WO2015162723A1 - Dispositif d'analyse de comportement - Google Patents

Dispositif d'analyse de comportement Download PDF

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Publication number
WO2015162723A1
WO2015162723A1 PCT/JP2014/061435 JP2014061435W WO2015162723A1 WO 2015162723 A1 WO2015162723 A1 WO 2015162723A1 JP 2014061435 W JP2014061435 W JP 2014061435W WO 2015162723 A1 WO2015162723 A1 WO 2015162723A1
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WIPO (PCT)
Prior art keywords
drop
rate
product
time
purchase
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PCT/JP2014/061435
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English (en)
Japanese (ja)
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教夫 大久保
幹 早川
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株式会社日立製作所
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Priority to JP2016514611A priority Critical patent/JP6205484B2/ja
Priority to PCT/JP2014/061435 priority patent/WO2015162723A1/fr
Publication of WO2015162723A1 publication Critical patent/WO2015162723A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions

Definitions

  • the present invention relates to an apparatus for analyzing behaviors of customers and the like in stores.
  • One aspect of the invention of the present application is a stop-by time that is a stoppage time in front of a product shelf in a store, and a purchase after stop-off that is a ratio of the number of people who stopped at the product shelf and the number of people who purchased the product on the product shelf.
  • a two-dimensional filter that smoothes the drop-off rate, which is the ratio of the number of customers visiting the product shelf and the number of people who visited the product shelf, on a flat surface for the entire store.
  • a stop-off time that is a stoppage time in front of a product shelf
  • a purchase after stop-off that is a ratio of the number of people who stopped at the product shelf and the number of people who purchased the product on the product shelf
  • It is a behavior analysis device equipped with a device for measuring the rate, and has a device for smoothing the stop-off rate, which is the ratio of the number of customers visiting the product shelf and the number of people who visited the product shelf, for each product shelf in a flat manner throughout the store
  • the first product shelf with a long drop-in time, low purchase rate after drop, low drop-off rate after smoothing, short drop-in time, high purchase rate after drop-off, and high drop-off rate after smoothing
  • a device for calculating a customer unit price improvement expected value when the product in the first product shelf and the product in the second product shelf are exchanged.
  • the input device has a function of inputting behavior history information from a portable sensor carried by a customer who has entered a predetermined geographical area while staying in the predetermined geographical area.
  • the action history information includes information regarding continuous or discrete time and place.
  • the processing device extracts stop-off information that indicates whether or not each customer has stopped at a plurality of predetermined locations in the geographical range.
  • the processing device maps, for each of a plurality of predetermined locations, a stoppage rate that is a ratio of the total number of visitors and the number of customers who have stopped at the predetermined location, and further smoothes the stopover rate two-dimensionally. .
  • the processing device displays the smoothed drop-in rate data on the output device, stores it in the storage device, or both.
  • Still another aspect of the present invention relates to an apparatus including an input device, an output device, a processing device, and a storage device.
  • the input device has a function of inputting behavior history information from a portable sensor carried by a customer who has entered a predetermined geographical area while staying in the predetermined geographical area.
  • the action history information includes information regarding continuous or discrete time and place.
  • the processing device extracts stop time information indicating the length of the stop time for each customer at each of the predetermined locations.
  • the input device inputs purchase information indicating whether or not each customer has purchased a product arranged at a plurality of predetermined locations for each of these locations.
  • the processing device calculates a post-stop purchase rate that is a ratio of the number of people who have stopped at a specific place and the number of people who have purchased a product placed at a predetermined place.
  • the processor further labels and / or sorts the products located in a plurality of predetermined locations based on the length of the stop-off time and the purchase rate after the stop-off. Do.
  • positioning of the goods in a store can be acquired automatically, and it is possible to provide a new value to a customer in a store. .
  • FIG. 1 is a system configuration diagram for performing behavior analysis in a store which is an embodiment of the present invention.
  • the entire system can be realized by a computer or server including an input device, an output device, an information processing device, and a storage device.
  • each processing unit to be described later is assumed to be executed by software operating on a computer, but may be configured by hardware.
  • each database described later can be stored in a storage device.
  • reference numeral 101 denotes a measurement result database that stores basic information for each customer ID, stop-by time by shelf, purchase price by shelf, purchase points by shelf, and the like. A data collection method will be described later with reference to FIG. The data stored in the measurement result database 101 will be described later with reference to FIG.
  • 102 is a post-stop purchase rate analysis processing unit
  • 103 is a drop-in rate analysis processing unit
  • 104 is a product placement optimization processing unit
  • 105 is a product placement list database showing the optimum placement candidates for the product. The data stored in the product arrangement list database 105 will be described later with reference to FIG.
  • the post-stop purchase rate analysis processing unit 102 includes a stop-time database 121, a purchase price database 122, a ratio analysis process 123, and a post-stop purchase rate analysis result database 124.
  • Data stored in the drop-in time database 121 will be described later with reference to FIG. This data is the total of the stop-by time for each customer at each store.
  • Data stored in the purchase price database 122 will be described later with reference to FIG.
  • the purchase amount 122 is a total of purchase amounts for each shelf for each customer visiting the store.
  • the purchase price may be the number of purchases if the price difference for each product is ignored. The data in this case will be described later with reference to FIG.
  • the ratio analysis process 123 analyzes the ratio or distribution of values for each of the drop-in time 121 and the purchase amount 122, and determines whether the value is relatively large or small. Each determination result may be digitized. Further, the ratio analysis process 123 calculates a post-stop purchase rate, which is a ratio purchased after the stop.
  • the after-stop purchase ratio refers to the drop-in time database 121 and totals the number C of customers who stopped by each shelf (that is, the number of customers having stop-by-shelf time other than 0) and refers to the purchase price database 122
  • the number of customers B who purchased the product for each shelf may be tabulated, and B may be divided by C for each shelf.
  • a threshold value for example, 30 seconds or more
  • the specific product on a specific shelf has a long customer visit time and the purchase rate after the visit is low, or the customer purchase time is short and the customer purchases after the visit.
  • a numerical value that can identify whether the rate is high may be calculated. For example, if a value obtained by dividing the stop time by the after-stop purchase rate is used, a product having a tendency of non-purpose purchase product has a large value. At this time, both values may be weighted with an appropriate coefficient.
  • These digitized data can be stored in the after-stop purchase rate analysis result database 124.
  • the determination result and the post-stop purchase rate are stored in the post-stop purchase rate analysis result 124.
  • FIG. 11 shows an example of after-stop purchase rate data stored in the after-stop purchase rate analysis result database 124.
  • the drop-in rate analysis processing unit 103 includes a drop-in rate database 131, a two-dimensional low-pass filter process 132, and a drop-in rate analysis result database 133.
  • the drop-in rate 131 is the ratio of the number of customers who visited by shelf among the number of customers visiting the store (for example, shown in FIG. 11).
  • the drop-in rate for each shelf is affected by the product characteristics and shelf layout characteristics.
  • the product characteristics are, for example, a high drop-in rate to a shelf with hit products.
  • the shelf arrangement characteristics include, for example, a high drop-in rate near the entrance. As described above, the drop-in rate is greatly different for each shelf due to various factors.
  • the two-dimensional low-pass filter process 132 that smoothes these values.
  • smoothing is performed in units of several meters as an example. Thereby, it is possible to remove local fluctuations.
  • the drop-in rate analysis result 133 stores the smoothed drop-in rate. The smoothed drop-in rate will be described later with reference to FIG.
  • the drop-in rate that reduces the influence of the characteristics of the displayed product. That is, in this embodiment, the drop-in rate determined by factors other than the product characteristics, such as the shape of the store, the entrance / exit arrangement, and the product shelf arrangement, is extracted. For this reason, the two-dimensional low-pass filter process is performed as described above. With the two-dimensional low-pass filter, it is possible to smooth a local drop-in rate variation (for example, a high drop-in rate around a popular product) that depends on the arrangement of the product. The fact that the influence of the drop-in rate due to the product characteristics can be reduced also means that the drop-in rate distribution obtained by this embodiment is not significantly affected even if the arrangement of the product is changed.
  • the characteristics of the low pass filter may be determined by the size and shape of the store, the characteristics of the product, and the display method. As in normal supermarkets and general stores, assuming that the range where one kind of product is placed is several tens of centimeters to 1 meter wide, a filter (for example, 3 to 5 meters) that makes the product distribution invisible can be used. That's fine. However, if the resolution is lowered too much, the distribution of the drop-in rate becomes invisible, so the filter characteristics should be such that necessary information can be obtained in consideration of the size of the store.
  • the product arrangement optimization processing unit 104 includes a calculation process 141 for a place with a high drop-in rate, a search process 142 for a non-purpose purchase product, and a calculation process 143 for a product arrangement change candidate.
  • the calculation process 141 for a place where the drop-in rate is high rearrangement is performed in descending order from the smoothed drop-off rate.
  • the shelves are sorted in the order of the smoothing stoppage rate shown in FIG.
  • the non-purpose purchase merchandise search process 142 a merchandise shelf having a long customer visit time and a low after-stop purchase rate is searched. For this reason, in this embodiment, the determination result obtained by the ratio analysis process 123 is used.
  • the digitized determination result is stored in the after-stop purchase rate analysis result database 124.
  • the stored data obtained by quantifying the characteristic that “the customer's visit time is long and the purchase rate after visit is low” is rearranged in descending order.
  • the customer's drop-in time is long, the purchase rate after drop is low, the customer's drop-in rate is low, the customer's drop-in time is short, the post-stop purchase rate is high, A product shelf with a high drop-in rate is calculated.
  • the product of the product characteristic quantified by the ratio analysis process 123 described above and the smoothed reciprocal of the stop-off rate are calculated for each shelf, and rearranged in descending order. At this time, each value may be weighted. In this way, a product shelf with a long customer visit time, a low after-stop purchase rate, and a low customer visit rate has a large value.
  • FIG. 2 explains the reason why sales can be expected to increase due to the above configuration.
  • the horizontal axis represents the customer drop-in rate
  • the vertical axis represents the post-stop purchase rate
  • the characteristics are plotted for each product.
  • the product A is a product with a large number of purpose purchases, a product with a high customer drop-in rate, and a high purchase rate after drop-in.
  • the product B is a product with a large number of non-purpose purchases, and is a product with a low customer drop-in rate and a low post-stop purchase rate. The result of replacing these two types of product arrangements is the right side of the figure.
  • the product A has been moved to a place with a low drop-in rate, it is a product with a large number of purpose-purchased items, indicating that the drop-in rate of customers does not decrease much.
  • the purchase rate after dropping does not change significantly, the sales of the product A do not change so much.
  • the post-stop purchase rate depends on the characteristics of the product, it is considered that the change in the arrangement does not change greatly.
  • the product B has moved to a place with a high drop-in rate, the drop-in rate increases.
  • the after-stop purchase rate does not change significantly, the sales of the product B increases as the drop-in rate increases.
  • the sales as a store increase by changing the arrangement of the two types of products.
  • FIG. 3 is a graph in which the horizontal axis represents the customer visit time and the vertical axis represents the post-stop purchase rate.
  • the purpose buying pattern is characterized by a short drop-in time and a high after-stop purchase rate.
  • a typical example is a case where what is going to be purchased is decided before coming to the store, and the decision to purchase a product is quick at the store and the purchase rate is high.
  • the non-purpose buying pattern is characterized by a long drop-in time and a low after-stop purchase rate.
  • a typical example is a case where purchase of a product is not decided before coming to the store, and purchase is decided after seeing the product at the store. The time until the decision is long and the purchase rate is low.
  • the purpose purchase and the non-purpose purchase are called, but it is not always necessary to accurately determine the purpose purchase or the non-purpose purchase.
  • the reason for this is that the product placement and exchange shown in FIG. 2 can be expected to increase sales by moving to a place where the drop-in rate is high if the drop-in time is long and the purchase rate is low.
  • the purpose purchase is when the drop-in time is short and the purchase rate after drop is high, and the case when the drop-in time is long and the purchase rate is low is considered non-purpose purchase.
  • FIG. 4 is a floor map in which a shelf with a high drop-in rate for each shelf is painted in a dark color, and a shelf with a low drop-in rate is painted in a light color.
  • the drop-in rate of the merchandise shelf is affected by the characteristics of the merchandise and the characteristics of the shelves in the store.
  • the characteristic of the product is, for example, a high drop-in rate for daily necessities.
  • the shelf arrangement characteristics include, for example, a high drop-in rate near the main flow line. As described above, the drop-in rate is greatly different for each shelf due to various factors.
  • reference numeral 501 denotes a flow line detection sensor device worn by a customer who visits the store
  • 502 denotes an infrared beacon that transmits an ID by infrared
  • 503 denotes a product shelf.
  • the flow line detection sensor device 501 includes an infrared sensor, an ID reception function, a memory, and the like.
  • infrared rays When infrared rays are used for the communication means, the directivity of infrared rays is strong, so that it is possible to measure the time when the customer is facing the product shelf depending on whether infrared rays are detected. By using this time as a drop-in time, it is possible to exclude a time when the vehicle simply passes by or is doing another thing without facing the product shelf. For this reason, it is possible to more accurately measure the time when there is a high possibility that the user is at a loss in purchasing the product. The measurement result is accumulated in the memory of the flow line detection sensor device 501.
  • FIG. 6 shows a procedure in which a customer at the store wears the flow line detection sensor device 501 and collects after shopping.
  • a request for mounting the flow line detection sensor device 501 is made.
  • the flow line detection sensor device 501 has a form that is lowered from the neck with a string, for example. This asks the customer for survey cooperation at the store.
  • you will have a normal shopping Next, you will have a normal shopping.
  • the flow line detected by the flow line detection sensor device 501 and the stay situation are measured.
  • the measured data is temporarily stored in a memory in the flow line detection sensor device 501.
  • a typical example of the measured data is the coordinate data in the store and data indicating the time existing at the coordinates.
  • the flow line detection sensor device 501 is connected to the cradle at the cash register, and the action data accumulated in the memory is collected.
  • purchase data including a product ID and price data is collected from tags attached to the product.
  • These data are accumulated in the measurement result database 101, and the action data and the purchase data can be matched. This can be verified later with the POS data by measuring the passage time of the cash register, or refraining from the customer's membership card number or refraining from the receipt number.
  • the flow line detection sensor device is collected at the time of leaving the store. After collection, the data in the sensor device is deleted.
  • the method of collecting and collecting data in the flow line detection sensor device 501 at the cash register is shown.
  • the flow line detection sensor device 501 has a transmission function, the information collected by the flow line detection sensor device 501 is sent to the management server in real time, and the sent information is stored in the measurement result database 101. You may comprise.
  • the method of measuring the stop-by time for each shelf is not limited to infrared communication means, but location measurement using RFID, distance measurement using laser, image recognition using a camera, and the like may be used.
  • FIG. 7 is summary data for each customer as a result of measuring behavior in the store. This data is stored in the measurement result database 101 of FIG. A unique customer ID is attached to each customer, and data such as store entry time, store exit time, and stay time in the store, which is the difference between them, is recorded. In addition, sex, age group, etc. may be recorded as personal information. Moreover, you may implement a questionnaire and summarize the result.
  • FIG. 8 is data on the drop-in time by shelf for each customer as a result of measuring the behavior in the store. This data is stored in the drop-in time database 121 of FIG. Assign a unique customer ID to each customer. Individual names are assigned to the shelves. Here, “shelf 1”, “shelf 2”, “shelf 3”,... The data of the time of stopping at each shelf is recorded for each customer. It can be seen that the customer ID 1234 has a long drop-in time of the shelf 6.
  • FIG. 9 is data on the purchase amount by shelf for each customer as a result of measuring the behavior in the store. This data is stored in the purchase price database 122 of FIG. Assign a unique customer ID to each customer. Individual names are assigned to the shelves. Here, “shelf 1”, “shelf 2”, “shelf 3”,... Data on the amount of money purchased on each shelf for each customer is recorded. It can be seen that the customer ID 1234 has a high purchase price for the shelf 8.
  • FIG. 10 shows data on the number of points purchased per shelf for each customer as a result of measuring the behavior in the store. This data is stored in the purchase price database 122 of FIG. 1 instead of the data of FIG. 9 or together with the data of FIG. Assign a unique customer ID to each customer. Individual names are assigned to the shelves. Here, “shelf 1”, “shelf 2”, “shelf 3”,... Data on the number of products purchased on each shelf for each customer is recorded. It can be seen that the customer ID 1234 has purchased one item each of the shelf 1 and the shelf 8.
  • FIG. 11 shows data of the drop-in rate for each customer and the purchase rate after the drop-down of the analysis result of measuring the behavior in the store.
  • This data is stored in the after-stop purchase rate analysis result database 124 of FIG.
  • Individual names are assigned to the shelves.
  • Data on the drop-in rate and purchase rate after drop is recorded for each shelf.
  • the drop-in rate is a value obtained by dividing the number of customers visiting the store among the customers who have measured the number by the number of customers visiting the store.
  • the after-stop purchase rate is a value obtained by dividing the number of customers who have purchased a product on the shelf among the customers who have been measured by the number of customers who have visited the shelf among the customers who have been measured.
  • FIG. 12 shows data on the drop-in rate and the smoothed drop-in rate for each customer in the analysis results of the in-store behavior measurement.
  • This data is stored in the drop-in rate analysis result database 133 of FIG.
  • Individual names are assigned to the shelves.
  • the data of the drop-in rate and the smoothed drop-in rate are recorded for each shelf.
  • the drop-in rate is a value obtained by dividing the number of customers visiting the store among the customers who have measured the number by the number of customers visiting the store.
  • the smoothed stoppage rate is a value obtained by performing a two-dimensional filter process with the stoppage rate arranged in a plane.
  • FIG. 13 shows data of a product arrangement list as an analysis result of measuring the behavior in the store.
  • This data is stored in the drop-off product arrangement list database 105 of FIG.
  • the target product, the replacement product candidate, and the expected value for improving the customer unit price are shown in the order in which the sales increase can be expected by exchanging the product arrangement.
  • the expected value of sales improvement is shown by the unit price of customers.
  • data calculated in the product arrangement change candidate calculation process 143 and rearranged in descending order of the values are used. As described above, in the calculation process 143, calculation is performed so that a product shelf with a long customer visit time, a low purchase rate after visit, and a low customer visit rate has a large value.
  • the products A, B, and C are cases where a large value is obtained by the calculation process 143, and the products X, Y, and Z are cases where a small value is obtained.
  • 160 yen improvement can be expected as a customer unit price by switching the arrangement of the product A and the product X.
  • product A is a product with a long drop-in time but a low purchase rate after drop-off, and a low drop-in rate after smoothing
  • product X has a short drop-in time but a high purchase rate after drop-off, and a drop-off after smoothing It is a product with a high rate.
  • the behavior analysis apparatus of the present embodiment it is possible to automatically determine whether a product should be placed in a place with a high drop-in rate or a product should be placed in a place with a low drop-in rate.
  • products that are not intended for purchase can be found in the store, and new value can be provided to the customer in the store.
  • the evaluation method or the quantification method of each element of the shelf on which the product is displayed, the length of the drop-off time, the size of the purchase rate after the drop-off, the size of the drop-off rate after the smoothing is as described above. It is not limited to examples. Various other statistical methods can also be used.
  • each element can be weighted.
  • FIG. 1 an example is shown in which the processing up to calculation of the product arrangement change candidate is performed based on the collected data.
  • Another embodiment of the present invention is a drop-in rate analysis system in which the measurement result database 101 and the drop-in rate analysis processing unit 103 are the main components in the configuration of FIG.
  • the other configurations in FIG. 1 can be omitted. That is, the drop-in rate analysis system of the present embodiment can extract points with high drop-in rates shown in the lower part of FIG. This drop-in rate is smoothed as described above, and the influence of the product characteristics is reduced. Therefore, even if the place where the product is placed is changed, it is hardly affected.
  • the manager can grasp a point at which the customer is likely to stop, so that it can be used as a reference for the product arrangement.
  • Another embodiment of the present invention is a post-stop purchase rate analysis system having the measurement result database 101, post-stop purchase rate analysis processing unit 102, and non-purpose purchase product search processing 142 in the configuration of FIG. It is.
  • the other configurations in FIG. 1 can be omitted.
  • the post-stop purchase rate analysis system of this embodiment classifies the product characteristics based on the customer's stop time and post-stop purchase rate from the data of the stop time database 121 and the purchase price database 122.
  • Example 1 shows a method of sorting trends by quantifying trends.
  • one or a plurality of threshold values are provided for the stop time and the post-stop purchase rate, and the product characteristics can be classified by the threshold values. For example, a product with a stop time of 30 seconds or less and a post-stop purchase rate of 80% or more is flagged as a target purchase product. In addition, products with a stop time of 3 minutes or more and a post-stop purchase rate of 30% or less are flagged as non-purpose purchase products. These flags are associated with the product type ID to create a database and stored in the storage device. The contents of the database can be displayed on the output device at any time.
  • a product with a long stop-off time and a low after-stop purchase rate and a product with a short stop-off time and a high after-stop purchase rate can be displayed in an identifiable manner.
  • the product characteristics can be grasped, it can be arranged on the product shelf according to the characteristics of the products, and sales can be improved.
  • the arrangement of products on the product shelves in the store has been described as an example.
  • the concept of the present invention is not limited to this, and can be applied, for example, to store-type arrangements in stores in shopping malls and shopping streets.
  • the present invention is not limited to the embodiments described above, and includes various modifications.
  • a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment.

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Abstract

La présente invention résout le problème qui est de distinguer automatiquement un achat prémédité et un achat non prémédité. L'invention porte sur un dispositif d'analyse de comportement équipé d'un dispositif servant à mesurer un temps d'arrêt, qui est un temps pendant lequel une personne s'est arrêtée devant un rayon dans un magasin, et un taux d'achat après arrêt, qui est un rapport du nombre de personnes qui se sont arrêtées au niveau d'un rayon sur le nombre de personnes qui ont acheté un produit présent sur le rayon. Le dispositif d'analyse de comportement est équipé d'un dispositif servant à appliquer un filtrage bidimensionnel dans le magasin entier à un taux d'arrêt, qui est un rapport du nombre de personnes qui sont arrivées au magasin sur le nombre de personnes qui se sont arrêtées au niveau du rayon, et d'un dispositif servant à calculer, en supposant qu'une valeur du taux d'arrêt après traitement par filtrage bidimensionnel est un taux d'arrêt après filtrage, un rayon dont le temps d'arrêt est long, le taux d'achat après arrêt est faible, et le taux d'arrêt après filtrage est faible.
PCT/JP2014/061435 2014-04-23 2014-04-23 Dispositif d'analyse de comportement WO2015162723A1 (fr)

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Cited By (9)

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JP2017162432A (ja) * 2016-03-07 2017-09-14 株式会社リコー 画像処理システム、情報処理装置、情報端末、プログラム
WO2018061623A1 (fr) * 2016-09-30 2018-04-05 パナソニックＩｐマネジメント株式会社 Dispositif d'évaluation et procédé d'évaluation
KR20180047794A (ko) * 2016-11-01 2018-05-10 주식회사 케이티 오프라인 매장의 방문자 행동 패턴에 기반한 o2o 마케팅 플랫폼 시스템 및 그 구축 방법
IT201700017690A1 (it) * 2017-02-17 2018-08-17 Centro Studi S R L Sistema intelligente PROCESS TOOL per il controllo dei processi che presiedono la vendita di beni e servizi
TWI651668B (zh) * 2017-01-10 2019-02-21 和碩聯合科技股份有限公司 基於消費交易紀錄之商品展售位置產生方法、商品展售位置產生裝置及電腦可讀取儲存媒體
JP2019105971A (ja) * 2017-12-12 2019-06-27 富士ゼロックス株式会社 情報処理装置及びプログラム
JP2019113889A (ja) * 2017-12-20 2019-07-11 ヤフー株式会社 算出装置、算出方法、及び算出プログラム
CN113674034A (zh) * 2021-08-31 2021-11-19 南京添益越科技有限公司 一种基于区块链技术的自治化网络社区管理系统
WO2024142543A1 (fr) * 2022-12-27 2024-07-04 コニカミノルタ株式会社 Dispositif d'analyse de déplacement de personnes, programme et procédé d'analyse de déplacement de personnes

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