CO2 MANAGEMENT SYSTEM, CO2 MANAGEMENT METHOD, AND STORAGE MEDIUM

Road traffic volume information is acquired, and CO2 recovery devices 30, 30a for recovering CO2 exhausted into the air from vehicles on a road and drifting around the road are installed. Activation of the CO2 recovery devices 30, 30a is controlled based on the road traffic volume information.

FIELD

The present invention relates to a CO2management system, a CO2management method, and a storage medium.

BACKGROUND

Publicly known is a management system designed to suppress the amount of emission of CO2into the air by recovering CO2in exhaust gas through CO2recovery devices mounted in vehicles, transmitting the amounts of CO2recovered by the CO2recovery device of the vehicles to a server, and having the server add up the amounts of CO2recovered by the CO2recovery device of the vehicles (for example, see Japanese Unexamined Patent Publication No. 2021-8852).

SUMMARY

The management system disclosed in this patent publication covers recovery of CO2in order to prevent CO2from being exhausted into the air. This patent publication does not suggest recovering CO2that has been exhausted into the air.

The present invention provides one technique which can efficiently recover CO2exhausted into the air.

That is, according to the present invention, there is provided a CO2management system comprising an information acquisition unit for acquiring road traffic volume information and a CO2recovery device for recovering CO2exhausted into the air from vehicles on a road and drifting around the road, wherein activation of the CO2recovery device is controlled based on the road traffic volume information.

Further, according to the present invention, there is provided a CO2management method comprising acquiring road traffic volume information and controlling activation of a CO2recovery device for recovering CO2exhausted into the air from vehicles on a road and drifting around the road based on the road traffic volume information.

Furthermore, according to the present invention, there is provided a non-transitory computer-readable storage medium storing a program that causes a computer to acquire road traffic volume information and control activation of a CO2recovery device for recovering CO2exhausted into the air from vehicles on a road and drifting around the road based on the road traffic volume information.

According to the present invention, it is possible to efficiently recover CO2in the air by controlling activation of a CO2recovery device based on road traffic volume information.

DESCRIPTION OF EMBODIMENTS

FIG.1Aschematically shows a “smart city”. The region surrounded by the dot and dash line represents the smart city1. First, referring toFIG.1A, what sort of region is meant by the smart city1will be explained simply. Referring toFIGS.1A,2indicates a communication network,3a base station of the communication network2,4a management server,5electricity, gas, or other infrastructure-related facilities and facilities such as stations, companies, factories, stores, hospitals, and schools,6public transportation such as automated buses,7residences, and8vehicles belonging to the residents of the residences7. Various electronic devices in each facility5are connected to the communication network2. Various electronic devices in the public transportation6, vehicles7, and residences8and portable terminals belonging to the residents of the residences7can wirelessly communicate with the base station3. In this case, the electronic devices in the facilities5, public transportation6, vehicles7, and residences and the portable terminals belonging to the residents of the residences7are managed by the management server4.

In this regard, in general, a smart city is defined to be a city or region which solves various urban or regional problems and continuously creates new value by using advanced management (planning, development, management, operations, etc.) while incorporating ICT and other new technologies. On the other hand, in the present invention, in case where various devices in facilities5, public transportation6, vehicles7, and residences8inside a region and the portable terminals belonging to the residents of the residences7are managed by the management server4, this region is called the smart city1.

Now then, in the embodiments according to the present invention, a plurality of CO2recovery devices are arranged distributed throughout the smart city1to recover CO2in the air. The activation of these CO2recovery devices are managed by the management server4so that the CO2in the air can be efficiently recovered. The management server4is shown inFIG.1B. As shown inFIG.1B, an electronic control unit10is provided inside the management server4. The electronic control unit10comprises a digital computer and is provided with a CPU (microprocessor)12, a memory13comprising a ROM and RAM, and an input/output port14, which are mutually connected by a bidirectional bus11. As shown inFIG.1B, the electronic control unit10is connected to the communication network2.

FIG.2schematically shows a map of a partial region of the smart city1. InFIG.2,20indicates roads,21indicates a facility such as a shopping center, and22indicates monitoring cameras for monitoring traffic. The images taken by these monitoring cameras22are transmitted to the management server4, and the traffic volume of the vehicles8and the like at each road segment is calculated in the electronic control unit10of the management server4.

In this regard, if the traffic volume of vehicles8and the like increases, the CO2exhausted into the air from the vehicles8and the like and drifting around roads will increase. Therefore, in the embodiments according to the present invention, the increased CO2is recoverd using CO2recovery devices. Example of such CO2recovery devices are indicated by reference sygns30inFIG.3AandFIG.3B. Note thatFIG.3Bshows a side view of the CO2recovery device30shown inFIG.3A. Referring toFIG.3AandFIG.3B,31indicates an air suction inlet,32an air suction pipe,33a solid adsorbent comprising activated carbon, zeolite, and the like for adsorbing CO2,34a suction pump driven by a suction fan motor,35a CO2recovery pipe,36a secondary battery or fuel cell, and37an electronic control device.

If the suction pump14is driven, air containing CO2is sucked in from the air suction inlet31through the air suction pipe32and CO2contained in air is adsorbed on the solid adsorbent33. At this time, the CO2recovery pipe35is closed. On the other hand, the CO2adsorbed on the solid adsorbent33desorbs when the pressure inside the solid adsorbent33is reduced or the solid adsorbent33is heated. Therefore, when collecting the CO2adsorbed on the solid adsorbent33into an external CO2recovery tank, the pressure inside the solid adsorbent33is reduced or the solid adsorbent33is heated and the CO2desorbed thereby is sent into the CO2recovery tank through the CO2recovery pipe35.

Various methods for recovery of CO2are known. Other than the above-mentioned CO2recovery method using the solid adsorbent33, a physical adsorption method using an amine or other liquid absorbent to make CO2be efficiently adsorbed on a solid adsorbent, a chemical absorption method using an amine or other liquid absorbent to absorb CO2, a method using a separation membrane to separate CO2, etc. are known. In the present invention, various known CO2recovery methods may be used in place of the CO2recovery method using the solid adsorbent33.

FIG.4shows the electronic control device37disposed inside the CO2recovery device30. Referring toFIG.4, an electronic control unit40is provided inside the electronic control device37. The electronic control unit40comprises a digital computer and is provided with a CPU (microprocessor)42, a memory43comprising a ROM and RAM, and an input/output port44, which are mutually connected by a bidirectional bus41. As shown inFIG.4, the suction fan motor of the suction pump34is connected to the electronic control unit40, the suction fan motor is controlled based on the output signal of the electronic control unit40. On the other hand, the electronic control unit40is connected to a communication device45, and the electronic control unit40can wirelessly communicate with the base station3.

Now then, as explained above, if the traffic volume of vehicles8and the like increases, CO2exhausted into the air from the vehicles8and the like and drifting around roads will increase. In the embodiments of the present invention, the increased CO2is recovered by the CO2recovery devices30. That is, the amount of CO2recovered per unit time by the CO2recovery device30when the CO2recovery device30is activated increases as the concentration of CO2in the air recovered by the CO2recovery device30increases. Therefore, the CO2recovery efficiency of the CO2recovery device30increases as the concentration of CO2in the air recovered by the CO2recovery device30increases.

On the other hand, if the traffic volume of vehicles8and the like in a certain road region increases, the concentration of CO2in the air around the road region with increased traffic volume will increase. Therefore, if CO2in the air around the road region with increased traffic volume is recovered by the CO2recovery device30, CO2can be efficiently recovered by the CO2recovery device30. Therefore, in the present invention, to efficiently recover CO2with the CO2recovery device30, activation of the CO2recovery device30is controlled based on the road traffic volume information.

Next, referring toFIG.2, a first embodiment of the present invention will be explained. In the first embodiment, the CO2recovery devices30are installed in advance in or near road regions in which there is a possibility of the traffic volume being greater than a predetermined traffic volume, the current traffic volume at each road region is detected, and CO2recovery devices30installed in or near road regions in which the detected current traffic volume is greater than the predetermined traffic volume are activated. InFIG.2, if, for example, the road regions in which there is a possibility of the traffic volume being greater than the predetermined traffic volume are road regions R1, R2, R3, and R4indicated by hatching, the CO2recovery devices30are disposed in the road regions R1, R2, R3, and R4or in near road regions within a predetermined range of distance from the road regions R1, R2, R3, and R4. In this case, the predetermined range of distance is, for example, 10 meters.

On the other hand, inFIG.2, for example, if the current traffic volume in the road region R2is greater than the predetermined traffic volume, one or more CO2recovery devices30located in a near road region within the predetermined range of distance from the road region R2are activated, and CO2recovery by the one or more CO2recovery devices30is started. CO2recovery by the CO2recovery devices30is continued while the traffic volume in the road region R2is greater than the predetermined traffic volume. If the traffic volume in the road region R2becomes less than the predetermined traffic volume, CO2recovery by the CO2recovery devices30is stopped.

FIG.5shows a routine for CO2recovery management for carrying out the first embodiment. This routine is performed at the electronic control unit10provided inside the management server4. Referring toFIG.5, first, at step50, the traffic volumes at road regions R1, R2, R3, R4, etc. are detected based on images currently taken by the monitoring cameras22. In this case, if the traffic volumes are being detected at a road management server separate from the management server4, road traffic volume information provided by the road management server can be used.

Next, at step51, a road region in which the current traffic volume is greater than the predetermined traffic volume is identified based on the traffic volumes at the road regions R1, R2, R3, R4, etc. detected at step50. If a road region in which the traffic volume is greater than the predetermined traffic volume is identified, the routine proceeds to step52where the CO2recovery devices30located in the identified road region or in a near road region within the predetermined range of distance from the identified road region are identified. Next, at step53, an activation command for the identified CO2recovery devices30is issued, and operation of the identified CO2recovery devices30are started.

In this way, in the first embodiment of the present invention, CO2recovery devices30located in road regions in which the traffic volume is greater than the predetermined traffic volume or in near road regions within the predetermined range of distance from such road regions are identified based on the road traffic volume information, and the identified CO2recovery devices30are activated.

Next, referring toFIG.6toFIG.11, a second embodiment of the present invention will be explained. In the second embodiment, a mobile CO2recovery device30ashown inFIG.6is used in place of the stationary CO2recovery device30shown inFIG.3AandFIG.3B. The mobile CO2recovery device30a, aside from being provided with wheels38for movement and a support base39, has a similar structure to that of the stationary CO2recovery device30shown inFIG.3AandFIG.3B. In the example shown inFIG.6, the CO2recovery device30ais moved by an automated tow vehicle60.

FIG.7schematically shows an example of the automated tow vehicle60. Referring toFIG.7,61indicates a vehicle driving unit for providing driving force to driving wheels of the tow vehicle60,62indicates a braking device for braking the tow vehicle60,63indicates a steering device for steering the tow vehicle60, and64indicates an electronic control unit mounted inside the tow vehicle60. As shown inFIG.7, the electronic control unit64comprises a digital computer and is provided with a CPU (microprocessor)66, a memory67comprising a ROM and RAM, and an input/output port68, which are mutually connected by a bidirectional bus65.

On the other hand, as shown inFIG.7, the tow vehicle60is provided with various sensors69necessary for the tow vehicle60to perform automated driving, that is, sensors for detecting the state of the tow vehicle60and sensors for detecting the surroundings of the tow vehicle60. In this case, an acceleration sensor, speed sensor, and azimuth angle sensor are used as the sensors for detecting the state of the tow vehicle60, and a camera for capturing the view in front of the tow vehicle60and the like, a laser imaging detection and ranging device (LIDAR), a radar device, etc., are used as the sensors for detecting the surroundings of the tow vehicle60. Further, the tow vehicle60is provided with a Global Navigation Satellite System (GNSS) reception device70, a map data storage device71, and a navigation device72. The GNSS reception device70can detect the current location of the tow vehicle60(for example, the latitude and longitude of the tow vehicle60) based on information acquired from a plurality of satellites. Therefore, it is possible to acquire the current location of the tow vehicle60with the GNSS reception device70. A GPS reception device, for example, is used as the GNSS reception device70.

The map data storage device71stores map data and the like necessary for the tow vehicle60to perform automated driving. The various sensors69, GNSS reception device70, map data storage device71, and navigation device72are connected to the electronic control unit64. Further, a communication device73capable of wirelessly communicating with the base station3is connected to the electronic control unit64. Further, a coupling mechanism74for coupling the CO2recovery device30ato the tow vehicle60is attached to the tow vehicle60. The driving wheels are driven based on the output signal from the electronic control unit64, the braking control for the tow vehicle60is performed by the braking device62according to the output signal from the electronic control unit64, the steering control for the tow vehicle60is performed by the steering device63according to the output signal from the electronic control unit64, and the coupling mechanism74is controlled according to the output signal from the electronic control unit64.

The movement destination of the automated tow vehicle60is determined at the management server4. The determined movement destination is transmitted through the communication network2to the communication device73. If the communication device73receives the movement destination, a travel route for the tow vehicle60is retrieved using the navigation device72, and the tow vehicle60undergoes automated travel while towing the CO2recovery device30aalong the retrieved travel route.

Next, the second embodiment will be explained referring toFIG.8schematically showing a map of a partial region of the smart city1. InFIG.8, if, for example, the road regions in which there is a possibility of the traffic volume becoming greater than the predetermined traffic volume are the road regions R1, R2, R3, and R4indicated by the hatching, in the second embodiment, unlike in the first embodiment, installation locations P1, P2, P3, and P4for installing the CO2recovery devices30are formed in the road regions R1, R2, R3, and R4or in near road regions within the predetermined range of distance from the road regions R1, R2, R3, and R4. On the other hand, in the second embodiment, as shown inFIG.8, a standby location23for the tow vehicle60and CO2recovery devices30ais provided. The tow vehicle60and CO2recovery devices30aare kept on standby at the standby location23.

In the second embodiment, if, for example, the current traffic volume in the road region R2is greater than the predetermined traffic volume, the CO2recovery device30aon standby at the standby location23is transported by the tow vehicle60to the installation location P2located in a near road region within the predetermined range of distance from the road region R2. When the CO2recovery device30aarrives at the installation location P2, the CO2recovery device30ais activated, and CO2recovery by the CO2recovery device30ais started. CO2recovery by the CO2recovery device30ais continued while the traffic volume in the road region R2is greater than the predetermined traffic volume. If the traffic volume in the road region R2becomes less than the predetermined traffic volume, CO2recovery by the CO2recovery device30ais stopped.

FIG.9shows a routine for CO2recovery management for carrying out the second embodiment. This routine is performed at the electronic control unit10provided inside the management server4. Referring toFIG.9, first, at step80, the current traffic volumes at the road regions R1, R2, R3, R4, etc. are detected based on images taken by the monitoring cameras22. In this case, if traffic volume is being detected at a road management server separate from the management server4, road traffic volume information provided by the road management server can also be used.

Next, at step81, road regions in which the current traffic volume is greater than the predetermined traffic volume are identified based on the traffic volumes at the road regions R1, R2, R3, R4, etc. detected at step80. If a road region in which the current traffic volume is greater than the predetermined traffic volume is identified, the routine proceeds to step82where the CO2recovery device30ainstallation location P1, P2, P3, or P4located in the identified road region or in a near road region within the predetermined range of distance from the identified road region is identified. Next, at step83, the identified CO2recovery device30ainstallation location P1, P2, P3, or P4is made a movement destination, and the movement destination and a movement command are transmitted to the tow vehicle60. If the tow vehicle60receives the movement destination and the movement command, the automated driving control routine shown inFIG.10is performed at the electronic control unit10provided in the tow vehicle60.

Referring toFIG.10, first, at step90, the movement destination transmitted from the management server4is determined as a destination. Next, at step91, at the standby location23, coupling processing is performed to couple the tow vehicle60to the CO2recovery device30aby automated driving. Next, at step92, it is judged whether the coupling processing between the tow vehicle60and the CO2recovery device30ais completed. If it is judged that the coupling processing between the tow vehicle60and the CO2recovery device30ais not completed, the routine returns to step91where the coupling processing between the tow vehicle60and a CO2recovery device30acontinues. On the other hand, at step92, if it is judged that the coupling processing between the tow vehicle60and the CO2recovery device30ais completed, the routine proceeds to step93.

At step93, the travel route for the tow vehicle60from the current location to the destination is determined by the navigation device72based on the destination determined at step90and the current location of the tow vehicle60acquired by the GNSS reception device70. Next, at step94, travel control for the tow vehicle60is performed to prevent contact with other vehicles or pedestrians based on detection results of the camera for capturing the view in front of the tow vehicle60and the like, LIDAR, radar device, etc. Next, at step95, it is judged whether the tow vehicle60has arrived at the destination determined at step90. When it is judged that the tow vehicle60has not arrived at the destination, the routine returns to step94where automated driving of the tow vehicle60continues. On the other hand, if it is judged at step95that the tow vehicle60has arrived at the destination, the routine proceeds to step96.

At step96, processing is performed to decouple the tow vehicle60and the CO2recovery device30a. If the tow vehicle60and the CO2recovery device30aare decoupled, the CO2recovery device30abecomes a state installed at the destination determined at step90. Next, at step97, an activation command to the CO2recovery device30is issued to start operation of the CO2recovery device30. On the other hand, when the tow vehicle60and the CO2recovery device30aare decoupled, the tow vehicle60returns to the standby location23by automated driving.

In this way, in the second embodiment of the present invention, the CO2recovery device30ainstallation location P1, P2, P3, or P4located in a road region in which the traffic volume is greater than the predetermined traffic volume or in a near road region within the predetermined range of distance from the road region is identified based on the road traffic volume information, and the mobile CO2recovery device30ais transported to the identified installation location and activated.

Now, in the first embodiment and second embodiment explained up to now, based on the actual current traffic volume at road regions R1, R2, R3, R4, etc. detected by the monitoring cameras22or the current road traffic volume information provided by a road management server separate from the management server4, activation of the CO2recovery devices30is controlled in the first embodiment and transportation and activation of the CO2recovery devices30aare controlled in the second embodiment. In this case, it is also possible to predict the current traffic volume based on the history of past traffic volume and to control the activation of predicted CO2recovery devices30and control the transportation and activation of the CO2recovery devices30abased on the predicted current traffic volume.

Next, referring toFIG.11toFIG.13, an example of a method of predicting the current traffic volumes at the road regions R1, R2, R3, and R4based on the history of past traffic volumes will be explained. In this example, a neural network100like that shown inFIG.11is used to predict the current traffic volumes at the road regions R1, R2, R3, and R4based on the history of past traffic volumes. Referring toFIG.11, in the neutral network100, L=1 indicates an input layer, L=2 and L=3 hidden layers, and L=4 an output layer. In the neutral network100, as shown inFIG.11, the input layer (L=1) comprises seven nodes, and input valuesx1,x2, ...,x6,x7of seven input parameters are input into the nodes of the input layer (L=1).

Further, in the neutral network100, the number of nodes in the output layer (L=4) is set to two. Output values from the nodes in the output layer (L=4) are indicated byy1′,y2′. The output valuesy1′,y2′ are sent to a softmax layer SM and converted to corresponding output valuesy1,y2. The total of the output valuesy1,y2is 1. Each output valuey1,y2represents a ratio relative to 1. Note that, in this case, it is also possible to not use the softmax layer SM , have there be one node in the output layer (L=4), and make the activation function at this node a sigmoid function to perform binary classification.

On the other hand,FIG.12Ashows a table listing input parameters for the neutral network100. Factors influencing the traffic volume in the road regions R1, R2, R3, R4, etc. are used as input parameters. In the example shown inFIG.12A, the calendar date, day of the week, weather, temperature, staging of events, and time periods are used as input parameters. Note that road regions R1, R2, R3, R4, etc. are also made input parameters.

On the other hand,FIG.12Bshows the correspondence relationship between the output valuesy1,y2and states. In the example shown inFIG.11toFIG.13, the neural network100is learned in weights so that the output value of the output valuey1is 1 or a value near 1 and the output value of the output valuey2is 0 or a value near 0 when it is predicted that the traffic volume will become greater than the predetermined traffic volume, and the output value of the output valuey2is 1 or a value near 1 and the output value of the output valuey1is 0 or a value near 0 when it is predicted that the traffic volume will be less than the predetermined traffic volume.

FIG.13shows a training data set created using input valuesx1,x2, ...,x6,x7as the input parameters and training data, that is, truth label yt, to learn weights of the neutral network100. InFIG.13, the input valuesx1,x2, ...,x6,x7, as explained above, represent the calendar date, day of the week, weather, temperature, staging of events, time periods, and road regions R1, R2, R3, R4, etc. On the other hand, inFIG.13, yt1, yt2represent training data, that is, truth labels, for the output valuesy1,y2. That is, inFIG.13, yt1represents a truth label when the traffic volume is greater than the predetermined traffic volume, and yt2represents a truth label when the traffic volume is less than the predetermined traffic volume. In this case, in the example shown inFIG.11toFIG.13, the truth label yt1is 1 and the truth label yt2is zero when the traffic volume is greater than the predetermined traffic volume, and the truth label yt2is 1 and the truth label yt1is zero when the traffic volume is less than the predetermined traffic volume.

Now then, in the example shown inFIG.11toFIG.13, it is measured if the traffic volume is greater than the predetermined traffic volume for each time increment of 10 minutes and the road regions R1, R2, R3, R4, etc. over several years, for example. The input valuesx1representing the calendar date, the input valuesx2representing the day of week, the input valuesx3representing the weather, the input valuesx4representing the temperature, the input values x5representing the staging of events, the input valuesx6representing the time period, the input valuesx7representing the road regions R1, R2, R3, R4, etc., and the values of the truth labels yt1, yt2over several years are stored in the memory13of the electronic control unit10provided in the management server4. In the electronic control unit10, a training data set like that shown inFIG.13is created based on the stored values. In the training data set, “m” pieces of data representing the relationship between the input valuesx1,x2, ...,x6,x7and the truth labels yt1, yt2are acquired. For example, in the second data entry (No. 2), the acquired input values x12, x22, ..., x62, x72and the acquired truth labels yt12, yt22are listed and, in the m-1th data entry (No. m-1), the acquired input values x1m-1, x2m-1, ..., x6m-1, x7m-1and the acquired truth labels yt1m-1, yt2m-1are listed.

Next, the method for leaning weights of the neural network100using the training data set will be simply explained. The leaning of weights of the neutral network100is performed at the electronic control unit10provided in the management server4. For example, first, the input valuesx1, ...,x7in the first entry of the training data set (No. 1) are input to the nodes of the input layer (L=1) of the neutral network100. The output valuesy1‘,y2′ are output from the nodes of the output layer of the neutral network100at this time. The output valuesy1‘,y2‘ are sent to the softmax layer SM and converted to corresponding output valuesy1,y2. Next, a cross entropy error E representing the error between the output valuesy1,y2and the truth labels yt1, yt2is calculated, and the weights of the neutral network100are learned using the back propagation so that the cross entropy error E becomes small.

When the leaning of weights of the neutral network100based on data in the first entry of the training data set (No. 1) is complete, the leaning of weights of the neural network100based on the data in the second entry (No. 2) of the training data set is performed using the back propagation. Similarly, the leaning of weights of the neural network100is sequentially performed until the mth entry of the training data set (No. m). The leaning of weights of the neutral network100is repeatedly performed until the cross entropy error E becomes less than a set error which is set in advance. Ultimately, a traffic volume predictive model comprised of the trained neutral network100which can predict traffic volume is created. This predictive model is created in the electronic control unit10provided in the management server4. If the input valuesx1, ...,x7are input into this predictive model, the output valuey1becomes1or a value near1when the traffic volume is predicted to become greater than the predetermined traffic volume, and the output valuey2becomes 1 or a value near 1 when the traffic volume is predicted to be less than the predetermined traffic volume. Therefore, it is possible to predict whether the traffic volume will be greater than the predetermined traffic volume from the output valuey1and output valuey2in the predictive model.

Next, a third embodiment which is a modification of the first embodiment will be explained. In the third embodiment, when it is predicted that the traffic volume at a road region R1, R2, R3, R4, etc. will become greater than the predetermined traffic volume, the CO2recovery device30located in a near road region within the predetermined range of distance from the road region in which it is predicted that the traffic volume will become greater than the predetermined traffic volume is activated, and CO2recovery by the CO2recovery device30is started.

FIG.14shows a routine for CO2recovery management for carrying out the third embodiment. This routine is performed at the electronic control unit10provided inside the management server4. Referring toFIG.14, first, at step200, for example, the input valuex1representing today’s calendar date, the input valuex2representing today’s day of the week, the input valuex3representing the current weather, the input valuex4representing the current temperature, the input value x5representing current state of staging of events, the input valuex6representing the current time period, and the input valuex7representing the road region R1, R2, R3, R4, etc., are acquired. In this case, the current weather and current temperature may be the weather and temperature based on a weather forecast.

Next, at step201, the input valuesx1, ...,x6and, for example, the input valuex7(=1) representing the road region R1are input into the above-mentioned predictive model. At this time, the output valuey1and output valuey2for the road region R1are output from the predictive model. As a result, the output valuey1and output valuey2for the road region R1are acquired as shown in step202. Next, at step203, it is judged whether the output valuey1and output valuey2are acquired for all road regions R1, R2, R3, R4, etc. When it is judged that the output valuey1and output valuey2have not been acquired for all road regions R1, R2, R3, R4, etc., the routine proceeds to step204where the input valuex7representing the road region is updated. In this example, the input valuex7representing the road region is made the input valuex7(=2) representing the road region R2. Next, the routine proceeds to step201.

At step201, the input valuesx1, ...,x6and the input valuex7(=2) representing the road region R2are input into the above-mentioned predictive model. At this time, the output valuey1and output valuey2for the road region R2are output from the predictive model. As a result, the output valuey1and output valuey2for the road region R2are acquired as shown in step202. If the output valuey1and output valuey2are acquired for all road regions R1, R2, R3, R4, etc. in this way, the routine proceeds to step205where, from the output valuey1and output valuey2acquired for the road regions R1, R2, R3, R4, etc. a road regions in which the current traffic volume is predicted to become greater than the predetermined traffic volume is identified. If the road regions in which the traffic volume will be greater than the predetermined traffic volume is identified, the routine proceeds to step206where the CO2recovery device30located in the identified road region or a near road region within the predetermined range of distance from the identified road regions is identified. Next, at step207, an activation command to the identified CO2recovery device30is issued, and the operation of the identified CO2recovery devices30is started.

In this way, in the third embodiment of the present invention, a prediction unit for predicting a road region and time period in which the traffic volume will be greater than the predetermined traffic volume based on a history of road traffic volume information is provided, the CO2recovery device30located in a road region in which it is predicted that the traffic volume will be greater than the predetermined traffic volume or a near road region within the predetermined range of distance from the road region is identified, and the identified CO2recovery device30is activated at the predicted time period. In this case, the electronic control unit10provided in the management server4constitutes the prediction unit. Further, in this case, the prediction unit predicts road regions and time periods in which the traffic volume will be greater than the predetermined traffic volume based on calendar date, day of week, weather, temperature, and state of staging of events.

Next, a fourth embodiment which is a modification of the second embodiment will be explained. In the fourth embodiment, when the traffic volume at the road region R1, R2, R3, R4, etc., is predicted to become greater than the predetermined traffic volume, the CO2recovery device30aon standby at the standby location23is transported by the tow vehicle60to the installation location P1, P2, P3, or P4located in a near road region within a predetermined range of distance from the road region in which it is predicted that the traffic volume will become greater than the predetermined traffic volume.

FIG.15shows a routine for CO2recovery management for carrying out the fourth embodiment. This routine is performed at the electronic control unit10provided inside the management server4.

Referring toFIG.15, first, at step300, for example, the input valuex1representing today’s calendar date, the input valuex2representing today’s day of the week, the input valuex3representing the current weather, the input valuex4representing the current temperature, the input value x5representing the current state of staging of events, the input valuex6representing the current time period, and the input valuex7representing the road regions R1, R2, R3, R4, etc. are acquired. In this case as well, the current weather and current temperature may be the weather and temperature based on a weather forecast.

Next, at step301, the input valuesx1, ....,x6and, for example, the input valuex7(=1) representing the road region R1are input into the above-mentioned predictive model. At this time, the output valuey1and output valuey2for the road region R1are output from the predictive model. As a result, the output valuey1and output valuey2for the road region R1are acquired as shown in step302. Next, at step303, it is judged whether the output valuey1and output valuey2have been acquired for all road regions R1, R2, R3, R4, etc. When it is judged that the output valuey1and output valuey2have not been acquired for all road regions R1, R2, R3, R4, etc., the routine proceeds to step304where the input valuex7representing the road region is updated. In this example, the input valuex7representing the road region is the input valuex7(=2) representing the road region R2. Next, the routine proceeds to step301.

At step301, the input valuesx1, ...,x6and the input valuex7(=2) representing the road region R2are input into the above-mentioned predictive model. At this time, the output valuey1and output valuey2for the road region R2are output from the predictive model. As a result, the output valuey1and the output valuey2for the road region R2are acquired as shown in step302. If the output valuey1and output valuey2have been acquired for all road regions R1, R2, R3, R4, etc., in this way, the routine proceeds to step305where, from the output valuey1and output valuey2acquired for road regions R1, R2, R3, R4, etc, a road region in which the current traffic volume is predicted to become greater than the predetermined traffic volume is identified. If a road region in which the traffic volume will become greater than the predetermined traffic volume is identified, the routine proceeds to step306where the CO2recovery device30ainstallation location P1, P2, P3, or P4located in the identified road region or a near road region within the predetermined range of distance from the identified road region is identified. Next, at step307, the identified CO2recovery device30ainstallation location P1, P2, P3, or P4is made a movement destination, and the movement destination and a movement command are transmitted to the tow vehicle60. If the tow vehicle60receives the movement destination and the movement command, the automated driving control routine shown inFIG.10is performed at the electronic control unit10provided in the tow vehicle60, and automated driving control for the tow vehicle60similar to in the second embodiment is performed.

In this way, in the fourth embodiment of the present invention, a prediction unit for predicting a road region and time period in which the traffic volume will become greater than the predetermined traffic volume based on a history of road traffic volume information is provided, the CO2recovery device30ainstallation location located in a road region in which it is predicted that the traffic volume will become greater than the predetermined traffic volume or a near road region within the predetermined range of distance from the road region is identified, and the mobile CO2recovery device30is transported to the identified installation location and activated at the predicted time period. In this case, the electronic control unit10provided in the management server4constitutes the prediction unit. Further, in this case, the prediction unit predicts road regions and time periods in which the traffic volume will become greater than the predetermined traffic volume based on calendar date, day of week, weather, temperature, and state of staging of events.

In this way, the CO2management system according to the present invention comprises an information acquisition unit for acquiring road traffic volume information and a CO2recovery device30,30afor recovering CO2exhausted into the air from vehicles on a road20and drifting around the road20, and activation of the CO2recovery device30,30ais controlled based on the road traffic volume information. In this case, the electronic control unit10in the management server4constitutes the information acquisition unit in the embodiments of the present invention.

Further, in the present invention, there is provided a CO2management method comprising acquiring road traffic volume information and controlling activation of the CO2recovery device30,30afor recovering CO2exhausted into the air from vehicles on the road20and drifting around the road20based on the road traffic volume information. Further, in the present invention, there is provided a non-transitory computer-readable storage medium storing a program that causes a computer to acquire road traffic volume information and control activation of the CO2recovery device30,30afor recovering CO2exhausted into the air from vehicles on the road20and drifting around the road20based on the road traffic volume information.