JP2017165509A - Transportation management system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide vehicle allocation matching with enhanced load efficiency.SOLUTION: A transportation management system of the present invention comprises: an operation information collection part for collecting operation status data including information on positions of delivery vehicles from delivery staff terminals with GPS function; an operation prediction part for creating operation prediction data including delivery prediction areas of delivery vehicles on the basis of the operation status data; a vehicle allocation matching part for performing vehicle allocation matching for vehicles to be allocated for delivery requests including cargo collection sites and delivery destinations; and an operation schedule planning control part for creating an operation schedule planning data by incorporating the delivery requests into an operation schedule plan for delivery vehicles. The vehicle allocation matching part calculates loading rates for the delivery vehicles on the basis of a cargo volume of a requested cargo for the delivery request and cargo volumes of determined delivery requests having been already incorporated in the operation schedule planning data, extracts first candidate vehicles having operation prediction areas including the cargo collection site and the delivery destination, and performing matching between the delivery request and the first candidate vehicles in a descending order of the loading rates.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a transportation management technique for collecting and delivering packages.

  2. Description of the Related Art Conventionally, as disclosed in Patent Document 1, there is a luggage transport system that performs vehicle allocation in consideration of information regarding the loading rate of a vehicle and the size or weight of the luggage. In Patent Document 1, the carrier presents the transportation conditions of the luggage on the vehicle side, and determines that the vehicle occupant can satisfy the presented transportation conditions in consideration of his / her work schedule. It is described that the vehicle is selected by returning a response to the supplier.

Japanese Patent No. 3948204

  An object of this invention is to provide the transportation management system which can aim at the improvement of the loading rate of a delivery vehicle.

(1) The present invention is a transportation management system for a delivery vehicle that collects and delivers requested parcels, receives position information of the delivery vehicle transmitted from a deliveryman terminal equipped with a GPS function, and is provided for each delivery vehicle. An operation information collection unit that collects operation status data based on delivery business results, an operation prediction unit that generates operation prediction data including an operation prediction area of the delivery vehicle based on the operation status data, a pickup location and delivery A dispatching matching unit that performs a dispatching matching process of the delivery vehicle with respect to a delivery request including a place, and an operation schedule plan data in which the delivery request is incorporated into an operation schedule plan of the delivery vehicle matched by the dispatching matching process, And an operation schedule plan control unit that transmits to the carrier terminal of the carrier to which the delivery vehicle belongs. The vehicle allocation matching unit calculates a loading rate for the delivery vehicle based on a package volume of the requested package of the delivery request and a package volume of the confirmed delivery request already incorporated in the operation schedule plan data, and the operation prediction A first candidate vehicle whose area includes the collection location and the delivery location is extracted, and a matching vehicle is extracted by matching the delivery requests in descending order of the loading ratio among the first candidate vehicles.

(2) In the above (1), when a plurality of matching vehicles are extracted, the vehicle allocation matching unit calculates a distance or time from a predetermined predicted position in the operation prediction area to a pickup location in the pickup time. It is possible to extract the matching vehicle that is calculated for each vehicle and is closest to the pickup location.

(3) In the above (1), the dispatching matching unit calculates a distance or time from a predetermined predicted position in the operation prediction area to a pickup location in the pickup time for each of the first candidate vehicles. The delivery vehicle whose distance or time is within a predetermined allowable range can be extracted as the first candidate vehicle.

(4) In the above (1) to (3), the operation prediction unit generates an operation result route connecting the vehicle positions of the delivery vehicles in time series based on the position information of the delivery vehicles, and the operation The operation prediction area including a plurality of virtual areas that include an actual result route and that divides the operation actual result route at predetermined time intervals can be generated.

(5) In the above (1) to (4), based on the delivery request reception unit that receives the delivery request transmitted from the shipper's shipper terminal, and the fare data set in advance for the delivery vehicle, And an estimate / order control unit that generates fare estimate data for the accepted delivery request and transmits the data to the shipper terminal. At this time, the operation schedule plan control unit generates an operation schedule plan data incorporating the delivery request after the order of the delivery request is confirmed with the shipper based on the fare estimate data, It can be sent to the article dealer terminal.

It is a network block diagram of the transportation management system of 1st Embodiment. It is a functional block diagram of the transportation management apparatus of 1st Embodiment. It is a functional block diagram of the delivery person terminal of a 1st embodiment. It is a figure which shows the flow of the transportation management system of 1st Embodiment. It is a figure which shows the flow of the transportation management system of 1st Embodiment. It is a figure which shows an example of the vehicle basic information (a) of 1st Embodiment, and delivery person information (b). It is a figure for demonstrating the loading information acquisition function of the delivery person terminal of 1st Embodiment. It is a figure which shows an example of the vehicle position and loading information of 1st Embodiment. It is a figure which shows an example of the operation performance information of 1st Embodiment. It is a figure which shows an example of the delivery request information of 1st Embodiment. It is a figure which shows the flow of the operation prediction data production | generation process of 1st Embodiment. It is a schematic diagram which shows an example of several past operation track record route information and a virtual area of 1st Embodiment. It is a schematic diagram of the operation prediction area of 1st Embodiment. It is a figure which shows an example of the operation prediction data containing the operation prediction area of 1st Embodiment, and an operation prediction load factor. It is the schematic diagram which showed the operation prediction data shown in FIG. 14 in the process table format (time chart format). It is a schematic diagram for demonstrating the vehicle allocation matching process of 1st Embodiment. It is the schematic diagram (a) which allocated the some delivery request in the operation prediction area of the operation prediction data of 1st Embodiment, and the figure (b) which shows an example of the delivery schedule plan data containing a some fixed schedule. It is a figure which shows the flow of the vehicle allocation matching process of 1st Embodiment. It is a flowchart which shows the modification of the vehicle allocation matching process of 1st Embodiment. It is a schematic diagram for demonstrating the vehicle allocation matching process using the prediction load factor of 2nd Embodiment. It is a figure which shows an example in which a delivery request is integrated in a delivery schedule by the vehicle allocation matching process of 2nd Embodiment. It is a figure which shows the dispatch matching process flow of 2nd Embodiment.

  Hereinafter, embodiments will be described with reference to the drawings.

(First embodiment)
1 to 19 are diagrams showing a first embodiment. FIG. 1 is a network configuration diagram of the transportation management system of this embodiment. As shown in FIG. 1, centering on a transportation management device (hereinafter referred to as a management device) 300, a shipper (shipper terminal 400) that makes a delivery request, and a shipper (shipper who is the delivery destination of the package based on the delivery request) A terminal 500) and a carrier (delivery company terminal 200 and delivery person terminal 100) delivering packages are connected via a network. The network may be a wireless communication network / wired communication network, a dedicated line network, or the like. For example, a network such as the Internet can be used.

  FIG. 2 is a functional block diagram of the management apparatus 300. The management device 300 includes a communication device 310, a control device 320, and a storage device 330. The communication device 310 controls data communication with each terminal connected via a network.

  The control device 320 includes a vehicle registration unit 321, an operation information collection unit 322, an operation information provision unit 323, an operation prediction unit 324, a delivery request reception unit 325, a dispatching matching unit 326, an estimate / order control unit 327, and an operation schedule plan control. A portion 328 is included.

  FIG. 3 is a functional block diagram of the delivery person terminal 100 of the present embodiment. The delivery person terminal 100 includes a communication unit 110, a control unit 120, a storage unit 130, a display unit 140, an operation unit 150, and an imaging unit 160. The control unit 120 includes an operation status control unit 121 and an operation schedule data control unit 122, and the operation status control unit 121 includes a GPS control unit 121A and a loading information control unit 121B. The delivery person terminal 100 is, for example, a portable information terminal having a wireless communication function.

  4 and 5 are flowcharts showing the processing flow of the transportation management system of this embodiment. The transportation management system is a management system that collects and manages delivery requests from shippers and creates and provides operation schedule plans for each delivery vehicle in response to delivery requests on behalf of the carrier responsible for the delivery vehicle. is there. In addition, a function of providing a delivery cost estimate and a purchase order for a delivery request on behalf of the carrier is provided, and the delivery request order management between the shipper and the carrier is supported.

  As shown in FIG. 4, the transportation company registers a delivery vehicle and a delivery person when using this transportation management system. The vehicle registration unit 321 of the management device 300 communicates with the carrier terminal 200 connected to the management device 300 with the carrier information of the carrier and a plurality of delivery vehicles, delivery personnel (for example, the carrier) Registration control of information on (driver) is performed. Note that the delivery vehicle and delivery person can also include an individual business owner who has a contract with a transportation company.

  For example, when the business entity terminal 200 connects to the management apparatus 300 and makes a vehicle registration request (S201), the vehicle registration unit 321 transmits a predetermined vehicle / delivery person registration screen to the business operator terminal 200, and the vehicle / delivery Perform input control and display control for the employee registration screen. The business operator can register each delivery vehicle and each delivery person having jurisdiction by inputting vehicle basic information and delivery person information via the vehicle / delivery person registration screen. The vehicle registration unit 321 performs a registration process of storing the basic vehicle information and the delivery person information input on the vehicle / delivery person registration screen in the storage device 330 (S301).

  FIG. 6A is a diagram illustrating an example of vehicle basic information, and includes a vehicle ID, a vehicle height, a luggage compartment volume, and the like for each business operator ID that identifies each transportation business operator. FIG. 6B is a diagram showing an example of delivery person information, which includes a delivery person ID for identifying each delivery person, name, age, qualification, and the like.

  The registration of the vehicle basic information and the delivery person information can also be performed on the management side of the management apparatus 300. For example, the transportation company sends paper medium-based vehicle basic information and delivery person information to the operation side of the management apparatus 300 by FAX or mail, and an input person on the operation side uses the registration function of the vehicle registration unit 321 to register the vehicle basic information. Information and delivery person information can be registered.

  The business entity terminal 200 receives the operation schedule data for the registered delivery vehicle from the management device 300, and transmits the operation schedule data to the corresponding delivery person terminal 100 (S202). The operation schedule data control unit 122 of the delivery person terminal 100 stores the received operation schedule data in the storage unit 130 (S101).

  The operation schedule data control unit 122 displays a delivery service start screen on the display unit 140 and controls the delivery person to input the vehicle ID and the delivery person ID. For example, the delivery person inputs the vehicle ID and the delivery person ID via the operation unit 150 such as an input button or a touch panel, and selects the job start button. When the business start button is selected, the operation schedule data control unit 122 transmits data indicating the start of the delivery business including the vehicle ID and the delivery person ID to the management device 300 (S102). Moreover, the operation schedule data control part 112 outputs the trigger signal which starts the process which acquires the positional information on the delivery person terminal 100 (delivery vehicle) to 121 A of GPS control parts. At this time, the operation schedule data control unit 122 can also display an operation schedule data plan table in which the delivery requests for today's day are arranged in time series on the display unit 140 (S103).

  The GPS control unit 121A of the delivery person terminal 100 acquires the position information of the moving delivery vehicle in the delivery service at predetermined intervals, and transmits the GPS data of the delivery vehicle to the management device 300 (S104). The delivery person also registers in the management device 300 the loading information after loading the packages and the loading information after unloading at the delivery locations at the collection location and delivery location of the delivery request. The loading information control unit 121B provides a loading information acquisition function as shown in FIG. 7 (S105).

  In the example of FIG. 7, the delivery person loads the package at the collection place, and then activates the loading information registration screen from the delivery person terminal 100. The loading information control unit 121B displays the screen A on the display unit 140. When the loading rate registration button is selected, the loading information control unit 121B displays the screen B and displays a message for photographing the loading room after loading on the delivery person with the photographing unit 160. Output. The delivery person selects a camera activation button (icon) on the screen B and activates the photographing unit 160 to photograph the cargo room after loading. The screen C is a screen that displays a captured image of the luggage compartment photographed by the photographing unit 160. On screen C, the delivery person confirms the captured image of the luggage compartment.

  Subsequently, the stacking information control unit 121B displays a screen D. The screen D includes a loading rate selection field for selecting or inputting a loading rate, and the delivery person selects, for example, the loading rate of the captured cargo room from a plurality of preset loading rate selection information. The loading information control unit 121B displays a pre-transmission confirmation screen E including the captured image of the cargo compartment and the selected loading rate on the display unit 140. When the transmission button is selected on the screen E, the loading information and the captured image are displayed. The transmission data (loading information) that is included is generated and transmitted to the management apparatus 300 (S106).

  In addition, when loading information is registered from the delivery person terminal 100, the captured image of the luggage compartment is transmitted to the management apparatus 300, but this is not restrictive. For example, without taking a picture of the cargo room itself, the loading rate checked by the delivery person can be selected from a plurality of preset loading rate selection information, or the delivery person can input the loading rate directly. Then, it may be configured to be registered in the management apparatus 300.

  The operation information collection unit 322 of the management apparatus 300 receives the GPS data and the loading information of the delivery vehicle transmitted from the delivery person terminal 100, and performs the operation information collection process for accumulating the operation status of each delivery vehicle in time series ( S302). The operation information collection unit 322 can receive the GPS data and the loading information from the delivery person terminal 100 in real time or collectively after the delivery service for one day is completed, and store it in the storage device 330. In addition, the operation information acquisition unit 322 can perform control so as to store the operation status data of each delivery vehicle in the storage device 330 for each daily delivery operation (daily).

  FIG. 8 is a diagram illustrating an example of vehicle position / loading information. As shown in FIG. 8, for each delivery vehicle (vehicle ID), the GPS data and the loading rate of the delivery vehicle are arranged in time series. Each time the operation information collection unit 322 receives GPS data and loading information from the delivery person terminal 100, the operation information collecting unit 322 assigns a sequence No., and operates the vehicle position / loading information including date, time, empty space ratio of the loading platform, and the like. Accumulate as situation data.

  Further, as shown in FIG. 9, the operation information collection unit 322 can generate operation result information based on the operation status data of FIG. 8 (S304). The operation result information includes the actual date, vehicle ID, delivery person ID, delivery request number, pickup time at the pickup location, GPS data and empty volume ratio (100-loading rate), load volume, receiving time at the receiving location, and receiving (delivery) ) Location GPS data, empty volume ratio (100-loading ratio), etc. are included.

  The operation status data and operation result information shown in FIGS. 8 and 9 are daily delivery histories (delivery work results) of each vehicle. The operation status data is information obtained by tracing the daily operation status in real time, and the operation result information is information indicating a daily operation result generated from the operation status data. For this reason, the information produced | generated based on operation performance information can also be produced | generated from operation status data.

  For example, the collection time can be calculated from the operation status data shown in FIG. 8 from the time of arrival at the collection place to the end of the collection. Here, the time at which the delivery vehicle arrives at the collection location is determined, for example, whether the vehicle location based on the GPS data is within the range of the location information of the collection location that can be grasped in advance from the delivery request at the desired collection time. By doing so, it is possible to grasp the time when the delivery vehicle arrives at the collection place. Further, by determining whether or not the delivery vehicle is stopped at the pickup location based on the GPS data (such as GPS data continuous in time series staying at the same position for a predetermined time), the delivery vehicle is set at the pickup location. The time of arrival can be determined.

  Further, the end of the collection can be determined based on the transmission data of the loading information received from the delivery person terminal 100 in the above-described loading information acquisition process. The empty volume ratio can be calculated based on the loading ratio received from the delivery person terminal 100. The same applies to the receiving time. As shown in the example of FIG. 8, when starting the delivery business or ending the delivery business, for example, at the base of the delivery vehicle, the delivery person performs the loading information acquisition process shown in FIG. It can be reflected in the situation data.

  The operation information providing unit 323 of the management device 300 can provide vehicle grasping information to each of the shipper main terminal 400 and the arrival main terminal 500 based on the operation status data shown in FIG. 8 (S303). For example, for each delivery request, it is possible to provide vehicle grasping information including a map map displaying the current vehicle position and delivery location with icons, and estimated arrival time. The operation information providing unit 323 acquires the operation status data of the corresponding delivery vehicle from the storage device 330 based on the vehicle grasping request including the delivery request number from the shipper terminal 400, and corresponds to the latest GPS data of the delivery vehicle. A map map in which the symbol mark (vehicle object) of the delivery vehicle is arranged on the map coordinates to be generated can be generated. A map map obtained by tracing the movement trajectory of the delivery vehicle can also be displayed on the map based on the operation status data including the time-series vehicle position.

  In addition, the operation information providing unit 323 can provide the operator terminal 200 with a map map including the current position and movement trajectory of one or more delivery vehicles for the operator. In this case, for example, the operation information providing unit 323 can also control to display the loading rate in the symbol mark of each delivery vehicle. By displaying each symbol mark of a plurality of delivery vehicles on one map map, the service provider can grasp the operation status of the plurality of delivery vehicles under its jurisdiction.

  The operation prediction unit 324 generates operation prediction data including an operation prediction area and an operation prediction load factor of each delivery vehicle based on the operation status data of FIG. 8 (S305). The operation prediction data of each delivery vehicle is used for generation of delivery schedule data of the delivery vehicle in response to a delivery request from the shipper, that is, a delivery vehicle matching process for the delivery request. The operation prediction data generation process will be described later. Note that the operation prediction area and the operation prediction load factor can also be generated as individual operation prediction data.

  The operation schedule data control unit 122 of the delivery person terminal 100 displays a delivery operation end screen on the display unit 140 based on an operation input by the delivery person, transmits data indicating the end of the delivery operation to the management device 300, and operates. Control is performed to end the GPS data acquisition process by the situation control unit 121.

  For example, the delivery person inputs the vehicle ID and the delivery person ID via the operation unit 150, and selects the business end button. When the work end button is selected, the operation schedule data control unit 122 transmits data indicating the end of the delivery work including the vehicle ID and the delivery person ID to the management apparatus 300 (S107). It is also possible to perform a delivery operation end process combined with the loading information acquisition process shown in FIG. For example, during the loading information acquisition process at the delivery location or delivery vehicle base where the delivery of the last delivery request has been completed, a button for selecting the delivery work end is displayed to indicate the end of the delivery work together with the loading information Data can be transmitted to the management apparatus 300.

  Next, with reference to FIG. 5, the processing flow until the delivery vehicle of the carrier is dispatched in response to the delivery request from the shipper will be described. As illustrated in FIG. 5, the delivery request reception unit 325 of the management apparatus 300 performs control related to the delivery request with the shipper terminal 400 connected to the management apparatus 300. For example, the delivery request reception unit 325 transmits a predetermined delivery request screen to the shipper terminal 400 (S311), and performs input control and display control on the delivery request screen. The shipper can input the delivery request data via the delivery request screen (S401) and make a delivery request. The delivery request receiving unit 325 receives the delivery request data input from the delivery request screen and stores it in the storage device 330 (S312).

  FIG. 10 is a diagram illustrating an example of delivery request information. For each delivery request, the delivery request number, the request date, the requester (shipper) ID, the collection location, the desired collection time (start), the desired collection time (end), the package volume, the number, the delivery (reception location) location, the delivery ( It is configured to include a desired delivery time (start), a desired delivery (reception) time (end), and the like. The delivery request screen is configured to include these input items.

  Here, the package volume can be input on the shipper side, but can also be configured such that the delivery request receiving unit 325 automatically calculates it. For example, when the shipper inputs the model number and the number of standard cardboards, the delivery request receiving unit 325 refers to the volume information for each model number stored in the storage device 330 in advance, and sends a delivery request based on the number to be delivered. The volume of the entire package can be calculated. Note that the method for calculating the volume of the entire package requested for delivery is not limited to this, and a known technique can be applied as appropriate.

  The vehicle allocation matching unit 326 performs vehicle allocation matching processing that allocates a delivery request from the shipper to the plurality of delivery vehicles registered by the carrier using the operation prediction data (S313). The vehicle allocation matching process will be described later.

  As a result of the dispatching matching process, when it is determined that there is no allocated vehicle, that is, there is no delivery vehicle capable of incorporating (contracting) a delivery request (NO in S314), the delivery request receiving unit 325 A correction notification process is performed (S315). The delivery request receiving unit 325 transmits a selection screen for selecting whether or not to modify the delivery request content to the shipper terminal 400. When the shipper selects “correct” (YES in S402), the process returns to step S401, and the delivery request reception unit 325 transmits a delivery request screen including the input delivery request content to the shipper terminal 400. The shipper modifies the desired collection time, the desired delivery time, the number of packages, etc. from the delivery request screen. The corrected delivery request is transmitted from the shipper terminal 400 to the management device 300. When the shipper selects “do not modify” (NO in S402), a delivery request cancel request is transmitted from the shipper terminal 400 to the management apparatus 300 (S403), and the delivery request reception unit 325 is input in step S401. Processing to stop accepting the delivery request is performed (S316).

  As a result of the vehicle allocation matching process, when it is determined that there is an allocated vehicle (YES in S314), the vehicle allocation matching unit 326 performs a temporary vehicle allocation notification process, indicating that a delivery request is assigned to the delivery vehicle of the carrier. A temporary notification is transmitted to the operator terminal 200 (S317).

  If it is determined that there is an allocated vehicle (YES in S314), the estimate / order control unit 327 calculates a fare related to the delivery request and generates estimate data. For example, it is possible to obtain transportation fee data from a carrier in advance and store it in the storage device 330, and calculate the fare for the delivery request according to the package volume, number, weight, etc. of the delivery request. The estimate / order control unit 327 generates estimate data and order form data for the delivery request, and performs an estimate / order form issuance process that is transmitted to the shipper terminal 400 (S318).

  The shipper transmits order data (order OK / NG) based on the order form data from the shipper terminal 400 to the management apparatus 300 (S404). The estimate / order control unit 327 receives the order data transmitted from the shipper terminal 400, and when the order is NG (NO in S319), performs a cancel process for the corresponding delivery request (S320). As the canceling process, for example, a notification for canceling the temporary vehicle delivery notification in step S317 can be transmitted to the business entity terminal 200, or the delivery request itself can be suspended or discarded (deleted).

  On the other hand, when it is determined by the estimate / order control unit 327 that the order data transmitted from the shipper terminal 400 is an order OK (YES in S319), the operation schedule plan control unit 328 matches the vehicle allocation matching process. The operation schedule plan data incorporating the delivery request is generated in the operation schedule plan data of the delivery vehicle, in other words, the operation schedule data of the delivery vehicle, and stored in the storage device 330 (S321). The operation schedule plan control unit 328 transmits the generated operation schedule plan data to the business operator terminal 200 (S322), and the business operator terminal 200 transmits the vehicle allocation schedule data based on the operation schedule plan data to the delivery person terminal 100. (S203).

  The estimate / order control unit 327 can also send the estimate data and the order data to the shipper by FAX. In this case, the shipper sends a purchase order including order OK or order NG to the management side of the management apparatus 300 by FAX, and uses the function of the estimate / order form control unit 327 on the management side of the management apparatus 300 to place an order. Data can be entered.

  As described above, in the transportation management system of the present embodiment, the management device 300 receives the delivery request data from each shipper terminal 400, assigns the delivery requests to a plurality of delivery vehicles registered by the carrier, and performs the dispatching matching process. Do. The management device 300 generates transportation schedule plan data for each delivery vehicle in which a delivery request based on the result of the dispatching matching process is incorporated, and provides the plan to the business operator terminal 200.

  In the present embodiment, the daily operation status of the delivery vehicle is grasped, and the delivery request is assigned to each delivery vehicle using the operation prediction data including the operation prediction area and the operation prediction loading rate. And provide an efficient dispatch schedule for a plurality of delivery vehicles under the jurisdiction of the carrier. Hereinafter, the operation prediction data generation process and the dispatching matching process of the present embodiment will be described in detail.

<Operation prediction data generation processing>
11 to 15 are diagrams for explaining the operation prediction data generation processing of the present embodiment. FIG. 11 is a diagram illustrating a process flow for generating operation prediction data. The operation prediction unit 324 can perform operation prediction data generation processing for each delivery vehicle, for example, at an arbitrary timing after the end of the delivery operation.

  The operation prediction unit 324 first acquires operation status data (delivery work history) of one delivery vehicle from the storage device 330 (S3051). Next, the operation prediction unit 324 generates operation result route information that connects the vehicle positions of the delivery vehicles in time series based on the GPS data (GPS coordinates) (S3052). FIG. 12 is a schematic diagram showing a plurality of past operation record route information. As shown in FIG. 12, for example, each operation result route on the past X day, X + 1 day, and X + 2 day can be generated. In the example of FIG. 12, a delivery vehicle that departs from a delivery vehicle base (a base where a delivery person such as a business operator's office or a predetermined parking lot parks a delivery vehicle for delivery work) performs the delivery work. It shows the operation result route from finishing to returning to the base.

  The operation prediction unit 324 divides the operation result route at a predetermined time interval, and sets a virtual area r for the vehicle position at a predetermined time (S3053). For example, the operation result route is sectioned every 30 minutes, and the position (coordinates) of the delivery vehicle located at 11:00 am is acquired. When there is only one operation result route, a predetermined range centering on the position at that time is set as the virtual area r. In the example of FIG. 12, the inside of a predetermined radius centering on the vehicle position is set as a virtual area r (circle indicated by a dotted line).

  Further, the virtual area r can be set using a plurality of operation result routes as in the example of FIG. For example, the position of each operation result route of the delivery vehicle located at 11:00 am can be acquired, the average center of a plurality of vehicle positions can be obtained, and used as the center position of the virtual area r. That is, the virtual area r (operation prediction area R described later) is updated by the daily operation result route.

  Here, each virtual area r can be arbitrarily set such as the same or different size at each time. For example, when the virtual area r is set using a plurality of operation result routes, the virtual area r can be set by obtaining the average center of each vehicle position as described above, depending on the degree of dispersion between the vehicle positions. The size of the radius when setting the virtual area may be variably controlled.

  For example, a known statistical variance is obtained from each vehicle position, and the virtual area r generated with respect to the average center can be set larger as the variance degree increases. Also, a threshold is provided for the degree of dispersion, the degree of dispersion relative to the threshold is determined, and the first virtual area r1 set when the degree of dispersion is larger than the threshold, set when the degree of dispersion is smaller than the threshold A second virtual area r2 that is smaller than the first virtual area r1 can be defined in advance.

  Further, when the average center of the virtual area is obtained based on the vehicle position of the operation results, if the current vehicle position is more than a predetermined value with respect to the center or average center of the previously set virtual area, the current vehicle It can also be configured not to update the average center of the virtual area by excluding the operation results of the position. By excluding the vehicle position of the irregular case, the operation prediction data can be generated with high accuracy in accordance with the daily operation results.

  In the present embodiment, since the loading rate is included in the operation status data, the operation predicting unit 324 can generate the loading rate for each virtual area, that is, at each time (S3054). As the loading rate at each time, for example, an average value, a maximum value, a median value, or the like of a plurality of loading rates can be used.

  A plurality of virtual areas r divided at predetermined time intervals set in this way are arranged in time series as shown in FIG. Then, the operation prediction unit 324 generates a region including at least each virtual area r as the operation prediction area R as shown in FIG. 13 (S3055). For example, it is possible to connect the virtual areas r adjacent to each other arranged in chronological order by tangent lines and generate one operation prediction area R that is closed around the base of the delivery vehicle.

  As the operation prediction area R, the plurality of virtual areas r shown in FIG. 12 may be used as they are. That is, in this embodiment, it is only necessary to set the area where each delivery vehicle set based on the daily operation track is located as the operation prediction area R, and the operation prediction area R is set by a method other than the above-described method. Also good. 12 and 13 show an example in which a circular virtual area is set with the vehicle position at a predetermined time as the center (average center) as the virtual area, but other shapes such as a polygonal shape are shown. The virtual area can be set, and the shape and size of the virtual area are arbitrary.

  As described above, in the present embodiment, the distribution history including the operation result route and the loading rate of each delivery vehicle is grasped, and each delivery vehicle is classified according to each unit such as day, day of the week, week, month, quarter, half year, and one year. The operation prediction area and the loading rate for each future day are generated from the present time.

  For example, as an example of generating a predicted future operation area and predicted operation load ratio, for example, if the current date (X day) is February 15, 2016 (Monday), February 16 of tomorrow ((X + 1) day) As the service forecast data for Sunday (Tuesday), the past delivery history of every Tuesday is extracted, the above-mentioned service forecast area is calculated for each predetermined time period from the delivery history on each Tuesday, and this is used as the service forecast area for tomorrow. Can be applied.

  The same applies to the predicted operation load ratio. The average load ratio in each time zone can be calculated from the past delivery history of every Tuesday, and this can be applied as the predicted operation load ratio for tomorrow. In this manner, operation prediction data for each future day ((X + 1) day, (X + 2) day,..., (X + n) day) from the current date is generated and stored in the storage device 330.

  The generation example of the operation prediction data described above is an example, and the present invention is not limited to this. The operation prediction data can be generated using various methods. For example, it is possible to generate operation prediction data in consideration of time rather than day of the week. When generating the forecast data for April 2016, with April 2016 as the current date, March 2015, April, May, and March 2016, It is possible to extract delivery history record data for April, and generate operation prediction data for each daily time zone from comparisons and trends of past data.

  FIG. 14 is a diagram illustrating an example of operation prediction data including an operation prediction area and an operation prediction load factor. The operation prediction data can be configured to include coordinate information (coordinate information on the map) of each operation prediction area R (n) that is partitioned at predetermined time intervals and arranged in time series, and the operation prediction loading rate. . As described above, in this embodiment, operation prediction data can be generated by associating the operation predicted loading ratio with the operation prediction areas arranged in time series (S3056). The operation prediction unit 324 generates the generated operation prediction data for each delivery vehicle and stores it in the storage device 330 (S3057).

  FIG. 15 is a schematic diagram showing the operation prediction data shown in FIG. 14 in a travel table format (time chart format). As shown in FIG. 15, the operation prediction data generated from the operation status for the past delivery request is used as information for grasping the future operation status based on the operation results.

  The operation prediction unit 324 can update the operation prediction data every time daily operation results (operation status data) are accumulated, and as described above, the operation prediction area and the operation prediction load ratio are determined by the operation prediction data. Each time the generation process is performed, it changes based on the operation result. For example, if the current operation status data on February 15 is accumulated, the operation prediction area and the operation predicted loading rate on February 22, 2016 (Monday) next week are as follows: Reflected and updated.

  The operation prediction data can be generated for each day of the week. In addition, the operation prediction data generation processing can be performed in real time or at a long-term timing such as one week, one month, or half a year.

<Vehicle allocation processing>
16 to 19 are diagrams for explaining the vehicle allocation matching process according to the present embodiment. FIG. 16 is a schematic diagram for explaining the vehicle allocation matching process.

  The vehicle allocation matching process according to the present embodiment is performed by the vehicle allocation matching unit 326 and uses the operation prediction area of the operation prediction data described above. That is, it is a process for incorporating a delivery request with a future delivery date at least tomorrow from the present time into the future operation schedule of the delivery vehicle, as shown in FIG. Using the image, the dispatching matching process is performed in which the delivery request is assigned to the delivery vehicle with the image of filling the delivery request in the operation prediction area.

  First, a delivery vehicle (first candidate vehicle) in which the collection location P and the delivery location Q of the delivery request are included in the operation prediction area is extracted. Since the coordinates on the map of the pickup location P and the delivery location Q can be grasped (calculated) from the address information etc., it is determined whether or not the coordinates of both the pickup location P and the delivery location Q are included in the operation prediction area. can do.

  Next, the loading rate at the desired collection time at the collection location Q is confirmed for one or a plurality of first candidate vehicles. Here, the volume of the requested package for delivery request is X, the volume of the package already loaded at the desired collection time, that is, the volume of the already loaded package of another delivery request (scheduled to be confirmed). Y. Since there is an upper limit on the cargo compartment volume of the delivery vehicle, in order to collect the requested luggage X, the delivery vehicle needs to have a total loading ratio of the requested luggage X + the determined scheduled Y smaller than the upper limit value of the cargo compartment volume. is there. Therefore, from the first candidate vehicles, a second candidate vehicle that satisfies the requested luggage X + confirmed schedule Y <the upper limit value is extracted.

  And the delivery vehicle nearest to the collection place is extracted as the allocation vehicle among the second candidate vehicles. For example, in the operation prediction area of the second candidate vehicle, the distance between the center (or the average center) of the virtual area including the pickup location P and the pickup location P is calculated, and the pickup location is set with the center of the virtual area as the predicted position. The nearest delivery vehicle that can go to P in the shortest distance is extracted as an allocation vehicle. In addition, as the shortest distance, a linear distance between the center of the virtual area and the pickup location P may be used, or the shortest distance to the pickup location P traced along the road on the map may be used. it can.

  At this time, as the requested baggage X + confirmed schedule Y is closer to the upper limit value, the loading rate can be expected to improve, and efficient delivery vehicle allocation (allocation) can be performed for the delivery request. Therefore, in the second candidate vehicle, for example, when the loading rate of the candidate vehicle A is 80% and the loading rate of the candidate vehicle B is 60%, and the candidate vehicle B is closest to the pickup location, it is not the candidate vehicle B The candidate vehicle A can be extracted.

  That is, by extracting the delivery vehicle closest to the pickup location as the allocated vehicle, the time (or distance) efficiency can be improved, but both the candidate vehicle A and the candidate vehicle B have the pickup location P in the operation prediction area. Therefore, the time loss between the candidate vehicle A and the candidate vehicle B is minimized. For this reason, in this embodiment, a delivery vehicle (delivery location P and delivery location Q) that includes a delivery vehicle included in the operation prediction area is extracted as a candidate vehicle, and a candidate having a high loading rate with respect to the requested package X of the delivery request. By extracting the vehicle A, a vehicle allocation matching process for improving the loading rate is provided.

  In a delivery vehicle having an operation prediction area that matches a delivery request, the delivery rate of each delivery vehicle is improved by matching the delivery requests in descending order of loading rate (requested baggage X + confirmed schedule Y). And the number of delivery vehicles can be optimized.

  For example, by improving the loading rate, the transportation company can reduce the number of necessary delivery vehicles, and can suppress a decrease in transportation service due to a shortage of delivery personnel (drivers). Moreover, since the loading rate per delivery vehicle can be improved, it is possible to reduce the cost by reducing the transportation cost and improve the profit of the carrier (delivery person). Moreover, the improvement of the loading ratio per delivery vehicle leads to, for example, providing a margin for assigning a new delivery request to the candidate vehicle B, and can reduce the loss of the delivery request acceptance opportunity. .

  Furthermore, since the transportation management system of the present embodiment centrally manages the extraction of allocated vehicles and the provision of delivery schedule plan data from a delivery request, the burden on the dispatch business of the transportation company can be reduced. For example, conventionally, a dispatcher of a transportation company has been required to mediate between a shipper and a delivery person and make adjustments by telephone communication or the like. Such communication is troublesome for the delivery person, leading to a loss of time in the delivery work, not being able to contact the delivery person, or not being able to request delivery even if the delivery person is contacted. There was a problem that the vehicle allocation could not be adjusted efficiently. On the other hand, in the present embodiment, it is not necessary to adjust vehicle allocation on the delivery person side and the transportation company side, and the efficiency of delivery work can be improved.

  As described above, in the present embodiment, the first matching condition for determining whether or not the collection location P and the delivery location Q of the delivery request are included in the operation prediction area and the delivery vehicle satisfying the first matching condition are requested. A vehicle allocation matching process including at least a second matching condition for preferentially matching a delivery vehicle having the highest total loading ratio of the package X + confirmation schedule Y (<upper limit value) with a delivery request is performed. And although the delivery vehicle nearest to a collection place is matched as 3rd matching conditions, the priority of the 2nd matching conditions is set higher than the 3rd matching conditions.

  In the vehicle allocation matching process, a distribution vehicle having the second matching condition is allocated among the distribution vehicles that satisfy the first matching condition and are located within a predetermined allowable range with respect to the collection location P. Can also be extracted. As in the third matching condition, the dispatching matching process may be performed based on the shortest distance, but the delivery vehicle satisfying the first matching condition includes the collection location P of the delivery request in the operation prediction area. Instead of the shortest distance, a delivery vehicle having the highest total loading ratio among delivery vehicles located within a predetermined allowable range (distance or time) can be configured to match the delivery request.

  In addition, since the confirmed schedule Y is not included in the delivery vehicle to which no confirmed delivery request is assigned, the above-described dispatching matching process is performed with the confirmed schedule = 0. In this case, when all of the first candidate vehicles extracted under the first matching condition are vehicles (free vehicles) that do not include the fixed schedule Y, the second matching condition is omitted and the third matching condition is applied, and the pickup is performed. The delivery vehicle closest to the place P is extracted as the allocation vehicle.

  The vehicle allocation matching process is performed on the premise that the desired collection time at the collection location P of the delivery request and the desired collection time or desired delivery time of the already confirmed delivery request are delivery vehicles that do not overlap. The logic is such that the delivery request that has been confirmed first is prioritized.

  As shown in FIG. 16, delivery schedule plan data is generated for the allocated vehicle. The delivery schedule plan data is plan data including a collection location, a desired collection time, a delivery location, and a desired delivery time for each delivery request. The delivery schedule plan data can also be generated, for example, by assigning a delivery request to the operation prediction data of each delivery vehicle shown in FIG. 14 or 15 and overwriting the operation prediction data with the delivery request. In this case, the confirmed schedule in which the delivery request is confirmed for the delivery vehicle and the operation forecast data are mixed, and the delivery schedule plan data is generated as delivery schedule / predicted data.

  FIG. 17A is a schematic diagram in which a plurality of delivery requests are assigned in the operation prediction area of the operation prediction data, and FIG. 17B is a diagram showing an example of the delivery schedule plan data including a plurality of confirmed schedules. It is. As shown in FIG. 17A, a plurality of delivery requests are assigned in an image that is laid out in the operation prediction area of the vehicle X.

  Then, as shown in FIG. 17B, after the delivery request 1 is collected, the delivery request 2 is assigned, the loading rate is 80%, and the delivery request 3 is assigned, and the loading rate is 90%. Yes. In this way, it is possible not only to assign the time period from the desired collection time to the desired delivery time so that they do not overlap with each other, but also the vehicle allocation matching process that assigns the delivery requests in an overlapping manner so as to increase the loading rate. Therefore, the loading rate per delivery vehicle is improved.

  FIG. 18 is a diagram illustrating a process flow of the vehicle allocation matching process. The vehicle allocation matching unit 326 extracts the requested package volume X, the collection location, the desired collection time, the delivery location, and the desired delivery time from the delivery request information from the shipper (S3131). At this time, when storing the delivery request information received from the shipper in the storage device 330, the delivery request accepting unit 325 converts the address information of the collection location and the delivery location into coordinate information on the map, and collects the collection location and the delivery location. The delivery request information including the coordinate information on the map can be generated together with the address information.

  Next, the vehicle allocation matching unit 326 refers to the delivery schedule plan data, and extracts delivery vehicles in which the scheduled collection desired time or delivery desired time and the requested delivery collection time or delivery desired time do not overlap. (S3132). Of the vehicles extracted in step S3132, the dispatching matching unit 326 extracts a delivery vehicle whose delivery schedule decision data includes the decision schedule Y (S3133).

  The vehicle allocation matching unit 326 refers to the operation prediction area of the operation prediction data for the delivery vehicle including the confirmed schedule Y, and the collection location and the delivery location of the delivery request are included in the operation prediction area based on the coordinate information. The delivery vehicle determined to be included is extracted as the first candidate vehicle (S3134, first area condition).

  Next, the vehicle allocation matching unit 326 targets the first candidate vehicle, and the total loading rate of the loading rate (requested baggage X + confirmed scheduled Y) at the desired collection time at the pickup location is smaller than the upper limit value of the cargo space volume. The delivery vehicle is extracted as the second candidate vehicle (S3135).

  The dispatching matching unit 326 extracts a delivery vehicle having the highest loading rate (requested baggage X + confirmed schedule Y) from the second candidate vehicles as an allocation vehicle (S3136). At this time, when a vehicle having the same loading rate is extracted as a plurality of allocated vehicles (YES in S3137), the vehicle allocation matching unit 326 includes a virtual area including the collection location P for each of the plurality of allocated vehicles. The distance between the center (or the average center) and the pickup location P is calculated, and the delivery vehicle located at the shortest distance to the pickup location is extracted as an allocated vehicle (S3138).

  In step S3139, the vehicle allocation matching unit 326 determines whether an allocated vehicle is extracted. If the allocated vehicle is extracted, the vehicle allocation matching process ends. On the other hand, when the allocated vehicle is not extracted at this stage, the second area condition relaxed from the collection place of the delivery request in step S3134 and the area matching condition (first area condition) between the delivery place and the operation prediction area, A vehicle allocation matching process is performed for a vehicle including the confirmed schedule Y.

  When it is determined that the allocated vehicle is not extracted under the first area condition (NO in S3139), the vehicle allocation matching unit 326 determines whether the vehicle allocation matching process is already performed under the second area condition, When it is determined that the vehicle allocation matching process under the second area condition is not performed (YES in S3140), the process proceeds to step S3141, and the operation prediction area of the operation prediction data is set for the delivery vehicle including the confirmed schedule Y. The delivery vehicle (second area) is determined by referring to whether or not either the collection location or the delivery location of the delivery request is included in the operation prediction area based on the coordinate information. (Condition vehicle) is extracted.

  The vehicle allocation matching unit 326 performs Steps S3135 to S3138 for the second area condition vehicle, and extracts a provision vehicle from the second area condition vehicle. When the allocation vehicle is extracted under the second area condition for the delivery request (YES in S3139), the dispatching matching unit 326 ends the dispatching matching process.

  On the other hand, if the allocation vehicle is not extracted even in the second area condition for the delivery request (NO in S3139, YES in S3140), the vehicle allocation matching unit 326 refers to the operation schedule plan data and does not include the confirmed schedule Y. Is extracted (S3142). At this time, the vehicle allocation matching unit 326 can extract a free vehicle in which the collection location P and the delivery location Q of the delivery request are included in the operation prediction area or a free vehicle included in the operation prediction area as a free vehicle. .

  The dispatching matching unit 326 calculates the distance between the center (or average center) of the virtual area including the collection location P and the collection location P based on the operation prediction data for each of the plurality of free vehicles, The free vehicle located at the shortest distance to the place is extracted as the allocated vehicle (S3143).

  The vehicle allocation matching unit 326 ends the vehicle allocation matching process when the allocated vehicle for the delivery request can be extracted from the free vehicles (YES in S3144). The vehicle allocation matching unit 326 determines that there is no matching vehicle for the delivery request (S3145) when the allocation vehicle for the delivery request cannot be extracted from the free vehicles (NO in S3144), and ends the process.

  FIG. 19 is a diagram illustrating a modified example of the vehicle allocation matching process according to the present embodiment. The modification shown in FIG. 19 has shown the aspect which matches a delivery request and a delivery vehicle, without utilizing operation prediction data. As shown in FIG. 19, as a condition for determining whether or not to use the operation prediction data, a future (collection receipt date> current + predetermined number of days) in which the delivery receipt date of the delivery request is relatively closer than the present is set. (S3151).

  When the collection date of the delivery request is the current time + the future in advance of the predetermined number of days, the vehicle allocation matching unit 326 performs a vehicle allocation matching process using the operation prediction data shown in FIG. Is the current + near future within a predetermined number of days, the delivery vehicle is matched to the delivery request regardless of the operation prediction data.

  As shown in FIG. 19, the vehicle allocation matching unit 326 determines the relationship between the requested package X for the delivery request and the loading rate of the delivery vehicle. When the requested baggage X is lower than the upper limit value and higher than the first threshold value (YES in S3152), the vehicle allocation matching unit 326 extracts candidate vehicles for free vehicles that do not include the fixed schedule Y, for example, free vehicles The distance from the base to the collection location is calculated, and the free vehicle located at the shortest distance to the collection location is extracted as the allocated vehicle (S3153).

  The vehicle allocation matching unit 326 ends the vehicle allocation matching process when the allocated vehicle for the delivery request can be extracted from the free vehicles that do not include the fixed schedule Y (YES in S3154). The dispatching matching unit 326 determines that there is no matching vehicle for the delivery request when the allocation vehicle for the delivery request cannot be extracted from the free vehicles that do not include the confirmed schedule Y (NO in S3154), and ends the process. To do.

  On the other hand, when it is determined in step S3152 that the requested luggage X is lower than the first threshold (NO in S3152), the vehicle allocation matching unit 326 targets the vehicle including the confirmed schedule Y for the loading rate (requested luggage X + The vehicle having the highest schedule Y) is extracted (S3155). When the vehicle allocation matching unit 326 can extract the allocated vehicle for the delivery request from the vehicles including the confirmed schedule Y (YES in S3156), the vehicle allocation matching unit 326 ends the vehicle allocation matching process and responds to the delivery request from the vehicles including the confirmed schedule Y. When the allocated vehicle cannot be extracted (NO in S3156), the process proceeds to step S3153, and the above-described vehicle allocation matching process for the free vehicle is performed.

  In the dispatching matching process shown in FIGS. 18 and 19, it is possible to consider the delivery work time (workable time frame) of the delivery person for one day. In this case, when it exceeds the workable time frame, it can be configured not to be extracted as an allocated vehicle. A workable time frame (for example, from 8:00 am to 6:00 pm) is defined in advance for each delivery person and stored in the storage device 330, and the dispatching matching unit 326 assigns the allocated vehicle determined to have a matching process. On the other hand, a workable time frame determination process is performed. For example, if the desired delivery time of the delivery request is 9:00 pm, the allocated vehicle can be excluded without being extracted as a matching vehicle, and the working hours of the delivery person can be optimized.

(Second Embodiment)
20 to 22 are diagrams for explaining the vehicle allocation matching process according to the second embodiment. The vehicle allocation matching process of the present embodiment realizes preferential vehicle reservation for a shipper who has a transaction record.

  The vehicle allocation matching unit 326 extracts candidate vehicles having a transaction record with the shipper of the delivery request on the basis of the transaction results for each shipper, and the volume of the requested baggage requested for the delivery is operated using the predicted operation load ratio of the candidate vehicle. A matching process for extracting a vehicle having a predicted loading ratio or less can be performed.

  FIG. 20 is a schematic diagram for explaining the dispatching matching process using the predicted operation load ratio. The delivery request includes a package V (package volume) and a shipper S. First, the vehicle allocation matching unit 326 refers to the transaction ratio information and extracts a delivery vehicle having a transaction record with the shipper S who has requested delivery.

  Here, as shown in FIG. 20, the transaction rate information is information including the shipper who has delivered in the past and the type of the package (package attribute) for each delivery vehicle. The ratio of the number of delivery requests of the shipper to the total number of delivery requests accepted within a certain period in the past. In addition to the number of delivery requests, it may be the ratio of the total amount of the delivery fee of the shipper to the total amount of the transportation fee described above within a certain past period. The management device 300 can include a transaction result section, generates transaction rate information for each registered delivery vehicle based on the operation result information and the delivery request information, and stores it in the storage device 330.

  The vehicle allocation matching unit 326 can extract the vehicle A and the vehicle C as a delivery vehicle having a transaction record with the shipper S who has requested delivery (circle character 1 in FIG. 20). The vehicle allocation matching unit 326 uses the extracted predicted operation load ratios of the vehicle A and the vehicle C, and the volume of the load V is equal to or less than the predicted operation load ratio, and the volume of the load V is the highest in the predicted operation load ratio. A nearby vehicle is extracted (circle character 2 in FIG. 20). In the case of the vehicle A, since the volume of the load V exceeds the predicted operation load ratio, the vehicle C whose volume V is equal to or less than the predicted operation load ratio is extracted as an allocated vehicle and A delivery request is assigned (circle character 3 in FIG. 20). The process of circle characters 1 to 3 in FIG. 20 corresponds to the matching process of the present embodiment.

  FIG. 21 is a diagram illustrating an example in which a delivery request is incorporated into a delivery schedule by the vehicle allocation matching process according to the present embodiment. As shown in FIG. 21, for each registered delivery vehicle, the predicted operation load ratio for each day from the present time is stored in the storage device 330 by the operation prediction data generation process. The predicted operation load ratio for each day in the example of FIG. 21 is shown in a travel table format as shown in FIG.

  As shown in FIG. 21, in the delivery schedule of each registered vehicle, for example, a delivery request is assigned to an operation predicted load ratio area (predicted load area) and a free area (100-predicted load ratio) ( It is generated by being overwritten). The fixed area is an area to which a delivery request has already been assigned. When a delivery request has already been assigned to a free area, the remaining area is a free area.

  FIG. 22 is a diagram showing a vehicle allocation matching process flow of the present embodiment. As shown in FIG. 22, the vehicle allocation matching unit 326 extracts the volume of the requested luggage and the shipper along with the collection location, the desired collection time, the delivery location, and the desired delivery time from the delivery request information of the shipper (S501).

  Next, the vehicle allocation matching unit 326 determines whether the collection date of the delivery request information to be matched is a future date after a predetermined number of days from the current date (S502). When it is determined that the collection date is a future date after a predetermined number of days from the current date, the process proceeds to step S503, and matching processing using the predicted operation load ratio is performed. If it is determined that the collection date is a future date less than the predetermined number of days from the current date, the process is terminated.

  In step S503, the vehicle allocation matching unit 326 refers to the transaction rate information, extracts a candidate vehicle when there is a transaction record with the shipper of the delivery request information (transaction rate> 0), and one or more extracted in step S503. The predicted operation load ratio at the desired collection time of the delivery request information is extracted from the operation prediction data and matched with the volume of the requested package (S504).

  The vehicle allocation matching unit 326 uses the predicted operation load ratio to extract a vehicle whose requested luggage volume is equal to or less than the predicted operation load ratio. When multiple vehicles are selected whose requested luggage volume is less than or equal to the predicted operation load ratio, the volume of the requested luggage is closest to the predicted operation load ratio, that is, the requested load capacity is subtracted from the estimated operation load ratio. The vehicle having the smallest difference is extracted (S505).

  In step S506, the vehicle allocation matching unit 326 determines whether a plurality of vehicles have been extracted in step S505. If it is determined that a plurality of vehicles have been extracted, the vehicle allocation matching unit 326 proceeds to step S507, and uses the operation prediction area data to locate the shortest distance (distance or time) from the plurality of vehicles to the pickup location. The vehicle to be extracted is extracted as an allocated vehicle for the delivery request information to be matched (S507).

  On the other hand, in step S505, when a vehicle whose requested luggage volume is less than or equal to the predicted operation load ratio is not extracted, it is determined that “there is no matching vehicle” in which the allocation vehicle for the delivery request information to be matched is not extracted (S508). Exit.

  According to the present embodiment, the predicted operation load ratio for each future day from the current time of each vehicle is generated from the daily delivery history (operation result). The predicted operation load ratio is used to incorporate a future delivery schedule from the present time. Based on the transaction results for each shipper and the predicted operation load ratio, a delivery request with the future as the collection date is matched to extract a provisioned vehicle.

  By configuring in this way, for the shipper side, the vehicle is preferentially secured, and the regular delivery cannot be performed because the vehicle cannot be secured, or the package may not be delivered on a specific delivery date. It is suppressed.

  The vehicle allocation matching process according to this embodiment is performed separately from the vehicle allocation matching process according to the first embodiment, and is combined with the vehicle allocation matching process according to the first embodiment, so that the vehicle is preferential to a shipper who has a transaction record. The loading rate can be improved while ensuring the security.

  For example, for vehicles that do not have a delivery schedule at a certain time in the future from the present time, vehicle allocation matching processing according to the present embodiment using the predicted operation load ratio is performed, and vehicles that are preferential to shippers who have a transaction history Securement can be realized.

  And the loading rate can be improved by performing the vehicle allocation matching process of the first embodiment for a vehicle including a fixed schedule area to which a delivery request is assigned by the vehicle allocation matching process of the present embodiment.

  In other words, with respect to future delivery schedules, preferentially securing vehicles to specific shippers is realized using transaction results and predicted operation loading ratio, and loading ratio is improved by the dispatching matching process of the first embodiment. Can be made. For this reason, for the shipper side, the vehicle is preferentially secured, and it is possible to prevent regular delivery without being able to secure the vehicle, or to prevent the package from being delivered on a specific delivery date, For the shipping company that provides vehicles, it is possible to improve the loading rate while preferentially securing vehicles to specific shippers, eliminating the need to have many vehicles and reducing delivery costs such as labor costs. Can be reduced.

  Note that the delivery request information and the transaction rate information of the present embodiment include a package type. The package type is information that defines an attribute of the requested package, and is information for identifying each package such as food, daily necessities, and clothes, for example. The package type can predefine whether or not mutual loading is possible between attributes of requested packages. For example, since the package type: food may give off an odor, it is stored in the storage device 330 as mutual loading rule information that defines whether or not the package type: clothes cannot be loaded together (OK / NG). I can leave. In addition, mutual loading is not only NG to be put together on the carrier, but after carrying one type of cargo between the types of cargo that mutual loading is considered NG, the other type of cargo Can be prohibited from carrying.

  Therefore, in the dispatching matching process of the present embodiment, the package type check process for determining whether the vehicle can carry the package type of the delivery request information, for example, the package type associated with the transaction record of the shipper, Processing for determining whether the package type is included or not based on the mutual loading rule information for the package type associated with the transaction result even if the package type is not included By doing this, it is possible to suppress the mutual loading that causes a drop in the quality of the luggage. The same applies to the first embodiment.

  The embodiment of the present invention has been described above. The delivery terminal 100, the carrier terminal 200, the shipper terminal 400, and the shipper terminal 500 include a portable information terminal having a communication function, a PDA (Personal Digital Assistant), and the like. Mobile communication terminal devices, information processing terminal devices having communication functions and calculation functions such as personal computers are included. In addition, a browser function for performing display control of screens and information can be provided.

  The management device 300 is, for example, a server device. In addition to the hardware configuration described above, the management device 300 includes a CPU that controls the entire device (each unit), a memory (main storage device), a mouse, a keyboard, a touch panel, a scanner, and the like. Operation input means, output means such as a printer and a speaker, an auxiliary storage device (hard disk or the like), and the like can be provided (or connected). The same applies to the hardware configurations of the delivery person terminal 100, the company terminal 200, the shipper terminal 400, and the shipper terminal 500.

  In addition, each function of the present invention can be realized by a program, a computer program prepared in advance for realizing each function is stored in the auxiliary storage device, and a control unit such as a CPU is stored in the auxiliary storage device. By reading the program into the main storage device and executing the program read into the main storage device, the control unit executes the functions of the units of the transportation management device 300 and the delivery person terminal 100. . On the other hand, each function of the present invention can be configured by an individual control device. For example, the transportation management device 300 can be configured by connecting a plurality of control devices directly or via a network.

  Further, the program can be provided to a computer in a state where the program is recorded on a computer-readable recording medium. Computer-readable recording media include optical disks such as CD-ROM, phase change optical disks such as DVD-ROM, magneto-optical disks such as MO (Magnet Optical) and MD (Mini Disk), floppy (registered trademark) disks, Examples include magnetic disks such as removable hard disks, memory cards such as compact flash (registered trademark), smart media, SD memory cards, and memory sticks. A hardware device such as an integrated circuit (IC chip or the like) specially designed and configured for the purpose of the present invention is also included as a recording medium.

100 delivery person terminal 110 communication unit 120 control unit (CPU)
121 operation status control unit 121A GPS control unit 121B loading information control unit 130 storage unit 140 display unit 150 operation unit 160 imaging unit 200 operator terminal 300 transportation management device 310 communication device 320 control device 321 vehicle registration unit 322 operation information collection unit 323 Operation information provision unit 324 Operation prediction unit 325 Delivery request reception unit 326 Allocation matching unit 327 Estimate / order control unit 328 Operation schedule plan control unit 330 Storage device 400 Shipper terminal 500 Shipper terminal

Claims (7)

A transportation management system for a delivery vehicle that collects and delivers requested luggage,
An operation information collection unit that receives position information of the delivery vehicle transmitted from a delivery person terminal having a GPS function, and collects operation status data based on a delivery work result for each delivery vehicle;
An operation prediction unit that generates operation prediction data including an operation prediction area of the delivery vehicle based on the operation status data;
A vehicle allocation matching unit that performs a vehicle allocation matching process of the delivery vehicle for a delivery request including a collection location and a delivery location;
Operation schedule plan control that generates operation schedule plan data in which the delivery request is incorporated into the operation schedule plan of the delivery vehicle matched by the vehicle allocation matching process, and transmits the operation schedule plan data to the carrier terminal of the carrier to which the delivery vehicle belongs. And
The vehicle allocation matching unit
A loading rate for the delivery vehicle is calculated based on the package volume of the requested package for the delivery request and the package volume of the confirmed delivery request already incorporated in the scheduled operation plan data, and the collection location is within the predicted operation area. And a first candidate vehicle including the delivery location is extracted, and a matching vehicle is extracted by matching the delivery requests in descending order of the loading ratio among the first candidate vehicles.   The vehicle allocation matching unit calculates, for each matching vehicle, a distance or a time from a predetermined predicted position in the operation prediction area to the pickup location in the pickup time when the plurality of matching vehicles are extracted. The transportation management system according to claim 1, wherein the closest matching vehicle is extracted.   The vehicle allocation matching unit calculates, for each first candidate vehicle, a distance or time from a predetermined predicted position in the operation prediction area in the pickup time to the pickup location, and a predetermined allowable range of the distance or time to the pickup location. The transportation management system according to claim 1, wherein the delivery vehicle that is a vehicle is extracted as the first candidate vehicle.   The operation prediction unit generates an operation result route in which the vehicle positions of the delivery vehicles are connected in time series based on the position information of the delivery vehicle, includes the operation result route, and sets the operation result route to a predetermined value. The transportation management system according to any one of claims 1 to 3, wherein the operation prediction area including a plurality of virtual areas partitioned at time intervals is generated. A delivery request reception unit that receives the delivery request transmitted from the shipper's shipper terminal;
An estimate / order control unit that generates fare estimate data for the received delivery request based on fare data set in advance for the delivery vehicle and transmits the fare estimate data to the shipper's terminal;
The operation schedule plan control unit generates the operation schedule plan data incorporating the delivery request after the order of the delivery request is confirmed with the shipper based on the fare estimate data, and the operator terminal The transportation management system according to any one of claims 1 to 4, wherein the transportation management system is transmitted. A transportation management device for a delivery vehicle that collects and delivers requested luggage,
An operation information collection unit that receives position information of the delivery vehicle transmitted from a delivery person terminal having a GPS function, and collects operation status data based on a delivery work result for each delivery vehicle;
An operation prediction unit that generates operation prediction data including an operation prediction area of the delivery vehicle based on the operation status data;
A vehicle allocation matching unit that performs a vehicle allocation matching process of the delivery vehicle for a delivery request including a collection location and a delivery location;
Operation schedule plan control that generates operation schedule plan data in which the delivery request is incorporated into the operation schedule plan of the delivery vehicle matched by the vehicle allocation matching process, and transmits the operation schedule plan data to the carrier terminal of the carrier to which the delivery vehicle belongs. And
The vehicle allocation matching unit
A loading rate for the delivery vehicle is calculated based on the package volume of the requested package for the delivery request and the package volume of the confirmed delivery request already incorporated in the scheduled operation plan data, and the collection location is within the predicted operation area. And a first candidate vehicle including the delivery location is extracted, and a matching vehicle is extracted by matching the delivery requests in descending order of the loading ratio among the first candidate vehicles. A program for causing a computer to execute transportation management processing of a delivery vehicle that collects and delivers requested luggage,
A first function for receiving position information of the delivery vehicle transmitted from a delivery person terminal equipped with a GPS function, and collecting operation status data based on delivery business results for each delivery vehicle;
A second function for generating operation prediction data including an operation prediction area of the delivery vehicle based on the operation status data;
A third function for performing a dispatching matching process of the delivery vehicle for a delivery request including a collection place and a delivery place;
A fourth function of generating operation schedule plan data in which the delivery request is incorporated in an operation schedule plan of the delivery vehicle matched by the vehicle allocation matching process, and transmitting the operation schedule plan data to an operator terminal of a transport company to which the delivery vehicle belongs; Realized,
The third function is:
A loading rate for the delivery vehicle is calculated based on the package volume of the requested package for the delivery request and the package volume of the confirmed delivery request already incorporated in the scheduled operation plan data, and the collection location is within the predicted operation area. And a first candidate vehicle including the delivery location is extracted, and a matching vehicle is extracted by matching the delivery requests in descending order of the loading ratio among the first candidate vehicles.