Traffic management system and traffic management method

Abstract

To improve an efficiency of traffic operations of a plurality of electric vehicles.SOLUTION: A traffic operation management system for managing traffic operations of a plurality of electric vehicles, comprises: a traffic operation planning unit that creates a traffic operation plan for the electric vehicles based on a precondition; a re-planning necessity determination unit that determines whether or not re-planning is necessary for the traffic operation plan by comparing a measured value of a battery residual amount with an estimated value of the battery residual amount in the traffic operation plan for an electric vehicle which is traveling according to the traffic operation plan; an influence range detecting unit that specifies a condition having a difference as a factor for the re-planning by comparing the precondition value at the time of forming a traffic operation plan with a measured value of the corresponding condition for the traffic operation plan that is determined to require the re-planning, wherein: the traffic operation planning unit performs re-planning for an unexecuted traffic operation plan that is influenced by the condition specified as the factor.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a traffic management system and a traffic management method. [Background technology]

[0002] Conventionally, there is a technology described in Japanese Patent Laid-Open No. 2016-064752 (Patent Document 1) for appropriately correcting a driving plan based on the energy consumption of a vehicle. This publication states that "in a vehicle energy management device (100) that predicts future energy consumption (vehicle state information), creates a control plan, and controls a vehicle, the device determines whether or not speed re-planning is necessary based on a comparison between an actual measurement value of energy consumption and a predicted value of energy consumption used in the control plan, and if it is determined that speed re-planning is necessary, the device calculates an error between the actual measurement value of vehicle speed for at least one of a certain period from the past to the present and the current instant and the speed plan before updating, and updates the speed plan from the present onwards by reducing the error, and uses the updated speed plan to recalculate energy and re-plan the control plan, thereby controlling the vehicle." [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-064752 Summary of the Invention [Problem to be solved by the invention]

[0004] When planning a vehicle trip, it is important to avoid situations where the vehicle cannot continue traveling due to a lack of necessary energy. Running out of gas is when the vehicle cannot continue traveling due to a lack of gasoline, and running out of electricity is when the battery power is insufficient and the vehicle cannot continue traveling. In recent years, the use of electric vehicles (EVs), which run on battery power, has increased. However, because the electricity used for running EVs is also used for other purposes such as air conditioning, there are concerns about unexpected power shortages. Furthermore, when electric vehicles are used as delivery vehicles to deliver cargo in logistics, if a vehicle runs out of power during delivery, the cargo will have to be transferred to another vehicle, which becomes a major problem that cannot be solved simply by moving the vehicle that has run out of power. Furthermore, if the temperature is lower than expected and a certain vehicle consumes a large amount of power due to heating, causing a power shortage, other vehicles may also experience a power shortage. However, in the conventional technology, the occurrence of a power shortage is not reflected in other vehicles, and the efficiency of vehicle operation as a whole is not taken into consideration.

[0005] Therefore, an object of the present invention is to improve the efficiency of operation of a plurality of electric vehicles. [Means for solving the problem]

[0006] In order to achieve the above object, a representative operation management system of the present invention is an operation management system that manages the operation of a plurality of electric vehicles, and includes: an operation planning unit that creates operation plans for the electric vehicles based on preconditions; a replanning necessity determination unit that compares, for electric vehicles traveling in accordance with the operation plan, an actual measured value of remaining battery capacity with a predicted value of remaining battery capacity in the operation plan to determine whether or not the operation plan needs to be replanned; and an influence range detection unit that, for operation plans that have been determined to require replanning, compares the values ​​of the preconditions at the time the operation plan was created with the actual measured values ​​of corresponding conditions, and identifies conditions with differences as factors for the replanning, and is characterized in that the operation planning unit replans unexecuted operation plans that are affected by the conditions identified as factors. Furthermore, one representative operation management method of the present invention is an operation management method for managing the operation of a plurality of electric vehicles, and is characterized by including: an operation planning step in which an operation management server creates an operation plan for the electric vehicles based on preconditions; a replanning necessity determination step in which, for electric vehicles traveling in accordance with the operation plan, an actual measured value of the remaining battery capacity with a predicted value of the remaining battery capacity in the operation plan are compared to determine whether the operation plan needs to be replanned; an influence range detection step in which, for an operation plan for which it has been determined that replanning is necessary, the value of the precondition at the time the operation plan was created is compared with the actual measured value of the corresponding condition, and conditions with differences are identified as factors for the replanning; and a replanning step in which replanning is carried out for unexecuted operation plans that are affected by the conditions identified as factors. [Effects of the Invention]

[0007] According to the present invention, it is possible to improve the efficiency of operation of a plurality of electric vehicles. Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0008] [Figure 1] Configuration diagram of a fleet management server according to the first embodiment [Figure 2] Flowchart explaining the processing of the fleet management server [Figure 3] Flowchart showing details of the decision on whether or not rescheduling is necessary [Figure 4] Flowchart detailing potential cause detection [Figure 5] Diagram of data columns for detecting potential causes [Figure 6] Diagram of changes under the same conditions [Figure 7] Specific examples of data [Figure 8] Illustration of setting the same conditions [Figure 9] Illustration of rescheduling [Figure 10] Flowchart for identifying vehicle and driver conditions as contributing factors DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an embodiment will be described with reference to the drawings. [Example]

[0010] In the first embodiment, a description will be given taking as an example a fleet management server 20 having the functions of a fleet management system. 1 is a configuration diagram of a fleet management server 20 according to a first embodiment. The fleet management server 20 is connected to a logistics operation management system 10. The logistics operation management system 10 transmits the origin and destination of cargo, deadlines, number of vehicles, etc. to the fleet management server 20. Here, the number of vehicles refers to the number of vehicles (delivery vehicles) that deliver cargo in logistics, and each vehicle is an electric vehicle. Furthermore, the fleet management server 20 is capable of communicating with each vehicle and receives remaining battery power and location information from each vehicle.

[0011] The fleet management server 20 creates an operation plan for each vehicle using data received from the logistics operation management system 10 and each vehicle. The fleet management server 20 transmits the operation plan corresponding to each vehicle, and each vehicle runs according to the operation plan. The fleet management server 20 can also transmit the operation plan and the status of each vehicle to the logistics operation management system 10.

[0012] The fleet management server 20 includes an operation planning unit 21, a vehicle state acquisition unit 22, a replanning necessity determination unit 23, an affected area detection unit 24, a similar plan extraction unit 25, and an information acquisition unit 26. The fleet management server 20 may be a computer equipped with a CPU (Central Processing Unit), a main storage device (memory), and an auxiliary storage device. When the fleet management server 20 is a computer, the CPU reads the fleet management program from the auxiliary storage device, expands it into the main storage device, and sequentially executes the programs to realize functions corresponding to the operation planning unit 21, the vehicle state acquisition unit 22, the replanning necessity determination unit 23, the affected area detection unit 24, the similar plan extraction unit 25, and the information acquisition unit 26.

[0013] The operation planning unit 21 creates an operation plan for the electric vehicle based on vehicle information, maps, driver and vehicle type information, and preconditions, and provides the plan to the vehicle. Vehicle information indicates the vehicle model, etc. The map shows the roads on which the vehicle travels by connecting links and nodes. Driver and vehicle model information indicates the driver's attributes and the characteristics of each vehicle model.

[0014] The preconditions are various conditions that affect the operation of the vehicle and are set by predicting them before the start of the operation. The conditions that affect the operation of the vehicle can include external conditions related to the outside of the vehicle, vehicle conditions related to the vehicle, and driver conditions related to the driver.

[0015] External conditions include weather, events, and traffic congestion. Weather includes rainfall, sunshine, temperature, etc. Events are events that temporarily affect traffic, such as local events, construction work, and accidents. Traffic congestion information is traffic congestion predicted based on the day of the week and time of day. Traffic congestion caused by accidents, etc. may be treated as an event. These conditions are associated with the links set on the roads.

[0016] If the operation planning unit 21 predicts that a vehicle traveling according to an operation plan will run out of power, it re-plans the operation plan of the vehicle that is predicted to run out of power. Also, if the operation planning unit 21 predicts that a vehicle traveling according to an operation plan will run out of power and identifies the cause of the power shortage, it re-plans the unexecuted operation plan that is affected by the cause. In other words, if it is determined that re-planning is necessary for a delivery vehicle that is currently making a delivery, the operation planning unit 21 re-plans the operation plan of the delivery vehicle before it departs. As a result, it is possible to create an operation plan for a vehicle before it departs that takes into account the causes of power shortages in other vehicles. Specifically, the operation planning unit 21 re-plans an operation plan that includes a link associated with a cause, among unexecuted operation plans.

[0017] The vehicle state acquisition unit 22 acquires information on the remaining battery level and the current location from each vehicle, and outputs the information to the replanning necessity determination unit 23 .

[0018] The replanning necessity determination unit 23 compares the actual measured value of the remaining battery capacity of an electric vehicle traveling according to the operation plan with the predicted value of the remaining battery capacity in the operation plan to determine whether replanning of the operation plan is necessary. The actual measured value of the remaining battery capacity is the remaining battery capacity acquired by the vehicle state acquisition unit 22. The predicted value of the remaining battery capacity in the operation plan can be identified by the operation plan created by the operation planning unit 21 and the planning conditions (prediction conditions used in the plan).

[0019] As an example, the re-planning necessity determination unit 23 determines whether or not the route indicated in the operation plan can be completed based on the actual measured value of the remaining battery charge of the vehicle while it is in motion and the operation plan, and determines that re-planning is necessary if the remaining battery charge is insufficient by the time the vehicle completes the motion. Alternatively, as an example, the replanning necessity determination unit 23 compares the remaining battery charge of the vehicle currently in motion with the remaining battery charge at the time of the operation plan, and determines that replanning is necessary when the remaining battery charge of the vehicle currently in motion is smaller than the remaining battery charge at the time of the operation plan. The replanning necessity determination unit 23 outputs the conditions of the plan that requires replanning and the operation plan that requires replanning to the influence range detection unit 24 as replanning data.

[0020] The information acquisition unit 26 acquires predicted values ​​and trends of various information (temperature information, weather information, event information, traffic congestion information, etc.), and manages them in association with links set on roads. The information acquisition unit 26 outputs predicted values ​​of various information (temperature information, weather information, event information, traffic congestion information, etc.) to the operation planning unit 21. The information acquisition unit 26 compares the actual transitions with the predictions for various information (temperature information, weather information, event information, traffic congestion information, etc.), and outputs the difference to the influence extent detection unit 24.

[0021] The influence range detection unit 24 identifies the cause of the need for replanning for an operation plan that has been determined to require replanning by the replanning necessity determination unit 23. Specifically, the influence range detection unit 24 compares the values ​​of the preconditions at the time of creating the operation plan that requires replanning with the actual measured values ​​of the corresponding conditions, and identifies the conditions with differences as the cause of replanning.

[0022] The affected range detection unit 24 outputs replanning data (conditions of the plan that requires replanning and the operation plan that requires replanning) and cause candidates (identified factors) to the similar plan extraction unit 25.

[0023] The similar plan extraction unit 25 extracts operation plans that include candidate causes as similar plans based on information on the replanning data, candidate causes, driver groups, and vehicle types. Similar plans may be extracted from unexecuted operation plans. For example, if a certain external condition is a candidate cause, operation plans that include a link associated with that external condition in the planned travel route are extracted as similar plans. At this time, the similar plans may be further narrowed down based on matching vehicle conditions and driver conditions.

[0024] The similar plan extraction unit 25 outputs similar plans that include the same conditions as the cause candidates to the operation planning unit 21. The operation planning unit 21 re-plans operation plans for the vehicle predicted to run out of power and the vehicles in the similar plans.

[0025] In this way, the fleet management server 20 estimates the predicted power consumption based on assumed conditions (predicted conditions) for electric vehicles during operation, and then performs a dispatch plan for electric vehicles. When it detects a vehicle that is predicted to run out of power while in operation due to a difference between the predicted conditions and the actual conditions, and thus requires rescheduling, it identifies a vehicle among multiple other vehicles that have similar condition differences to the vehicle that requires rescheduling, and performs a rescheduling for the identified vehicle. This avoids the need for rescheduling while the vehicle is in operation, minimizes the impact on logistics operations, and reduces the load on the customer business management system.

[0026] 2 is a flowchart illustrating the processing of the fleet management server 20. First, the fleet planning unit 21 uses the information acquisition unit 26, driver and vehicle type information, vehicle information, and maps to create an operation plan including a buffer (step S101). The operation planning unit 21 transmits the created operation plan to the corresponding vehicles. Each vehicle starts traveling in sequence according to the operation plan it received.

[0027] The vehicle state acquisition unit 22 acquires the remaining battery power from the currently traveling vehicle (step S102). The re-planning necessity determination unit 23 determines whether re-planning is necessary for the currently traveling vehicle (step S103). If re-planning is not necessary, the process ends. If re-planning is necessary, the influence range detection unit 24 determines, as a cause candidate, any difference between the predicted value used at the time of planning and the current predicted value (step S104). The similar plan extraction unit 25 sets the driver group (grouping by attributes) and vehicle type at the time of planning as the same conditions (step S105).

[0028] The operation planning unit 21 re-plans the cause candidates and subsequent plans (not yet departed) that meet the same conditions (step S106), and then distributes the re-planned operation plan to each vehicle (step S107), thereby ending the process.

[0029] 3 is a flowchart showing the details of the process of determining whether replanning is necessary (step S103). The replanning necessity determination unit 23 first compares the remaining battery charge planned in the operation plan with the current remaining battery charge (step S201). This comparison is to determine whether the trip will be completed if the current remaining charge changes according to the predicted future change.

[0030] If the comparison result shows that the current consumption is greater than the planned amount (even when the buffer is taken into consideration) (step S202; Yes), the re-planning necessity determination unit 23 extracts the operation plan as a plan in which electricity will run out (step S203) and ends the processing. If the current consumption is not greater than the planned amount (step S202; No), the processing ends as is.

[0031] 4 is a flowchart showing details of the detection of cause candidates (step S104). The affected area detection unit 24 first extracts the time until the power shortage occurs during travel and the passing area (the series of links passed through) (step S301).

[0032] The influence range detection unit 24 extracts factors that cause a difference between the predicted value at the time of planning and the actual change at the extracted time and area (step S302). The influence range detection unit 24 compares the latest conditions in the plan for the undeparted train with the conditions at the time of planning, based on the extracted factors (step S303).

[0033] If the comparison reveals differences in the conditions (step S304; Yes), the influence extent detection unit 24 determines the conditions, time periods, and areas that differ as possible causes (step S305) and terminates the process. If there are no differences in the conditions (step S304; No), the process terminates.

[0034] Figure 5 is an explanatory diagram of the data string for detecting potential causes. In Figure 5, a prediction that "a power shortage will occur at 11:00" for a certain vehicle is made at the time of 10:00. In addition, this power shortage prediction associates the areas through which the vehicle passes as a link string.

[0035] Based on this time and the passed areas, the affected area detection unit 24 determines the difference between the actual progress and the plan. That is, it compares the predicted and actual progress of weather information, event information, and traffic congestion information in the areas passed through up to 10:00. As a result, there is no difference in the event information, there is a temporary difference in the traffic congestion information, and there is a difference in the weather information for all times and areas.

[0036] Therefore, the influence extent detection unit 24 compares the difference between the forecast at the time of planning and the latest forecast for the weather information. As a result, there is a difference between the weather information forecast at the time of planning and the latest forecast, so the influence extent detection unit 24 determines the weather information as a candidate cause. In FIG. 5, the influence extent detection unit 24 determines "Weather information 11:00-13:00, sunny, 25°C (conditions at the time of planning)" and "Area: D[], E[]" as candidate causes.

[0037] When there are multiple conditions (factors), each factor may be weighted. The weight of each factor is determined according to the magnitude of its influence and its duration over time. For example, differences in weather have a large influence and also have a duration over time, so a large value is set as the weight. By setting weights in this way, when looking at the differences between the planned value and the latest forecast value, items with a large influence can be prioritized. Differences in items with a small influence can be ignored as errors.

[0038] In the explanation so far, we have explained the case where the search is narrowed down based on the condition that the vehicle model and driver group are the same, but it is also possible to set the condition that only either the vehicle model or the driver group is the same. Figure 6 is an explanatory diagram of changing the same condition.

[0039] In FIG. 6, the replanning necessity determination unit 23 further outputs the replanned data to the similar plan extraction unit 25. The similar plan extraction unit 25 extracts operation plans including candidate causes as similar plans based on the replanned data, candidate causes, driver group, and vehicle type information. When a certain external condition is a candidate cause, the similar plan extraction unit 25 usually narrows down the candidates based on the conditions of the same vehicle type and the same driver group. However, if there is a plan for the same vehicle type that does not run out of battery, it is possible to consider that the driver group has a large influence as a factor, and remove the vehicle type from the same conditions. In other words, in this case, the search is narrowed down based only on the driver group to extract similar plans.

[0040] FIG. 7 shows a specific example of the data. Weather information associates data such as "sunny, 25°C" with time and link ID. Event information associates time with a string of links affected by the event. Traffic congestion information associates the degree of traffic congestion with time and link.

[0041] Fig. 8 is an explanatory diagram of setting the same condition (step S105). In Fig. 8, the search is narrowed down to the condition that the vehicle type and driver group are the same, so all of the unexecuted operation plans that are expected to run out of battery are of vehicle type "A" and driver group "Group 1".

[0042] Here, the vehicle type is not limited to a broad classification such as a passenger car or a truck, but may be a specific vehicle type name such as a vehicle type A1 of a company A, a vehicle type B2 of a company B, or the like, among trucks. The driver group can be based on years of experience, carefulness of driving, gender, sensitivity to heat or cold, etc.

[0043] FIG. 9 is an explanatory diagram of replanning (step S106). In FIG. 9, the vehicle type and driver group are the same conditions. The candidate causes are "weather information 11:00-13:00, sunny, 25°C (conditions at the time of planning)" and "area: D[], E[]", and the same conditions are "vehicle type: A, driver group: Group1". In FIG. 9, the operation plan that includes the candidate causes and satisfies the same conditions is operation plan ID2. Therefore, the operation planning unit 21 selectively replans the operation plan with operation plan ID2.

[0044] In the above explanation, we have shown the case where external conditions are the factors, but vehicle conditions and driver conditions may also be identified as factors. Figure 10 is a flowchart for identifying vehicle conditions and driver conditions as factors.

[0045] The influence range detection unit 24 first selects the external conditions as the factors with priority (step S401). Next, the influence range detection unit 24 determines whether there are any vehicles that will not run out of power under the same external factors. This determination is to determine whether there are electric vehicles that require replanning under the same external conditions and electric vehicles that do not require replanning. If there are no vehicles that will not run out of power under the same external factors (step S402; No), the process ends with the external conditions being the factors.

[0046] If there is a vehicle that will not run out of power with the same external factors (step S402; Yes), the influence range detection unit 24 selects the vehicle conditions as the factors with priority (step S403). Next, the influence range detection unit 24 determines whether there is a vehicle that will not run out of power with the same external factors and vehicle conditions. This determination is to determine whether there are electric vehicles that require replanning with the same vehicle conditions and electric vehicles that do not require replanning. If there is no vehicle that will not run out of power with the same external factors and vehicle conditions (step S404; No), the process ends with the vehicle conditions being the factor.

[0047] If there is a vehicle that has the same external factors and vehicle factors and does not run out of power (step S404; Yes), the influence range detection unit 24 selects the driver condition as the factor (step S405) and ends the process.

[0048] As described above, the operation management server 20 is an operation management system that manages the operation of a plurality of electric vehicles, and includes an operation planning unit 21 that creates operation plans for the electric vehicles based on preconditions, a replanning necessity determination unit 23 that compares, for electric vehicles traveling in accordance with the operation plan, an actual measurement value of the remaining battery capacity with a predicted value of the remaining battery capacity in the operation plan, and determines whether or not the operation plan needs to be replanned, and an influence range detection unit 24 that, for an operation plan that has been determined to need replanning, compares the values ​​of the preconditions at the time the operation plan was created with the actual measurement values ​​of the corresponding conditions, and identifies conditions with differences as factors for the replanning, and the operation planning unit 21 is characterized in that it replans unexecuted operation plans that are affected by the conditions identified as factors. Therefore, if a vehicle is predicted to run out of power after departure, this can be reflected in the vehicle's operation plan before departure to avoid other vehicles running out of power, thereby making the operation of multiple electric vehicles more efficient.

[0049] In addition, the replanning necessity determination unit 23 determines whether or not it is possible to complete the route indicated in the operation plan based on the actual measured value of the remaining battery charge and the operation plan, and determines that the replanning is necessary if the remaining battery charge is insufficient by the time the operation is completed. Therefore, it is possible to accurately determine when an electric vehicle is out of power and to carry out replanning.

[0050] Furthermore, the fleet management server 20 uses at least one of weather, an event, and traffic congestion as the pre-condition and the condition. This makes it possible to predict power shortages based on various external conditions that affect operation.

[0051] In addition, the operation management server 20 further includes an information acquisition unit 26 that acquires the preconditions and the conditions and manages them in association with the links set on the road, and the operation planning unit 21 re-plans the operation plans that include the links associated with the factors among the unexecuted operation plans. In this way, by associating conditions with links, it is possible to selectively replan for vehicles that are scheduled to travel the same route.

[0052] The preconditions and the conditions include external conditions relating to the outside of the vehicle, vehicle conditions relating to the vehicle, and driver conditions relating to the driver. Therefore, the cause of the power shortage can be determined with high accuracy, taking into consideration the conditions related to the vehicle and the conditions related to the driver.

[0053] Furthermore, the influence range detection unit 24 preferentially identifies the external conditions as the factor, and when there are electric vehicles that require re-planning and electric vehicles that do not require re-planning under the same external conditions, it preferentially identifies the vehicle conditions as the factor, and when there are electric vehicles that require re-planning and electric vehicles that do not require re-planning under the same vehicle conditions, it identifies the driver conditions as the factor. This makes it possible to predict and avoid power shortages caused by the vehicle or the driver.

[0054] In addition, the plurality of electric vehicles are delivery vehicles that deliver cargo in logistics, and when it is determined that replanning is necessary for a delivery vehicle that is currently making a delivery, the operation planning unit 21 replans the operation plan of the delivery vehicle before departure. This reduces the burden of managing logistics and allows for efficient delivery.

[0055] The present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, not only can the configurations be deleted, but also replacements and additions of configurations are possible. [Explanation of symbols]

[0056] 10: Logistics operation management system, 20: Operation management server, 21: Operation planning unit, 22: Vehicle status acquisition unit, 23: Replanning necessity determination unit, 24: Impact range detection unit, 25: Similar plan extraction unit, 26: Information acquisition unit

Claims

1. A traffic management system that manages the operation of a plurality of electric vehicles, an operation planning unit that creates an operation plan for the electric vehicle based on a precondition; a replanning necessity determination unit that determines whether or not replanning of the operation plan is necessary by comparing an actual measured value of remaining battery capacity with a predicted value of remaining battery capacity in the operation plan for an electric vehicle that is traveling according to the operation plan; an influence range detection unit that compares values ​​of the preconditions at the time of creating the operation plan for which replanning is determined to be necessary with actual measured values ​​of the corresponding conditions, and identifies conditions with differences as factors for the replanning; Equipped with the operation planning unit re-plans an unexecuted operation plan that is affected by the condition identified as the factor, the precondition and the condition include an external condition related to an outside of the vehicle, a vehicle condition related to the vehicle, and a driver condition related to a driver; The influence range detection unit preferentially identifies the external conditions as the factors, and when there are electric vehicles that require the replanning and electric vehicles that do not require the replanning under the same external conditions, it preferentially identifies the vehicle conditions as the factors, and when there are electric vehicles that require the replanning and electric vehicles that do not require the replanning under the same vehicle conditions, it identifies the driver conditions as the factors. A traffic management system characterized by:

2. The traffic management system according to claim 1, The operation management system is characterized in that the re-planning necessity determination unit determines whether it is possible to complete the route indicated in the operation plan based on the actual measured value of the remaining battery charge and the operation plan, and determines that the re-planning is necessary if the remaining battery charge is insufficient by the time the operation is completed.

3. The traffic management system according to claim 1, 10. A traffic management system using at least one of weather, an event, and traffic congestion as the precondition and the condition.

4. The traffic management system according to claim 1, an information acquisition unit that acquires the preconditions and the conditions and manages them in association with links set on the road; The operation management system is characterized in that the operation planning unit re-plans an operation plan that includes a link associated with the factor, among the unexecuted operation plans.

5. The traffic management system according to claim 1, the plurality of electric vehicles are delivery vehicles that deliver cargo in logistics, The operation management system is characterized in that the operation planning unit re-plans the operation plan of the delivery vehicle before departure when it is determined that re-planning is necessary for the delivery vehicle during delivery.

6. A traffic management method for managing the traffic of a plurality of electric vehicles, The operation management server an operation planning step of creating an operation plan for the electric vehicle based on a precondition; a replanning necessity determination step of comparing an actual measured value of remaining battery capacity with a predicted value of remaining battery capacity in the operation plan for an electric vehicle traveling according to the operation plan to determine whether or not replanning of the operation plan is necessary; an influence range detection step of comparing values ​​of the preconditions at the time of creating the operation plan for which it is determined that replanning is necessary with actual measured values ​​of the corresponding conditions, and identifying conditions with differences as factors for the replanning; a re-planning step of re-planning an unexecuted operation plan that is affected by the conditions identified as the factors; and the precondition and the condition include an external condition related to an outside of the vehicle, a vehicle condition related to the vehicle, and a driver condition related to a driver; The influence range detection step preferentially identifies the external conditions as the factors, and when there are electric vehicles that require the replanning and electric vehicles that do not require the replanning under the same external conditions, preferentially identifies the vehicle conditions as the factors, and when there are electric vehicles that require the replanning and electric vehicles that do not require the replanning under the same vehicle conditions, preferentially identifies the driver conditions as the factors. A traffic management method characterized by the above.

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