Traffic management method and traffic management system

Abstract

To provide an operation management method and an operation management system capable of promptly responding to health condition deterioration risk of a driver who is in service.SOLUTION: An operation management system includes: an operation plan acquisition unit 101 which acquires operation plan information including operation start time and operation end time scheduled for a driver D1; a biometric information acquisition unit 102 which acquires biometric information of the driver before the operation start time; a self-reported information acquisition unit 107 which acquires self-reported information regarding the health of the driver D1, from the driver D1 before the operation start time; a determination unit 103 which determines an alert level of the driver D1 in a scheduled operation period from the operation start time to the operation end time, on the basis of the acquired biometric information and the self-reported information; and a determination result presentation unit (presentation unit 104) which presents a result of the determination.SELECTED DRAWING: Figure 2

Description

[Technical field]

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

[0002] 2. Description of the Related Art Transportation companies that own a large number of vehicles such as trucks are required to manage their drivers so as to operate them safely.

[0003] One operation management system used by transportation companies and the like is one in which a removable biosensor is attached to the driver while on duty (driving) to obtain biometric information such as the driver's heart rate and body temperature, and the driver's health condition is managed while on duty (see, for example, Patent Document 1). Such a system can increase safety by issuing a warning or encouraging the driver to take a rest if the driver feels drowsy while driving. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2002-74599 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, in a method of managing health based on the driver's biological condition while driving, as in Patent Document 1, there was a problem in that a warning or the like could not be given until the driver's health condition while driving deteriorated.

[0006] On the other hand, there is also a method to create a driving plan based on the driver's biometric information or self-reporting obtained before the start of the drive. However, this method does not adequately deal with cases where a driver's health condition is good at the start of the drive but fatigue accumulates during the drive and causes poor health.

[0007] The present disclosure has been made in consideration of the above points, and provides a traffic management method and a traffic management system that can address the risk of a driver's health deteriorating during driving at an early stage. [Means for solving the problem]

[0008] One aspect of the traffic management method of the present disclosure is to An operation plan acquisition step of acquiring operation plan information including a scheduled operation start time and an operation end time for a driver; A biometric information acquisition step of acquiring biometric information of the driver before the start time of the trip; a self-reported information acquisition step of acquiring self-reported information on the health of the driver from the driver before the start time of the trip; a determination step of determining an alert level of the driver during a scheduled operation period from the operation start time to the operation end time based on the acquired biometric information and the self-reported information; a determination result presentation step of presenting a result of the determination; Includes.

[0009] One aspect of the traffic management system of the present disclosure is: An operation plan acquisition unit that acquires operation plan information including a scheduled operation start time and an operation end time for a driver; A biometric information acquisition unit that acquires biometric information of the driver before the operation start time; a self-reported information acquisition unit that acquires self-reported information on the health of the driver from the driver before the start time of the trip; a determination unit that determines an alert level of the driver during a scheduled operation period from the operation start time to the operation end time based on the acquired biometric information and the self-reported information; a determination result presentation unit that presents a result of the determination; has. Effect of the Invention

[0010] According to the present disclosure, it becomes possible to address early the risk of a driver's health deteriorating while driving. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram showing an overall configuration of a traffic management system according to an embodiment. [Diagram 2] FIG. 1 is a block diagram showing a configuration of a main part of a traffic management system according to an embodiment of the present invention; [Diagram 3] 1 is a flowchart for explaining the operation of a traffic control system according to an embodiment of the present invention; [Figure 4] Flowchart showing the contents of alert level determination processing [Diagram 5] FIG. 1 is a diagram for explaining level classification and judgment by a judgment unit. [Figure 6A] A diagram showing an example of a judgment made for a normal driver [Figure 6B] A diagram showing an example of judgment for a patient driver [Figure 6C] A diagram showing an example of judgment for a hypersensitive driver DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment of the present disclosure will be described in detail with reference to the drawings.

[0013] <1> Overall configuration of the traffic management system FIG. 1 is a diagram showing an overall configuration of a traffic management system according to an embodiment.

[0014] The vehicle operation control system 1 of this embodiment includes an administrator-side system 10, a driver-side system 20, and a cloud 30.

[0015] The management system 10 has a terminal 11. The terminal 11 is installed in the office of the transportation company's transportation management department. The terminal 11 is a so-called personal computer, and executes operations required for safe transportation management in response to operations by a manager M1.

[0016] The terminal 11 displays information about the driver D1, such as lack of sleep, poor health, self-reported information, and driving information. The manager M1 calls out to the driver D1 during roll call and provides follow-up support. The manager M1 also creates a driving plan and inputs it into the terminal 11.

[0017] The driver's side system 20 includes a terminal 21 and a biological information sensor 22. In the case of this embodiment, the terminal 21 is a so-called smartphone carried by the driver D1, and the biological information sensor 22 is a ring-shaped sensor.

[0018] The biological information acquired by the biological information sensor 22 is wirelessly transmitted to the terminal 21. The biological information sensor 22 acquires information related to sleep and information related to physical condition as biological information. The biological information sensor 22 measures, for example, heart rate, body temperature, respiratory rate, SpO2, etc. during sleep.

[0019] The biological information sensor 22 may be a wristwatch type sensor, or in other words, any sensor capable of acquiring biological information of the driver D1, including during sleep, may be used.

[0020] An application program for measuring the biological information of the driver D1 in cooperation with the biological information sensor 22 and an application program for accepting self-reported information from the driver D1 are pre-installed in the terminal 21. Examples of the biological information measured by the terminal 21 include the average heart rate, minimum heart rate, average respiratory rate, body temperature, etc. during sleep. The terminal 21 accumulates the biological information of the driver D1 in chronological order.

[0021] The terminal 21 may also have installed therein a health management application program capable of determining the sleep level of the driver D1 based on the biometric information of the driver D1. Since the method of determining the sleep level based on the biometric information is known, a detailed description thereof will be omitted here. For example, the terminal 21 detects when the driver D1 falls asleep and wakes up based on acceleration information from the biometric information sensor 22. The terminal 21 also determines the sleep level based on the heart rate and body temperature of the driver D1. The sleep score may be calculated by the terminal 21 or the cloud 30.

[0022] The cloud 30 is configured to be able to wirelessly exchange data with the administrator side system 10 and the driver side system 20.

[0023] The cloud 30 inputs manager assessment information from the manager-side system 10. The manager assessment information is information about the driver D1 observed by the manager M1 during roll call, such as information that the driver D1 looks sleepy or unwell.

[0024] In addition, the cloud 30 inputs biometric information and self-reported information from the driver's side system 20.

[0025] The cloud 30 also receives operation information from a vehicle B1, such as a truck owned by a transport company. The operation information is information obtained by an on-board device, such as information on the actual time the vehicle B1 has been traveling and information on the distance traveled. The operation information may also include information on the driver's drowsiness based on the driver's line of sight, etc.

[0026] The cloud 30 is configured to include at least a storage device and an information processing device, and generates various types of information by executing a specified program using biometric information and self-reported information input from the driver's side system 20, administrator assessment information input from the administrator side system 10, and operation information input from the vehicle B1.

[0027] Of the various types of information generated, the cloud 30 sends the health advice information to the terminal 21 of the driver-side system 20, and the roll-call health comment information to the terminal 11 of the manager-side system 10. The roll-call health comment information includes information about the sleep and physical condition of the driver D1. The roll-call health comment information also includes information indicating the alert level of the driver D1 during the scheduled operation period from the start time to the end time of operation, determined based on the biometric information and self-reported information.

[0028] <2> Main components of the traffic management system FIG. 2 is a functional block diagram of the traffic control system according to the present embodiment.

[0029] Each element of the functional block shown in Fig. 2 is provided in any one of the administrator-side system 10, the driver-side system 20, and the cloud 30 in Fig. 1. Specifically, the operation plan acquisition unit 101 is provided in the administrator-side system 10, the biological information acquisition unit 102 is provided in the driver-side system 20, the determination unit 102 is provided in the administrator-side system 10, the presentation unit 104 is provided in the administrator-side system 10, the sleep detection unit 105 and the physical condition determination unit 106 are provided in the administrator-side system 10, and the self-reported information acquisition unit 107 is provided in the driver-side system 20.

[0030] It should be noted that the determination unit 103, the sleep detection unit 105 and the physical condition determination unit 106 do not necessarily have to be provided in the administrator-side system 10, and may be provided in the cloud 30 or in the driver-side system 20.

[0031] In practice, each of the administrator side system 10, the driver side system 20, and the cloud 30 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The CPU reads out a program corresponding to the processing content from the ROM, loads it into the RAM, and works with the loaded program to realize the functions of each unit shown in FIG. 2. Note that all or part of the functions of each unit shown in FIG. 2 may be realized by a hardwired circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array). Each functional block of the traffic management system 10 can be realized by one or more processors, one or more electronic circuits, or a combination thereof.

[0032] The operation plan acquisition unit 101 acquires operation plan information including an operation start time and an operation end time scheduled for the driver D1. The operation plan is inputted, for example, by the manager M1 from the terminal 11 and stored in the cloud 30 or the terminal 11. The operation plan acquisition unit 101 acquires the operation plan by reading out the stored operation plan.

[0033] The biometric information acquisition unit 102 acquires biometric information of the driver D1 before the start time of the trip. This biometric information is measured by the terminal 21 in cooperation with the biometric information sensor 22, and is stored in the terminal 21, the cloud 30, or the terminal 11.

[0034] The determination unit 103 determines the alert level of the driver D1 during the scheduled operation period from the operation start time to the operation end time based on the driver biological information before the operation start time. In the present embodiment, the determination unit 103 inputs the detection result by the sleep detection unit 105, the determination result by the physical condition determination unit 106, and the self-reported information of the driver D1, and determines the alert level of the driver D1 during the scheduled operation period from the operation start time to the operation end time based on these. Here, the "alert level" represents the level of risk that the driver D1 will become drowsy or feel unwell while driving.

[0035] The sleep detection unit 105 detects the sleep of the driver D1 based on the biological information acquired by the biological information acquisition unit 102. Specifically, the sleep detection unit 105 detects the sleep start time (i.e., the time when the driver D1 fell asleep), the sleep end time (i.e., the time when the driver D1 woke up), and the sleeping time of the driver D1 based on the heart rate, body temperature, etc. of the driver D1, and outputs these to the determination unit 103.

[0036] The physical condition determination unit 106 inputs the biometric information acquired by the biometric information acquisition unit 102 and the sleep information detected by the sleep detection unit 105, and determines the physical condition of the driver D1 based on the biometric information while the driver D1 is sleeping. For example, the physical condition determination unit 106 determines that the higher the heart rate while sleeping, the more fatigue has accumulated in the driver D1, and the worse the physical condition of the driver D1. In this embodiment, by determining the physical condition based on the biometric information while sleeping, it is possible to perform a more stable physical condition determination than when determining the physical condition based on the biometric information while not sleeping.

[0037] The physical condition determined by the physical condition determining unit 106 is not limited to the degree of fatigue. The physical condition determining unit 106 may determine the physical condition of the driver D1 based on, for example, body temperature, respiratory rate, heart rate variability, and the like.

[0038] The self-reported information acquisition unit 107 is an input unit that accepts a self-report from the driver D1, and is embodied, for example, by a touch panel or a voice input unit of the terminal 21. The self-reported information acquisition unit 107 may also be embodied, for example, by a touch panel or a voice input unit of the terminal 11.

[0039] The self-reported information acquisition unit 107 acquires self-reported information on the physical condition, etc., from the driver D1 before driving. "Before driving" refers to, for example, within a predetermined time before the start of driving (for example, within one hour before driving) or at the time of roll call. The self-reported information is, for example, the degree of fatigue and physical condition felt by the driver D1, medication status, etc.

[0040] The determination process of the determination unit 103 will now be described in more detail.

[0041] The determination unit 103 determines whether the driver D1 will feel sleepy or whether the degree of fatigue will become too large during the scheduled operation period from the operation start time to the operation end time. The determination unit 103 acquires biological information of the driver D1 during a period including at least one sleeping period before the operation start time, and determines the possibility that the driver D1 will feel sleepy during the scheduled operation period based on the length and timing of the sleeping period. The period including at least one sleeping period is preferably at least one day (24 hours), for example, but may be about half a day (12 hours) as long as the most recent sleeping period can be determined. The determination unit 103 calculates the sleeping period taken by the driver D1 in the sleep closest to the start of operation. Alternatively, the determination unit 103 calculates the sleeping period taken within, for example, 24 hours before the start of operation. The determination unit 103 also calculates the time from when the driver D1 ends his sleep immediately before the start of operation (from when he wakes up) to when he ends operation, that is, the non-sleep scheduled time.

[0042] The determination unit 103 determines that the driver D1 becomes sleepy during driving as the sleeping time is shorter. In addition, the determination unit 103 determines that the driver D1 becomes sleepy during driving as the non-sleeping planned time is longer. That is, in this embodiment, the determination unit 103 determines whether the driver D1 is likely to become sleepy by taking into account not only the sleeping time but also the non-sleeping planned time. For example, when the sleeping time is shorter than the threshold value X1 and the non-sleeping planned time is longer than the threshold value X2, the determination unit 103 determines that the alert level of drowsiness during driving is high. When the sleeping time is shorter than the threshold value X1 or the non-sleeping planned time is longer than the threshold value X2, the determination unit 103 determines that the alert level of drowsiness during driving is medium. When the sleeping time is equal to or greater than the threshold value X1 and the non-sleeping planned time is equal to or less than the threshold value X2, the determination unit 103 determines that the alert level of drowsiness during driving is low. Note that a high level indicates that drowsiness is more likely to occur during driving. The drowsiness levels are not limited to three levels, and the assessment result may be shown in more levels.

[0043] Furthermore, it is more preferable that the determination unit 103 performs the determination using conventional knowledge regarding drowsiness as described below. -Depending on your sleep rhythm (it's easy to wake up during the day and get sleepy at night), for example, even if you've stayed up all night, you won't feel sleepy because roll call is in the morning, but you may get sleepy before the end of your shift. The longer you stay awake, the more sleepy you feel.

[0044] In addition, in this embodiment, the judgment unit 103 judges the alert level of the driver D1 while driving based on the physical condition of the driver D1 obtained by the physical condition judgment unit 106 and the self-reported information obtained by the self-reported information acquisition unit 107.

[0045] Note that the judgment unit 103 does not necessarily need to make the judgment using all of the outputs of the sleep detection unit 105, the physical condition judgment unit 106, and the self-reported information acquisition unit 107, and may make the above judgment using, for example, the outputs of the sleep detection unit 105 and the self-reported information acquisition unit 107, or may make the above judgment using the outputs of the physical condition judgment unit 106 and the self-reported information acquisition unit 107. In short, it is sufficient to judge the alert level of the driver D1 during the scheduled operation period from the operation start time to the operation end time based on the biometric information and the self-reported information.

[0046] For example, the determination unit 103 may acquire the heart rate during sleep from the physical condition determination unit 106, and if the heart rate during sleep is higher than the threshold H, determine that fatigue has accumulated in the driver D1, and raise the drowsiness alert by one level from the determination result of the determination unit 103. Furthermore, the determination unit 103 may output parameters indicating other physical conditions, such as a fatigue level, in addition to the drowsiness alert, based on the drowsiness alert and information indicating the physical condition obtained from the physical condition determination unit 106.

[0047] The presentation unit 104 is embodied by the display unit and speaker of the terminal 11, and presents the judgment result by displaying or outputting audio. In addition to the classification result in the second classification step, the presentation unit 104 can also present the detection result of the sleep detection unit 105, the judgment result of the physical condition judgment unit 106, and the like, in response to the operation of the manager M1. The contents of the presentation include, for example, "The sleep time is shorter than usual, so the scheduled operation time should be shortened," "It seems that the driver woke up early, and the desire to sleep is likely to increase around the end of the operation. A nap during the lunch break is recommended," "Attention is required as this is poor physical condition. An early rest is recommended," "Attention is required as this is poor physical condition. Check the driver's awareness and consider a medical examination at a hospital," "The driver has reported that he is not feeling well and would like to take the day off tomorrow. Please consider whether he can take a holiday," "The driver has reported that he is feeling a bit tired for three consecutive days. It seems that he has not fully recovered. Please remake the operation plan," "It seems that he forgot to take his medicine. Please give him the medicine stored at the station," and so on.

[0048] The presentation unit 104 may display the result of the determination by the determination unit 103 and the self-reported alert level of the driver D1 side by side. For example, the presentation unit 104 may display the sleep alert by the determination unit 103, information indicating the physical condition, and information indicating the self-reported physical condition. The presentation unit 104 may be, for example, a monitor or a tablet terminal.

[0049] FIG. 3 is a flowchart illustrating the operation of the traffic control system according to the present embodiment.

[0050] In step S1, the operation plan acquisition unit 101 acquires operation plan information including a scheduled operation start time and operation end time for the driver D1. In step S2, the biometric information acquisition unit 102 acquires biometric information of the driver D1 before the operation start time.

[0051] In the following step S3, the sleep detection unit 105 detects that the driver D1 is sleeping based on the biological information acquired in step S2. In the following step S4, the physical condition determination unit 106 determines the physical condition level of the driver D1 based on the biological information during sleeping. In the following step S5, the self-reported information acquisition unit 107 acquires the self-reported information of the driver D1. Note that the processes of steps S2-S3, step S4, and step S5 may be performed in parallel.

[0052] In the next step S6, the determination unit 103 determines the alert level of the driver D1 during the scheduled operation period from the operation start time to the operation end time based on the biological information before the operation start time. In this embodiment, the determination unit 103 determines the alert level of the driver D1 during the scheduled operation period based on the sleep information and physical condition level obtained from the biological information before the operation start time and the self-reported information.

[0053] In the following step S7, the presentation unit 104 presents the alert level.

[0054] Fig. 4 is a flowchart showing the process of determining the alert level (step S6), and Fig. 5 is a diagram showing an example of level classification of biometric information and self-reported information, and classification of alert levels.

[0055] 4, when the determination unit 103 starts the alert level determination process (step S6), first, in step S6a1, it determines whether the biometric information is equal to or greater than a threshold Th1, and in step S6a2 it determines whether the self-reported information is equal to or greater than a threshold Th2. Here, the biometric information being equal to or greater than the threshold Th1 means that the health condition is better than a predetermined level. Similarly, the self-reported information being equal to or greater than the threshold Th2 means that the health condition is better than a predetermined level.

[0056] In steps S6a1, S6a3, and S6a4, the determination unit 103 performs threshold determination of the biological information to classify the health state of the driver D1 into a first level or a second level. Note that this level classification may be performed directly using the biological information, but in this embodiment, it is performed on the detection result of the sleep detection unit 105 and the determination result of the physical condition determination unit 106.

[0057] Similarly, in steps S6a2, S6a5, and S6a6, the determination unit 103 classifies the health state of the driver D1 into the first level or the second level by determining the self-reported information against a threshold value.

[0058] Next, in step S6b1, the judgment unit 103 judges whether both the health condition level based on the biometric information and the health condition level based on the self-reported information are at the first level, and if a positive result is obtained (step S6b1; YES), the judgment unit 103 proceeds to step S6b3 and classifies the alert level of driver D1 as "good" (i.e., the alert level is low).

[0059] If the judgment unit 103 obtains a negative result in step S6b1 (step S6b1; NO), it proceeds to step Sb2 and judges whether the health condition level based on the biometric information and the health condition level based on the self-reported information are both at the second level, and if it obtains a positive result (step S6b2; YES), it proceeds to step S6b4 and classifies the alert level of driver D1 as "poor" (i.e., the alert level is high).

[0060] If the judgment unit 103 obtains a negative result in step S6b2 (step S6b2; NO), which means that either the health condition level based on the biometric information or the health condition level based on the self-reported information is at the first level and the other is at the second level, the judgment unit 103 proceeds to step Sb5 and classifies the alert level of driver D1 as "caution" (i.e., the alert level is medium).

[0061] As described above, in this embodiment, the judgment unit 103 executes a first classification step (steps S6a1 to S6a6) of classifying the health state into levels for each of the biometric information and the self-reported information, and a second classification step (steps S6b1 to S6b5) of classifying the alert level of the driver D1 during the scheduled driving period based on a combination of the level classification results for each of the biometric information and the self-reported information.

[0062] The classification results in the second classification step (steps S6b1 to S6b5) are presented by the presentation unit 104.

[0063] <3> summary As described above, according to this embodiment, there are provided an operation plan acquisition unit 101 that acquires operation plan information including the scheduled operation start time and operation end time for driver D1, a biometric information acquisition unit 102 that acquires biometric information of driver D1 before the operation start time, a self-reported information acquisition unit 107 that acquires self-reported information on the health of driver D1 from driver D1 before the operation start time, a judgment unit 103 that judges the alert level of driver D1 during the scheduled operation period from the operation start time to the operation end time based on the acquired biometric information and self-reported information, and a judgment result presentation unit (presentation unit 104) that presents the result of the judgment.

[0064] This allows the manager M1 to know the alert level of the driver D1 during the scheduled operation period from the operation start time to the operation end time based on the content presented on the judgment result presentation unit (presentation unit 104), and thus allows the manager M1 to make appropriate comments to the driver D1, make appropriate changes to the operation plan, etc. As a result, it is possible to realize a traffic management system and a traffic management method that can deal with the risk of the driver's health condition deteriorating during operation at an early stage.

[0065] Furthermore, according to the driving management system of this embodiment shown in FIG. 1, even if there is a leak of vital signs from the vital signs sensor 22, support can be provided based on the driver D1's self-reporting or the manager M1's assessment.

[0066] According to the configuration of the embodiment described above, the manager M1 can comprehensively determine the alert level of the driver D1 while driving based on the driver D1's appearance at the time of roll call, the self-reported content, and the biometric information.

[0067] The above-described embodiment is merely an example of the embodiment of the present invention, and the technical scope of the present invention should not be interpreted as being limited by the embodiment. In other words, the present invention can be embodied in various forms without departing from the gist or main characteristics of the present invention.

[0068] In the above-described embodiment, the judgment unit 103 calculates the sleep time taken by the driver D1 in the sleep closest to the start of operation, or the sleep time taken within, for example, 24 hours prior to the start of operation, and calculates the time from when the driver D1 ends his sleep immediately before the start of operation (from when he wakes up) to when he ends operation as the non-sleep planned time, but the present disclosure is not limited to this.

[0069] For example, the determination unit 103 may determine the alert level of the driver during the scheduled driving period based on the average value of the sleeping time and the scheduled non-sleep time up to the previous driving, in addition to the sleeping time and the scheduled non-sleep time.

[0070] In this way, the determination unit 103 can make a determination that takes into consideration not only the relationship between the sleeping time and the scheduled non-sleep time during the current operation, but also the relationship between the sleeping time and the scheduled non-sleep time up until the previous operation. For example, there are drivers who are not likely to feel sleepy even if the ratio of the sleeping time to the scheduled non-sleep time is small, and conversely, there are drivers who are likely to feel sleepy if the ratio of the sleeping time to the scheduled non-sleep time is not large. If a determination is made that takes into consideration the relationship between the sleeping time and the scheduled non-sleep time up until the previous operation, it becomes possible to make a determination that reflects such individual differences of drivers.

[0071] In other words, by calculating the average values ​​of sleep time and planned non-sleep time on working days not only from a specified time before the working day but also from a specified number of days before (2 days, 5 days, 14 days, etc.), and determining and presenting the relationship between sleep time and planned non-sleep time on the working day taking into account the relationship between these, it becomes possible to more accurately determine the driver's alert level during the scheduled driving period.

[0072] In the above-described embodiment, as shown in Fig. 5, the determination unit 103 determines the alert level of the driver D1 during the scheduled operation period from the operation start time to the operation end time based on the biometric information and the self-reported information. Furthermore, this determination may be made taking into consideration the type of the driver D1. The type of the driver D1 can be classified into, for example, a normal type, a patient type who self-reports that they are not tired even though they are tired, and a hypersensitive type who self-reports that they are tired even though they are not very tired.

[0073] The determination unit 103 classifies the type of the driver D1 based on the past history of the biometric information and the self-reported information. Specifically, the determination unit 103 compares the biometric information, which is objective information, with the self-reported information in the past history, and classifies the driver D1 whose self-reported information tends to be good despite the biometric information being poor as a patient type. Conversely, the determination unit 103 classifies the driver D1 whose self-reported information tends to be poor despite the biometric information being good as a sensitive type.

[0074] For example, in a distribution chart of an individual's self-reporting and biometric information history, in which the self-reporting is plotted on one axis and the biometric information is plotted on the other axis, the type of driver D1 can be classified based on the fact that the self-reporting and the biometric information are not simply proportionally correlated but have a bias. Also, by comparing the distribution bias within a group, the type of driver D1 can be classified.

[0075] This classification may be made based on the manager M1's judgment, not limited to the past history of the biometric information and the self-reported information. In other words, the manager M1 may determine the type of the driver D1 and input it from the terminal 11. The judgment unit 103 judges the alert level of the driver D1 based on the classified type of the driver D1 in addition to the biometric information and the self-reported information. This judgment may be called a weighting judgment according to the type of the driver D1.

[0076] Fig. 6A is a diagram showing a judgment example for a normal driver, Fig. 6B is a diagram showing a judgment example for a patient driver, and Fig. 6C is a diagram showing a judgment example for a hypersensitive driver. The judgment example for the normal driver in Fig. 6A is the same as the judgment example shown in Fig. 5.

[0077] In the judgment example for a patient driver in Fig. 6B, when the self-reported information is at the second level (bad) and the biological information is at the first level (good), the judgment result (overall judgment) is output as "bad" rather than "caution." In the judgment example for a hypersensitive driver in Fig. 6C, when the self-reported information is at the second level (bad) and the biological information is at the first level (good), the judgment result (overall judgment) is output as "good" rather than "caution."

[0078] In this way, by classifying the type of driver D1 and determining the alert level of driver D1 based on the type of driver D1 in addition to biometric information and self-reported information (in other words, by weighting and determining the self-reported information according to the type of driver D1), it becomes possible to more accurately determine the alert level of driver D1 during the scheduled operation period from the start time to the end time of operation.

[0079] In the above embodiment, the biometric information and self-reported information are classified into a first level (good) and a second level (bad), but the biometric information and self-reported information may be classified more finely. Also, in the above embodiment, the type of the driver D1 is classified into a normal type, a tolerant type, and a hypersensitive type, but the classification is not limited to this. [Industrial Applicability]

[0080] The present disclosure is useful, for example, as a technique for transport companies to safely manage transport operations. [Explanation of symbols]

[0081] 1 Traffic control system 10 Administrator System 11, 21 Terminal 20 Driver's side system 22 Biometric Sensor 30. Cloud 101 Operation Plan Acquisition Department 102 Biometric information acquisition unit 103 Judgment section 104 Presentation section 105 Sleep detection unit 106 Physical condition assessment section 107 Self-reported Information Acquisition Department

Claims

1. An operation plan acquisition step of acquiring operation plan information including a scheduled operation start time and an operation end time for a driver; A biometric information acquisition step of acquiring biometric information of the driver before the start time of the trip; a self-reported information acquisition step of acquiring self-reported information on the health of the driver from the driver before the start time of the trip; a determination step of determining an alert level of the driver during a scheduled operation period from the operation start time to the operation end time based on the acquired biometric information and the self-reported information; a determination result presentation step of presenting a result of the determination; Including, The determining step includes: a first threshold determination step of determining the biological information into a first level and a second level worse than the first level; a second threshold determination step of determining the self-reported information as a first level and a second level worse than the first level; a level classification step of classifying an alert level of the driver during the scheduled operation period based on a determination result of the first threshold determination step and a determination result of the second threshold determination step; Including, In the level classification step, when the judgment results of the first threshold judgment step and the second threshold judgment step are both the first level, the alert level is classified as a first alert level, when one of the judgment results of the first threshold judgment step and the second threshold judgment step is the first level and the other is the second level, the alert level is classified as a second alert level that is higher than the first alert level, and when the judgment results of the first threshold judgment step and the second threshold judgment step are both the second level, the alert level is classified as a third alert level that is higher than the second alert level, In the determination result presentation step, the alert level classified in the level classification step is presented as a result of the determination. Operation management method.

2. The first alert level is "good", the second alert level is "caution", and the third alert level is "bad". The operation management method according to claim 1.

3. classifying the type of the driver; In the determination step, an alert level of the driver is determined based on the acquired biometric information and the self-reported information as well as the classified type of the driver. The operation management method according to claim 1.

4. An operation plan acquisition unit that acquires operation plan information including a scheduled operation start time and an operation end time for a driver; A biometric information acquisition unit that acquires biometric information of the driver before the operation start time; a self-reported information acquisition unit that acquires self-reported information on the health of the driver from the driver before the start time of the trip; a determination unit that determines an alert level of the driver during a scheduled operation period from the operation start time to the operation end time based on the acquired biometric information and the self-reported information; a determination result presentation unit that presents a result of the determination; having The determination unit is performing a first threshold determination for threshold-determining the biometric information into a first level and a second level worse than the first level, and performing a second threshold determination for threshold-determining the self-reported information into a first level and a second level worse than the first level; based on a result of the first threshold judgment and a result of the second threshold judgment, if the judgment results of the first threshold judgment and the second threshold judgment are both the first level, the alert level is classified as a first alert level, if one of the judgment results of the first threshold judgment and the second threshold judgment is the first level and the other is the second level, the alert level is classified as a second alert level having a higher alert level than the first alert level, and if the judgment results of the first threshold judgment and the second threshold judgment are both the second level, the alert level is classified as a third alert level having a higher alert level than the second alert level. Traffic management system.

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