Method for recommending a user's future individual use of at least one means of transport and / or at least one alternative means of transport, computer program and / or computer-readable medium, data processing device and vehicle-external server

Abstract

A method for recommending a user's future individual use of at least one means of transport and / or at least one alternative means of transport, comprising: retrieving usage data relating to the user's use of the at least one means of transport and recorded in the past; determining a movement pattern based on the usage data; determining, based on the movement pattern, forecast data relating to a possible future use of the at least one alternative means of transport; and outputting, taking into account the usage data and the forecast data, a recommendation for recommending the future individual use of the at least one means of transport and / or the at least one alternative means of transport.

Description

[0001] The present disclosure relates to a method for recommending a user's future individual use of at least one means of transport and / or at least one alternative means of transport. The disclosure relates equally to a computer program and / or computer-readable medium, a data processing device, and an external server.

[0002] The choice of transport mode – that is, whether a user travels a route by motor vehicle, public transport (e.g., long-distance and / or local public transport), bicycle, and / or on foot – is typically heavily influenced by the user's habits. This means that many people choose to travel by motor vehicle or car without considering whether there are alternative modes of transport (in this case: bicycle, walking, public transport) that could be used for that journey.

[0003] It is known from the prior art to plan the travel of a route. It is also known that a travel time for a planned route can be displayed, differentiated by mode of transport. Furthermore, it is known to consider additional information about the journey. In addition to the travel time, this can include determining the route itself, its length, costs, and / or energy requirements.

[0004] DE 10 2011 080 758 A1 discloses a navigation method and a navigation device. These enable the determination of a route between a starting point and a destination point and then guidance to the destination point, taking into account a currently determined position, which is regularly determined by means of a GPS system. The navigation devices are regularly configured to determine a route taking into account various optimization criteria, whereby an expected energy consumption for a given route can be determined.

[0005] For many road users, especially drivers, using their chosen mode of transport, particularly a motor vehicle, is a familiar part of everyday life. Therefore, these road users utilize their chosen mode of transport without informing themselves about alternative options.

[0006] At the time of departure or the start of the journey, switching to one or more alternative means of transport may no longer be possible and / or practical, as circumstances such as departure time, clothing, items to be transported, etc., are already geared towards a journey by car or the use of a car, and switching to alternative means of transport (in this case, for example: bicycle, walking, public transport) could require time-consuming replanning. Furthermore, specific recommendations for action may be flawed, as not all circumstances of a journey are observable and could therefore lead to confusion for the user.

[0007] Patent application DE 10 2024 116 908.7, which was not yet published on the filing date of the present disclosure, describes a method for evaluating an individual use of at least one means of transport by a user, wherein the method comprises: retrieving usage data relating to the use of the at least one means of transport by the user and recorded in the past; determining, based on the usage data, comparative data relating to a possible use of at least one alternative means of transport; and outputting, taking into account the usage data and the comparative data, an evaluation information for assessing the individual use of the at least one means of transport in view of the possible use of the at least one alternative means of transport.

[0008] This approach achieves a transparent presentation that retrospectively evaluates the individual use of one or more modes of transport by the user. This can be helpful for the user to reconsider their usage patterns and preferences. It can also show the user the possibility of using one or more alternative modes of transport.

[0009] However, the user then finds themselves in the position of having to plan the future use of one or more modes of transport based on an ex-post evaluation of past usage patterns. It is possible that characteristics of the ex-post evaluation, or of the potential use of at least one alternative mode of transport in the past, may not be applicable to the future. For example, specific use cases, timetable changes, and / or construction work may influence future usage options.

[0010] Against the background of this state of the art, one objective of the present disclosure is to specify a method that is suitable for enriching the state of the art and improving at least the aforementioned aspects of the state of the art. In particular, the disclosure aims to provide a user with an efficient and user-friendly recommendation for the future individual use of at least one means of transport and / or at least one alternative means of transport.

[0011] The problem is solved by the features of the independent claims. The dependent claims contain further developments of the disclosure.

[0012] The problem is then solved, according to one aspect of the disclosure, by a method for recommending a user's future individual use of at least one means of transport and / or at least one alternative means of transport, the method comprising: retrieving usage data relating to the user's use of the at least one means of transport and recorded in the past; determining a movement pattern based on the usage data; determining, based on the movement pattern, forecast data relating to a possible future use of the at least one alternative means of transport; and outputting, taking into account the usage data and the forecast data, a recommendation for recommending the future individual use of the at least one means of transport and / or the at least one alternative means of transport.

[0013] It has been recognized that it is possible to support users in their mobility decisions regarding the future use of at least one mode of transport and / or at least one alternative mode of transport by providing recommendations. In other words, this can be achieved by recording and analyzing individual mobility behavior using at least one mode of transport based on usage data in order to identify movement patterns. By evaluating these movement patterns and optionally incorporating forecast data from external data sources, a tailored and, if desired, timely recommendation for action can be determined for the user. This recommendation is then presented to the user as guidance on their future individual use of at least one mode of transport and / or at least one alternative mode of transport.

[0014] By recognizing movement patterns and combining them with predictive data, an intelligent method for optimized mobility recommendations is created. The individuality of the usage data and the processing of additional information allow the recommendation to be tailored to the user's specific circumstances, enabling a targeted and beneficial recommendation.

[0015] Optionally, the movement pattern represents temporal and / or spatial correspondences, similarities, and / or correlations in the use of at least one mode of transport. These correspondences, similarities, and / or correlations can, for example, spatially link a stop at a journey's starting point and / or destination with a parking space. Temporally, commuting times and / or weekly rhythms can be identified, which can then be used to determine the forecast data. This makes it possible to provide comprehensive information for determining the forecast data.

[0016] Optionally, the forecast data includes future route planning and / or planning data that correlate spatially and / or temporally with the movement pattern. Route planning can be carried out, for example, by a navigation application, which can plan a journey start, destination, departure time, and / or arrival time based on the movement pattern, ensuring that these points correlate with the movement pattern. The planning data can be readily available from databases, particularly for public transport, and thus form a robust data source.

[0017] Optionally, the forecast data and / or the recommendation information can be influenced by user input and / or a user profile. For example, user input and / or the user profile can take into account a valid local public transport ticket, a valid long-distance public transport ticket, and / or a preference for certain modes of transport. Furthermore, it is possible to implement user activities in the user profile that can influence the determination of the forecast data and / or the recommendation information.

[0018] Optionally, the usage data can relate to one or more journeys and / or movements using one or more modes of transport. This makes it possible, for example, to combine several individual journeys into a tour to see if an alternative can replace all related individual journeys. A tour can, for example, include individual journeys between several locations (e.g., between home, office, shopping, and home) and / or using different modes of transport.

[0019] Alternatively or additionally, the usage data relates to usage within a limited time period. Besides analyzing individual trips, i.e., the use of one or more modes of transport, a more comprehensive analysis is also possible. This allows for the determination, within an optionally selectable usage period or time interval, of the proportion of trips that could have been made using alternative modes of transport.

[0020] Optionally, the forecast data and / or recommendation information are determined taking into account seat occupancy and / or load of each motor vehicle and / or weather and / or traffic information as a constraint. This allows for the inclusion of additional individual factors, such as weather or seat occupancy, in the suitability assessment of using at least one alternative mode of transport. Recurring journeys that, due to unobservable circumstances such as the transport of heavy goods, are only possible for the user by motor vehicle, for example, can be excluded from the analysis. Such situations can be learned through machine learning, and potential alternatives can be hidden and / or disregarded for these journeys.

[0021] Optionally, the recommendation information is determined taking into account a time aspect, emissions, costs, and / or a health aspect of the individual use of at least one mode of transport in relation to the future individual use of at least one alternative mode of transport. In addition to simply considering the time required for using at least one alternative mode of transport, such as cycling, public transport, or walking, route details and other variables such as calories burned and / or CO2 emissions saved when using an alternative can also be considered.

[0022] Optionally, the recommendation information can be displayed on the user's device. This allows the user to be informed of the recommendation before choosing a mode of transport and thus conveniently adjust their future use accordingly.

[0023] According to one aspect of the disclosure, a computer program and / or a computer-readable medium is provided. The computer program and / or the computer-readable medium includes instructions that, when executed by a data processing device, cause the device to perform the method according to the disclosure and / or steps thereof. Optionally, the computer program and / or the computer-readable medium includes instructions that, when executed by a data processing device, cause the device to perform the process steps described as advantageous or optional in order to achieve an associated technical effect.

[0024] According to one aspect of the disclosure, a data processing device is provided. The data processing device is configured to perform the procedure described above. Optionally, the data processing device is configured to perform a procedure step described as advantageous or optional and / or to implement a procedure feature in order to achieve an associated technical effect.

[0025] According to one aspect of the disclosure, an external server comprising the data processing device described above is provided. Optionally, the server's data processing device and / or the server(s) are configured to perform a process step described as advantageous or optional and / or to implement a process feature in order to achieve an associated technical effect.

[0026] One embodiment of each is described below with reference to the figures. Fig. Figure 1 schematically shows a motor vehicle, a user, a user terminal device and a server for carrying out a procedure according to one aspect of the disclosure; Fig. Figure 2 schematically shows a flowchart of a procedure according to one aspect of the revelation; and Fig. Figure 3 shows a schematic representation of a computer program and / or computer-readable medium according to one aspect of the revelation.

[0027] Fig. Figure 1 schematically shows a motor vehicle 50, a user 10, a user terminal device 15 and a server 90 for carrying out a process 100 according to an aspect of the disclosure.

[0028] The vehicle-external server 90, or the cloud and / or the backend, has a data processing device 91. The data processing device 91 is configured to process the data relating to Fig. 2 described procedures to be carried out 100 times.

[0029] This shows Fig. Figure 1 is only an exemplary illustration of an application for carrying out the method 100. In other embodiments (not shown), the user terminal 15 and / or the motor vehicle 50 may have a data processing device 91 for carrying out the method 100, thereby eliminating the need for the vehicle-external server 90 and enabling, for example, direct communication between the motor vehicle 50 and the user terminal 15. In a further embodiment (not shown), the user terminal 15 may be dispensed with and output is made to an output device (not shown) of the motor vehicle 50 or vice versa.

[0030] The data processing device 91 is configured to retrieve usage data 55 relating to the use of at least one means of transport 50a by the user 10 and recorded in the past. The means of transport 50a is in Fig. 1. The means of transport 50a is exemplified as a motor vehicle 50. The usage data 55 can be retrieved using navigation information from the motor vehicle 50 and / or the user terminal 15 carried with the means of transport 50a. The usage data 55 comprise a trajectory of the means of transport 50a and / or the user terminal 10, i.e., a temporally resolved sequence of locations where the means of transport 50a and / or the user terminal 10 was situated. Thus, the usage data 55 are representative of the use of the means of transport 50a by the user 10. The usage data 55 characterize a journey start and end in terms of time and / or location. The usage data 55 relate to one or more journeys and / or movements with one or more means of transport 50a. The means of transport 50a may have traveled multiple routes or completed multiple journeys when used by the user 10.Alternatively or additionally, instead of one means of transport 50a, several means of transport 50a (not shown) may have been used.

[0031] The data processing device 91 is configured to determine a movement pattern 57 based on the usage data 55. The movement pattern 57 represents temporal and / or spatial correspondences, similarities, and / or correlations in the use of the at least one means of transport 50a. Correspondences, similarities, and / or correlations can be determined, for example, by distances between locations and / or time intervals between points in time of several journey starts by the user 10 and / or by distances between locations and / or time intervals between points in time of several destinations of the user 10. The correspondences, similarities, and / or correlations can relate to temporal and spatial movement patterns 57, such as the user 10 commuting to work and / or a regular, for example, weekly trip for shopping and / or for leisure activities.

[0032] The data processing device 91 is configured to determine, based on the movement pattern 57, a possible future use of the forecast data 60 relating to at least one alternative means of transport 40. To determine the forecast data 60, the data processing device 91 can, for example, retrieve planning data via the internet, i.e., for example, planning data corresponding to a usage period in the future as forecast data 60 for a usage period corresponding to the movement pattern 57. Alternatively or additionally, route planning can be carried out using a navigation application for a usage period corresponding to the movement pattern 57, whereby the navigation application can be implemented with regard to various means of transport 40, 40a.

[0033] The forecast data 60 comprises future route planning and / or planning data that correlates spatially and / or temporally with the movement pattern 57. The forecast data 60 is determined taking into account a time corridor around the start and / or end of the trip, as well as a spatial corridor around the start and / or destination. The time corridor allows, for example, an earlier or later start and / or end of the trip to be considered when determining the forecast data 60. The time corridor can be defined and / or influenced by user input 16 and / or a user profile 17 and can be defined according to a default setting. The spatial corridor allows, for example, a different start and / or destination and can be considered when determining the forecast data 60.The spatial corridor can be defined and / or influenced by a user input 16 and / or a user profile 17 and can be defined according to a default setting.

[0034] The data processing device 91 is designed to determine, taking into account the usage data 55 and the forecast data 60, a recommendation information 70 for the recommendation of the future individual use of at least one means of transport 50a and / or at least one alternative means of transport 40.

[0035] The forecast data 60 and / or the recommendation information 70 are determined taking into account the seat occupancy and / or load of each means of transport 50a designed as a motor vehicle 50 and / or weather and / or traffic information as constraint 18. The constraint 18 can, for example, be retrieved from the internet in the case of weather and / or traffic information. It is also possible for the user 10 to define the constraint 18 via a user input 16 and / or for the constraint 18 to be retrieved via a user profile 17, in particular with regard to seat occupancy and / or load. In addition, the constraint 18, in particular with regard to seat occupancy and / or load, can be recorded by vehicle sensors and transmitted to the data processing device 91.

[0036] The data processing device 91 is configured to output the recommendation information 70. The recommendation information 70 is output to the user terminal 15, and is perceptibly displayed to the user 10 via an output device on the user terminal. The output of the recommendation information 70 is optional and occurs before the user 10 uses one of the means of transport 40 or 40a.

[0037] The means of transport 50a is, for example, designed as a motor vehicle 50a, but can also be another means of transport. Fig. Figure 1 schematically shows several alternative means of transport 40, the number of which is arbitrary. These alternative means of transport 40 can include, for example, a bicycle, public transport (including long-distance and local transport, optionally with bus), bicycle, e-bike, walking, scooter, e-scooter, etc. The user device 15 is, for example, a smartphone, smart glasses, another smart device, and / or a computer. User input 16 can be made via the user device 15, for example, by touch, voice, and / or gesture input, and can also be made via the motor vehicle 50.

[0038] The following example is intended to illustrate the implementation of procedure 100: Using, for example, vehicle sensor data (GPS, timestamps) as usage data 55, the mobility behavior of user 10 can be observed over time. It can be calculated where user 10 lives, where user 10 works, and which other locations user 10 regularly visits. Based on this information, mobility patterns or movement patterns 57 can be identified: for example, commuting patterns to work, a weekly visit to a gym, a drop-off and pick-up service for children, and / or a shopping trip on the weekend are examples of movement patterns 57. Based on seat occupancy, it is also possible to determine when user 10 uses the vehicle 50a, which is classified as a motor vehicle 50, alone and / or when other people are traveling with them.In addition to vehicle sensor data, additional external data sources such as POI data, current traffic information (traffic jams, road closures, construction sites, toll costs), weather data, and information on alternative means of transport (departure times, costs, etc.) can be accessed as forecast data. Personal background information, such as owning a bicycle and / or a public transport ticket, can be entered by the user. After an optional "learning phase," the movement pattern is recognized; for example: the user lives with their wife and son on the outskirts of Munich, commutes to the office in Pasing three times a week (Mon, Tue, Thu), takes their son to kindergarten every Tuesday morning, and their wife does so on the other days. Every other Thursday, the user drives directly to football training outside of Munich after work.Based on movement pattern 57, the system identifies where user 10 works and determines, as predictive data 60, that user 10 is faster using a combination of bicycle and S-Bahn (suburban train) as alternative means of transport 40 than using a car 50. It is determined that user 10 drops off their son on Tuesday mornings and goes to sports practice every other Thursday on their way home, but the sports field is not easily accessible by public transport as alternative means of transport 40. Therefore, the system only issues a recommendation 70 to the user's device 15 shortly before the calculated departure time on Wednesdays and every other Thursday, advising them to use public transport as alternative means of transport 40. If the S-Bahn is on strike, this is detected based on predictive data 70, and the system recommends that user 10 choose to use a car 50.Based on movement pattern 70, the family's preferred supermarket chain, which they regularly visit, is also identified. If user 10 is driving vehicle 50 on Tuesday evening and there is a major traffic jam, this is detected based on forecast data 70, and it is recommended that they go shopping in the meantime and choose a route to a nearby supermarket.

[0039] Fig. Figure 2 schematically shows a flowchart of a procedure 100 according to one aspect of the disclosure. The procedure 100 according to Fig. 1 is a method 100 for recommending a future individual use of at least one means of transport 50a and / or at least one alternative means of transport 40 by a user 10. Such a means of transport 50a, such an alternative means of transport 40 and features of the method 100 are related to Fig. 1 described. Fig. 2 is referred to Fig. 1 described.

[0040] The procedure 100 according to Fig. 2 indicates: Retrieving 110 of the usage data 55 relating to the use of at least one means of transport 50a by the user 10 and recorded in the past.

[0041] The procedure 100 involves: Determining 115 a movement pattern 57 based on the usage data 55. The movement pattern 57 represents temporal and / or spatial similarities, similarities and / or correlations in the use of at least one means of transport 50a.

[0042] Procedure 100 involves: Determining, based on the movement pattern 57, forecast data 60 relating to a possible future use of at least one alternative means of transport 40. The forecast data 60 comprise a future route plan and / or planning data that correlates spatially and / or temporally with the movement pattern 57.

[0043] The procedure 100 includes: Output 130, taking into account the usage data 55 and the forecast data 60, a recommendation information 70 for the recommendation of the future individual use of at least one means of transport 50a and / or at least one alternative means of transport 40.

[0044] The forecast data 60 and / or the recommendation information 70 can be influenced by user input 16 and / or a user profile 17.

[0045] The forecast data 60 and / or the recommendation information 70 are determined taking into account a seat occupancy and / or a load of a means of transport 50a designed as a motor vehicle 50 and / or weather and / or traffic information as a constraint 18.

[0046] Recommendation information 70 is determined taking into account a temporal aspect, emissions, costs and / or a health aspect of the individual use of at least one means of transport 50a in relation to the future individual use of at least one alternative means of transport 40.

[0047] The output of recommendation information 70 (130) takes place on a user device (15) belonging to user 10.

[0048] The expert recognizes that the procedure 100 according to Fig. 2. The procedure can also be carried out in a different order than shown. In particular, it is possible to swap, shift, repeat and / or perform steps of procedure 100 simultaneously.

[0049] Fig. Figure 3 shows a schematic representation of a computer program and / or computer-readable medium 200 according to one aspect of the disclosure. The computer program and / or computer-readable medium 200 comprises instructions 201 which, when the program or instructions 201 are executed by a data processing device 91, cause it to execute the method 100 and / or the steps of the method 100 according to Fig. 2 to be carried out.

[0050] The commands 201 can be in the form of program code in any code or language, in particular in code suitable for monitoring motor vehicles 90. The computer program and / or computer-readable medium 200 can be or comprise any digital data storage device, such as a USB flash drive, hard drive, CD-ROM, SD card, or SSD card. The computer program does not necessarily have to be stored on such a computer-readable storage medium, but can also be accessed via the Internet or otherwise. Reference symbol (part of the description) 10 users 15 User terminal 16 User input 17 User profile 18 Constraint 40 alternative means of transport 50 motor vehicles 50a Means of transport 55 Usage data 57 movement patterns 60 forecast data 70 Recommendation Information 90 servers 91 Data processing device 100 procedures 110 Retrievals 115 Determine 120 Determine Spend 130 200 computer program and / or computer-readable medium 201 commands QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] DE 10 2011 080 758 A1

[0004] DE 10 2024 116 908.7

[0007]

Claims

[1] Method (100) for recommending a future individual use of at least one means of transport (50a) and / or at least one alternative means of transport (40) by a user (10), wherein the method (100) comprises: - Retrieving (110) data relating to the use of at least one means of transport (50a) by the user (10) and recorded in the past (55); - Determining (115) a movement pattern (57) based on usage data (55); - Determine (120), based on the movement pattern (57), a possible future use of the forecast data (60) relating to at least one alternative means of transport (40); and - Issue (130), taking into account the usage data (55) and the forecast data (60), a recommendation information (70) to recommend the future individual use of at least one means of transport (50a) and / or at least one alternative means of transport (40). [2] Method (100) according to claim 1, wherein the movement pattern (57) represents temporal and / or spatial similarities, similarities and / or correlations in the use of the at least one means of transport (50a). [3] Method (100) according to claim 1 or 2, wherein the forecast data (60) comprise a future route planning and / or planning data that correlates spatially and / or temporally with the movement pattern (57). [4] Method (100) according to one of the preceding claims, wherein the forecast data (60) and / or the recommendation information (70) can be influenced by a user input (16) and / or a user profile (17). [5] Method (100) according to one of the preceding claims, wherein the forecast data (60) and / or the recommendation information (70) are determined taking into account a seat occupancy and / or a load of a means of transport (50a) designed as a motor vehicle (50) and / or weather and / or traffic information as a constraint (18). [6] Method (100) according to one of the preceding claims, wherein the recommendation information (70) is determined taking into account a temporal aspect, emissions, costs and / or a health aspect of the individual use of the at least one means of transport (50a) in relation to the future individual use of the at least one alternative means of transport (40). [7] Method (100) according to one of the preceding claims, wherein the output (130) of the recommendation information (70) is performed on a user terminal device (15) of the user (10). [8] Computer program and / or computer-readable medium (200) comprising instructions (201) which, when the program or instructions (201) are executed by a data processing device (91), cause the device to perform the method (100) and / or the steps of the method (100) according to any one of claims 1 to 7. [9] Data processing device (91) wherein the data processing device (91) is configured to perform the method (100) according to any one of claims 1 to 7. [10] Vehicle-external server (90) comprising the data processing device (91) according to claim 9.

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