Traffic operation management system and traffic operation 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 JP 2016-064752 A (Patent Document 1) for appropriately correcting a travel plan based on the energy consumption of a vehicle. This publication describes 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 judges whether or not speed replanning 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 when it is judged that speed replanning is necessary, the device calculates an error between the actual measurement value of the vehicle speed and the speed plan before updating for at least one of a certain period from the past to the present and the current moment, and updates the speed plan from the present onward by reducing the error, and uses the updated speed plan to recalculate energy and replan the control plan to control the vehicle." [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-064752 A Summary of the Invention [Problem to be solved by the invention]

[0004] When planning a vehicle trip, it is important to avoid a situation where the vehicle cannot continue traveling due to lack of necessary energy. Running out of gas means that the vehicle cannot continue traveling due to lack of gasoline, and running out of electricity means that the battery does not have enough power to continue traveling. In recent years, the use of electric vehicles (EVs), which run on battery power, has increased. However, because the electricity used by EVs to run is also used for other purposes such as air conditioning, there are concerns that they may unexpectedly run out of power. Furthermore, when electric vehicles are used as delivery vehicles to deliver cargo in logistics, if a vehicle runs out of power during a delivery, the cargo will have to be transferred to another vehicle, which can be a major problem that cannot be solved simply by moving the vehicle that has run out of power. Furthermore, for example, 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 conventional technology, no consideration is given to reflecting the occurrence of a power shortage on other vehicles and improving the efficiency of vehicle operation as a whole.

[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-mentioned object, a representative operation management system of the present invention is a 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 pre-conditions; a replanning necessity determination unit that compares, for electric vehicles traveling according to the operation plan, an actual measured value of remaining battery power with a predicted value of remaining battery power in the operation plan to determine whether or not replanning of the operation plan is necessary; and an influence range detection unit that, for operation plans for which replanning is determined to be necessary, compares the values ​​of the pre-conditions at the time of creation of the operation plan with 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 carries out replanning for 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 in including: an operation planning step in which an operation management server creates an operation plan for the electric vehicles based on pre-conditions; a replanning necessity determination step in which, for electric vehicles traveling according to the operation plan, an actual measured value of remaining battery power with a predicted value of remaining battery power in the operation plan to determine whether or not replanning of the operation plan is necessary; an influence range detection step in which, for the operation plan for which it has been determined that replanning is necessary, values ​​of the pre-conditions at the time of creation of the operation plan are compared with actual measured values ​​of corresponding conditions, and conditions with differences are identified as factors for the replanning; and a replanning step in which replanning is implemented for unexecuted operation plans that are affected by the conditions identified as factors. Effect 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 description of the drawings]

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

[0009] Hereinafter, the embodiments will be described with reference to the drawings. EXAMPLES

[0010] In the first embodiment, a fleet management server 20 having the functions of a fleet management system will be described as an example. 1 is a configuration diagram of a traffic management server 20 according to a first embodiment. The traffic 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, deadline, number of vehicles, etc. to the traffic 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 charge and location information from each vehicle.

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

[0012] The traffic management server 20 has an operation planning unit 21, a vehicle state acquisition unit 22, a replanning necessity determination unit 23, an affected range detection unit 24, a similar plan extraction unit 25, and an information acquisition unit 26. The traffic 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 traffic management server 20 is a computer, the CPU reads out a traffic management program from the auxiliary storage device, expands it into the main storage device, and executes it sequentially to realize functions corresponding to the operation planning unit 21, the vehicle state acquisition unit 22, the replanning necessity determination unit 23, the affected range detection unit 24, the similar plan extraction unit 25, and the information acquisition unit 26.

[0013] The operation plan unit 21 creates an operation plan for the electric vehicle based on the vehicle information, the map, the driver and vehicle type information, and the pre-conditions, and provides the operation plan to the vehicle. Vehicle information is information that 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 attributes of the driver and the characteristics of each vehicle model, etc.

[0014] The pre-conditions are various conditions that affect the operation of the vehicle, which are predicted and set 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] The external conditions include weather, events, traffic jams, etc. Weather includes rainfall, sunshine, temperature, etc. Events are events that temporarily affect traffic, such as special events, construction, and accidents taking place in the vicinity. Traffic jam information is information on traffic jams predicted based on the day of the week and time of day. Traffic jams caused by accidents, etc. may be treated as events. Moreover, these conditions are associated with the links set on the road.

[0016] If the operation planning unit 21 predicts that a vehicle traveling according to an operation plan will run out of power, the operation planning unit 21 re-plans the operation plan of the vehicle predicted to run out of power. In addition, 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, the operation planning unit 21 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, an operation plan can be created for a vehicle before it departs, taking into account the causes of power shortage for 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 charge 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 charge of an electric vehicle traveling according to the operation plan with the predicted value of the remaining battery charge in the operation plan to determine whether or not replanning is necessary for the operation plan. The actual measured value of the remaining battery charge is the remaining battery charge acquired by the vehicle state acquisition unit 22. The predicted value of the remaining battery charge in the operation plan can be specified by the operation plan created by the operation planning unit 21 and the planning conditions (the prediction conditions used in the plan).

[0019] As an example, 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 battery level of the vehicle while traveling and the operation plan, and determines that replanning is necessary if the battery level is insufficient to complete the trip. 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 less 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 affected 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 to the operation planning unit 21 predicted values ​​of various information (temperature information, weather information, event information, traffic congestion information, etc.). The information acquisition unit 26 compares the actual transitions with 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 affected range detection unit 24 identifies a cause of the need for replanning for an operation plan that is determined to require replanning by the replanning necessity determination unit 23. Specifically, the affected range detection unit 24 compares values ​​of preconditions at the time of creating the operation plan that requires replanning with actual measured values ​​of the corresponding conditions, and identifies a condition with a difference as a 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 including the cause candidate as similar plans based on the replanning data, the cause candidate, the driver group, and the vehicle type information. The similar plans may be extracted from unexecuted operation plans. For example, if a certain external condition is a cause candidate, an operation plan including a link associated with the external condition in the planned travel route is extracted as a similar plan. At this time, the similar plans may be further narrowed down by matching vehicle conditions and driver conditions.

[0024] The similar plan extraction unit 25 outputs a similar plan that includes the same conditions as the cause candidate 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 of the similar plans.

[0025] In this way, the traffic management server 20 estimates the predicted power consumption by assuming the conditions (predicted conditions) when the electric vehicles will be traveling, and performs a dispatch plan for the electric vehicles. Then, when it detects a vehicle that is predicted to run out of power while traveling due to a difference between the predicted conditions and the actual conditions and requires replanning, it identifies a vehicle that has a condition difference of the same degree as the vehicle that requires replanning among multiple other vehicles that have not yet traveled, and performs replanning for the identified vehicle. This makes it possible to avoid replanning while traveling, suppress the impact on logistics operations, and reduce the load on the customer business management system.

[0026] 2 is a flowchart explaining the processing of the operation management server 20. First, the operation planning unit 21 uses the information acquisition unit 26, driver and vehicle type information, vehicle information, and a map 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 charge from the currently traveling vehicle (step S102). The re-planning necessity determination unit 23 determines whether or not re-planning is necessary for the currently traveling vehicle (step S103). If re-planning is not necessary, the process ends. If replanning is necessary, the impact range detection unit 24 determines, as a cause candidate, a 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 planner 21 re-plans the cause candidates and subsequent plans (not yet departed) that meet the same conditions (step S106). Then, the operation planner 21 distributes the re-planned operation plan to each vehicle (step S107), and ends the process.

[0029] 3 is a flowchart showing details of the process of judging whether or not replanning is necessary (step S103). The replanning necessity judgment unit 23 first compares the battery remaining amount planned in the operation plan with the current battery remaining amount (step S201). This comparison is a judgment of whether or not the trip will be completed if the current remaining amount changes according to the future predicted 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 replanning necessity determination unit 23 extracts the operation plan as a plan that will run out of electricity (step S203) and ends the process. If the current consumption is not greater than the planned amount (step S202; No), the process ends as it 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 runs out during travel and the passing area (the series of links passed) (step S301).

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

[0033] If the comparison shows that there is a difference in the conditions (step S304; Yes), the influence extent detection unit 24 sets the different conditions, time period, and area as possible causes (step S305) and ends the process. If there is no difference in the conditions (step S304; No), the process ends as it is.

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

[0035] Based on the time and the passed areas, the affected area detection unit 24 judges the difference between the actual transition and the plan. That is, it compares the predicted and actual transition of the weather information, event information, and traffic congestion information in the areas passed by 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 affected range detection unit 24 compares the difference between the weather information forecast at the time of planning and the latest forecast. As a result, since there is a difference between the weather information forecast at the time of planning and the latest forecast, the affected range detection unit 24 determines the weather information as a cause candidate. In FIG. 5, the affected range detection unit 24 determines "Weather information 11:00-13:00, sunny, 25°C (conditions at the time of planning)" and "Area: D[], E[]" as cause candidates.

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

[0038] In the above explanation, the search is narrowed down to the condition that the vehicle model and the 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 cause candidates as similar plans based on the replanned data, cause candidates, driver group, and vehicle type information. When a certain external condition is a cause candidate, the similar plan extraction unit 25 usually narrows down the candidates using 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 cause a power shortage, it is possible to remove the vehicle type from the same conditions, considering that the driver group has a large causal influence. That is, in this case, the similar plans are extracted by narrowing down the candidates only by the driver group.

[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 information associates the degree of traffic congestion with time and link.

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

[0042] Here, the vehicle type is not limited to a large classification such as passenger car or truck, and specific vehicle type names such as vehicle type A1 of company A and vehicle type B2 of company B among trucks can be used. 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 description, the case where the external conditions are the factors has been described, but the vehicle conditions and the driver conditions may also be specified as factors. Fig. 10 is a flowchart for specifying the vehicle conditions and the 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 judges whether or not there are any vehicles that do not run out of power with the same external factors. This judgement is to judge whether or not there are electric vehicles that require replanning and electric vehicles that do not require replanning with the same external conditions. If there are no vehicles that do not run out of power with the same external factors (step S402; No), the process ends with the external conditions being the factors.

[0046] If there is a vehicle that does not run out of power with the same external factors (step S402; Yes), the influence range detection unit 24 preferentially selects the vehicle conditions as the factors (step S403). Next, the influence range detection unit 24 determines whether there is a vehicle that does not run out of power with the same external factors and vehicle conditions. This determination is a determination of 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 does 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 factors.

[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 pre-conditions, a replanning necessity determination unit 23 that compares an actual measured value of remaining battery power with a predicted value of remaining battery power in the operation plan for electric vehicles traveling according to the operation plan to determine whether or not replanning of the operation plan is necessary, and an influence range detection unit 24 that compares, for an operation plan for which replanning is determined to be necessary, the values ​​of the pre-conditions at the time of creation of the operation plan with actual measured values ​​of corresponding conditions, and identifies conditions with differences as factors for the replanning, and the operation planning unit 21 is characterized in that it carries out replanning for 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 operation plan of the vehicle before departure to avoid running out of power in other vehicles, 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 battery remaining charge and the operation plan, and determines that replanning is necessary if the battery remaining charge is insufficient before the operation is completed. Therefore, it is possible to accurately determine when an electric vehicle is running out of power and to perform replanning.

[0050] In addition, 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 pre-conditions 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 to which the factors are associated, 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] In addition, the pre-conditions and the conditions include external conditions related to the outside of the vehicle, vehicle conditions related to the vehicle, and driver conditions related to the driver. Therefore, the cause of the power shortage can be accurately determined by taking into account the conditions related to the vehicle and the conditions related to the driver.

[0053] In addition, 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 multiple 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 enables efficient delivery.

[0055] The present invention is not limited to the above-mentioned embodiment, and various modifications are included. For example, the above-mentioned embodiment is described in detail to easily explain the present invention, and is not necessarily limited to the embodiment having all the described configurations. Moreover, the present invention is not limited to the deletion of the configurations, and it is also possible to replace or add the configurations. [Explanation of symbols]

[0056] 10: Logistics operation management system, 20: Traffic management server, 21: Operation planning unit, 22: Vehicle state 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 pre-condition; a replanning necessity determination unit that determines whether or not replanning of the operation plan is necessary by comparing an actual measurement value of a remaining battery level with a predicted value of the remaining battery level in the operation plan for an electric vehicle 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 a condition with a difference as a cause of 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 pre-condition 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 factor, and, when an electric vehicle that requires the replanning and an electric vehicle that does not require the replanning exist under the same external conditions, preferentially identifies the vehicle conditions as the factor, and, when an electric vehicle that requires the replanning and an electric vehicle that does not require the replanning exist under the same vehicle conditions, identifies the driver conditions as the factor. A traffic management system characterized by the above.

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 or not it is possible to complete the route indicated in the operation plan based on the actual measured value of the remaining battery level and the operation plan, and determines that the re-planning is necessary if the remaining battery level is insufficient before the operation is completed.

3. The traffic management system according to claim 1, 11. A traffic management system using at least one of weather, events, and traffic congestion as the pre-conditions and the conditions.

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 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, when it is determined that replanning is necessary for a delivery vehicle currently in delivery, the operation planning unit replans the operation plan of the delivery vehicle before departure.

6. A method for managing the operation of a plurality of electric vehicles, comprising: The operation management server An operation planning step of creating an operation plan for the electric vehicle based on a pre-condition; a replanning necessity determination step of determining whether or not replanning of the operation plan is necessary by comparing an actual measured value of a remaining battery charge with a predicted value of the remaining battery charge in the operation plan for an electric vehicle traveling according to the operation plan; an influence range detection step of comparing values ​​of the preconditions at the time of creating the operation plan with actual measured values ​​of the corresponding conditions for the operation plan determined to require replanning, and identifying a condition having a difference as a cause of the replanning; A replanning step of replanning an unexecuted operation plan affected by the condition identified as the factor; and The pre-condition 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 an electric vehicle that requires the replanning and an electric vehicle that does not require the replanning exist under the same external conditions, preferentially identifies the vehicle conditions as the factors, and when an electric vehicle that requires the replanning and an electric vehicle that does not require the replanning exist under the same vehicle conditions, identifies the driver conditions as the factors. A traffic management method comprising:

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