Method, computer program, device, vehicle and network component for estimating the departure time of a user with a vehicle

Abstract

An embodiment provides a method, a computer program, an apparatus, a vehicle, and a network component for estimating a departure time of a user with a vehicle. The method (10) for estimating a departure time of a user with a vehicle (100) comprises obtaining (12) vehicle-related data about the vehicle (100) and obtaining (14) person-related data about the user. The method (10) further comprises estimating (16) the departure time based on the vehicle-related data and the person-related data.

Description

[0001] The present invention relates to a method, a computer program, a device, a vehicle and a network component for estimating the departure time of a user together with a vehicle. The present invention relates in particular, but not exclusively, to a method for determining the departure time of a user together with a vehicle based on data related to the user as a person and data related to the vehicle as a vehicle.

[0002] Mobile devices and vehicles are constantly being developed. Efforts are underway to make them increasingly intelligent, for example by integrating more and more communication components and computing power (processors, control units, controllers, etc.). This allows vehicles to adapt to a wide range of situations. One goal could be, for example, increased safety, comfort, and mobility.

[0003] Document DE 10 2009 054 002 A1 describes a solution for estimating travel time for a driver to travel from their current location to an appointment based on their preferred route. A notification period is determined based on the estimated travel time and the appointment time, and the user is reminded of the appointment at the notification time. This presents a challenge: promptly reminding users with appointments at locations far from their current location so that they depart and arrive at their appointments on time, but not too early. This can be difficult for users to estimate if they are unfamiliar with the area or unsure where they will be staying before their appointment.

[0004] DE 10 2017 111 711 A1 proposes a concept for finding and displaying available vehicles in a car-sharing system.

[0005] Here, the user can select a vehicle in consideration of the arrival time. A list of information related to vehicle types and vehicle arrival times is displayed on a display of the user terminal device, and the user can select a desired vehicle type in consideration of the arrival time.

[0006] Document DE 10 2015 007 490 A1 describes a method for operating a vehicle, in which a time span is determined that indicates the permitted parking duration in a parking space provided for the vehicle. The vehicle user is informed of the impending expiration of this time span. To determine the time span, an evaluation device evaluates at least one piece of information transmitted to the evaluation device from the vehicle and / or from at least one other vehicle. To detect parking conditions, a vehicle already involved in the traffic movement is used, in which the relevant information is detected and transmitted to the evaluation device.

[0007] Document EP 2 772 876 A1 relates to a parking guidance system. This concept provides for monitoring parking spaces and their status (vacant / occupied). Based on the user's driving destination, the arrival time is estimated and a parking space reservation proposal is made to the user.

[0008] GB 2552360 A describes a concept for adjusting the space in which the vehicle occupants' status or behavior parameters, such as temperature, lighting, and humidity, are taken into account. This can include, for example, determining the average length of time a user spends in a room or building. The concept provides for the evaluation of the user's current and past navigation data.

[0009] The solutions described in the prior art relate to the automated transmission of data intended to simplify vehicle handling. This does not take into account that the vehicle can already be prepared for the corresponding use before the journey begins.

[0010] This results in the technical problem underlying the present invention being to provide an improved approach for controlling a vehicle.

[0011] The above technical problem is solved according to the attached independent claims.

[0012] Embodiments of the present invention are based on the recognition that the arrival time (of a user at a vehicle) or the departure time of a user and the vehicle can reveal important information. If this information is available, measures can be taken in the vehicle to prepare for the user's departure or boarding. Examples include timely ventilating, cooling, heating the vehicle, defrosting the windshield, and accessing current traffic information. Furthermore, personalized settings can be implemented, such as a vehicle welcome, system startup, and so on.

[0013] Furthermore, the exit time can be estimated and parking spaces can be scheduled in advance, so that parking seekers can navigate to a parking space that is about to be released before it becomes vacant.

[0014] An embodiment implements a method for estimating the departure time of a user and a vehicle. The method includes obtaining vehicle-related or vehicle-based data about the vehicle and obtaining person-related or person-based data about the user. The method also includes estimating the departure time based on the vehicle-related data and the person-related data. Estimating the departure time based on the vehicle-related data and the person-related data is more reliable than estimating based on vehicle-related data or person-related data alone. In addition to the advantages already mentioned, the embodiment also enables mobility providers to recognize the arrival of their customers at a stop early. This allows for better, even optimal, fleet planning and route planning, making services more comfortable and efficient.

[0015] In a further embodiment, the method includes receiving the person-related data from a mobile device of the user and the vehicle-related data from the vehicle. Thus, data from mobile radio devices (e.g., smartphones, handheld phones, etc.) can be further used. In particular, this data can be correlated with or evaluated together with the vehicle-related data, for example, directly from the vehicle or from a corresponding database storing such data. This results in a higher reliability of the estimate, as both historical and current data are available.

[0016] Estimating the departure time may include determining the user's arrival time at the vehicle. Determining the arrival time provides an indication of the approximate departure time. The arrival time may be estimated, for example, from a user's movement history. This may improve the accuracy of the estimated departure time.

[0017] In another embodiment, the location of the vehicle can be determined based on the vehicle-related data, and the user's arrival at the vehicle's location can be determined based on the person-related data. The arrival time can be reliably estimated from the vehicle's current location together with the user's data.

[0018] For example, in some embodiments, a user behavior routine can be determined based on the person-related data, and a vehicle routine can be determined based on the vehicle-related data. The departure time can then be estimated based on a comparison of the user behavior routine and the vehicle routine. Thus, by comparing these data, an advantageous cost estimate can be made.

[0019] Determining the vehicle's behavior can include evaluating vehicle starts or vehicle starting points and destinations within a time period based on vehicle-related data. Determining the user's behavior can include evaluating person-related data regarding the user's mobility. This allows for a correlation that improves the departure time estimate.

[0020] The person-related data may include one or more elements from the group consisting of the following characteristics: the user's mobility status, the user's location, the user's movements, the user's data connection, temporal changes in the user's mobility status, temporal changes in the user's location, temporal changes in the user's movements, and temporal changes in the user's data connection. Thus, a wide range of data (at least some of which already exist) can be used to estimate the departure time, thereby preparing the vehicle for the upcoming journey and the user.

[0021] In some embodiments, real-time events are determined based on data related to the person, and the departure time is also estimated based on the real-time events. For example, an event such as a data connection interruption can be used to identify the imminent departure of a vehicle with a specific user. An example of this is a user leaving the office on their way to their vehicle, where a data connection to a wireless network (e.g., a wireless local area network, WLAN) is interrupted and can be used to identify the user's immediate departure.

[0022] Another real-time event is the availability of the radio network, which can be repeated, for example, on the user's usual route to his vehicle and thus can also be an indication of an imminent departure.

[0023] Depending on the data used to estimate the departure time, the reliability of the estimate can vary. Therefore, a determination of reliability information for the estimated departure time can also be performed. Any measures on / in the vehicle can then be adjusted based on this reliability information, or a query can be sent to the user to confirm the estimated departure time.

[0024] Furthermore, in other embodiments, the estimated departure time can be further communicated to the vehicle (and / or the user) or departure preparation measures can be activated in / on the vehicle. Thus, the vehicle can be ready when the user logs in and is about to start the journey, for example, ventilation, heating, cooling, defrosting, or seat and / or mirror adjustment and one or more other personalized settings can be already performed.

[0025] Since the release of the parking space of the vehicle can also be foreseen according to the estimated departure time, the parking space of the vehicle can also be arranged based on the estimated departure time. The earlier arrangement or occupancy planning of the parking space thus achieved can be used for more efficient utilization of the parking space.

[0026] Another embodiment is a computer program for executing the method described herein, which executes the method when executed on a computer, a processor, or a programmable hardware component. Another embodiment also includes a device having a control module configured to implement one of the methods described herein. Furthermore, an embodiment provides a network component including a corresponding device, and a system including a vehicle, a mobile device, and a network component according to the present description.

[0027] Further advantageous embodiments will be explained in more detail below with reference to the exemplary embodiments shown in the drawings, without, however, being restricted to these exemplary embodiments as a whole.

[0028] in:

[0029] Figure 1 A flow chart showing an exemplary embodiment of a method for estimating a departure time of a user with a vehicle;

[0030] Figure 2 An exemplary embodiment of a device, network components, and a system for estimating the departure time of a user with a vehicle is shown in a simplified diagram.

[0031] Various embodiments will now be described in more detail with reference to the accompanying drawings, in which some embodiments are shown. Optional features or components are shown herein with dashed lines.

[0032] Although the embodiments can be modified and changed in various ways, the embodiments in the drawings are shown as examples and are described in detail. It should be understood that the embodiments are not limited to the corresponding disclosed forms, but rather include all functional and / or structural variations, equivalents, and alternatives within the scope of the present invention.

[0033] It should be noted that an element that is described as being "connected" or "coupled" to other elements can be directly connected or coupled to the other elements, or there can be elements disposed therebetween. Conversely, when an element is described as being "directly connected" or "directly coupled" to another element, there are no elements disposed therebetween. Other terms used to describe relationships between elements should be interpreted in a similar manner (e.g., "between" versus "directly between," "adjacent" versus "directly adjacent," etc.).

[0034] The technical terms used herein are intended only to illustrate specific embodiments and should not be construed as limiting such embodiments. As used herein, the singular forms "the indefinite article one" and "the definite article this" include the plural forms unless the context explicitly indicates otherwise. Furthermore, it should be understood that, as used herein, expressions such as "comprises," "contains," "has," "includes," "includes," and / or "has" indicate the presence of the described features, integers, steps, workflows, elements, and / or components, but do not preclude the presence or addition of one or more features, integers, steps, workflows, elements, components, and / or groups.

[0035] Figure 1 A flow chart shows an exemplary embodiment of a method 10 for estimating the departure time of a user with a vehicle. The method 10 includes obtaining vehicle-related data about the vehicle and obtaining 14 person-related data about the user. The method also includes estimating 16 the departure time based on the vehicle-related data and the person-related data.

[0036] In the exemplary embodiment, any vehicle can be considered, such as a passenger car, truck, two-wheeled vehicle, etc., but also a watercraft or aircraft. According to this exemplary embodiment, estimating here means predetermining, predicting, or calculating the departure time of the vehicle, which may have a certain degree of inaccuracy. For example, the departure time may be determined with an accuracy of one or more seconds, one or more minutes, etc., or with a certain degree of accuracy. The user or user can be one of multiple potential users, drivers, or passengers of suitable vehicles. Examples include family vehicles or business vehicles used by multiple drivers. In this regard, the content presented here is targeted at one of multiple conceivable users, if necessary. The vehicle-related data can be, for example, sensor data or corresponding data profiles. Examples include travel time, speed, parking process, location, etc. This data can be detected and provided by components located in the vehicle itself, such as sensors, navigation equipment, etc., but it is also conceivable that it can be detected by devices brought into the vehicle by the user, such as mobile radio equipment.

[0037] In this case, the person-related data is user-related and can also be detected by corresponding devices, such as mobile radios or other sensors. Scenarios are also conceivable where the corresponding sensor data indicates that a user has left an office, shopping mall, or home. For example, shutting down a computer or turning off lights in an office / home can indicate that the vehicle is about to be used. Furthermore, person-related data can also be detected by the vehicle itself, such as which user used the vehicle and when.

[0038] Figure 2 The schematic diagram shows an exemplary embodiment of a device, network components 200, 300 and a system 400 for estimating the departure time of a user with a vehicle 100. Figure 2 A vehicle 100, a network component 200, and a mobile device 300 are shown, wherein these components also constitute an embodiment of a system 400. However, embodiments, in particular embodiments of the system 400, are not limited to the presence of all three components, as will be explained below. In this case, the above-described method 10 can be performed on all the components shown. Figure 1 As shown, vehicle-related data and person-related data are used to estimate the departure time of vehicle 100. These data can be collected / detected in vehicle 100, in network component 200, or in mobile device 300, where method 10 can then be carried out.

[0039] For the communication of data, for example, conventional techniques of wireless communication are used, wherein corresponding radio modems or other components allowing data communication can be used. Figure 2The network component 200 shown in the upper center receives data relating to the vehicle from the vehicle 100 and data relating to the person from the mobile device 300. Additionally or alternatively, the vehicle 100 can receive these data from the mobile device 300, for example via a network component 200 provided for this purpose (base station, access point, Internet) or even directly from the mobile device 300. Wireless technologies such as mobile radio, WLAN, Bluetooth, etc. or even other interfaces such as USB (Universal Serial Bus) can also be considered for direct communication, if the mobile device 300 is coupled in the vehicle, for example. Also additionally or alternatively, data can be transmitted from the vehicle 100 directly or via a network to the mobile device 300. Another variant is to detect the data relating to the vehicle directly via the mobile device 300. It is emphasized here and hereinafter that the mobile device 300 itself is also a network component. In this respect, the method 10 described herein can be carried out in all the components shown and in Figure 2 The dashed arrows shown in represent different communication paths or communication possibilities. In addition, in some embodiments, the detection of data related to people and the detection of data related to vehicles can even be achieved only by vehicle 100 or only by mobile device 300.

[0040] In an embodiment, method 10 may also be implemented as a computer program. Thus, one embodiment is a computer program for executing method 10 described herein, which executes the method when executed on a computer, processor, or programmable hardware component. In this regard, an embodiment also includes an apparatus having a control module configured to execute method 10 described herein.

[0041] For example, this method can be implemented in software and can then be executed by corresponding hardware. In an embodiment, the control module can be equivalent to one or more arbitrary controllers or processors or programmable hardware components. For example, the device can also be implemented as software programmed for the corresponding hardware components. In this regard, the control module can be implemented as programmable hardware with corresponding adapted software. Any processor, such as a digital signal processor (DSP), can be used here. Here, the embodiment is not limited to a specific type of processor. It is conceivable that any processor or even multiple processors or microcontrollers can be used to implement the device or control module. It is also conceivable to implement it in the form of integration with other control units, such as integration in a control unit for a vehicle, an ECU (electronic control unit), a user terminal device (such as a mobile radio device, a network component), a server (such as a network component), which can additionally include one or more other functions. The embodiment also implements a network component (base station, vehicle, mobile device, server) with a corresponding device or control module.

[0042] In some embodiments, the location of the user's smartphone can also be known, and as the time remaining before the ride passes, many other feasible solutions will appear. For example, a notification or message can be sent to the user in the last few minutes before the ride. This available time window is valuable because once in the vehicle, the user's attention may no longer be on the smartphone. For example, possible notifications or messages may be triggered by advertising companies, office management services, etc. If the ride time is known, it can also be derived from this how long the vehicle has been parked. This makes other services / businesses possible. Some examples are software updates, refueling, cleaning, parcels, laundry delivery, charge management for electric vehicles, charging before departure in winter to save preheating, etc.

[0043] Continue from Figure 2 Method 10 is described from the perspective of network component 200 in FIG. In other embodiments, when data is aggregated in vehicle 100 or mobile device 300, the details apply similarly. In this regard, method 10 includes receiving the person-related data from the user's mobile device 300 and receiving the vehicle-related data from vehicle 100. For example, the data is transmitted to network component 200 via the Internet via a wireless interface and a corresponding access point.

[0044] In some embodiments, method 10 implements, on the one hand, an algorithm that predicts arrival at a specific location (parking space or parking station) based on data related to the customer / person involved (e.g., GPS movement data, GPS standing for Global Positioning System), and, on the other hand, a cloud service that makes this information available to all interested or relevant functions. Estimating 16 the departure time may include determining the arrival time / ride time of the user at vehicle 100. It should be emphasized that in many cases, the arrival time of the user at vehicle 100 is very close to or shortly before the vehicle's departure time. However, other situations are also conceivable in which the user first remains in or at the vehicle for a certain period of time before commencing travel. Conceivable scenarios include waiting for multiple passengers and certain routine or customary or even planned journeys for the user.

[0045] Method 10 can, for example, include determining the location of vehicle 100 based on vehicle-related data. In addition to the aforementioned GPS data, other positioning mechanisms can also be used, particularly in locations where GPS signals are unavailable, such as underground garages, parking structures, and garages. Alternative mechanisms include, for example, positioning based on available mobile radio networks or WLAN networks, vehicle sensor data (e.g., optically detected parking space numbers), and so on. Furthermore, method 10 can include determining the location of a user's arrival at vehicle 100 based on person-related data. GPS-based or positioning-based mechanisms can also be used in this context.

[0046] In this regard, in some embodiments, a user behavior routine can be determined based on the person-related data, and a vehicle routine can be determined based on the vehicle-related data. The departure time estimation 16 can be based on a comparison of the user behavior routine and the vehicle routine. The routine can be learned, for example, by detecting information about the time of the vehicle's starting point or vehicle start and the destination, as used in predictive navigation, also known as P-NAV (predictive navigation). User behavior can also be learned from smartphone data, for example, to suggest the next destination and provide navigation instructions.

[0047] The determination of vehicle routines can be performed by evaluating the vehicle starting point and / or vehicle destination over a time period based on the data related to the vehicle (e.g., regular travel back and forth between two travel destinations at a certain time). The determination of user behavior routines can be completed by evaluating the data related to the person in terms of the user's mobility (e.g., regular departure from home / residence / office, or regular movement from home / residence to a certain work address on weekdays, or even using different means of transportation). This embodiment enables the combination of data related to the person, such as smartphone data, with vehicle data, and thus enables the centralized availability of information on "ride times" or "departure times" in a cloud service. For example, a smartphone ( Figure 2 The mobile device 300 in the device collects data such as mobility status (resting, walking, running, driving), location and movement (GPS location), and data connection (WLAN, Bluetooth, mobile radio) and sends them to the cloud backend ( Figure 2 is sent to the network component 200).

[0048] On the server (network component 200), this data is processed and, in at least some embodiments, analyzed according to two principles. On the one hand, behavioral routines are studied, which predict the occurrence of specific locations at specific times (learning mobility patterns). On the other hand, real-time events are observed and their correlations with subsequent rides / departures are learned. Neural network and artificial intelligence mechanisms can also be used here. A learning mechanism can be generated, for example, by comparing estimated departure times with actual departure times and adjusting or training corresponding algorithms based on successes or failures.

[0049] In this context, the person-related data may include one or more elements from the group consisting of the following characteristics: the user's mobility status, the user's location, the user's movements, the user's data connection, the temporal changes in the user's mobility status, the temporal changes in the user's location, the temporal changes in the user's movements, and the temporal changes in the user's data connection. Other examples include mobility status, geolocation, WLAN connection, Bluetooth connection, etc. Further examples of used vehicle data include ignition start and end (Keyword 15), parking position, temporal regularity, etc.

[0050] Furthermore, an embodiment of method 10 may provide for determining real-time events based on person-related data, and for estimating 16 the departure time based on real-time events. Real-time events may be, for example, the disconnection of a home WLAN connection or even the geographic proximity of a smartphone (mobile device 300) to a parking location.

[0051] As a result, method 10 of the service outputs the arrival or departure time of the person at the boarding location. In addition to the time, the probability of the prediction being fulfilled can also be provided. In some embodiments, the smaller the time interval relative to the boarding time, the greater the probability of the prediction being fulfilled, as information becomes increasingly reliable. In other words, method 10 can also include determining reliability information for the estimated departure time.

[0052] Some embodiments enable the connection of smartphone data and vehicle data. This allows for a central provision of boarding or departure forecasts for use in a variety of functions. The provision of boarding times / departure times together with probabilities can be made available at any time from a central location. As described above, it is also conceivable that vehicle-related data is detected by the mobile device 300. Some embodiments enable pure smartphone applications, which do not detect vehicle data directly from the vehicle, but are based on vehicle-related data detected by the smartphone itself and its sensor technology. The local sensor system (infrastructure) of the mobile device 300, such as a camera or microphone, can then be used to predict user behavior.

[0053] Because, at least in some embodiments, these data have a direct personnel association or vehicle association, a high accuracy of the estimate 16 can be achieved. Because the data related to the personnel remains verifiable in quantity (according to the principle of data simplification) and the number of partners in the action chain is small, the estimate 16 can be performed effectively. Here, data security, transparency and deletability are guaranteed in an understandable manner and a strong trust relationship can be established with the customer. The embodiment can be used for many vehicles here. For example, bicycles, scooters, motorcycles, trucks (trucks), buses, balance cars, aviation, railways, etc. Many services can benefit from the embodiment. For example, ride-sharing services, taxi services, shuttle services, shared car services, parking services, charging station services, traffic forecast services (where and how many vehicles are leaving), local advertising, urban planning, etc.

[0054] In another embodiment, the method provides for transmitting the estimated departure time to vehicle 100 or initiating measures in vehicle 100 to prepare for departure. Preparatory measures can be initiated remotely or even by vehicle 100 itself. Examples of these measures include heating, cooling, ventilation, seat and mirror settings, or user-specific navigation system settings (destination, route preferences) or entertainment system settings (radio stations, online services, etc.). In another embodiment, parking spaces for vehicle 100 can also be allocated based on the estimated departure time. This results in efficient parking space allocation and utilization. Another embodiment is a computer program for executing the method described herein, which executes the method when executed on a computer, processor, or programmable hardware component. Depending on the specific implementation requirements, embodiments of the present invention can be implemented in hardware or software. This implementation can be performed using digital storage media, such as floppy disks, DVDs, Blu-ray discs, CDs, ROMs, PROMs, EPROMs, EEPROMs, or flash memory, hard disks, or other magnetic or optical storage devices storing electronically readable control signals, which can interact or cooperate with programmable hardware components to execute the corresponding method.

[0055] A programmable hardware component can be formed by a processor, a computer processor (CPU = Central Processing Unit), a graphics processing unit (GPU = Graphics Processing Unit), a computer, a computer system, an application-specific integrated circuit (ASIC = Application-Specific Integrated Circuit), an integrated circuit (IC = Integrated Circuit), a single-chip system (SOC = System on a Chip), a programmable logic element or a field programmable gate array (FPGA = Field Programmable Gate Array) with a microprocessor.

[0056] Thus, the digital storage medium may be machine-readable or computer-readable. Thus, some embodiments include a data carrier having electronically readable control signals capable of cooperating with a programmable computer system or programmable hardware component to perform the methods described herein. Thus, one embodiment is a data carrier (or digital storage medium or computer-readable medium) having recorded thereon a program for performing the methods described herein.

[0057] In general, embodiments of the present invention can be implemented as a program, hardware, a computer program, or a computer program product having a program code, or as data, wherein when the program code is executed on a processor or a programmable hardware component, the program code or data can perform one of the above-described methods. For example, the program code or data can also be stored on a machine-readable carrier or a data carrier. The program code or data can also exist as source code, machine code, bit code, or other intermediate code.

[0058] The above embodiments merely illustrate the principles of the present invention. Of course, variations and modifications of the arrangements and features described herein will be apparent to those skilled in the art. It should be noted that the present invention is limited solely by the scope of the claims set forth below, and not by the specific features presented in this specification and the explanation of the embodiments.

[0059] List of reference numerals:

[0060] 10 Method for estimating the departure time of a user and a vehicle

[0061] 12 Obtain vehicle-related data about the vehicle

[0062] 14. Obtaining personal data about the user

[0063] 16 Estimate the departure time based on the data of the vehicle and the person involved

[0064] 100 vehicles

[0065] 200 Network Components

[0066] 300 mobile devices

[0067] 400 system

Claims

1. A method (10) for estimating a departure time of a user with a vehicle (100), comprising: obtaining (12) vehicle-related data about the vehicle (100); obtaining (14) personal data about the user; and The departure time is estimated (16) using a neural network based on the vehicle-related data and the person-related data, wherein: determining real-time events based on the data concerning the persons involved, and additionally estimating (16) a departure time based on the real-time events, and Arrange parking spaces for vehicles based on the estimated departure time and anticipate the release of parking spaces for vehicles. wherein user behavior routines are determined based on the data concerning the persons, and vehicle routines are determined based on the data concerning the vehicles, and the departure time point is estimated (16) based on a comparison of the user behavior routines and the vehicle routines, wherein the determination of the vehicle routines includes evaluating the vehicle starting point and the vehicle destination over a time period based on the data concerning the vehicles.

2. The method (10) according to claim 1, further comprising receiving the person-related data from the user's mobile device (300) and receiving the vehicle-related data from the vehicle (100).

3. The method (10) according to claim 1, wherein: The estimation (16) of the departure time point includes determining the arrival time of the user at the vehicle (100).

4. The method (10) according to claim 3 further comprises determining the location of the vehicle (100) based on the data related to the vehicle, and determining the location of the user arriving at the vehicle (100) based on the data related to the person.

5. The method (10) according to claim 1, wherein: The determination of the user behavior routine includes an evaluation of the person-related data with respect to the user's mobility.

6. The method (10) according to claim 1, wherein: The data involving the person includes one or more elements from a group consisting of the following characteristics, namely the user's mobility status, the user's location, the user's movement, the user's data connection, the temporal change of the user's mobility status, the temporal change of the user's location, the temporal change of the user's movement and the temporal change of the user's data connection.

7. The method (10) according to claim 1, further comprising determining reliability information for the estimate (16) of the departure time point.

8. The method (10) according to claim 1, further comprising transmitting the estimated departure time to the vehicle or initiating measures in the vehicle (100) to prepare for departure.

9. The method (10) according to claim 1, further comprising arranging a parking space for the vehicle (100) based on the estimated departure time.

10. A computer program for carrying out the method (10) according to one of claims 1 to 9, which executes the method when the computer program is run on a computer, a processor or a programmable hardware component.

11. A device comprising a control module, which is designed to carry out the method (10) according to one of claims 1 to 9.

12. A network component (200) having the device according to claim 11.

13. A vehicle (100) having a device according to claim 11.

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