Operation management system and operation management method

Abstract

This operation management system 200 comprises: a load transition estimation unit 224 that estimates a load change at a delivery point in consideration of density which is defined by the number of stop times due to delivery in a travel route of a vehicle 300 which is an electric vehicle; and a power consumption calculation unit 225 that calculates power consumption along the travel route on the basis of the estimated load. Thus, provided are an operation management system and an operation management method by which it is possible to estimate the power consumption of an electric vehicle with high accuracy when planning a travel route for delivery.

Description

Traffic management system and traffic management method

[0001] The present invention relates to a traffic management system and a traffic management method, and more particularly to a traffic management system and the like that can be suitably used when estimating the power consumption of an electric vehicle and planning a travel route based on the estimated power consumption.

[0002] In recent years, from the viewpoint of environmental consideration, electric vehicles that run on electricity have come into use, and attempts have been made to use electric vehicles for delivery.

[0003] Patent Literature 1 describes a device for estimating the amount of power consumption (cost value) of an electric vehicle, which includes a storage unit that stores multiple types of driving models that indicate the manner in which the electric vehicle travels on a road link, for each number of stops on the road link, a stop count estimation unit that estimates the number of stops of the electric vehicle on the road link, and a processing unit that calculates the amount of power consumption (cost value). The processing unit calculates the amount of power consumption required for the electric vehicle to travel on the road link using a specific driving model that corresponds to the number of stops estimated by the stop count estimation unit and is selected from the multiple types of driving models.

[0004] JP 2015-18392 A

[0005] When estimating the power consumption of an electric vehicle, if the actual power consumption exceeds the predicted power consumption, the vehicle will run out of power and stop. In this case, costs will be incurred to revise the plan due to the power shortage, such as arranging for another vehicle or transferring cargo. On the other hand, if the actual power consumption is significantly lower than the predicted power consumption, the buffer in the driving plan will be too large, and more vehicles than necessary will need to be used for delivery. In this case, for example, steady-state costs will increase. The present invention aims to provide a traffic management system and a traffic management method that can accurately estimate the power consumption of an electric vehicle when planning a driving route for delivery by an electric vehicle.

[0006] In order to solve the above problems, the present invention provides a traffic management system that includes a load transition estimation unit that estimates load changes at delivery points by taking into account the density, which is determined by the number of stops made for deliveries along the electric vehicle's route, and a power consumption calculation unit that calculates the power consumption of the route based on the estimated load.In this case, a traffic management system can be provided that can accurately estimate the power consumption of the electric vehicle when planning a route for delivery by the electric vehicle.

[0007] Here, for example, the power consumption calculation unit calculates the power consumption including the load when the electric vehicle departs from the delivery point and the stop point where the electric vehicle stops due to a traffic light. In this case, the power consumption of the electric vehicle can be estimated with high accuracy by taking the influence of the load into account. Furthermore, for example, the power consumption calculation unit calculates the load by multiplying the total vehicle weight at each of the delivery point and the stop point by a predetermined power consumption coefficient. In this case, a more suitable calculation formula can be used to calculate the power consumption including the load. Furthermore, for example, the system further includes a traffic light stop point estimation unit that estimates the stop points where the electric vehicle stops due to a traffic light. In this case, the number of stops due to traffic lights can be determined more accurately. Furthermore, for example, the traffic light stop point estimation unit calculates a traffic light stop coefficient, which is the rate at which the electric vehicle stops due to a traffic light, based on the electric vehicle's driving history. In this case, the traffic light stop points can be estimated more accurately. And, for example, the load transition estimation unit estimates the load change based on the rate at which redelivery is estimated to be necessary, calculated based on past delivery records. In this case, the power consumption including the load can be calculated by taking into account whether the delivery can be delivered. Furthermore, for example, the weight transition estimation unit estimates the load change based on the number of deliveries estimated to require redelivery. In this case, the load change can be estimated even if the weight of each delivery is unknown. Furthermore, for example, the system may further include a route planning unit that plans a driving route for the electric vehicle, and a power shortage determination unit that determines whether the electric vehicle will run out of power by comparing the power consumption calculated by the power consumption calculation unit for one driving route planned by the route planning unit with the remaining battery power of the electric vehicle. If the power shortage determination unit determines that one driving route will run out of power, the route planning unit re-plans another driving route. In this case, a more suitable driving route can be determined.

[0008] The present invention also provides a traffic control system that includes a traffic light stop point estimation unit that estimates stop points on the electric vehicle's driving route where the electric vehicle will stop due to a traffic light, a load transition estimation unit that estimates load changes at delivery points on the driving route where the electric vehicle will stop for delivery, and a power consumption calculation unit that calculates power consumption based on the estimated load, including the load at the estimated stop points and delivery points when the electric vehicle departs. In this case, a traffic control system can be provided that can estimate the power consumption of the electric vehicle with high accuracy when planning a driving route for delivery by the electric vehicle.

[0009] Furthermore, the present invention provides a traffic management method in which a processor executes a program stored in a memory to estimate the load change at a delivery point taking the load change into consideration as a density based on the number of stops made for deliveries along the electric vehicle's route, and calculates the power consumption of the route based on the estimated load. In this case, a traffic management method can be provided that can accurately estimate the power consumption of an electric vehicle when planning a route for delivery by the electric vehicle.

[0010] According to the present invention, it is possible to provide an operation management system and an operation management method that can estimate the power consumption of an electric vehicle with high accuracy when planning a driving route for delivery by the electric vehicle.

[0011] 1 is a block diagram showing the overall configuration of a vehicle management system according to an embodiment; FIG. 2 is a diagram showing driving history data and a traffic light stop coefficient; (a) to (b) are diagrams showing a power consumption model; FIG. 3 is a flowchart explaining the operation of the traffic management system; FIG. 4 is a flowchart explaining S402 in FIG. 4 in more detail; and FIG. 5 is a flowchart explaining S403 in FIG. 4 in more detail.

[0012] An embodiment of the present invention will be described in detail below with reference to the accompanying drawings. <Overall Description of Vehicle Management System 1> FIG. 1 is a block diagram showing the overall configuration of a vehicle management system 1 according to this embodiment. The vehicle management system 1 according to this embodiment includes a delivery planning system 100 that manages deliveries, delivery destinations, and delivery records, and an operation management system 200 that creates delivery routes for vehicles 300, which are electric vehicles. The delivery planning system 100 manages the delivery destinations and delivery points of deliveries, and formulates a delivery plan for deliveries to be delivered in a single delivery. In this embodiment, the delivery planning system 100 is included in the delivery core system. The delivery plan may also include the weight of the deliveries.

[0013] The traffic management system 200 creates a driving route for each vehicle 300 to make a delivery based on the delivery plan formulated by the delivery planning system 100. The driving route includes, in addition to the driving route, information such as the time of delivery and the vehicle making the delivery. The traffic management system 200 includes a route planning unit 210 that plans the driving route for the vehicle 300, a power consumption estimation unit 220 that estimates the power consumption of the vehicle 300, and a power shortage determination unit 230 that determines whether the vehicle 300 will run out of power.

[0014] The route planning unit 210 plans a driving route for delivery based on the delivery points of the delivery items. Based on the delivery points of the delivery items and the map data included in the map database 240, the route planning unit 210 plans a route from the point where the vehicle 300 departs, via each delivery point, to the point where the vehicle 300 finally arrives. The point where the vehicle 300 departs is, for example, a delivery center where the delivery items are collected. The point where the vehicle 300 arrives is, for example, the delivery center from which the vehicle departed or a charging facility. The map data is acquired in advance and stored in the map database 240. There are no particular limitations on the map data, as long as it can identify the delivery points and the positions of traffic lights on the driving route.

[0015] The power consumption estimation unit 220 estimates the power consumption of the travel route based on the congestion of the travel route of the vehicle 300 and changes in load at stopping points. The power consumption estimation unit 220 includes a traffic light stop coefficient calculation unit 221, a traffic light stop point estimation unit 222, a delivery coefficient calculation unit 223, a load transition estimation unit 224, and a power consumption calculation unit 225.

[0016] The traffic light stop coefficient calculation unit 221 calculates a traffic light stop coefficient, which is the rate at which the vehicle 300 will stop at a traffic light, based on the driving history database 250. The driving history database 250 stores driving history data of the vehicle 300 in the past, and this driving history data includes data on whether the vehicle 300 stopped at a traffic light. That is, the driving history database 250 stores data on whether the vehicle 300 was able to pass through each traffic light on its driving route because the light was green, or whether the vehicle 300 was unable to pass because the light was red and had to stop. The traffic light stop coefficient calculation unit 221 acquires this data as traffic light stop performance data and calculates the rate at which the vehicle 300 will stop for each traffic light on its driving route. This rate is output to the traffic light stop point estimation unit 222 as a traffic light stop coefficient.

[0017] FIG. 2 is a diagram showing driving history data and a traffic light stop coefficient. In FIG. 2, the driving history data is composed of a traffic light ID, which is the ID of the signal, an entry link indicating where the vehicle entered the signal, an exit link indicating where the vehicle exited the signal, the number of passes, which is the number of times the vehicle passed through the signal, and the number of times the vehicle stopped without being able to pass (number of stops). The number of passes is the sum of the number of times the vehicle was able to pass through the traffic light without being able to pass and the number of times the vehicle stopped without being able to pass (number of stops). The coefficient is the traffic light stop coefficient. This coefficient can be calculated by dividing the number of stops by the number of passes. Note that the number of driving history data and traffic light stop coefficients are not limited to one for each signal, and multiple coefficients may be prepared depending on, for example, the time of travel or the day of the week.

[0018] Returning to FIG. 1 , the traffic light stop point estimation unit 222 estimates a stop point on the travel route of the vehicle 300. In this embodiment, a stop point is defined as a point where the vehicle stops due to a traffic light. The traffic light stop point estimation unit 222 predicts a stop point based on the traffic light stop coefficient calculated by the traffic light stop coefficient calculation unit 221. In this case, it can also be said that the traffic light stop point estimation unit 222 calculates a traffic light stop coefficient, which is the rate at which the vehicle stops due to a traffic light, based on the actual number of stops at traffic lights in the travel history of the vehicle 300, and estimates a stop point.

[0019] The delivery coefficient calculation unit 223 calculates a redelivery coefficient, which is the probability that a package cannot be delivered and will have to be redelivered, based on the delivery record. The delivery record is managed by the delivery planning system 100, and the delivery coefficient calculation unit 223 obtains the delivery record from the delivery planning system 100.

[0020] The load transition estimation unit 224 estimates load changes at delivery points taking into account density. In this embodiment, a delivery point is defined as a point where a delivery stops due to delivery. The load transition estimation unit 224 estimates load changes at a delivery point based on the load at departure (shown as the load at departure in FIG. 1 ) and the redelivery coefficient. In this case, density refers to the number of stops due to deliveries along the vehicle 300's travel route. One feature of this embodiment is that it estimates the power consumption of the vehicle 300 taking into account the density of the delivery area. The number of stops due to deliveries along the travel route is used as the density. In other words, there is a correlation between density and the number of stops due to deliveries along the travel route. A high number of stops indicates a high number of traffic lights and delivery points, which corresponds to a higher density. Conversely, a low number of stops indicates a low number of traffic lights and delivery points, which corresponds to a lower density.

[0021] In this case, the load transition estimation unit 224 can be said to estimate the load change based on the rate at which redelivery is estimated to be necessary, calculated based on past delivery records. The load transition estimation unit 224 also estimates the load change based on the number of deliveries estimated to require redelivery. It is difficult to accurately determine the weight of each delivery. On the other hand, the number of deliveries can be clearly determined. Therefore, a predetermined weight is set for each delivery, and when a delivery is delivered, the load is reduced by the number of deliveries. This weight can also be calculated, for example, by dividing the load at departure by the number of deliveries at departure. The delivery coefficient calculation unit 223 and the load transition estimation unit 224 can also be considered as a density effect estimation unit that estimates the effect of load changes on density.

[0022] The power consumption calculation unit 225 calculates the power consumption of the travel route based on the load estimated by the load transition estimation unit 224. In this embodiment, the power consumption calculation unit 225 calculates the power consumption including the load when the vehicle 300 departs from the delivery point and the stopping point. The load estimated at this time is used. More specifically, the estimated load is input into the power consumption model 260 to calculate the power consumption.

[0023] The delivery planning system 100 is, for example, a server computer that manages the entire vehicle management system 1. Although the delivery planning system 100 is shown as one system, its functions may be realized by multiple server computers. The delivery planning system 100 may also be a virtual server device. The operation management system 200 is a computer device, such as a PC (Personal Computer). However, it is not limited to this and may also be a mobile computer, smartphone, tablet, etc.

[0024] The delivery planning system 100 and the operation control system 200 include a processor such as a CPU (Central Processing Unit) as a computing means, and a main memory as a storage means. Here, the processor executes various software such as an OS (operating system) and applications (application software). The main memory is a storage area that stores various software and data used to execute the software. Furthermore, the delivery planning system 100 and the operation control system 200 include storage such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive) as an auxiliary storage device, and a communication interface for communicating with the outside. They may also include input devices such as a mouse and a keyboard, and output devices such as a display.

[0025] 3(a) and 3(b) are diagrams illustrating power consumption models. Of these, FIG. 3(a) is a diagram illustrating an example of a conventional power consumption model. The illustrated power consumption model calculates propulsive force and calculates power consumption based on the propulsive force. FIG. 3(b) is a diagram illustrating a power consumption model 260 according to the present embodiment. In comparison with conventional power consumption models, the power consumption model 260 according to the present embodiment adds a power consumption term at the time of departure to the second term on the right side to calculate power consumption. After the vehicle 300 stops at a delivery point or a stopping point, the vehicle 300 must start traveling again. The power consumption term represents the power consumption due to the load when the vehicle 300 departs at this time. The power consumption term is defined by multiplying the sum of the vehicle weights (load + vehicle weight) Wi at each of the delivery point and the stopping point by a predetermined power consumption coefficient Cs.

[0026] Returning to FIG. 1 , the power shortage determination unit 230 determines whether the vehicle 300 will run out of power by comparing the power consumption calculated by the power consumption calculation unit 225 for each travel route planned by the route planner 210 with the remaining battery level of the vehicle 300. Running out of power refers to a state in which the vehicle 300 runs out of power while traveling and is unable to travel. The remaining battery level is sent from the vehicle 300 to and stored in the vehicle information database 270, which stores vehicle information, which is information about the vehicle 300. The power shortage determination unit 230 then obtains the remaining battery level of the vehicle 300 from the vehicle information database 270. If the power shortage determination unit 230 determines that there is a travel route that will run out of power, the power shortage determination unit 230 requests the route planner 210 to replan the route. Upon receiving the replanning, the route planner 210 replans another travel route.

[0027] <Explanation of Operation of Traffic Management System 200> Next, the operation of the traffic management system 200 will be described. FIG. 4 is a flowchart illustrating the operation of the traffic management system 200. First, the route planning unit 210 plans a driving route that passes through a delivery point based on the delivery point (S401). Next, the traffic light stop point estimation unit 222 estimates whether or not there will be a traffic light stop on the driving route based on the traffic light stop coefficient (S402). Next, the load transition estimation unit 224 estimates whether delivery is possible based on the redelivery coefficient and estimates the load change when passing through a delivery point (S403). Furthermore, the power consumption calculation unit 225 estimates the power consumption of the driving route based on the power consumption model 260 (S404). Next, the power shortage determination unit 230 determines whether the power consumption on the driving route is greater than the remaining battery power included in the vehicle information (S405). If it is greater (Yes in S405), the process returns to S401. In other words, this is the case where the power shortage determination unit 230 requests the route planning unit 210 to replan, and the route planning unit 210 replans another travel route. On the other hand, if the difference is not larger (the same or smaller) (No in S405), the traffic management system 200 distributes the travel route to the vehicle 300 (S406).

[0028] Fig. 5 is a flowchart illustrating in more detail S402 in Fig. 4. First, the traffic light stop coefficient calculation unit 221 calculates a traffic light stop coefficient for each traffic light based on the driving history, using the "number of stops / number of passes" (S501). Then, the traffic light stop point estimation unit 222 multiplies the traffic light stop coefficient by the traffic light on the driving route to estimate whether or not a stop will be made (S502).

[0029] FIG. 6 is a flowchart that provides more detailed information about S403 in FIG. 4. First, the delivery coefficient calculation unit 223 calculates the redelivery coefficient based on the delivery record as "number of redeliveries / number of deliveries" (S601). Next, the load transition estimation unit 224 multiplies the delivery point on the travel route by the redelivery coefficient to estimate whether delivery is possible (S602). Furthermore, the load transition estimation unit 224 determines whether delivery was successful (S603). As a result, if delivery was successful (Yes in S603), the load transition estimation unit 224 subtracts the delivered amount from the load (S604). The subtraction amount is calculated as "load at departure / delivery point × redelivery coefficient." On the other hand, if delivery was not successful (No in S603), the series of processes ends.

[0030] According to the traffic management system 200 described above, it is possible to estimate the power consumption of the vehicle 300 with high accuracy when planning a delivery route. As a result, the vehicle 300 is less likely to run out of power, and the cost of revising the plan due to a power shortage in the vehicle 300 is less likely to be incurred. Furthermore, it is possible to make deliveries using an appropriate number of vehicles 300, thereby reducing steady-state costs.

[0031] Furthermore, the operation management system 200 described above can also be said to include a traffic light stop point estimation unit 222 that estimates stop points on the driving route of the vehicle 300, which are points where the vehicle 300 will stop due to a traffic light, a load transition estimation unit 224 that estimates load changes at delivery points on the driving route, which are points where the vehicle 300 will stop for delivery purposes, and a power consumption calculation unit 225 that calculates power consumption based on the estimated load, including the load when the vehicle 300 departs from the estimated stop points and delivery points.

[0032] <Explanation of Traffic Management Method> The processing performed by the traffic management system 200 described above is realized by the cooperation of software and hardware resources. That is, a processor such as a CPU provided in the traffic management system 200 loads into main memory and executes a program that realizes each function of the traffic management system 200, thereby realizing each of these functions.

[0033] Therefore, the processing performed by the above-mentioned operation management system 200 can be considered to be an operation management method in which the processor executes a program recorded in memory, and the number of stops due to deliveries on the vehicle's 300 route is used as density, the load change at the delivery point is estimated taking into account the density, and the power consumption of the route is calculated based on the estimated load.

[0034] The program for realizing this embodiment can be provided not only by communication means but also by being stored on a recording medium such as a CD-ROM.

[0035] Although the present embodiment has been described above, the technical scope of the present invention is not limited to the scope of the above embodiment. It is clear from the claims that various modifications and improvements to the above embodiment are also included in the technical scope of the present invention.

[0036] 1... Vehicle management system, 100... Delivery planning system, 200... Operation management system, 210... Route planning unit, 220... Power consumption estimation unit, 221... Signal stop coefficient calculation unit, 222... Signal stop point estimation unit, 223... Delivery coefficient calculation unit, 224... Load transition estimation unit, 225... Power consumption calculation unit, 230... Power shortage determination unit

Claims

1. A traffic management system comprising: a load transition estimation unit that estimates load changes at delivery points by taking into account the density of the number of stops made for deliveries along an electric vehicle's driving route; and a power consumption calculation unit that calculates the power consumption of the driving route based on the estimated load.

2. The traffic management system according to claim 1, wherein the power consumption calculation unit calculates the power consumption including the load when the electric vehicle departs from the delivery point and the stopping point where the electric vehicle stops due to traffic lights.

3. The traffic management system according to claim 2, wherein the power consumption calculation unit calculates the load by multiplying the sum of the vehicle weights at the delivery points and the stopping points by a predetermined power consumption coefficient.

4. The traffic control system according to claim 1, further comprising a traffic light stop point estimation unit that estimates a stop point where the electric vehicle will stop due to a traffic light.

5. The traffic light stop point estimation unit of claim 4 calculates a traffic light stop coefficient, which is the rate at which the electric vehicle will stop at a traffic light, based on the electric vehicle's driving history of having stopped at a traffic light, and estimates the stopping point.

6. The traffic management system according to claim 1, wherein the load transition estimation unit estimates load changes based on a rate at which redelivery is estimated to be necessary, the rate being calculated based on past delivery records.

7. The traffic management system according to claim 6, wherein the load transition estimation unit estimates the load change based on the number of deliveries estimated to require redelivery.

8. An operation management system as described in claim 1, further comprising: a route planning unit which plans a driving route for the electric vehicle; and a power shortage determination unit which determines whether the electric vehicle will run out of power by comparing the power consumption calculated by the power consumption calculation unit for one driving route planned by the route planning unit with the remaining battery charge of the electric vehicle, wherein if the power shortage determination unit determines that the one driving route will run out of power, the route planning unit re-plans another driving route.

9. A traffic light stop point estimation unit that estimates a stop point on the driving route of an electric vehicle, which is a point where the electric vehicle will stop due to a traffic light; a load transition estimation unit that estimates a load change at a delivery point on the driving route, which is a point where the electric vehicle will stop for a delivery; and a power consumption calculation unit that calculates the power consumption including the load at the estimated stop point and the estimated delivery point when the electric vehicle departs based on the estimated load.

10. An operation management method in which a processor executes a program recorded in a memory, the method determining the density of an electric vehicle's route based on the number of stops made for deliveries, estimating the change in load at a delivery point taking into account the density, and calculating the power consumption of the route based on the estimated load.

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