of building b, u is the traffic unit and the utilization rate, P ^b _age is the personal mobility device utilization rate of building b considering the age ratio,
Calculation of the plurality of center building indicators and the plurality of center stop indicators calculates the plurality of center building indicators and the plurality of center stop indicators using Equation 4 below,
[Equation 4]

where I ^b and I ^s are the central building index and the central station index, respectively, s is a subscript indicating a stop, b is a subscript indicating a building,

is the distance between the station and the building,

is the utilization rate according to the distance,
The calculation of the plurality of central building indices and the plurality of central station indices is performed by calculating index values for all stations existing in the aggregate district for each building for extracting the central building, and calculating the index value for each station for extracting the central station. Calculate the index value for all buildings existing in the aggregate district,
The calculation of the at least one central building and the at least one central station determines a central building corresponding to a central building indicator having a value greater than a median value of the plurality of central building indicators as the at least one central building, and determining as at least one central stop a central stop corresponding to a central stop index having a value greater than a median value of the central stop indices of ;
In setting the personal mobile device area, minimum distances each capable of reaching all routes related to the station are calculated from the at least one central building, and an average value of the calculated minimum distances is used as a radius, and the at least one A central building area in which the central building is the center is set, a plurality of Voronoi polygons corresponding to all stations in the aggregate district are generated, and Voronoi polygons corresponding to at least one central station among the plurality of Voronoi polygons are generated. Setting the polygon as the central stop area, and setting both the central building area and the central stop area as the personal mobile device area, or setting only the overlapping area of the central building area and the central stop area as the personal mobile device area. Characterized by a device for predicting demand for new use of a personal mobile device.

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