Operation management device, control system, operation management method, and mobile body

Abstract

To appropriately manage the operation of mobile vehicle 13, for which task objectives based on arbitrary perspectives have been set. [Solution] The operation management device 15 includes an input unit 1 that inputs mobile body information, including current and future traffic condition information and operation status information related to the mobile body 13; an update unit 8 that updates the task queue each time a new task arises; a prediction section setting unit 33 that sets a prediction section according to the length of the updated task queue; a calculation unit 5 that calculates a task target value based on an arbitrary viewpoint; a setting unit 7 that sets the priority of the task target using mobile body information, etc., belonging to the prediction section set by the prediction section setting unit 33 from among the mobile body information; an operation evaluation unit 55 that evaluates the operation of the mobile body 13 using the mobile body information, the task target value, and the priority of the task target; and an operation plan creation unit 57 that creates an operation plan for the mobile body 13 using the operation evaluation results related to the mobile body 13.

Description

Technical Field

[0001] The present invention relates to an operation management device, a control system, an operation management method, and a moving body.

Background Art

[0002] Patent Document 1 discloses, for example, an invention of a vehicle management system that optimizes resources related to fleet vehicles (vehicles used in business by corporations, etc.) including charging, refueling, and parking overheads in a Mobility as a Service (MaaS) network architecture. Here, MaaS means all services such as search, reservation, and settlement for optimizing the combination of transportation means to reach a destination.

[0003] In the vehicle management system according to Patent Document 1, a scheduling subsystem generates a scheduling command using machine learning based on the acquired vehicle parameters, infrastructure resource availability information, and historical usage information, and transmits a scheduling command for scheduling the use of at least one infrastructure resource by one or more of the vehicles in the fleet to the fleet of vehicles.

[0004] According to the vehicle management system according to Patent Document 1, by optimizing the resources related to fleet vehicles, it is possible to avoid congestion at charging / gasoline stations, which causes an increase in road traffic and a long waiting time in densely populated urban areas, and realize efficient management of fleet vehicles.

Prior Art Documents

Patent Documents

[0006] Incidentally, KPIs (Key Performance Indicators) are known as evaluation metrics for MaaS services. KPIs are indicators used to evaluate the degree of goal achievement in the process of achieving a goal. If we were to set KPIs based on arbitrary perspectives as evaluation metrics for MaaS services for mobile entities such as vehicles, and then appropriately adjust the KPI values ​​in accordance with changes in the environment in which the mobile entities are located, it would be possible to carry out appropriate operational management of the mobile entities. In this regard, the vehicle management system described in Patent Document 1 does not describe or suggest setting KPIs (task targets) based on an arbitrary viewpoint for a moving object, or appropriately setting KPI values ​​(task target values) in accordance with changes in the environment in which the moving object is located. In this invention, as described above, "KPI" will be read as "task target" and "KPI value" will be read as "task target value".

[0007] The present invention aims to provide an operation management device, a control system, an operation management method, and a mobile body that can appropriately perform operation management of a mobile body for which task objectives based on any viewpoint are set. [Means for solving the problem]

[0008] To solve the above problems, the operation management device according to the present invention is an operation management device for managing the operation of a moving object, An input unit for inputting traffic information regarding traffic conditions in the section traveled by the mobile body, and operational status information regarding the operational status of the mobile body, A storage unit that stores the task of the mobile body and the task objective which is the goal of the task, A calculation unit that uses the traffic condition information and the operation status information to calculate a task target value indicating the degree of achievement of the task target stored in the storage unit, A setting unit sets the priority of the task objectives related to the moving body using the task objective values ​​calculated by the calculation unit, The most important feature is that it includes a creation unit that creates a movement plan for the moving body using the priority of the task objective set by the setting unit. [Effects of the Invention]

[0009] According to the operation management device of the present invention, it is possible to appropriately perform operation management of a moving object for which task objectives based on any viewpoint have been set. Other issues, configurations, and effects not mentioned above will be described in detail in the following embodiments. [Brief explanation of the drawing]

[0010] [Figure 1A] This is a block diagram illustrating the general functions of the operation management system based on the first perspective. [Figure 1B] This is a functional block diagram illustrating the schematic configuration of a control system according to an embodiment of the present invention. [Figure 2] This is a flowchart illustrating the operation of a traffic management device according to an embodiment of the present invention. [Figure 3] This is an explanatory diagram showing an example of displaying the operational evaluation results related to mobile vehicles: time delay (KPI1) and average fuel consumption (KPI2). [Figure 4A] This diagram illustrates the operation of a traffic management system according to an embodiment of the system installed on a route bus, with a route bus being used as an example of a mobile vehicle. [Figure 4B] This diagram illustrates the operation of a traffic management system according to an embodiment of the system installed on a route bus, with a route bus being used as an example of a mobile vehicle. [Figure 5A] This diagram illustrates the relationship between the time-series changes related to the second relative time and the trend of delay times for route buses, using KPI1 as the key performance indicator (KPI). [Figure 5B] This diagram illustrates the trend of average fuel consumption for route buses in relation to time-series changes related to the second relative time, using KPI2 as the key performance indicator (KPI). [Figure 6A] This diagram illustrates the operation of a first modified example of a traffic management device installed on a route bus, with a route bus being used as an example of a mobile vehicle. [Figure 6B] FIG. is a diagram for explaining the operation of an operation management device according to a first modification provided in a route bus when the route bus is exemplified as a moving body. [Figure 7A] FIG. is a diagram for explaining the operation of an operation management device according to a second modification provided in a route bus when the route bus is exemplified as a moving body. [Figure 7B] FIG. is a diagram for explaining the operation of an operation management device according to a second modification provided in a route bus when the route bus is exemplified as a moving body. [Figure 8] FIG. is a diagram for explaining the operation of an operation management device according to a third modification provided in an autonomous guided vehicle when the autonomous guided vehicle is exemplified as a moving body. [Figure 9A] FIG. is an explanatory diagram in which the transition of the number of cargos transported per unit time related to the autonomous guided vehicle with respect to the time-series change related to the sixth relative time is associated as KPI11. [Figure 9B] FIG. is an explanatory diagram in which the transition of the safety distance related to the autonomous guided vehicle with respect to the time-series change related to the sixth relative time is associated as KPI12. [Figure 10] FIG. is a diagram for explaining the operation of an operation management device according to a fourth modification provided in a dump truck when the dump truck is exemplified as a moving body. DETAILED DESCRIPTION OF THE INVENTION

[0011] The operation management device, control system, operation management method, and moving body according to the embodiments of the present invention will be described in detail with reference to appropriate drawings. In the description of the operation management device, control system, operation management method, and moving body according to the embodiments of the present invention, components having common functions are denoted by common reference numerals, and redundant descriptions thereof are omitted.

[0012] (Schematic Functions of the Operation Management Device 15 Based on the First Aspect) First, the schematic functions of the operation management device 15 based on the first aspect will be described with reference to FIG. 1A. FIG. 1A is a block diagram showing the schematic functions of the operation management device 15 based on the first aspect.

[0013] The operation management device 15, based on the first perspective, has the function of managing the operation of the mobile body 13 (see Figure 1B). As shown in Figure 1A, the operation management device 15 based on the first perspective comprises: an input unit 1 that inputs traffic condition information regarding traffic conditions in the travel section of the mobile body 13 and operation condition information regarding the operating state of the mobile body 13; a storage unit 3 that stores the tasks of the mobile body 13 and task objectives which are the goals of the tasks; a calculation unit 5 that uses the traffic condition information and the operation condition information to calculate a task objective value indicating the degree of achievement of the task objectives stored in the storage unit 3; a setting unit 7 that uses the task objective value calculated by the calculation unit 5 to set the priority of the task objectives related to the mobile body 13; and a creation unit 9 that uses the priority of the task objectives set by the setting unit 7 to create a travel plan for the mobile body 13.

[0014] According to the operation management device 15 based on the first perspective, the setting unit 7 creates a movement plan for the mobile body 13 using the priority of the task objectives set by the setting unit 7. By using the created movement plan to manage the operation of the mobile body 13, it is possible to appropriately manage the operation of the mobile body 13 for which KPIs (task objectives) based on any perspective have been set.

[0015] [Schematic configuration of the control system 11 according to an embodiment of the present invention] Next, the schematic configuration of the control system 11 according to an embodiment of the present invention will be described with reference to Figure 1B. Figure 1B is a functional block diagram showing the schematic configuration of the control system 11 according to an embodiment of the present invention.

[0016] As shown in Figure 1B, the control system 11 according to an embodiment of the present invention comprises a mobile body 13, a traffic management device 15, and a communication medium 17 that connects the mobile body 13 and the traffic management device 15 in a manner that enables information exchange.

[0017] The term "mobile entity 13" is not particularly limited, but it is a comprehensive concept that includes public transportation vehicles such as taxis and route buses, as well as public transportation systems such as trains, ships, and aircraft. In the embodiments shown in Figures 2-7B (including modified examples), a route bus 13A (see, for example, Figures 3A and 3B) is used as an example to explain the mobile entity 13.

[0018] The operation management device 15 has the function of performing operation management to move the mobile body 13 along the required route to the required target location. To realize these functions, the operation management device 15 is configured as shown in Figure 1B, and includes an update unit 8, a prediction section setting unit 33, a calculation unit 5, a setting unit 7, a KPI database 35 and prediction model 37 in the storage unit 3, a traffic condition prediction unit 51, an operation status prediction unit 53, an operation evaluation unit 55, and an operation plan creation unit 57.

[0019] The update unit 8 updates the task queue each time a new task is added via the task input unit 21 by adding the new task to the task queue and removing the processed task from the task queue. Here, the task queue is a list describing the queue of tasks. The task queue corresponds to a part of the storage unit 3 (see Figure 1A). Also, a task refers to work related to the movement of the mobile body 13. For example, a task could be the work of setting the target location, movement route, and movement speed related to the mobile body 13.

[0020] In this embodiment, the update unit 8 adds new tasks to the task queue each time a new task is added. In other words, in this embodiment, the process of adding new tasks to the task queue is triggered by the addition of a new task.

[0021] However, the present invention is not limited to this example. For example, the update unit 8 may be configured to add new tasks to the task queue in a timely manner at an appropriate timing that does not jeopardize the effects of the present invention (substantial real-time performance).

[0022] The prediction interval setting unit 33 sets a prediction interval according to the length of the updated task queue by the update unit 8. A prediction interval refers to a unit interval used when predicting the operation of the mobile object 13 as a whole. The length (distance or time) of the updated prediction interval is set appropriately according to the length of the task queue based on the new task.

[0023] The calculation unit 5 calculates KPI values ​​(target values) for each KPI (target) based on multiple perspectives, for example, using a required objective function, based on traffic condition information related to the travel route of the mobile body 13 in the predicted section set by the prediction section setting unit 33 (e.g., congestion, temperature, weather, etc. related to the travel route), operational status information related to the mobile body 13 (e.g., travel speed, average fuel consumption, etc. related to the mobile body 13), operational evaluation results by the operational evaluation unit 55 (details will be described later), and the contents of the KPI database 41 in the memory unit 3 [multiple key performance indicators (KPIs) are set in advance].

[0024] Here, "Key Performance Indicator (KPI)" refers to a variable, and "KPI value" refers to the value that the variable called KPI can take. In the operation management device 15 according to the embodiment of the present invention, as described above, multiple KPIs (variables: task targets) based on multiple perspectives are set in advance, and a KPI value (value that the variable can take: task target value) is associated with each of the multiple KPIs.

[0025] Traffic information regarding the travel route of the mobile unit 13 is input via the traffic condition input unit 23. Furthermore, the operating status of the mobile unit 13 is input via the operating status input unit 27.

[0026] The traffic condition input unit 23 is equipped with an infrastructure sensor 25. The infrastructure sensors 25 are installed in multiple locations at appropriate intervals from each other along the route of the route bus 13A, including the bus stops, if the moving object 13 is a route bus 13A. The infrastructure sensors 25 consist of a group of sensors (not shown) including, for example, a camera, a laser sensor, a temperature sensor, a sunlight sensor, and a weather sensor, and play a role in detecting traffic information, including the location information of the route bus 13A. To detect the location information of the route bus 13A, for example, absolute location information from GPS (Global Positioning System) can be referenced.

[0027] Traffic information, including the location of bus route 13A, includes, for example, whether there is traffic congestion along the route of bus route 13A, if so, where the congestion is located, how many passengers are waiting at the bus stop, and the weather at the bus stop (rainy / sunny, hot / cold).

[0028] Traffic condition information, including the location information of the route bus 13A detected by the infrastructure sensor 25 in the traffic condition input unit 23, is sent via a communication medium (whether wired or wireless) 17 to the calculation unit 5 and traffic condition prediction unit 51 in the operation management device 15.

[0029] Traffic information, including the location information of route bus 13A, is referenced in the calculation unit 5 when calculating KPI values, which are values ​​of multiple pre-set KPIs. Furthermore, traffic information, including the location information of route bus 13A, is referenced by the traffic situation prediction unit 51 when predicting the estimated arrival times for each of the multiple bus stops along the route of route bus 13A.

[0030] The operation status input unit 27 is equipped with a status monitoring sensor 29. The status monitoring sensors 29 are installed at various locations on the route bus 13A, for example, when the mobile object 13 is a route bus 13A. The status monitoring sensors 29 consist of a group of sensors (not shown) including, for example, an indoor camera, a room temperature sensor, a speed sensor, a fuel consumption sensor, and a position sensor, and play a role in detecting information related to the operating status of the route bus 13A.

[0031] Information regarding the operating status of route bus 13A includes, for example, the speed of route bus 13A, average fuel consumption, number of passengers, fuel level, and current location.

[0032] Information regarding the operating status of the route bus 13A, detected by the status monitoring sensor 29 in the operating status input unit 27, is sent to the calculation unit 5 in the operation management device 15 via a communication medium (whether wired or wireless) 17.

[0033] Information regarding the operating status of route bus 13A is referenced in the calculation unit 5 when calculating KPI values, which are values ​​for each of the multiple KPIs that have been set in advance.

[0034] Examples of KPIs (task targets) that are pre-registered in the KPI database 41 include indicators such as the time delay (KPI1), average fuel consumption (KPI2), and ride comfort of the route bus 13A, which is the mobile entity 13.

[0035] Furthermore, the input unit 1 may also accept input of mobile environment information indicating the environment in which the mobile body 13 moves.

[0036] The setting unit 7 sets the priority for each of the multiple KPIs (target values) based on the KPI values ​​(target values) for each KPI (target) calculated by the calculation unit 5, and the operation plan for the mobile body 13 generated by the operation plan creation unit 57 (details below). The priority levels for each of the multiple KPIs set by the setting unit 7 are sent to the operation evaluation unit 55.

[0037] Furthermore, if the mobile entity 13 is a public transport vehicle such as a route bus or taxi (not shown), the setting unit 7 may adopt a configuration that sets priorities for multiple KPIs based on customer evaluation values ​​(customer needs) obtained from passengers of the public transport vehicle. For example, one could adopt a configuration where travel time to the destination is used as a KPI, and priorities are set for multiple KPIs based on customer evaluations (customer needs) indicating that travel time to the destination is a priority. Alternatively, for example, one could adopt ride comfort as a KPI and set priorities for multiple KPIs based on customer evaluations (customer needs) indicating that ride comfort should be prioritized. With this configuration, for example, if the mobile entity 13 is a public transport vehicle such as a route bus or taxi, priorities for each of the multiple KPIs can be set based on customer evaluation values ​​(customer needs) obtained from passengers of the public transport vehicle, thereby enabling appropriate operational management of the public transport vehicle as a mobile entity 13 in accordance with customer needs.

[0038] Furthermore, the setting unit 7 may adopt a configuration that sets priorities for multiple KPIs based on information about newly added tasks. Specifically, for example, if the mobile unit 13 is an autonomous electric vehicle (not shown), the system may adopt a configuration that sets priorities for multiple KPIs, such as recommending a task to add a charging station as a waypoint in cases where the onboard battery (not shown) needs to be charged before arriving at the target location, based on information about the location of the target destination and charging station, or raising the priority of KPIs related to energy-saving driving. With this configuration, for example, if the mobile unit 13 is an autonomous electric vehicle, the system can set priorities for multiple KPIs, such as raising the priority of KPIs related to energy-saving driving, based on information about the location of the target destination and charging station, based on information about the location of the charging station, as information about the newly added task, thereby enabling appropriate operational management of the electric vehicle as the mobile unit 13, taking into account the charge status of the onboard battery.

[0039] The traffic condition prediction unit 51 predicts future traffic conditions (over the predicted section) for the mobile object 13 based on the predicted section set by the prediction section setting unit 33 and traffic condition information (for example, congestion, temperature, weather, etc.) related to the travel route of the mobile object 13 input via the traffic condition input unit 23.

[0040] The operation state prediction unit 53 predicts the future operation state of the mobile body 13 over the predicted section (predicted section) based on the predicted section set by the prediction section setting unit 33, operation state information about the mobile body 13 input via the operation state input unit 27 (for example, the speed of movement of the mobile body 13, average fuel consumption, etc.), and the prediction model 43 provided in the storage unit 3. Furthermore, the prediction model 43 provided in the memory unit 3 has pre-stored equations of motion and other data relating to the operating state of the mobile body 13, for predicting the operating state including the future behavior of the mobile body 13.

[0041] Furthermore, the operation status prediction unit 53 may adopt a configuration that updates the prediction model 43 when a task is added, based on information regarding the operation status of the mobile body 13 and demand forecasts. Specifically, for example, if the moving object 13 is a public transport vehicle such as a route bus or a taxi, a configuration may be adopted to improve the prediction accuracy of the prediction model 43 by changing the predicted motion characteristics related to the public transport vehicle based on the number of passengers (loading weight) on the public transport vehicle. Furthermore, for example, if the mobile body 13 is a route bus, a configuration may be adopted to improve the prediction accuracy of the prediction model 43 by changing the load mass of the prediction model 43 related to the route bus based on the predicted number of passengers boarding and alighting at bus stops. With this configuration, for example, the predicted motion characteristics of the public transport vehicle can be changed based on the number of passengers (load weight) on the public transport vehicle, thereby improving the prediction accuracy of the prediction model 43. This allows for appropriate operation management of the public transport vehicle as a mobile entity 13, even when the number of passengers (load weight) on the public transport vehicle changes.

[0042] The task input unit 21, the traffic situation input unit 23, and the operation status input unit 27 may work together to function as an input unit 1 for inputting various information related to the operation management of the mobile unit 13. The task input unit 21, the traffic situation input unit 23, and the operation status input unit 27 may also have their functions consolidated into a single input unit 1. Furthermore, the prediction section setting unit 33, the traffic condition prediction unit 51, and the operation state prediction unit 53 may work together to function as a single generation unit 10. The prediction section setting unit 33, the traffic condition prediction unit 51, and the operation state prediction unit 53 may also have their functions integrated into a single generation unit 10. The generation unit 10 uses the tasks of the mobile body 13 input by the input unit 1 and the tasks of the mobile body 13 stored in the storage unit 3 to modify the prediction section, which includes the section for predicting traffic conditions and the section for predicting the state of the mobile body 13, and generates traffic condition information and operation state information for the modified prediction section.

[0043] The operation evaluation unit 55 evaluates the operation of the mobile vehicle 13 based on mobile vehicle information, including current and future operation status information and traffic condition information, input via the input unit 1, KPI values ​​for each of the multiple KPIs calculated by the calculation unit 5, and the priorities for each of the multiple KPIs set by the priority setting unit 37. The operation evaluation results for the mobile vehicle 13 by the operation evaluation unit 55 are sent to the operation plan creation unit 57 and the operation evaluation display unit 69 (details below) provided on the mobile vehicle 13, respectively.

[0044] The operation plan creation unit 57 creates an operation plan for the mobile vehicle 13 based on the operation evaluation results for the mobile vehicle 13 by the operation evaluation unit 55. The operation plan for the mobile vehicle 13 includes, for example, the travel speed (timetable delay time) of the mobile vehicle 13 in the predicted section and operation control instructions to achieve average fuel consumption. The operation plan for the mobile vehicle 13 created by the operation plan creation unit 57 is sent to the operation control unit 61 installed in the mobile vehicle 13 via the communication medium 17. The operation evaluation unit 55 and the operation plan creation unit 57 may work together to function as a single creation unit 9. Alternatively, the operation evaluation unit 55 and the operation plan creation unit 57 may have their functions integrated into a single creation unit 9.

[0045] On the other hand, the mobile unit 13 is configured to include an operation control unit 61, a steering mechanism 63, a hybrid prime mover 65 equipped with an internal combustion engine and an electric motor, a braking mechanism 67, and an operation evaluation display unit 69.

[0046] The operation control unit 61 controls the operation (driving, turning, stopping) of the mobile body 13 by appropriately using the steering mechanism 63, the prime mover 65, and the braking mechanism 67, in accordance with the operation plan (operation control instruction) for the mobile body 13 generated by the operation plan generation unit 57. Furthermore, the operation control unit 61 controls the operation evaluation display unit 69 to display the operation evaluation results (for example, timetable delay time / average fuel consumption) related to the mobile vehicle 13 from the operation evaluation unit 55.

[0047] [Outline operation of the operation management device 15 according to an embodiment of the present invention] Next, the general operation of the operation management device 15 according to an embodiment of the present invention (the processing flow of the operation management method for the mobile body 13) will be described with reference to Figures 2 and 3 as appropriate. Figure 2 is a flowchart illustrating the operation of the operation management device 15 according to an embodiment of the present invention. Figure 3 is an explanatory diagram showing an example of displaying the operation evaluation results for the mobile unit 13, namely the timetable delay time (KPI1) and the average fuel consumption (KPI2).

[0048] As a premise, in the case of the route bus 13A exemplified as the mobile body 13, if a new task is added by the task addition unit 21 (for example, changing the route of route bus 13A to a detour route that avoids congestion), the operation plan creation unit 57 of the operation management device 15 shall create an operation plan to operate route bus 13A along the changed detour route. Furthermore, as examples of KPIs (task targets) based on multiple perspectives, we will explain using the time delay (KPI1) and average fuel consumption (KPI2) of route bus 13A as examples.

[0049] In step S11 shown in Figure 2, the operation management device 15 sets the task queue, which is a list describing the queue of tasks, to an initial value (for example, route bus 13A patrols a predetermined route), and performs initialization processing to initialize various variables.

[0050] In step S12, the operation management device 15 determines whether a new task has been added. If the determination in step S12 indicates that a new task has been added, the operation management device 15 proceeds to the next step S13. On the other hand, if the determination in step S12 is that no new tasks have been added, the operation management device 15 will jump the processing flow to step S15.

[0051] In step S13, the update unit 8 in the operation management device 15 performs a task queue update, adding new tasks generated in step S12 to the task queue and removing processed tasks from the task queue.

[0052] In step S14, the prediction section setting unit 33 of the operation management device 15 sets a prediction section according to the length of the updated task queue by the update unit 8. This changes the length of the updated prediction section as appropriate.

[0053] In step S15, the calculation unit 5 of the operation management device 15 calculates KPI values ​​for each of the multiple KPIs within the set predicted section based on traffic condition information regarding the movement route of the mobile body 13 in the predicted section set by the predicted section setting unit 33, the operation status of the mobile body 13, the operation evaluation results (operation evaluation values) from the operation evaluation unit 55, and the contents of the KPI database 41 in the storage unit 3. The setting unit 7 in the operation management device 15 sets the priority for each of the multiple KPIs based on the KPI values ​​for each of the multiple KPIs calculated by the calculation unit 5 and the operation plan for the mobile body 13 generated by the operation plan generation unit 57.

[0054] The important point here is that when setting priorities for each of the multiple KPIs [time delays related to route bus 13A (KPI1), average fuel consumption (KPI2)], traffic condition information and operational status information related to route bus 13A in the predicted section set by the predicted section setting unit 33 should be given full consideration.

[0055] Specifically, for example, suppose that in order to improve the average fuel consumption (KPI2) of route bus 13A, route bus 13A was operated at a relatively low speed, but a situation arose where the time delay (KPI1) for route bus 13A tended to increase. In these operational scenarios, the priority given to the time delay (KPI1) related to route bus 13A will be increased compared to the average fuel consumption (KPI2) related to route bus 13A. This will reduce the time delay (KPI1) related to route bus 13A. With this configuration, it is possible to appropriately manage the operation of route bus 13A (mobile vehicle 13) by appropriately setting priorities for each KPI based on multiple perspectives [time delay related to route bus 13A (KPI1), average fuel consumption (KPI2)].

[0056] Returning to Figure 2, we continue the explanation. In step S16, the traffic condition acquisition unit 23 and the operation status acquisition unit 27 acquire current mobile body information (current traffic condition information and operation status information related to the mobile body 13).

[0057] In step S17, the traffic condition prediction unit 51 and the operation condition prediction unit 53 acquire future mobile information (future traffic condition information and operation condition information regarding the mobile body 13) for the predicted section set in step S14.

[0058] In step S18, the operational status of the mobile vehicle 13 in the predicted section is evaluated based on the priority of each KPI [time delay time for route bus 13A (KPI1), average fuel consumption (KPI2)] which was set in step S15.

[0059] The important point here is that when evaluating the operational status of the moving object 13 in the predicted section, current and future moving object information, KPI values ​​(KPI1 / KPI2) for each of the multiple KPIs calculated by the calculation unit 5, and the priority of each of the multiple KPIs (KPI1 / KPI2) are taken into consideration.

[0060] Specifically, for example, in a case where the average fuel consumption (KPI2) for route bus 13A is good, but the time delay (KPI1) for route bus 13A is on an increasing trend, the operation evaluation unit 55 outputs an operation evaluation result (operation evaluation value) indicating that the priority of time delay (KPI1) for route bus 13A should be increased compared to the average fuel consumption (KPI2) for route bus 13A. This configuration allows us to prioritize each KPI based on multiple perspectives [time delays related to route bus 13A (KPI1), average fuel consumption (KPI2)] to an appropriate level.

[0061] In step S19, the operation evaluation unit 55 of the operation management device 15 determines whether the operation evaluation value has been sufficiently improved. If the determination in step S19 is that the operation evaluation value has not been sufficiently improved, the operation management device 15 proceeds to the next step S20. On the other hand, if the judgment in step S19 determines that the operational evaluation value has been sufficiently improved, the operation management device 15 jumps the process to step S21.

[0062] In step S20, the operation plan creation unit 57 of the operation management device 15 searches for operation control instructions that can improve the operation evaluation value. The operation control instructions extracted through this search that can improve the operation evaluation value are fed back to the setting unit 7 in step S15.

[0063] In step S21, the operation plan creation unit 57 of the operation management device 15 outputs an operation control instruction to the operation control unit 61 of the mobile body 13, which has been determined to be capable of significantly improving the operation evaluation value. In response, the operation control unit 61 of the mobile body 13 controls the operation of the mobile body 13 (driving, turning, stopping) by appropriately using the steering mechanism 63, the prime mover 65, and the braking mechanism 67, in accordance with the operation plan (operation control instruction) for the mobile body 13 generated by the operation plan creation unit 57.

[0064] Furthermore, as shown in Figure 3, the operation control unit 61 of the mobile unit 13 controls the operation evaluation display unit 69 to display the operation evaluation results (predicted operation evaluation values) for the mobile unit 13, which are the schedule delay time (KPI1) and the average fuel consumption (KPI2), which has been scored for the convenience of intuitive information recognition. In the case where the mobile object 13 is, for example, a route bus 13A, passengers of the route bus 13A who see the information displayed in the operation evaluation display unit 69 installed in the passenger compartment of the route bus 13A (for example, the time of delay, the estimated arrival time at each of the multiple bus stops, and the average fuel consumption) can find out how much the route bus 13A is delayed compared to the timetable. In the case where the mobile object 13 is, for example, a taxi (not shown), a taxi passenger who sees the display contents (for example, the estimated time of arrival at the destination) of the operation evaluation display unit 69 installed in the passenger compartment of the taxi can find out the estimated time of arrival at the destination. Furthermore, in cases where the operation evaluation display unit 69 is installed in a control room that manages the operation of the mobile vehicle 13, the manager of the mobile vehicle 13 who sees the contents of the operation evaluation display unit 69 (for example, the time of delay, the estimated arrival time for each of the multiple bus stops, and the average fuel consumption) can find out how much the route bus 13A is delayed compared to the timetable, whether energy-saving operation is being carried out through the average fuel consumption, etc. Furthermore, in the operation evaluation display unit 69 shown in Figure 3, the fact that the time delay is enclosed in a thick black frame indicates that improving the time delay (KPI1) should be prioritized over improving the average fuel consumption (KPI2).

[0065] [Operation of the operation management device 15 according to the embodiment of the present invention] Next, the operation of the operation management device 15 according to an embodiment of the present invention will be described with reference to Figures 4A and 4B as appropriate. Figures 4A and 4B are diagrams illustrating the operation of the operation management device 15 according to the embodiment, with a route bus 13A as an example of the mobile body 13.

[0066] As a prerequisite, we introduce the concept of "relative time" for the purpose of simplifying explanations by expressing the current time relatively when setting the "prediction interval." For example, the section in the time series system related to the first relative time ta in which the operating status of route bus 13A as mobile entity 13 is predicted is called the first prediction interval PI_1. Also, the task of route bus 13A, as mobile entity 13, moving from its current location to the required target location is called a task.

[0067] In the example shown in Figure 4A, in the first prediction interval (time series system related to the first relative time ta) PI_1, the task of moving from the current location P(ta0) corresponding to the first relative time ta0 (current time) to the first bus stop BS1 is called the firsta task, and the task of moving from the first bus stop BS1 to the second bus stop BS2 is called the seconda task. The first prediction interval PI_1 consists of the first task queue, which is the queue for the firsta task and the seconda task.

[0068] In the route bus 13A shown in Figure 4A, it is assumed that the bus will make a temporary stop at the first bus stop BS1 and the second bus stop BS2 in the first predicted section PI_1 to allow passengers to board and alight.

[0069] When the aforementioned new task (operating a temporary stop for passengers to board and alight at both the first bus stop BS1 and the second bus stop BS2) is added, the update unit 8 performs a task queue update to add the new task to the first task queue, as shown in Figure 4A.

[0070] The prediction interval setting unit 33 sets the first prediction interval PI_1 as the prediction interval.

[0071] Based on the above assumptions and settings, the traffic condition prediction unit 51 predicts the traffic conditions for route bus 13A for each of the following periods belonging to the first prediction section PI_1 (time series system related to the first relative time ta): first relative time ta0 (current time), ta1, ta2, ta3 (predicted arrival times for the first bus stop BS1), and ta4, ta5 (predicted arrival times for the second bus stop BS2).

[0072] The operation status prediction unit 53, similar to the traffic condition prediction unit 51, predicts the operation status of the route bus 13A at each of the following prediction points belonging to the first prediction section PI_1 (time series system related to the first relative time ta): the first relative time ta0 (current time), ta1, ta2, ta3 (predicted arrival times related to the first bus stop BS1), and ta4, ta5 (predicted arrival times related to the second bus stop BS2).

[0073] As described above, the prediction of traffic conditions and operating status for route bus 13A shown in Figure 4A is performed at time-discrete times (first relative times ta0, ta1, ta2, ta3, ta4, ta5). The intervals between each of the first relative times ta0, ta1, ta2, ta3, ta4, ta5 may be equal or unequal.

[0074] On the other hand, the prediction of traffic conditions and operating status for route bus 13A shown in Figure 4B is performed in the second prediction section (time series system related to the second relative time tb) PI_2, which is a system that is temporally different from the first prediction section (time series system related to the first relative time ta) PI_1. Incidentally, the second relative time tb can be expressed as [first relative time ta + tx] (where tx is a predetermined time length).

[0075] In the example shown in Figure 4B, in the second prediction interval (time series system related to the second relative time tb) PI_2, the task of moving from the current location P(tb0) corresponding to the second relative time tb0 (current time) to the first bus stop BS1 is called the 1b task, the task of moving from the first bus stop BS1 to the second bus stop BS2 is called the 2b task, and the task of moving from the second bus stop BS2 to the third bus stop BS3 is called the 3b task. The second prediction interval PI_2 consists of a second task queue, which is a waiting list for the 1b task, the 2b task, and the 3b task.

[0076] In the route bus 13A shown in Figure 4B, it is assumed that in the second predicted section PI_2, the bus will make a temporary stop at the first bus stop BS1, the second bus stop BS2, and the newly added third bus stop BS3 for passengers to board and alight. The addition of the third bus stop BS3 is communicated to the update unit 8 and the prediction section setting unit 33 via the task addition unit 21.

[0077] When the aforementioned new task (temporarily stopping at the third bus stop BS3) is added, the update unit 8 performs a task queue update to add the new task to the first task queue, as shown in Figure 4B.

[0078] The prediction interval setting unit 33 sets the second prediction interval PI_2 as the prediction interval, which is longer in time (essentially synonymous with distance) than the first prediction interval PI_1.

[0079] Based on the above assumptions and settings, the traffic condition prediction unit 51 predicts the traffic conditions for route bus 13A at each of the following prediction points belonging to the second prediction section PI_2 (time series system related to the second relative time tb): second relative time tb0 (current time), tb1 (predicted arrival time for the first bus stop BS1), tb2, tb3, tb4 (predicted arrival time for the second bus stop BS1), tb5, tb6, tb7 (predicted arrival time for the third bus stop BS3).

[0080] The operation status prediction unit 53, similar to the traffic condition prediction unit 51, predicts the operation status of the route bus 13A at each of the following prediction points belonging to the second prediction section PI_2 (time series system related to the second relative time tb), the second relative time tb0 (current time), tb1 (predicted arrival time related to the first bus stop BS1), tb2, tb3, tb4 (predicted arrival time related to the second bus stop BS1), and tb5, tb6, tb7 (predicted arrival time related to the third bus stop BS3).

[0081] The traffic conditions and operating status of bus route 13A shown in Figure 4B are predicted at time-discrete times (second relative times tb0, tb1, tb2, tb3, tb4, tb5, tb6, tb7). The time intervals between each of these second relative times tb0, tb1, tb2, tb3, tb4, tb5, tb6, tb7 may be equal or unequal.

[0082] Figure 5A shows the trend of the time delay time for route bus 13A in relation to the time series change (tb0-tb7) related to the second relative time tb, as KPI1, and Figure 5B shows the trend of the average fuel consumption for route bus 13A in relation to the time series change (tb0-tb7) related to the second relative time tb, as KPI2. The time delay (KPI1) and average fuel consumption (KPI2) for route bus 13A are in a trade-off relationship with each other.

[0083] During the period from time tb0, shown in Figure 5A, to the transition time tb_th, which is located midway between tb3 and tb4, the time delay time (KPI1) for route bus 13A gradually decreased. Also, during the same period (tb0-tb_th), shown in Figure 5B, the average fuel consumption (KPI2) for route bus 13A remained at a high level (a level where average fuel consumption was relatively poor).

[0084] During the period from the aforementioned switching time tb_th to tb7, as shown in Figure 5A, the time delay time (KPI1) for route bus 13A shows a gradual increasing trend. Also, during the same period (tb_th-tb7), as shown in Figure 5B, the average fuel consumption (KPI2) for route bus 13A decreases sharply immediately after the aforementioned switching time tb_th (average fuel consumption improves rapidly), and remains at a low level (a level where average fuel consumption is relatively good) in the subsequent period (tb4-tb7).

[0085] This is based on the coordinated operation of the operation evaluation unit 55 and the operation plan creation unit 57, as described below. In other words, the operation evaluation unit 55 outputs an operation evaluation result (operation evaluation value) indicating that, for the period from the second relative time tb0 to the switching time tb_th, the priority of the time delay time (KPI1) for the route bus 13A should be increased compared to the average fuel consumption (KPI2) for the route bus 13A. The operation plan creation unit 57 outputs an operation control instruction to the operation control unit 61 installed in the mobile unit 13, in accordance with the operation evaluation value output by the operation evaluation unit 55.

[0086] Here, the change in the characteristics of the time delay time (KPI1) / average fuel consumption (KPI2) for route bus 13A, at the aforementioned switching time tb_th, is based on the coordinated operation of the operation evaluation unit 55 and the operation plan creation unit 57 (creation unit 9) described below. In other words, the operation evaluation unit 55 monitors the trend of the time delay time (KPI1) related to the route bus 13A. When the time delay time (KPI1) related to the route bus 13A falls to the KPI1 threshold KPI1_th which defines the acceptable level (corresponding to the switching time tb_th), the operation evaluation unit 55 switches the priority of each of the multiple KPIs (KPI1 / KPI2) to a default value [for example, increasing the priority of the average fuel consumption (KPI2) related to the route bus 13A compared to the time delay time (KPI1) related to the route bus 13A].

[0087] As a result, the operation evaluation unit 55 outputs an operation evaluation result (operation evaluation value) indicating that, for the period after the switching time tb_th, the priority of the average fuel consumption (KPI2) for route bus 13A will be increased compared to the timetable delay time (KPI1) for route bus 13A. The operation plan creation unit 57 outputs an operation control instruction to the operation control unit 61 installed in the mobile unit 13, in accordance with the operation evaluation value output by the operation evaluation unit 55.

[0088] Then, the setting unit 7 provided in the mobile unit 13 sets priorities for each KPI based on multiple perspectives, optimized so that the average fuel consumption (KPI2) for the route bus 13A does not fall below the KPI1 threshold KPI1_th. As a result, both the time delay (KPI1) and the average fuel consumption (KPI2) related to route bus 13A are considered in a balanced manner, enabling appropriate operational management of the mobile vehicle 13, for which KPIs based on multiple perspectives are set.

[0089] [Operation of the operation management device 15 according to the first modified example of the present invention] Next, the operation of the operation management device 15 according to the first modified example of the present invention will be described with reference to Figures 6A and 6B as appropriate. Figures 6A and 6B illustrate the operation of the operation management device 15 according to the first modified example, with the example of a route bus 13A as the mobile body 13.

[0090] The operation management device 15 according to the first modified version of the present invention adjusts the number and interval (distance or time) of prediction points belonging to the prediction section according to the length of the task queue when setting a prediction section according to the length of the task queue. This ensures real-time acquisition of prediction information while maintaining a high level of prediction accuracy when predicting the traffic conditions and operating status of route buses 13A in the set prediction section.

[0091] As a prerequisite, the section in which the operating status of the route bus 13A as a mobile entity 13 is predicted in the time series system related to the third relative time tc is set to an appropriate time length (synonymous with distance) according to the length of the task queue.

[0092] In the example shown in Figure 6A, the section in which the operating status of the route bus 13A as a mobile entity 13 is predicted in the time series system related to the third relative time tc is divided into two unit sections consisting of the 11th prediction section PI_11 and the 12th prediction section PI_12. The prediction interval setting unit 33 sets the 11th prediction interval PI_11 and the 12th prediction interval PI_12 as prediction intervals, respectively. However, in this first modified example, both the 11th prediction interval PI_11 and the 12th prediction interval PI_12 are predicted together.

[0093] As shown in Figure 6A, the 11th prediction interval PI_11 has an 11-hour length (tc3-tc0) that includes the 3rd relative time tc0 (current time)-tc3, which includes the 1st bus stop BS1. On the other hand, the 12th prediction interval PI_12 has a 12-hour length (tc7-tc4) that includes the 2nd bus stop BS2 and the 3rd bus stop BS3, and extends to the 3rd relative time tc4-tc7.

[0094] The 11th time interval (tc3-tc0) is set to be shorter than the 12th time interval (tc7-tc4). The number of prediction points (tc0, tc1, tc2, tc3) belonging to the 11th prediction interval PI_11 [4] is the same as the number of prediction points (tc4, tc5, tc6, tc7) belonging to the 12th prediction interval PI_12 [4] (symmetrical).

[0095] Furthermore, the computation time required for prediction in the 11th prediction interval PI_11 or the 12th prediction interval PI_12 is proportional to the number of prediction points belonging to each prediction interval. Also, the computation time required for prediction in the 11th prediction interval PI_11 or the 12th prediction interval PI_12 does not depend on the length of time for each prediction interval [11th time length (tc3-tc0), 12th time length (tc7-tc4)].

[0096] The granularity of the information obtained in the 11th prediction interval PI_11 (number of prediction points belonging to the prediction interval / time length related to the prediction interval) is finer than the granularity of the information obtained in the 12th prediction interval PI_12. As a result, when predicting the traffic conditions and operating status of bus route 13A in the 11th prediction section PI_11, it is possible to maintain a high level of prediction accuracy while ensuring real-time acquisition of prediction information, compared to predicting the traffic conditions and operating status of bus route 13A in the 12th prediction section PI_12.

[0097] As shown in Figure 6B, the section in which the operating status of the route bus 13A as the mobile entity 13 is predicted in the time series system related to the fourth relative time td is set to an appropriate time length (synonymous with distance) according to the length of the task queue, similar to the example shown in Figure 6B.

[0098] In the example shown in Figure 6B, the section in which the operating status of the route bus 13A as a mobile entity 13 is predicted in the time series system related to the fourth relative time td is divided into two unit sections consisting of the 21st prediction section PI_21 and the 22nd prediction section PI_22, similar to the example shown in Figure 6B. The prediction interval setting unit 33 sets the 21st prediction interval PI_21 and the 22nd prediction interval PI_22 as prediction intervals, respectively. However, in this first modified example, both the 21st prediction interval PI_21 and the 22nd prediction interval PI_22 are predicted together.

[0099] As shown in Figure 6B, the 21st prediction interval PI_21 has a 21-hour length (td4-td0) that includes the 4th relative time td0 (current time)-td4, which includes the 1st bus stop BS1 and the 2nd bus stop BS2. On the other hand, the 22nd prediction interval PI_22 has a 22-hour length (td7-td4) that includes the 3rd bus stop BS3 and extends to the 4th relative time td4-td7.

[0100] The 21st time interval (td4-td0) is set to be shorter than the 22nd time interval (td7-td4). The number of prediction points (td0, td1, td2, td3, td4) belonging to the 21st prediction interval PI_21 [5] is different from the number of prediction points (td5, td6, td7) belonging to the 22nd prediction interval PI_22 [3] (asymmetric).

[0101] Furthermore, the computation time required for prediction in the 21st prediction interval PI_21 or the 22nd prediction interval PI_22 is proportional to the number of prediction points belonging to each prediction interval. In addition, the computation time required for prediction in the 21st prediction interval PI_21 or the 22nd prediction interval PI_22 does not depend on the length of time for each prediction interval [21st time length (td4-td0), 22nd time length (td7-td4)].

[0102] The granularity of the information obtained in the 21st prediction interval PI_21 (number of prediction points belonging to the prediction interval / time length related to the prediction interval) is finer than the granularity of the information obtained in the 22nd prediction interval PI_22. As a result, the prediction accuracy of the traffic conditions and operating status of bus route 13A in the 21st prediction section PI_21 can be maintained at a high level while ensuring high real-time availability of prediction information, compared to the prediction accuracy of the traffic conditions and operating status of bus route 13A in the 22nd prediction section PI_22.

[0103] Comparing the example shown in Figure 6A and the example shown in Figure 6B, the length of the batch prediction interval in the example shown in Figure 6B [21st hour length (td4-td0) + 22nd hour length (td7-td4)] is shorter than the length of the batch prediction interval in the example shown in Figure 6A [11th hour length (tc4-tc0) + 12th hour length (tc7-tc4)] by the amount of time that has elapsed (the predetermined time length ty below). Incidentally, the fourth relative time td can be expressed as [third relative time tc + ty] (where ty is a predetermined time length).

[0104] In this regard, in the example shown in Figure 6B, the number of prediction points in the 21st prediction interval PI_21 [5] is increased by the amount by which the number of prediction points in the 22nd prediction interval PI_22 [3] has decreased compared to the number of prediction points in the 12th prediction interval PI_12 [4], so that the number of prediction points in the 21st prediction interval PI_21 [8] in the 21st prediction interval PI_21 and the 22nd prediction interval PI_22 [8] is equal to the number of prediction points in the 11th prediction interval PI_11 and the 12th prediction interval PI_12 [8].

[0105] This makes it possible to make the number of prediction points [8] in the 21st prediction section PI_21 and the 22nd prediction section PI_22 equivalent to the number of prediction points [8] in the 11th prediction section PI_11 and the 12th prediction section PI_12, thereby making the overall calculation time required for prediction in the 21st prediction section PI_21 and the 22nd prediction section PI_22 equivalent to the overall calculation time required for prediction in the 11th prediction section PI_11 and the 12th prediction section PI_12, while improving the prediction accuracy of traffic conditions and operating status, as well as multiple KPI values, for route bus 13A in the prediction section including the 1st bus stop BS1 to the 3rd bus stop BS3. As a result, improved operational evaluation values ​​can be obtained that lead to an appropriate level for the priority of each of the multiple KPIs related to route bus 13A, thus enabling appropriate operational management of the mobile entity 13 (route bus 13A) for which KPIs (targets) based on multiple perspectives are set.

[0106] [Operation of the operation management device 15 according to the second modified example of the present invention] Next, the operation of the operation management device 15 according to the second modified example of the present invention will be described with reference to Figures 7A and 7B as appropriate. Figures 7A and 7B illustrate the operation of the operation management device 15 according to a second modified example, provided on a route bus 13A, where the route bus 13A is an example of the mobile body 13.

[0107] The operation management device 15 according to a second modification of the present invention, when setting a prediction section according to the length of the task queue, calculates a computational load CL for predicting the traffic conditions and operating status of the route bus 13A in the prediction section based on the length of the task queue, the number of prediction points belonging to the prediction section, and the interval (distance or time length), and limits the setting specifications of the prediction section according to the length of the task queue (number of prediction points belonging to the prediction section and the interval) so that the calculated computational load CL does not exceed a predetermined computational load threshold CL_Th. This ensures real-time acquisition of predictive information while maintaining a high level of prediction accuracy when predicting traffic conditions and operating status of route bus 13A within a set prediction section.

[0108] As a prerequisite, the section in which the operating status of the route bus 13A as a mobile entity 13 is predicted in the time series system related to the fifth relative time te is set to an appropriate time length (synonymous with distance) according to the length of the task queue.

[0109] In the comparative example of the second modified example shown in Figure 7A, the 31st prediction section PI_31 is given as an example of a section in which the operating status of the route bus 13A as a moving object 13 is predicted in the time series system related to the 5th relative time te.

[0110] As shown in Figure 7A, the 31st prediction interval PI_31 has a 31-hour length (te6-te0) that includes the 1st bus stop BS1, the 2nd bus stop BS2, and the 3rd bus stop BS3, and extends from the 5th relative time te0 (current time) to te6.

[0111] Now, let's assume that in the route bus 13A shown in Figure 7A, in addition to the task of temporarily stopping at the first bus stop BS1, the second bus stop BS2, and the third bus stop BS3 for passengers to board and alight, a new task has been added: temporarily stopping at the fourth bus stop BS4 for passengers to board and alight.

[0112] The addition of the fourth bus stop, BS4, is communicated to the update unit 8 and the prediction section setting unit 33 via the task addition unit 21.

[0113] When the aforementioned new task (temporarily stop at bus stop BS4) is added, the update unit 8 performs a task queue update to add the new task to the task queue.

[0114] As shown in Figure 7B, the prediction interval setting unit 33 sets the 33rd prediction interval PI_33 as the prediction interval, which is longer in time (essentially synonymous with distance) than the 31st prediction interval PI_31.

[0115] As shown in Figure 7B, the 33rd prediction interval PI_33 has a 33-hour length (te6-te0) that includes the 1st bus stop BS1, the 2nd bus stop BS2, the 3rd bus stop BS3, and the 4th bus stop BS4, and extends from the 5th relative time te0 (current time) to te6.

[0116] The important point here is that, in the operation management device 15 according to the second modified example, when the prediction section setting unit 33 sets a prediction section (33rd prediction section PI_33) according to the length of the task queue, it calculates a computational load CL, which includes a prediction of the traffic conditions and operating status of the route bus 13A in the prediction section, based on the length of the task queue, the number of prediction points belonging to the prediction section, and the interval (distance or time length) between them, and restricts the setting method of the prediction section according to the length of the task queue (number of prediction points belonging to the prediction section) so that the calculated computational load CL does not exceed a predetermined computational load threshold CL_Th. The predetermined computational load threshold CL_Th should be set to an appropriate value, taking into consideration that the computation time required to predict the traffic conditions and operating status of route bus 13A in the prediction section converges to a predetermined allowable time based on the perspective of ensuring real-time performance.

[0117] The 33rd time length (te6*-te0) for the 33rd prediction interval PI_33, shown in Figure 7B, is set to a longer value than the 31st time length (te6-te0) for the 31st prediction interval PI_31, shown in Figure 7A. Furthermore, the number of prediction points [te0, te1, te2, te3, te4*, te5*, te6*] belonging to the 33rd prediction interval PI_33 shown in Figure 7B [7] is set to be the same as the number of prediction points [te0, te1, te2, te3, te4, te5, te6] belonging to the 31st prediction interval PI_31 shown in Figure 7A [7]. Furthermore, the prediction points [te4*, te5*, te6*] belonging to the 33rd prediction interval PI_33 shown in Figure 7B are set at different temporal positions in the time series system related to the 5th relative time te compared to the prediction points [te4, te5, te6] belonging to the 31st prediction interval PI_31 shown in Figure 7A.

[0118] Furthermore, the computation time required for prediction in the 31st prediction interval PI_31 or the 33rd prediction interval PI_33 is proportional to the number of prediction points belonging to each prediction interval. In addition, the computation time required for prediction in the 31st prediction interval PI_31 or the 33rd prediction interval PI_33 does not depend on the length of time for each prediction interval [31st time length (te6-te0), 33rd time length (te6*-te0)].

[0119] In the operation management device 15 according to a second modification of the present invention, when setting a prediction section according to the length of the task queue, the device calculates a computational load CL related to predicting the traffic conditions and operating status of the route bus 13A in the prediction section based on the length of the task queue, the number of prediction points belonging to the prediction section, and the interval (distance or time length). The device then restricts the setting specifications of the prediction section according to the length of the task queue (number of prediction points belonging to the prediction section and the interval) so that the calculated computational load CL does not exceed a predetermined computational load threshold CL_Th. According to the operation management device 15 of the second modified version of the present invention, when predicting the traffic conditions and operating status of route buses 13A in a set prediction section, the real-time acquisition of prediction information is ensured while maintaining a high level of prediction accuracy.

[0120] [Operation of the operation management device 15 according to the third modified example of the present invention] Next, the operation of the operation management device 15 according to the third modified example of the present invention will be described with reference to Figure 8. Figure 8 is a diagram illustrating the operation of the operation management device 15 according to a third modified example, when the autonomous transport vehicle 13B is used as an example of the mobile body 13.

[0121] As shown in Figure 8, an unmanned transport area 73 is partitioned off within the logistics warehouse 71. Multiple luggage racks 75 are arranged in a line within the unmanned transport area 73, and transport paths 76 are provided to weave between the luggage racks 75. In addition, unmanned autonomous transport vehicles 13B move along the transport paths 76 provided to weave between the luggage racks 75 and perform the task of transporting luggage 77 placed on each of the luggage racks 75, and workers 79 coexist in the unmanned transport area 73 to assist in the task of transporting luggage 77.

[0122] The autonomous transport vehicle 13B, which functions as the mobile unit 13, is equipped with a third modified operation management device 15 (see Figure 1B). The third modified operation management device 15 installed in the autonomous transport vehicle 13B controls the driving of the autonomous transport vehicle 13B in order to improve multiple set KPIs. The autonomous transport vehicle 13B has several set KPIs, including the number of packages transported per unit time (KPI 11) and the safe distance maintained between it and the worker 79 (KPI 12).

[0123] The autonomous transport vehicle 13B, in accordance with the control instructions of the operation management device 15, picks up packages 77 placed on each of the multiple luggage racks 75 within the unmanned transport area 73 where workers 79 are present, and transports them to the shipping area 81.

[0124] The packages 77 placed in shipping area 81 are loaded into the cargo compartment of transport truck 85 by forklift 83.

[0125] Figure 9A shows an example of the change in the number of packages transported per unit time by the autonomous transport vehicle 13B in relation to the time series change (tf0-tf7) related to the sixth relative time tf, as KPI11, and Figure 9B shows an example of the change in the safe distance related to the autonomous transport vehicle 13B in relation to the time series change (tf0-tf7) related to the sixth relative time tf, as KPI12. Each of the time-series changes (tf0-tf7) related to the sixth relative time tf is associated with a transport task included in the task queue (a task in which the autonomous transport vehicle 13B transports packages 77 placed on each of the multiple luggage racks 75 to the shipping area 81). The number of packages transported per unit time (KPI11) and the safe distance (KPI12) for the autonomous transport vehicle 13B are in a trade-off relationship with each other.

[0126] During the period from time tf0, shown in Figure 9A, to the switching time tf_th, which is located midway between tf3 and tf4, the number of packages transported per unit time (KPI11) by the autonomous transport vehicle 13B gradually increases. Furthermore, during the same period (tf0-tf_th) shown in Figure 9B, the safe distance (KPI12) for the autonomous transport vehicle 13B gradually decreased (the safe distance gradually worsened).

[0127] During the period from the aforementioned switching time tf_th to tf7, as shown in Figure 9A, the number of packages transported per unit time (KPI11) by the autonomous transport vehicle 13B remained at a high level. Furthermore, during the same period (from tf_th to tf7), as shown in Figure 9B, the safety distance (KPI12) related to the autonomous transport vehicle 13B was stably maintained at a relatively low level (a level at which the separation distance between the autonomous transport vehicle 13B and the workers 79 could ensure safety).

[0128] This is based on the coordinated operation of the operation evaluation unit 55 and the operation plan creation unit 57, as described below. In other words, the operation evaluation unit 55 outputs an operation evaluation result (operation evaluation value) indicating that, during the period from the sixth relative time tf0 to the switching time tf_th, the priority of the number of packages transported per unit time (KPI 11) for the autonomous transport vehicle 13B is increased compared to the safety distance (KPI 12) for the autonomous transport vehicle 13B. The operation plan creation unit 57 outputs an operation control instruction to the operation control unit 61 equipped in the autonomous transport vehicle 13B as the mobile unit 13, in accordance with the operation evaluation value output by the operation evaluation unit 55.

[0129] Here, the characteristic of the number of packages transported per unit time (KPI11) / safe distance (KPI12) for the autonomous transport vehicle 13B changes at the aforementioned switching time tf_th, based on the coordinated operation of the operation evaluation unit 55 and the operation plan creation unit 57 described below. In other words, the operation evaluation unit 55 monitors the trend of the safe distance (KPI12) related to the autonomous transport vehicle 13B. When the safe distance (KPI12) related to the autonomous transport vehicle 13B falls to the safety distance threshold KPI12_th which defines the acceptable level (corresponding to the switching time tf_th), the operation evaluation unit 55 switches the priority of each of the multiple KPIs (KPI11 / KPI12) to a default value [for example, increasing the priority of the safe distance (KPI12) related to the autonomous transport vehicle 13B compared to the number of packages transported per unit time related to the autonomous transport vehicle 13B (KPI11)].

[0130] As a result, the operation evaluation unit 55 outputs an operation evaluation result (operation evaluation value) indicating that, for the period after the switching time tf_th, the priority of the safe distance (KPI 12) for the autonomous transport vehicle 13B will be increased compared to the number of packages transported per unit time (KPI 11) for the autonomous transport vehicle 13B. The operation plan creation unit 57 outputs an operation control instruction to the operation control unit 61 equipped in the autonomous transport vehicle 13B as the mobile unit 13, in accordance with the operation evaluation value output by the operation evaluation unit 55.

[0131] Then, the setting unit 7 provided in the autonomous transport vehicle 13B, which is the mobile unit 13, sets the priority of multiple KPIs, which are optimized so that the safety distance (KPI 12) related to the autonomous transport vehicle 13B does not fall below the safety distance threshold KPI 12_th. As a result, by appropriately considering both the number of packages transported per unit time (KPI11) and the safe distance (KPI12) for the autonomous transport vehicle 13B, it becomes possible to properly manage the operation of the autonomous transport vehicle 13B, which has KPIs set based on multiple perspectives. According to the third modified example of the operation management device 15, efficient logistics transportation can be achieved.

[0132] [Operation of the operation management device 15 according to the fourth modified example of the present invention] Next, the operation of the operation management device 15 according to the fourth modified example of the present invention will be described with reference to Figure 10. Figure 10 is a diagram illustrating the operation of the operation management device 15 according to the fourth modified example, provided on a dump truck 13C, where the dump truck 13C is used as an example of the mobile body 13.

[0133] As shown in Figure 10, the construction work area 101 is provided with one excavation area 91 and two excavation areas 93a and 93b, and a transport path 95 is provided to connect the excavation area 91 and the excavation areas 93a and 93b.

[0134] In construction work area 101, one excavator 103 and several dump trucks 13C (three in the example shown in Figure 10) are in operation. Excavator 103 is positioned in the excavation area 91 and excavates the soil in the excavation area 91. The excavated soil is loaded onto the beds of several dump trucks 13C. Each of the dump trucks 13C loaded with soil travels along a suitable transport path 95 towards one of the discharge areas 93a or 93b. Each dump truck 13C that arrives at either dumping site 93a or 93b operates to dump the soil and sand it has loaded onto its bed into either (available) dumping site 93a or 93b.

[0135] The dump truck 13C, which serves as the mobile unit 13, is equipped with a fourth modified operation management device 15 (see Figure 1B). The fourth modified operation management device 15 installed in the dump truck 13C controls the operation of the dump truck 13C in order to improve multiple set KPIs. The dump truck 13C has several KPIs set, including average driving speed (KPI31) and cumulative volume (KPI32). The average travel speed (KPI31) and cumulative volume (KPI32) for dump truck 13C are in a trade-off relationship with each other.

[0136] The dump truck 13C, following the control instructions of the operation management device 15, loads the soil excavated by the excavator 103 into its cargo bed within the construction work area 101 and travels via an appropriate transport path 95 toward one of the (available) unloading areas 93a or 93b. Upon arriving at one of the (available) unloading areas 93a or 93b, the dump truck 13C dumps the soil it had loaded into the corresponding unloading area 93a or 93b.

[0137] The operation management device 15 according to the fourth modified example, which is installed in the dump truck 13C, performs operation control for the dump truck 13C in order to improve several set KPIs [average driving speed (KPI 31) and accumulated volume (KPI 32) for the dump truck 13C]. Specifically, the operation management device 15 according to the fourth modification, which is installed in the dump truck 13C, performs operation control of the dump truck 13C in such a way as to increase the load capacity of the dump truck 13C as possible and to increase the average driving speed as possible.

[0138] By the way, at dumping sites 93a and 93b (when it is not necessary to distinguish between dumping sites 93a and 93b, they are simply referred to as "dumping site 93"), the total amount of soil and sand discarded may exceed the storage capacity, making it impossible for dump trucks 13C to discharge the soil. In this case, the operation management device 15 according to the fourth modified example monitors the total amount of soil and sand dumped in each of the dumping areas 93a and 93b, and selects the available dumping area 93 as the appropriate dumping area 93 based on the monitoring results. As a result, the destination of the dump truck 13C that departed from the excavation area 91 may change.

[0139] When the destination of the dump truck 13C that has departed from the excavation area 91 changes, the prediction section setting unit 33 of the operation management device 15 according to the fourth modified example sets a new prediction section by changing the prediction section, for example, by adding prediction points along the route to the appropriate (available) excavation site 93 as prediction points connected to prediction points 97a, 97b, and 97c.

[0140] This enables the prediction of a route from the excavation area 91 to the appropriate (available) discharge area 93, and realizes operational control for the dump truck 13C that can improve multiple KPIs [average travel speed (KPI 31) and cumulative volume (KPI 32) for the dump truck 13C].

[0141] By the way, the availability and location of the excavation site 93 change from moment to moment. According to the operation management device 15 of the fourth modified version of the present invention, even if the availability and location of the dumping area 93 change, operation control for the dump truck 13C can be realized that can improve multiple KPIs [average driving speed (KPI 31) and accumulated volume (KPI 32) for the dump truck 13C] by predicting and selecting an appropriate (available and short-distance) dumping area 93 in response to such changes.

[0142] [Configuration and operation of the operation management device 15 according to an embodiment of the present invention] Next, the configuration and operation of the operation management device 15 according to an embodiment of the present invention will be described. The operation management device 15 according to an embodiment of the present invention is based on an operation management device 15 that manages the operation of a mobile body 13 that moves along a required route. The operation management device 15 according to an embodiment of the present invention includes an input unit 1 for inputting mobile body information, including current and future operation status information and traffic condition information, and an update unit 8 for updating the task queue by adding a new task to the task queue. A prediction interval setting unit 33 sets a prediction interval that follows the length of the task queue after updating by the update unit 8, A setting unit 7 calculates KPI values, which are the values ​​of multiple pre-set key performance indicators (KPIs), based on the information of the moving object that belongs to the prediction interval set by the prediction interval setting unit 33 from the moving object information input via the input unit 1, and sets the priority for each of the multiple KPIs. An operation evaluation unit 55 evaluates the operation of the mobile body 13 based on the mobile body information input via the input unit 1, the multiple KPI values ​​calculated by the setting unit 7, and the priority for each of the multiple KPIs set by the setting unit 7. The system includes an operation plan creation unit 57 that creates an operation plan for the mobile body 13 based on the operation evaluation results for the mobile body 13 by the operation evaluation unit 55, The setting unit 7 adopts a configuration that sets priorities for each of the multiple KPIs based on the multiple KPI values ​​calculated and the operation plan for the mobile body 13 generated by the operation plan creation unit 57.

[0143] According to the operation management device 15 of the embodiment of the present invention, the setting unit 7 sets priorities for each of the multiple KPIs based on the multiple calculated KPI values ​​and the operation plan for the mobile body 13 generated by the operation plan creation unit 57, thereby enabling appropriate operation management of the mobile body 13 for which KPIs (task targets) based on multiple perspectives are set.

[0144] Furthermore, in the operation management device 15 according to the embodiment of the present invention, the setting unit 7 may adopt a configuration that sets priorities for each of the multiple KPIs based on customer evaluation values ​​(customer needs) related to the mobile body 13. With this configuration, for example, if the mobile body 13 is a public transport vehicle such as a route bus or a taxi, priorities for each of the multiple KPIs are set based on customer evaluation values ​​(customer needs) obtained from passengers of the public transport vehicle, so that operation management of the public transport vehicle as a mobile body 13 in accordance with customer needs can be appropriately carried out.

[0145] Furthermore, in the operation management device 15 according to the embodiment of the present invention, the setting unit 7 may adopt a configuration that sets priorities for each of the multiple KPIs based on the information of the new task. With this configuration, for example, if the mobile unit 13 is an autonomous electric vehicle, prioritizing multiple KPIs, such as raising the priority of KPIs related to energy-saving operation based on information about the target location and the location of the charging station, as newly added task information, will enable appropriate operation management of the electric vehicle as the mobile unit 13, taking into account the charge status of the onboard battery.

[0146] Furthermore, in the operation management device 15 according to an embodiment of the present invention, the input unit 1 may input the mobile body information and demand forecast information relating to the mobile body 13, and update the prediction model 43 relating to the mobile body 13 each time a new task occurs based on the prediction section set by the prediction section setting unit 33 and the input mobile body information and demand forecast information relating to the mobile body, and use the updated prediction model 43 to predict the operation status relating to the mobile body 13. With this configuration, for example, the predicted motion characteristics of the public transport vehicle can be changed based on the number of passengers (load weight) on the public transport vehicle, thereby improving the prediction accuracy of the prediction model 43. This allows for appropriate operation management of the public transport vehicle as a mobile entity 13, even when the number of passengers (load weight) on the public transport vehicle changes.

[0147] Furthermore, in the operation management device 15 according to an embodiment of the present invention, the prediction section setting unit 33 may adopt a configuration that adjusts the setting method of the prediction section based on the length of the task queue after updating by the update unit 8. With this configuration, for example, when predicting the traffic conditions and operating status of route bus 13A in the prediction section set by the prediction section setting unit 33, it is possible to ensure real-time acquisition of prediction information while maintaining a high level of prediction accuracy.

[0148] Furthermore, in the operation management device 15 according to an embodiment of the present invention, when the prediction section setting unit 33 sets a prediction section according to the length of the updated task queue by the update unit 8, it may employ a configuration that limits the setting of the prediction section (number of predicted points and intervals) so that the calculated calculation load CL does not exceed a predetermined calculation load threshold CL_th. With this configuration, for example, even as a mobile unit 13, it is possible to obtain improved operational evaluation values ​​that guide the priority of each of the multiple KPIs related to the route bus 13A to an appropriate level, thereby enabling proper operational management of the mobile unit 13 (route bus 13A) for which KPIs based on multiple perspectives are set.

[0149] Furthermore, in the operation management device 15 according to the embodiment of the present invention, the operation evaluation unit 55, which constitutes a part of the creation unit 9, may be configured to output operation evaluation values ​​for improving the multiple KPIs based on the mobile information input via the input unit 1, the KPI values ​​for each of the multiple KPIs calculated by the calculation unit 5, and the priority for each of the multiple KPIs set by the setting unit 7. With this configuration, the operation evaluation unit 55 outputs operation evaluation values ​​to improve multiple KPIs based on the priority set by the setting unit 7. For example, both the time delay (KPI1) and the average fuel consumption (KPI2) for the route bus 13A are given balanced consideration, resulting in the proper execution of operation management for the mobile vehicle 13, which has KPIs set based on multiple perspectives.

[0150] [Configuration and operation of the control system 11 according to an embodiment of the present invention] Next, the configuration and operation of the control system 11 according to an embodiment of the present invention will be described. The control system 11 according to an embodiment of the present invention adopts a configuration comprising a traffic management device 15 based on a first viewpoint and a mobile body that moves based on the traffic plan created by the traffic management device.

[0151] According to the control system 11 of the embodiment of the present invention, the setting unit 7 sets priorities for each of the multiple KPIs based on the calculated multiple KPI values ​​and the operation plan for the mobile body 13 generated by the operation plan creation unit 57, thereby enabling appropriate operation management of the mobile body 13 for which KPIs based on multiple perspectives are set.

[0152] Furthermore, in the control system 11 according to the embodiment of the present invention, the mobile body 13 may be configured to include an operation evaluation display unit 69 that displays the results of the operation evaluation by the operation evaluation unit 55 (operation evaluation value). With this configuration, for example, if the mobile body 13 is a route bus 13A, passengers of the route bus 13A who see the display contents of the operation evaluation display unit 69 installed in the passenger compartment of the route bus 13A (for example, the time of delay, the estimated arrival time for each of the multiple bus stops, and the average fuel consumption) can find out how much the route bus 13A is delayed compared to the timetable.

[0153] [Configuration and operation of the operation management method according to an embodiment of the present invention] Next, the configuration and operation of the operation management method according to an embodiment of the present invention will be described. An operational management method according to an embodiment of the present invention is an operational management method used when managing the operation of a mobile body 13, The process includes inputting traffic information regarding traffic conditions in the travel section of the mobile body 13, and operational status information regarding the operational status of the mobile body, A step of storing the task of the mobile body 13 and the task objective which is the goal of the task, A step of calculating a task target value indicating the degree of achievement of the stored task target using the traffic condition information and the operation status information, A step of setting the priority of the task objectives related to the mobile body 13 using the calculated task objective values, A configuration may also be adopted that includes the step of creating a movement plan for the mobile body 13 using the priority of the task objectives set above.

[0154] According to the operation management method according to the embodiment of the present invention, a movement plan for the mobile body 13 is created using the priority of the set task objectives. By performing operation management of the mobile body 13 using the created movement plan, operation management of the mobile body 13, for which KPIs (task objectives) based on any perspective are set, can be appropriately carried out.

[0155] [Configuration and operation of the mobile body 13 according to the embodiment of the present invention] Next, the configuration and operation of the mobile body 13 according to the embodiment of the present invention will be described. The mobile body 13 according to the embodiment of the present invention incorporates an operation management device 15 based on the first aspect. According to the mobile body 13 of the present invention, it is possible to obtain a mobile body 13 that can set task objectives based on any viewpoint and perform appropriate operational management.

[0156] [Other Embodiments] The embodiments described above are examples of the embodiment of the present invention. Therefore, the technical scope of the present invention should not be interpreted as being limited by these embodiments, as the present invention can be implemented in various forms without departing from its gist or its main features.

[0157] For example, the present invention may be implemented in a manner in which a program that realizes the functions according to the above embodiment is supplied to a system or device via a network or storage medium, and a processor in the computer of that system or device reads and executes the program. Alternatively, the present invention may be implemented using hardware circuits that realize the above functions. Information including the program that realizes the above functions can be stored in a recording device such as a memory or hard disk, or in a recording medium such as a memory card or optical disc. [Explanation of Symbols]

[0158] 1 Input section 3 Storage section 5. Calculation Section 7. Settings section 8 Update section 9. Creation Section 10 Generation part 11. Air Traffic Control Systems 13 Mobile Units 15 Traffic control device 17 Communication media 21 Task Input Section (Input Section) 23 Traffic Condition Input Section (Input Section) 27. Operation status input section (input section) 33 Prediction interval setting unit (generation unit) 35 Database (storage unit) 37 Predictive Model (Memory Unit) 51 Traffic Condition Prediction Unit (Generation Unit) 53 Operation Status Prediction Unit (Generation Unit) 55 Operation Evaluation Department (Creation Department) 57. Operation Planning Department (Planning Department) 61 Operation Control Unit 69 Operation Evaluation Display Unit

Claims

1. In an operation management device that manages the operation of a mobile object, An input unit for inputting traffic information regarding traffic conditions in the section traveled by the mobile body, and operational status information regarding the operational status of the mobile body, A storage unit that stores the task of the mobile body and the task objective which is the goal of the task, A calculation unit that uses the traffic condition information and the operation status information to calculate a task target value indicating the degree of achievement of the task target stored in the storage unit, A setting unit sets the priority of the task objectives related to the moving body using the task objective values ​​calculated by the calculation unit, The system includes a creation unit that creates a movement plan for the moving object using the priority of the task objective set by the setting unit. A traffic control device characterized by the following features.

2. In the operation management device according to claim 1, The input unit further inputs movement environment information indicating the environment in which the moving object is moving, The system further comprises a generation unit that uses the mobile environment information and the task of the mobile body input by the input unit to predict traffic conditions and operating status, generate traffic condition information and operating status information based on the prediction, and transmit the generated traffic condition information and operating status information to the input unit. A traffic control device characterized by the following features.

3. In the operation management device according to claim 2, The input unit receives the task of the mobile body, The generation unit modifies a prediction section, which includes a section for predicting traffic conditions and a section for predicting the state of the mobile body, using the task of the mobile body input by the input unit and the task of the mobile body stored in the storage unit, and generates the traffic condition information and the operating state information for the modified prediction section. A traffic control device characterized by the following features.

4. In the operation management device according to claim 3, The system further comprises an update unit that adds the task of the mobile body input by the input unit to the storage unit and updates the stored information in the storage unit. A traffic control device characterized by the following features.

5. In the operation management device according to claim 1, The memory unit stores a plurality of task objectives, The calculation unit calculates task target values ​​that indicate the degree of achievement for each of the multiple task targets, The setting unit sets the priority for each of the multiple task objectives using the task objective values ​​for each of the multiple task objectives calculated by the calculation unit. A traffic control device characterized by the following features.

6. In the operation management device according to claim 5, The operating state of the aforementioned mobile body is the current and future operating state of the mobile body. A traffic control device characterized by the following features.

7. In the operation management device according to claim 6, The setting unit sets priorities for each of the multiple KPIs based on the customer evaluation values ​​related to the mobile device. A traffic control device characterized by the following features.

8. In the operation management device according to claim 6, The setting unit sets the priority for each of the multiple KPIs based on the information of the new task. A traffic control device characterized by the following features.

9. In the operation management device according to claim 4, The aforementioned input unit is Further inputting the aforementioned mobile body information and demand forecast information related to the mobile body, The aforementioned update unit is, Based on the prediction interval set by the generation unit, the input mobile body information, and the demand forecast information related to the mobile body, the prediction model for the mobile body is updated each time a new task arises. Using the updated prediction model, the operating status of the mobile object is predicted. A traffic control device characterized by the following features.

10. In the operation management device according to claim 6, The generation unit adjusts the setting configuration of the prediction interval based on the length of the task queue after it has been updated by the update unit. A traffic control device characterized by the following features.

11. In the operation management device according to claim 10, When the generation unit sets a prediction interval according to the length of the task queue after it has been updated by the task queue update unit, it calculates a computational load, including the prediction of the operating state of the mobile object in the prediction interval, based on the length of the updated task queue, the number of prediction points belonging to the prediction interval, and the interval between them, and restricts the way the prediction interval is set so that the calculated computational load does not exceed a predetermined computational load threshold. A traffic control device characterized by the following features.

12. The operation management device according to claim 6, The aforementioned creation unit, The operation of the mobile object is evaluated using the mobile object information input via the input unit, the KPI values ​​for each of the multiple KPIs calculated by the calculation unit, and the priority for each of the multiple KPIs set by the setting unit. Based on the priority of each of the multiple KPIs set by the setting unit, the unit outputs operational evaluation values ​​to improve those multiple KPIs. A traffic control device characterized by the following features.

13. The operation management device according to claim 1, The system includes a mobile body that moves based on the operation plan created by the operation management device. A control system characterized by the following features.

14. A control system according to claim 13, The aforementioned moving body is The system includes an operation evaluation display unit that displays the results of the operation evaluation performed by the aforementioned creation unit. A control system characterized by the following features.

15. In operation management methods used when managing the operation of mobile vehicles, A step of inputting traffic information regarding traffic conditions in the section traveled by the mobile body, and operational status information regarding the operational status of the mobile body, A step of storing the task of the mobile body and the task objective which is the goal of the task, A step of calculating a task target value indicating the degree of achievement of the stored task target using the traffic condition information and the operation status information, A step of setting the priority of the task objectives related to the mobile body using the calculated task objective values, The process includes creating a movement plan for the moving body using the priority of the task objectives set above. A method for managing operations characterized by the following features.

16. The operation management device described in claim 1 is incorporated. A mobile body characterized by the following features.

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