WO2024227783A1 - Method and device for outputting a predicted occupancy distribution in a public means of transportation - Google Patents

Abstract

The invention relates to a method for outputting a predicted occupancy distribution in a public means of transportation between two stops, having the steps of ascertaining empirical data, storing the empirical data in a storage device of the occupancy distribution prediction system, detecting the actual state of the occupancy distribution in the public means of transportation between two stops using a sensor unit of the occupancy distribution prediction system, reading empirical data from a database of the occupancy distribution prediction system, generating a prediction of the occupancy distribution from the detected actual state and the empirical data read from the storage device by means of a control unit of the occupancy distribution prediction system, and outputting first data derived from the predicted occupancy distribution on an output device of the occupancy distribution prediction system. The invention also relates to an occupancy distribution prediction system.

Description

METHOD AND DEVICE FOR OUTPUTTING A FORECAST OCCUPANCY DISTRIBUTION IN A PUBLIC TRANSPORTATION

The invention describes a method for outputting a predicted occupancy distribution in a public transport means between two stops with the method steps of determining empirical data, storing the empirical data in a database of the occupancy distribution forecasting system, recording an actual state of the occupancy distribution in the public transport means between two stops with the aid of a sensor unit of an occupancy distribution forecasting system, reading empirical data from a database of the occupancy distribution forecasting system, creating a forecast of the occupancy distribution from the recorded actual state and the empirical data read from the database by a control unit of the occupancy distribution forecasting system and outputting first data derived from the predicted occupancy distribution on an output device of the occupancy distribution forecasting system, as well as an occupancy distribution forecasting system.

State of the art

In buses, trams and other public transport vehicles, the situation regularly occurs at peak times that large groups of passengers (e.g. schoolchildren) get stuck in the aisles or passageways shortly after boarding the vehicle, thus not properly filling other areas of the vehicle and making it difficult or impossible for other passengers to board.

This results in delays and vehicle delays for the public transport company (transport company), and inconveniences and even delays for passengers because they have to wait for the next scheduled vehicle. In some cases, the transport company may also be forced to use additional vehicles during such phases, even though in terms of sheer numbers, all passengers could be transported with the scheduled vehicle deployment. If the transport company wants to influence the passenger distribution in the vehicle, the driver must make the appropriate announcements after having gained an overview of whether passenger redistribution is even an option, ie whether there are any areas of the vehicle that are less busy. This is very tedious for the driver and one-off announcements or instructions are usually overheard or ignored by the passengers concerned. A transport company therefore has an interest in achieving a homogeneous passenger distribution in its vehicles and using as little material and personnel as possible.

A simple, inexpensive and largely automated device that can be installed in vehicles and that detects an uneven passenger distribution and helps to resolve this represents a desirable investment for transport companies. In this case, only those vehicles that are also used on the affected routes during the critical phases need to be equipped accordingly.

A system for recording passenger distribution is known, for example, from document DE 10 2004 040 057 A1, which provides for the identification of passengers by equipping tickets with transmitters, preferably RFID chips. Using suitable receivers, the distribution of tickets and thus the distribution of passengers in the area can be recorded. This data can then be made available to a traffic control system.

It is therefore an object of the present invention to provide an improved method for outputting a predicted occupancy distribution.

It is also an object of the invention to provide a occupancy distribution forecasting system.

Description of the Invention

The object is achieved by means of the method according to the invention for outputting a predicted occupancy distribution in a public transport system. Advantageous embodiments of the invention are set out in the subclaims. The method according to the invention for outputting a predicted occupancy distribution in a public transport vehicle between two stops has six method steps: In the first method step, empirical data is recorded. For the purposes of this document, empirical data has been obtained on a plurality of determined occupancy distributions under different reproducible conditions. The empirical data in particular includes data on occupancy distributions and passenger behavior from previous trips of the public transport vehicle and optionally the stops served by the public transport vehicle. Empirical data is recorded over a period of time that lies before the output of the predicted occupancy distribution and is optionally constantly updated. The empirical data is optionally recorded by a suitable sensor unit of an occupancy distribution forecasting system arranged in the public transport vehicle and/or at the stop.

In the second step, the experience data is saved in a database of the staffing distribution forecasting system. Optionally, all experience data that was recorded over an adjustable period of time is saved in the database, as well as optionally further recorded experience data. The database is therefore optionally constantly updated with newly recorded experience data.

In the third step, the current state of the occupancy distribution in public transport between two stops is recorded using a sensor unit of an occupancy distribution forecasting system.

In the fourth step, empirical data is read from a database of the staffing distribution forecasting system. The database is optionally connected to a control unit of the staffing distribution forecasting system, which has a memory with a software program.

In the fifth step of the process, a forecast of the staffing distribution is created from the recorded actual state and the experience data read from the database by a control unit of the staffing distribution forecasting system. The database is optionally connected to a control unit of the staffing distribution forecasting system, which has a memory with a software program with which a forecast staffing Distribution can be determined. The control unit can be located locally or decentrally, i.e. in the means of transport or at the stop. Optionally, it is also possible to provide a central control system that communicates, for example, via cloud computing and/or fog computing. Mixed forms of central and decentralized control are also possible.

In the sixth method step, first data derived from the predicted occupancy distribution are output on an output device of the occupancy distribution forecasting system.

An occupancy distribution in the sense of the invention is the number of passengers per unit area as a function of the length of the public transport vehicle. The occupancy distribution therefore indicates how many passengers are located where over the length of the public transport vehicle. Furthermore, the occupancy distribution is the number of passengers per unit area as a function of the length of the platform of a stop. The actual state of the occupancy distribution also indicates how many passengers are located where over the length of the public transport vehicle and/or the platform of a stop at a given time. The actual state of the occupancy distribution is recorded and determined, for example, by suitable sensors. The predicted occupancy distribution, on the other hand, indicates how many passengers will be located where over the length of the public transport vehicle and/or the platform of a stop at a given time.

In particular, passengers are signaled a direction of decreasing occupancy distribution and the direction to an area with low occupancy distribution. The output device can, for example, output such information optically and/or acoustically. Optionally, the output device can be a smartphone, tablet, or notebook with a suitable app on which the data from the predicted occupancy distribution is output.

For the purposes of this document, public transport is understood to mean any means of transport for the transport of persons and/or goods that is generally accessible to everyone. This includes means of transport operated by specially licensed transport companies (for example railways, aviation, cable cars, shipping, lifts) or means of transport on licensed lines or routes or means of transport operated by licensed service providers, as well as any commercially operated means of transport for passengers and/or goods in the private sector.

For the purposes of this document, stops are all locations where public transport stops, where people get on and/or off and/or goods are loaded and/or unloaded. Stops can be fixed stopping points according to a timetable. However, individually agreed stopping points are also considered stops for the purposes of this document.

The sensors suitable for carrying out the process are, for example, optical sensors for image recognition (cameras) and sensors that work with emitted and received light. Furthermore, sensors can also be based on the frequency modulated or FMCW (Frequency Modulated Continuous Wave) method, in which the distance is determined from the frequency shift of the reflected light beam based on a frequency modulated light beam. Also known as the Doppler effect. FMCW is considered the next generation LiDAR technology. TOF, structured light, 905nm LiDAR are the classic technologies.

In a further development of the invention, the experience data is determined by the occupancy distribution forecasting system. The occupancy distribution forecasting system has a plurality of sensors with which an actual state of the occupancy distribution of passengers in public transport can be recorded. The occupancy distribution forecasting system also has a database in which the recorded experience data can be stored.

In a further embodiment of the invention, the empirical data is determined from experimentally recorded data. The sensors of the occupancy distribution forecasting system determine an actual state of the occupancy distribution; optionally, the sensors determine a plurality of actual states of different occupancy distributions over a definable period of time before the forecast occupancy distribution is output. This plurality of actual states of different occupancy distributions are stored in the database as empirical data. In a further aspect of the invention, the experience data is determined by a sensor unit of the occupancy distribution forecasting system. The sensor unit of the occupancy distribution forecasting system determines an actual state of the occupancy distribution; optionally, the sensor unit determines a plurality of actual states of different occupancy distributions over a definable period of time before the forecast occupancy distribution is output. This plurality of actual states of different occupancy distributions are stored in the database as experience data.

In a further embodiment of the invention, the sensor unit is arranged in the public transport and/or at a stop. The sensor unit can be used to record the actual state of the occupancy distribution in the public transport and/or at a stop; a plurality of actual states of different occupancy distributions are stored in the database as empirical data.

In an advantageous embodiment of the invention, the experience data is determined at least one year, preferably one month, particularly preferably 14 days and especially preferably one week before the forecast staffing distribution is output. This ensures that sufficient experience data is recorded over a period of time. This period is optionally also such that it is shortly before the forecast staffing distribution is output. The experience data is therefore up-to-date.

In a further embodiment of the invention, the experience data includes the occupancy and passenger behavior in the public transport and/or the stop. The experience data in particular includes data on occupancy distributions and passenger behavior from previous trips of the public transport and optionally the stops served by the public transport. Experience data is recorded over a period of time that lies before the output of the predicted occupancy distribution and is optionally constantly updated.

In a further embodiment of the invention, the forecast of the occupancy distribution is created from event data, whereby the event data is used to create the forecast of the occupancy distribution in order to select the empirical data used to create the forecast of the occupancy distribution. Event data in the sense of the determination contain data from temporary events that can influence the staffing distribution.

In a further development of the invention, the event data includes time and location information. The location information includes in particular the route of the public transport and the location of the stops that the public transport stops at. The time information optionally includes the date and time of day at which the public transport stops at the stops.

In a further aspect of the invention, time information includes the time of day, time of year, holidays, vacation times, weather forecasts, reservations. All of these time parameters influence the occupancy distribution or the occupancy distribution is different for different time parameters. The information is used to select the experience data that have the same time parameters.

In a further embodiment of the invention, the forecast of the occupancy distribution is created from structural data. Structural data contains data from permanent parameters of the stop and/or the public transport, in particular, for example, spatial and room conditions. The parameters can also be recorded for a limited time, e.g. from a construction site. These parameters influence the occupancy distribution both in the public transport and at the stop.

In a further embodiment of the invention, the structural data includes structural information about the stop and/or the public transport. Structural information such as construction sites at a stop and the type and number of carriages of a public transport are also essential for the occupancy distribution at the stop or in the public transport.

In a further embodiment of the invention, the structural information includes the architectural design and/or the spatial and/or room conditions of the stop and/or the public transport. This includes, for example, the number and location of entrances and exits, elevators, construction sites, sales outlets at the stop, as well as, for example, the number and Location of entrances and exits, type and number of carriages, standing and seating areas of public transport.

In a further development of the invention, the structural information includes the functionality of the respective structure. This includes, for example, the number and location of entrances and exits, elevators, construction sites, sales points at the stop, as well as, for example, the number and location of entrances and exits, type and number of carriages, standing and seating areas of the public transport.

In a further embodiment of the invention, the experience data, structure data and/or the event data are different for different stops. Experience data, structure data and/or the event data are determined for each stop that the public transport stops at and stored in the database.

In a further embodiment of the invention, the current passenger behavior in public transport or at the next stop is recorded, with the forecast of the occupancy distribution being created from the recorded actual state and the recorded current passenger behavior. The actual state of the occupancy distribution indicates how many passengers are where along the length of the public transport and/or the platform of a stop at a given time. The actual state of the occupancy distribution is recorded and determined, for example, by suitable sensors. By knowing the actual state of the occupancy distribution and the current passenger behavior at a given time, a forecast of the occupancy distribution is created at a later time.

In a further development of the invention, the actual state of the occupancy distribution is recorded in a first time interval, wherein the first time interval is on the route between two consecutive stops. In the first time interval, the public transport is usually in motion, all passengers have boarded the public transport at a first stop and are usually moving within the public transport in order to find and take their standing and/or sitting places or have already taken their standing and/or sitting places. In a further embodiment of the invention, the actual state of the occupancy distribution is recorded when passenger movement in public transport is low. Passenger movement in public transport is not low immediately after departure from a stop, but at a later point in time when passengers have taken their standing and/or sitting places and possibly stowed their luggage. Passenger movement at this later point in time usually only occurs, for example, to use the toilets and/or to consume drinks and/or food. The proportion of these passengers is usually small, however. When passenger movement is low, occupied and/or complementary to this, unoccupied standing and/or sitting places in public transport can be recorded and determined more reliably than when passenger movement is high.

In a further embodiment of the invention, the actual state of the occupancy distribution is recorded by a first sensor, the first sensor being a sensor for counting passengers at doors and/or passageways and/or a sensor for capturing image material, and the data recorded by the first sensor being analyzed with regard to the actual state of the occupancy distribution. A first sensor is optionally advantageously arranged at entrance doors to the public transport and/or doors in the interior, e.g. passenger compartments with standing and/or seating areas, and records the number of passengers there. Optionally, the first sensor is a depth image camera. The first sensor can record a depth image of the transport space, which is used to determine an occupancy distribution of the transport space based on the depth image. In a further embodiment of the invention, the first sensor is a sensor for counting passengers at doors and/or passageways and/or a sensor for capturing image material.

In a further embodiment of the invention, the passenger behavior is analyzed with regard to the preparatory actions for passengers to get off. In a further embodiment of the invention, the preparatory actions for passengers to get off the public transport vehicle include the passengers getting up from their seats, moving towards the exit of the public transport vehicle and/or stowing personal items. By recording the preparatory actions of the passengers to get off, a predicted occupancy distribution after the passengers get off when the public transport vehicle stops at a stop is possible. In a further embodiment of the invention, the analysis of passenger behavior is supported by AI/ML. The memory of the control unit contains machine learning data that is applied to the data recorded by the sensor. This significantly distinguishes the preparatory actions of passengers to get off from other actions.

In a further embodiment of the invention, the analysis of passenger behavior includes the analysis of movement flows of people and/or a distribution of people at the next stop. By recording the distribution of people at the next stop, it is possible to predict in which area of the platform of the stop passengers will board the public transport and to calculate a corresponding passenger distribution in the public transport.

In a further embodiment of the invention, the analysis of passenger behavior takes place in a second time interval. The second time interval is usually in a period of time shortly before reaching the second stop. In the second time interval, the passengers have stowed their personal items (e.g. luggage), have left their seats and are moving towards the exits of the public transport or are in the immediate vicinity of them.

In a further development of the invention, the second time interval is different from the first time interval. While in the first time interval the passenger movement in the public transport is low, the passenger movement in the second time interval is high because the passengers stow their personal belongings and/or move towards the exits of the public transport.

In a further embodiment of the invention, the second time interval is before reaching the next stop. In the second time interval shortly before reaching the second stop, the passengers have stowed their personal items (e.g. luggage), left their seats and are moving towards the exits of the public transport or are in the immediate vicinity of it. In a further development of the invention, the second time interval is 2 minutes, preferably 1 minute, particularly preferably 30 seconds and especially preferably 15 seconds before reaching the next stop. In a further embodiment of the invention, the passenger behavior is analyzed by a second sensor. The second sensor is optionally an image recognition sensor, which can optionally be used to distinguish people from objects.

In a further embodiment of the invention, the second sensor is different from the first sensor. While passenger counting and localization is possible using the first sensor, the second sensor records the passenger behavior of individual passengers.

In a further aspect of the invention, the second sensor is a camera for image capture. The second sensor is a sensor for image recognition, which can optionally be used to distinguish people from objects.

In an advantageous embodiment of the invention, the forecast of the occupancy distribution is created for a point in time immediately after passengers get off the public transport at the next stop. By recording the distribution of people at the next stop, it is possible to predict in which area of the platform at the stop passengers will board the public transport and to calculate a corresponding passenger distribution in the public transport. In addition, occupied and/or complementary unoccupied standing and/or seated places in the public transport are recorded and determined. By displaying unoccupied standing and/or seated places in the public transport, the passenger flow can be directed, passengers can board and disembark more quickly and the utilization of the public transport is improved.

In a further development of the invention, the time is not in the first time interval and/or the second time interval. The first time interval is on the way between two consecutive stops. In the first time interval, the public transport is usually in motion, all passengers have boarded the public transport at a first stop and are usually moving within the public transport to find and take their standing and/or seated places or have already taken their standing and/or seated places. The second time interval is usually in a period of time shortly before reaching the second stop. In the second time interval, the passengers have have stowed their personal belongings (e.g. luggage), have left their seats and are moving towards the exits of the public transport or are in the immediate vicinity of them. The time of the forecast of the occupancy distribution is immediately after passengers have exited the public transport at the next stop.

The object is further achieved with the occupancy distribution forecasting system according to the invention. Advantageous embodiments of the invention are also set out in the subclaims.

The occupancy distribution forecasting system for a public means of transport according to the invention has a first sensor unit with a first sensor, wherein the first sensor unit is intended and suitable for counting passengers in public means of transport and/or recording passenger behavior in public means of transport. A first sensor is optionally advantageously arranged on entrance doors of the public means of transport and/or doors in the interior, e.g. passenger compartments with standing and/or seating areas, and records the number of passengers present there. Optionally, the first sensor is a depth image camera. The first sensor can record a depth image of the transport space, with which an occupancy distribution of the transport space in the public means of transport is determined on the basis of the depth image. In a further embodiment of the invention, the first sensor is a sensor for counting passengers at doors and/or passageways and/or a sensor for recording image material.

The occupancy distribution forecasting system according to the invention further comprises a second sensor unit with a second sensor, wherein the second sensor unit is intended and suitable for counting passengers at a stop and/or recording passenger behavior at a stop. The second sensor is, like the first sensor, a depth camera. The second sensor can also record a depth image of the transport space, which is used to determine an occupancy distribution of the transport space in the public transport vehicle based on the depth image. In a further embodiment of the invention, the second sensor is a sensor for counting passengers at doors and/or passageways and/or a sensor for recording image material. Furthermore, the occupancy distribution forecasting system according to the invention has a control unit which is intended and suitable for receiving the data from the first and second sensor units and for creating a forecast occupancy distribution from the received data, as well as a database coupled to the control unit, wherein empirical data, structural data and/or event data are stored in the database. The control unit has a memory with a suitable software program.

The occupancy distribution forecast system according to the invention also has an output device for outputting data derived from the forecast occupancy distribution. The output device has a plurality of optical displays and acoustic display devices (loudspeakers). Displays and loudspeakers are arranged, for example, in the ceiling area of the passenger compartment. In the area of a high forecast passenger distribution, the displays are intended to light up red, for example, while in areas with a low forecast passenger distribution the displays light up green. Due to the color signaling, a passenger is motivated to move from an area with a high forecast passenger distribution to an area with a low forecast passenger distribution. A preferred direction can also be indicated, for example, by a running direction of an LED running light arranged in the floor of a carriage and/or a platform at a stop. It is also conceivable to signal a direction in which carriages with a low predicted passenger distribution are to be expected. In addition, announcements are played over the loudspeakers, informing passengers of an area in the carriages with a low predicted passenger distribution.

In a further development of the invention, the first sensor unit has a further sensor, which differs in its functionality from the first sensor. While the first sensor can be used to count and locate passengers, the second sensor records the passenger behavior of individual passengers. The second sensor is optionally a sensor for image recognition, which can optionally be used to distinguish people from objects. In a further aspect of the invention, the first sensor unit is arranged in the public transport and/or the second sensor unit is arranged at the stop. Both sensor units each have two different sensors with which passengers can be counted both in the public transport and at the stop and passenger behavior can be recorded.

In another embodiment, the functionality of the first sensor is different from that of the second sensor. While the first sensor can be used to count and locate passengers, the second sensor records the behavior of individual passengers.

In a further embodiment of the invention, the first sensor is intended and suitable for counting passengers and/or the further first sensor is intended and suitable for determining passenger behavior. A first sensor is optionally advantageously arranged on entrance doors of the public transport vehicle and/or doors in the interior, e.g. passenger compartments with standing and/or seating areas, and records the number of passengers present there. Optionally, the first sensor is a depth image camera. The first sensor can record a depth image of the transport space, which is used to determine an occupancy distribution of the transport space in the public transport vehicle based on the depth image. The further first sensor is a sensor for image recognition, which can optionally be used to distinguish people from objects.

In a further embodiment of the invention, the control unit is coupled to the first sensor unit, to the second sensor unit, to the database and/or to the output unit. A method for outputting a predicted occupancy distribution in a public transport vehicle can be carried out by means of the control unit.

In a further aspect of the invention, the second sensor unit comprises a plurality of second sensors. The number and arrangement of the second sensors are selected such that all passengers in a public transport vehicle and/or on the platform of a stop can be detected. In a further embodiment of the invention, the plurality of second sensors of the second sensor unit monitor passenger behavior in public transport and/or at the next stop. The second sensors are image recognition sensors, which can optionally be used to distinguish people from objects.

In a further embodiment of the invention, the data recorded by the second sensor is evaluated using AI/ML. The memory of the control unit contains machine learning data that is applied to the data recorded by the sensor. This allows the passengers' preparatory actions for getting off to be significantly differentiated from other actions.

The embodiments are shown schematically in a simplified manner in the following drawings and explained in the following description.

Fig. 1 a: Occupancy distribution forecasting system in a public transport system

Fig. 1 b: Current state of the occupancy distribution in a public transport vehicle

Fig. 2 a: Occupancy distribution forecasting system in a railway platform

Fig. 2 b: Current state of the occupancy distribution in a platform

Fig. 3 a: Occupancy distribution forecasting system in a public transport vehicle, first and second sensors

Fig. 3 b: Occupancy distribution forecasting system in a railway platform, first and second sensors

Fig. 4 a: Actual state of the occupancy distribution and predicted occupancy distribution in a public transport system

Fig. 4 b: Actual state of the occupancy distribution and predicted occupancy distribution in a platform

Fig. 5 a: Occupancy distribution forecasting system in a public transport vehicle, first and second sensors, output device

Fig. 5 b: Occupancy distribution forecasting system on a railway platform, first and second sensors, output device

Fig. 6: Flowchart of the procedure for outputting a predicted occupancy distribution in a public transport system Fig. 7 a: Flowchart of the procedure for outputting a predicted occupancy distribution in a public transport system

Fig. 7 b: Flowchart of the procedure for outputting a predicted occupancy distribution in a public transport system, permanent output of the actual state and the predicted occupancy distribution

Fig. 8 a: Connections of first sensors, second sensors, AI/ML unit, database with the control unit and output devices as well as data flow, sensors in public transport

Fig. 8 b: Connections of first sensors, second sensors, AI/ML unit, database with the control unit and output devices as well as data flow, sensors in public transport and at second stop

Fig. 8 c: Connections of first sensors, second sensors, AI/ML unit, database with the control unit and output devices as well as data flow, sensors in public transport and at second stop, output devices in public transport and at second stop

Fig. 8 d: Connections of first sensors, second sensors, AI/ML unit, database with the control unit and output devices as well as data flow, sensors in public transport and at stops, output devices in public transport and at stops

Fig. 1 shows an embodiment of the occupancy distribution forecast system BVPS according to the invention arranged in a public transport OV. The public transport OV in this and all other embodiments is a light rail which runs on tracks G at regular intervals to a plurality of stops H1, H2 and stops at the stops H1, H2. The public transport OV has a plurality of carriages W coupled to one another and continuously connected. The carriages W have passenger seats P. By means of the doors T, a passenger F can enter and exit the carriages W and the passenger compartments A. Passengers F get on and off at the stops H1, H2, H3, with platform H3 being reached after platform H2 and platform H2 after platform H1. The stops H1, H2 each have a platform B. In this exemplary embodiment, the actual state of the occupancy distribution with passengers F of the carriages W of the public transport OV is recorded at a given time during the journey of the public transport OV. For this purpose, the interior of the carriages W of the public transport OV has the occupancy distribution forecasting system BVPS (Fig. 1 a). The occupancy distribution forecasting system BVPS has the first sensor unit SE1 with a plurality of first sensors S1. The first sensors S1 are arranged in the passenger compartments A of the carriages W and in the areas of the doors T. Each first sensor S1 is a depth image camera. The first sensors S1 are connected to the control unit C, which is arranged in the public transport OV. Passengers F and their positions in the carriages W are recorded by means of the first sensors S1.

The evaluation of three-dimensional depth images captured by the first sensors S1 has the advantage that, in contrast to a pure person count, a space actually occupied by the people and/or pieces of luggage can be recorded. For example, it is easily possible, particularly in rail transport, to detect an excessive occupancy of an area of the transport space with pieces of luggage or, in particular, bicycles.

At this point in time, the passenger seats P of the carriages W are not fully occupied; the carriages W have free passenger seats FP. The total number of the first sensors S1 detect the position of each passenger F in the carriages W at this given point in time. The control unit C determines the actual state of the occupancy distribution based on the data provided by the first sensors S1, whereby the occupancy distribution is the function of the number of passengers F over the length L(OV) of the public transport OV. The actual state of the occupancy distribution therefore indicates how many passengers F are where in the public transport OV at a given point in time. Complementary to the occupancy distribution is the distribution of the free passenger seats FP over the length L(OV) of the public transport OV (Fig. 1 b). This distribution of the free passenger seats FP over the length L(OV) of the public transport OV is particularly interesting for the passengers F and the transport company itself in order to ensure an even and as complete as possible occupancy of the public transport OV. Fig. 2 shows an embodiment of the occupancy distribution forecasting system BVPS according to the invention arranged on a platform B. The platform B has a plurality of doors T and elevators E for passengers F to enter or exit from the platform B. The platform B itself is separated from the track G, on which the public transport OV runs, by a platform edge BK.

In this exemplary embodiment, the actual state of the occupancy distribution with passengers F on platform B of public transport OV is recorded at a given point in time. For this purpose, platform B has the occupancy distribution forecasting system BVPS (Fig. 2 a). The occupancy distribution forecasting system BVPS has the first sensor unit SE1 with a plurality of first sensors S1, which are connected to the control unit C. The control unit C determines the actual state of the occupancy distribution based on the data provided by the first sensors S1, the occupancy distribution being the function of the number of passengers F over the length L(V) of platform B (Fig. 2 b). The actual state of the occupancy distribution therefore indicates how many passengers F are where on platform B at a given point in time.

Fig. 3 and Fig. 4 show embodiments of the occupancy distribution forecast system BVPS according to the invention arranged in a public transport vehicle OV (Fig. 3 a) and on a platform B (Fig. 3 b). In addition to the embodiments shown so far (see Fig. 1 a, 2 a), the occupancy distribution forecast system BVPS has a second sensor unit SE2 with a plurality of second sensors S2. The first sensors S1 are arranged in the passenger compartments A of the carriages W in the areas of the doors T. The second sensors S2 are also arranged in the passenger compartments A of the carriages W in such a way that they have an unobstructed line of sight (LoS = Line of Sight) of the passengers in the passenger compartments A and in the areas between the carriages W (Fig. 3 a). On platform B (Fig. 3 b), the second sensors S2 of the second sensor unit SE2 are also arranged in a structural unit with the first sensors S1 in such a way that they have an unobstructed line of sight to passengers F.

A first sensor S1 is, as explained, a depth image camera. The first sensors S1 are connected to the control unit C, which is arranged in the public transport OV. By means of the first sensors S1, passengers F and their positions in the carriages W are recorded. The control unit C determines the actual state of the occupancy distribution based on the data provided by the first sensors S1, whereby the occupancy distribution is the function of the number of passengers F over the length L(OV) of the public transport OV. The actual state of the occupancy distribution therefore indicates how many passengers F are where in the public transport OV at a given time.

The control unit C determines the actual state of the occupancy distribution of platform B based on the data provided by the first sensors S1, whereby the occupancy distribution is the function of the number of passengers F over the length L(V) of platform B (Fig. 3 b). The actual state of the occupancy distribution therefore indicates how many passengers F are where on platform B at a given time. The control unit C for determining the occupancy distribution of platform B is arranged on platform B.

A second sensor S2 is also a camera that is suitable for capturing images. In contrast to the first sensor S1, the second sensor S2 is not a depth-image camera, but rather creates images of the passengers F. The second sensor S2 constantly updates the images, digitizes the images and forwards them to the control unit C. The control unit C analyzes the passenger behavior from the images created by the second sensors S2. The control unit C has a memory and a suitable software program for analyzing the images. The first S1 and second sensors S2 are arranged in such a way that the entire transport area A and the entire platform B can be captured in three dimensions over the sum of all detection areas.

The analysis of passenger behaviour includes the analysis of movement flows of passengers F in the public transport OV (Fig. 3 a). In the public transport OV, the analysis of movement flows of passengers F includes the predicted passenger distribution FP(PRO) over the length L(OV) of the public transport OV at the time when the public transport OV reaches the stop H2 (see Fig. 4 a). The predicted passenger distribution FP(PRO) therefore indicates how many passengers F will be where in the public transport OV at the time when the public transport OV reaches the stop H2. On platform B, the analysis of movement flows of passengers F includes the predicted passenger distribution FP(PRO) over the Length L(B) of platform B at the time when the public transport OV leaves the

Stop H2 reached (see Fig. 4 b).

To analyze the predicted passenger distribution FP(PRO), the control unit C uses the determined actual state of the occupancy distribution at a given point in time, which was determined using the first sensors S1 (see Fig. 1, Fig. 2). In addition, the preparatory actions of the passengers F to get off are analyzed using the second sensors S2. The preparatory actions include, for example, the passengers F getting up from their seats, stowing personal items, putting on clothing and, in particular, the movement of passengers in the direction of the exit T of the public transport vehicle. The memory of the control unit C also contains machine learning data that is applied to the image recordings. This significantly distinguishes the preparatory actions of the passengers F to get off from other actions.

Fig. 5 shows further embodiments of the occupancy distribution forecast system BVPS according to the invention arranged in a public transport OV (Fig. 5 a) and on a platform B of a stop H2 (Fig. 5 b). The arrangement and functioning of the occupancy distribution forecast system BVPS has already been described in the previous embodiment (see Fig. 3). The analysis of the predicted passenger distribution FP(PRO) is also described in the previous embodiment (see Fig. 3, Fig. 4).

In addition, the occupancy distribution forecast system BVPS has an output device A. The output device A has a plurality of optical displays D and acoustic display devices (loudspeakers) L. Displays D and loudspeakers L are arranged in the ceiling area of the passenger compartment A (Fig. 5 a). In the area of a high predicted passenger distribution FP(PRO), the displays D are intended to light up red, while in areas with a low predicted passenger distribution FP(PRO), the displays D light up green. Due to the color signaling, a passenger F is motivated to move from an area with a high predicted passenger distribution FP(PRO) to an area with a low predicted passenger distribution FP(PRO). A preferred direction can also be shown, for example, by a running direction of an LED running light arranged in the floor of a carriage. In particular in the case of multi-section For trains, it is conceivable to signal a direction in which carriages W with a low predicted passenger distribution FP(PRO) are to be expected. In addition, corresponding announcements are played through the loudspeakers L, which inform passengers F of an area in the carriages W with a low predicted passenger distribution FP(PRO).

In the same way, on platform B, passengers F are shown an area with a low predicted passenger distribution FP(PRO) in the incoming public transport OV visually by the displays D and acoustically by the loudspeakers L (Fig. 5 b). This also motivates passengers F to find an area with a low predicted passenger distribution FP(PRO) on platform B before boarding the public transport OV. This speeds up the boarding and alighting of passengers F from and to the public transport OV.

The BVPS occupancy distribution forecasting system is also used to determine empirical data (see Fig. 6). For empirical data, data on the actual state of the occupancy distribution is used, which was determined one week (7 days) before the forecast occupancy distribution is output. Optionally, empirical data is determined at least one year, preferably one month and particularly preferably 14 days before the forecast occupancy distribution is output. The empirical data is recorded using the sensors S1, S2 of the BVPS occupancy distribution forecasting system. The actual state of the occupancy distribution is recorded as already described. In particular, the empirical data is determined over a period of time, i.e. a plurality of actual states are recorded by the sensors S1, S2 within a definable period of time before the forecast occupancy distribution is output; the respective actual states of the occupancy distribution are stored in the database DB and can be called up by the control unit C.

The experience data also includes data on the current state of passenger behavior, which is also determined using the BVPS occupancy distribution forecast system as described. The data on the current state of passenger behavior is also determined several times over a period of time and stored in the DB database.

Fig. 6 shows embodiments of the method for outputting a-PROG of a predicted

Staffing distribution and the recording of experience data. It shows which distribution driving step on which component of the occupancy distribution forecasting system BVPS is carried out at which time t. The public transport OV leaves the first stop H1 at time v-H1, reaches the second stop H2 at time a-H2 and requires the travel time FZ for this.

In the time interval Z1, the first sensors S1 record the actual state of the occupancy distribution in the public transport OV d-IST when the passenger movement in the public transport OV is low. In this time interval Z1, the passenger movement within the carriages W is usually low, the passengers F have taken their seats. The sensor data recorded by the first sensors S1 are sent to the control unit C, which determines the actual state of the occupancy distribution in the public transport OV c-IST. This determined c-IST actual state of the occupancy distribution is stored in the database DB (see Fig. 8) s-IST.

Within the second time interval Z2 15 s before reaching the next stop H2, the passenger behavior FV of the passengers F is recorded d- FV using the second sensors S2. In further embodiments of the method, the second time interval Z2 can be 2 min, preferably 1 min, and particularly preferably 30 s. The recorded d-FV sensor data from the second sensors S2 are also sent to the control unit C, which determines a predicted passenger distribution c-PROG. This created c-PROG is also stored in the database DB s-PROG. The predicted passenger distribution is determined for the time ZP c-PROG, which is immediately after passengers F get off the public transport OV at the next stop H2. The determined c-PROG predicted passenger distribution is also stored in the database DB. The predicted passenger distribution is sent to the output unit A, which outputs data of the predicted passenger distribution via loudspeakers L and displays D.

Fig. 7 shows embodiments of the method for outputting a-PROGn of predicted occupancy distributions as well as the implementation of experience data, structural data and event data. In a first variant (Fig. 7 a), in the time interval Z1 at the time Zp1, the actual state of the occupancy distribution is recorded d-OV by means of the sensors S1 arranged in the public transport OV when the passenger movement in the public transport OV is low. In this time interval Z1 at the time Zp1.1, the passenger movement within the carriages W is usually low, the passengers F have taken their seats. In addition, at the same time Zp1.1, the current passenger behavior is recorded using the first sensors S1. The recorded sensor data d-OV of the first sensors S1 are sent to the control unit C, which determines the actual state of the occupancy distribution and the current passenger behavior and stores it as experience data in the database DB.

Within the second time interval Z2 15 s before reaching a-H2 the next stop H2, the actual state of the occupancy distribution and the passenger behavior FV of the passengers F are recorded d-H by means of the second sensors S2 on the platform B of the second stop H2 at the time Zp2. The second time interval Z2 can be 2 min, preferably 1 min, and particularly preferably 30 s in further embodiments of the method. The recorded sensor data d-H of the second sensors S2 are also sent to the control unit C, which determines a predicted passenger distribution C-PROG2. The predicted passenger distribution is determined C-PROG2 for the time ZP, which is immediately after passengers F get off the public transport OV at the next stop H2. The predicted passenger distribution is sent to the output unit A, which outputs data of the predicted passenger distribution via loudspeakers L and displays D at time Zp2 a-PROG2.

In a variant (Fig. 6 b) of the method, in the time interval Z1 at time Zp1.1, using the sensors S1 arranged in the public transport OV within the third time interval Z3, which lies within the first time interval Z1 and has a shorter duration than the first time interval Z1, multiple detections d-OVn, determinations c-OVn taking into account the empirical data, structural data and event data stored in the database DB and outputs a-OVn of actual states of the occupancy distribution at regular, adjustable time intervals at the times Zp1 .1 to Zp1 .n. The outputs a-OVn are made via loudspeakers L and displays D within the public transport OV. Passengers F thus receive updated information about free seats within the public transport OV during the travel time FZ.

Fig. 8 shows variants of the connections of first sensors S1, second sensors S2, the AI/ML unit and the database DB with the control unit C and the output devices L, D and the data flow between the components mentioned both in the public transport OV and on the platform B of the second stop H2. In contrast to previously shown embodiments, the control unit C is arranged in such a way that a preferably wireless data exchange can take place between the components arranged in the moving public transport OV and in the stop H2.

The control unit C is connected to an AI/ML unit AI/ML, which has stored AI-ML data. The AI/ML unit AI/ML has machine learning data that is applied to the data recorded by the sensors S1, S2. This optionally significantly distinguishes the preparatory actions of the passengers F to get off from other actions. The database DB, which is also connected to the control unit C, has experience data, structural data and event data.

Experience data are occupancy distributions recorded by sensors S1, S2 at different times of stops Hn and of the public transport OV that stops at the Hn stops. The database DB also contains structural data, which is also applied to the recorded sensor data to create the predicted occupancy distribution. Structural data contains data about the architectural design, the spatial and/or room conditions of the stop and the public transport. Event data includes time and location information. The location information includes in particular the route of the public transport OV and the location of the stops Hn that the public transport OV stops at. The time information includes the date and time of day when the public transport OV stops at the Hn stops. The time information is used to select the experience data that has the same time parameters.

In a first variant (Fig. 7 a), the first sensors S1 are arranged in the public transport OV and the second sensors S2 on the platform of the stop H2 and send the recorded d-OVn, d-Hn data to the control unit C, which sends the determined actual states c- OVn, c-Hn and predicted states c-PROGn of the occupancy distribution to the output devices L, D of the stop H2, where they are output a-OVn, a-PROGn. In a further variant (Fig. 7 b), first sensors S1 are arranged in the public transport OV and send the recorded d-OVn data to the control unit C. Second sensors S2 are arranged at the stop H2 and also send their recorded d-Hn data to the control unit C. The control unit C determines c-OVn, c-Hn actual states and predicted states c-PROGn of the occupancy distribution of both the public transport OV and the stop H2, sends the determined c-OVn, c-Hn actual states and predicted states c-PROGn of the occupancy distribution to the output devices L, D of the stop H2 and the public transport OV, where they are output a-OVn, a-PROGn respectively.

In a further variant (Fig. 8 c), sensors S1, S2 are arranged in the public transport OV and send the recorded data to the control unit C. Sensors S1, S2 are also arranged at the stop H2 and also send their recorded data to the control unit C. The control unit C determines actual states c-ISTn and predicted states c-PROG of the occupancy distribution both of the public transport OV and of the stop H2, sends the determined actual states c-ISTn and predicted states c-PROG of the occupancy distribution to the output devices L, D of the stop H2 and of the public transport OV, where they are respectively output a-IST, a-PROG.

In a variant of the above embodiment (see Fig. 8 c), sensors S1, S2 are arranged at three different stops H1, H2, H3 and send their recorded data to the control unit C. First and second sensors S1, S2 are also arranged in the public transport OV and send the recorded data to the control unit C. The control unit C determines actual states c-ISTn and predicted states c-PROG of the occupancy distribution both of the public transport OV and of each individual stop H1, H2, H3 and sends the determined actual states c-ISTn and predicted states c-PROG of the occupancy distribution to the output devices L, D of each stop H1, H2, H3 and of the public transport OV, where they are respectively output a-IST, a-PROG.

According to the invention, the method presented here and the arrangement of the connections of first sensors S1, second sensors S2 with the control unit C and the output device directions L, D as well as the data flow between the mentioned components in public transport OV with any number of stops Hn.

Fig. 5 shows further embodiments of the occupancy distribution forecast system BVPS according to the invention arranged in a public transport OV (Fig. 5 a) and on a platform B of a stop H2 (Fig. 5 b). The arrangement and functioning of the occupancy distribution forecast system BVPS has already been described in the previous embodiment (see Fig. 3). The analysis of the predicted passenger distribution FP(PRO) is also described in the previous embodiment (see Fig. 3, Fig. 4).

In addition, the occupancy distribution forecast system BVPS has an output device A. The output device A has a plurality of optical displays D and acoustic display devices (loudspeakers) L. Displays D and loudspeakers L are arranged in the ceiling area of the passenger compartment A (Fig. 5 a). In the area of a high predicted passenger distribution FP(PRO), the displays D are intended to light up red, while in areas with a low predicted passenger distribution FP(PRO), the displays D light up green. Due to the color signaling, a passenger F is motivated to move from an area with a high predicted passenger distribution FP(PRO) to an area with a low predicted passenger distribution FP(PRO). A preferred direction can also be shown, for example, by a running direction of an LED running light arranged in the floor of a carriage. Particularly in the case of multi-unit trains, it is conceivable to signal a direction in which carriages W with a low predicted passenger distribution FP(PRO) are to be expected. In addition, corresponding announcements are played through the loudspeakers L, which inform passengers F of an area in the carriages W with a low predicted passenger distribution FP(PRO).

In the same way, on platform B, passengers F are shown an area with a low predicted passenger distribution FP(PRO) in the arriving public transport OV visually by the displays D and acoustically by the loudspeakers L (Fig. 5 b). This also motivates passengers F to identify an area with a low predicted passenger distribution FP(PRO) on platform B before boarding. get on public transport OV. This speeds up the boarding and alighting of passengers F from or onto public transport OV.

Fig. 6 shows embodiments of the method for outputting a-PROG of a predicted occupancy distribution. It shows which method step is carried out on which component of the occupancy distribution prediction system BVPS at which time t.

The public transport OV leaves the first stop H1 at time v-H1, reaches the second stop H2 at time a-H2 and requires the travel time FZ to do so (Fig. 6 a). In the time interval Z1, the actual state of the occupancy distribution is recorded d-IST using the first sensors S1 if the passenger movement in the public transport OV is low. In this time interval Z1, the passenger movement within the carriages W is usually low, the passengers F have taken their seats. The sensor data recorded by the first sensors S1 are sent to the control unit C, which determines the actual state of the occupancy distribution c-IST. Within the second time interval Z2, 15 s before reaching the next stop H2 a-H2, the passenger behavior FV of the passengers F is recorded d-FV using the second sensors S2. In further embodiments of the method, the second time interval Z2 can be 2 min, preferably 1 min, and particularly preferably 30 s. The recorded sensor data from the second sensors S2 are also sent to the control unit C, which determines a predicted passenger distribution c-PROG. The predicted passenger distribution is determined for the time ZP c-PROG, which is immediately after passengers F get off the public transport OV at the next stop H2. The predicted passenger distribution is sent to the output unit A, which outputs data on the predicted passenger distribution via loudspeakers L and displays D a-PROG.

In a variant (Fig. 6 b) of the method, an output a-IST of the actual state of the occupancy distribution is also made within the time interval Z1. Passengers F, particularly within public transport OV, are thus informed about the actual state of the occupancy distribution and can specifically take free seats, especially in a heavily occupied public transport OV. In a further variant (Fig. 6 c) of the method, multiple recordings d-ISTn, determinations c-ISTn and outputs a-ISTn of actual states of the occupancy distribution are also carried out within the first time interval Z1 at regular, adjustable time intervals. In this way, passengers F receive updated information about free seats, particularly within public transport OV.

Fig. 7 shows variants of the connections of first sensors S1, second sensors S2 with the control unit C and the output devices L, D as well as the data flow between the mentioned components both in the public transport OV and on the platform B of the second stop H2. In contrast to previously shown embodiments, the control unit C is arranged in such a way that a preferably wireless data exchange can take place between the components arranged in the moving public transport OV and in the stop H2.

In a first variant (Fig. 7 a), the sensors S1, S2 are arranged in the public transport OV and send the recorded data to the control unit C, which sends the determined actual states c-ISTn and predicted states c-PROG of the occupancy distribution to the output devices L, D of the stop H2, where they are output a-IST, a-PROG.

In a further variant (Fig. 7 b), sensors S1, S2 are arranged in the public transport OV and send the recorded data to the control unit C. Sensors S1, S2 are also arranged at the stop H2 and also send their recorded data to the control unit C. The control unit C determines actual states c-ISTn and predicted states c-PROG of the occupancy distribution both of the public transport OV and of the stop H2, sends the determined actual states c-ISTn and predicted states c-PROG of the occupancy distribution to the output devices L, D of the stop H2, where they are output a-IST, a-PROG.

In a further variant (Fig. 7 c), sensors S1, S2 are arranged in the public transport OV and send the recorded data to the control unit C. Sensors S1, S2 are also arranged at the stop H2 and also send their recorded data to the control unit C. The control unit C determines actual states c-ISTn and predicted states c-PROG of the occupancy distribution of both the public transport OV and also from the stop H2, sends the determined actual states c-ISTn and predicted states c-PROG of the occupancy distribution to the output devices L, D of the stop H2 and the public transport OV, where they are output a-IST, a-PROG respectively.

In a variant of the above embodiment (see Fig. 7 c), sensors S1, S2 are arranged at three different stops H1, H2, H3 and send their recorded data to the control unit C. First and second sensors S1, S2 are also arranged in the public transport OV and send the recorded data to the control unit C. The control unit C determines actual states c-ISTn and predicted states c-PROG of the occupancy distribution both of the public transport OV and of each individual stop H1, H2, H3 and sends the determined actual states c-ISTn and predicted states c-PROG of the occupancy distribution to the output devices L, D of each stop H1, H2, H3 and of the public transport OV, where they are respectively output a-IST, a-PROG.

According to the invention, the method presented here and the arrangement of the connections of first sensors S1, second sensors S2 with the control unit C and the output devices L, D as well as the data flow between the said components can be carried out in the public transport OV with any number of stops Hn.

REFERENCE SYMBOL LIST

A passenger compartment

B platform

BK platform edge

BVPS occupancy distribution forecasting system

C control unit

D Display

E elevator

DB database

F Passenger

FP Free Passenger PI rates

FV Current Passenger Behavior

vehicle driving time

G track

H1, H2, H3 stops

AI/ML storage with AI/ML data

L speaker

L(OV) Length of public transport

L(B) length of the platform

P Unoccupied seat/standing place

PB Predicted Occupation Distribution

PF Occupied seat/standing space

OV Public Transport

SE1 First sensor unit

SE2 Second sensor unit

S1 First Sensor

S2, Second Sensor

T timeline T door

Z1 First time interval

Z2 Second time interval

Z3 Third time interval

ZP Time of the predicted occupancy distribution v-H1 Leaving the first stop a-H2 Arrival at the second stop d-IST, d-IST1, d- Recording of the actual state of the occupancy distribution IST2, d-ISTn d-FV Recording of the current passenger behavior c-IST, C-IST1, C-IST2, Determination of the actual state of the occupancy distribution c-ISTn s-IST Saving of the actual state of the occupancy distribution a-IST, a-IST1, a- Output of the actual state of the occupancy distribution IST2, a-ISTn c-PROG Calculation of a predicted occupancy distribution a-PROG Output of a predicted occupancy distribution

Claims

PATENT CLAIMS 1. Method for outputting a predicted occupancy distribution (PB) in a public transport (OV) between two stops (H1, H2) with the following procedural steps: • Determination of empirical data, • Storing the experience data in a database (DB) of the staff distribution forecasting system (BVPS), • Recording an actual state of the occupancy distribution (IB) in public transport (OV) between two stops (H1, H2) using a sensor unit (SE1) of an occupancy distribution forecasting system (BVPS), • Reading out experience data from a database (DB) of the staff distribution forecasting system (BVPS), • Creating a forecast of the occupancy distribution (PB1) from the recorded actual state and the experience data read from the memory by a control unit (C) of the occupancy distribution forecast system (BVPS), • Output of first data derived from the forecast occupancy distribution (PB) on an output device (L, D) of the occupancy distribution forecasting system (BVPS). 2. Method for outputting a predicted occupancy distribution (PB) in a public transport (OV) between two stops (H1, H2) according to claim 1, characterized in that the empirical data are determined by the occupancy distribution forecasting system (BVPS). 3. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the empirical data are determined from experimentally recorded data. 4. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to claim 3, characterized in that the empirical data are determined by a sensor unit (SE1, SE2) of the occupancy distribution forecasting system (BVPS). 5. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to claim 4, characterized in that the sensor unit (SE1, SE2, SEn) is arranged in the public transport means (OV) and/or at a stop (H1, H2, Hn). 6. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the empirical data are determined at least one year, preferably one month, particularly preferably 14 days and especially preferably one week before the output of the predicted occupancy distribution (PB). 7. Method for outputting a predicted occupancy distribution (PB) in a public transport (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the experience data include the occupancy and passenger behavior in the public transport (OV) and/or the stop (H1, H2, Hn). 8. Method for outputting a forecast occupancy distribution (PB) in a public transport (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the forecast of the occupancy distribution (PB1) is created from event data, wherein the event data are used to create the forecast of the occupancy distribution (PB) in order to select the empirical data used for creating the forecast of the occupancy distribution (PB). 9. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to claim 8, characterized in that the event data comprise time and location information. 10. Method for outputting a predicted occupancy distribution (PB) in a public transport (OV) between two stops (H1, H2) according to claim 9, characterized in that Time information including time of day, season, holidays, vacation times, weather forecasts, reservations. 11. Method for outputting a predicted occupancy distribution (PB) in a public transport (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the forecast of the staffing distribution (PB1) is created from structural data. 12. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to claim 11, characterized in that the structural data comprise structural information of the stop (H1, H2, Hn) and/or the public transport means (OV). 13. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to claim 12, characterized in that the structural information comprises the architectural design and/or the spatial and/or space conditions of the stop (H1, H2, Hn) and/or the public transport means (OV). 14. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to claim 11 or 12, characterized in that the structural information comprises the functionality of the respective structure. 15. Method for outputting a predicted occupancy distribution (PB) in a public transport system (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the experience data, structure data and/or the event data are different for different stops (H1, H2, Hn). 16. Method for outputting a forecast occupancy distribution (PB) in a public transport (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the current passenger behavior (FV) in the public transport (OV) or at the next stop (H2) is recorded, wherein the forecast of the occupancy distribution (PB) is created from the recorded actual state and the recorded current passenger behavior (FV). 17. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the analysis of the passenger behaviour (FV) is carried out with regard to the preparatory actions for the alighting of passengers (F). 18. Method for outputting a predicted occupancy distribution (PB) in a public transport (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that Preparatory actions for passengers (F) to disembark from public transport (OV) include passengers (F) getting up from their seats, moving towards the exit of the public transport (OV) and/or stowing personal belongings. 19. Method for outputting a predicted occupancy distribution (PB) in a public transport (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the analysis of the passenger behavior (FV) is carried out with the aid of AI/ML. 20. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the analysis of the passenger behaviour (FV) comprises the analysis of movement flows of persons and/or a distribution of persons at the next stop (H2). 21. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the analysis of the passenger behaviour (FV) is carried out by a second sensor (S2). 22. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the second sensor (S2) is different from the first sensor (S1). 23. Method for outputting a predicted occupancy distribution (PB) in a public transport means (OV) between two stops (H1, H2) according to one or more of the preceding claims, characterized in that the second sensor (S2) is a camera for image capture. 24. Occupancy distribution forecasting system (OVPS) for a public transport (OV) with: • a first sensor unit (SE1) with a first sensor (S1), wherein the first sensor unit (SE1) is intended and suitable for counting passengers (F) in public transport (OV) and/or recording passenger behaviour (FV) in public transport (OV), • a second sensor unit (SE2) with a second sensor (S2), wherein the second sensor unit (SE2) is intended and suitable for counting passengers (F) at a stop (H 1 , H2) and/or recording passenger behavior (FV) at a stop (H1, H2), • a control unit (C) which is designed and suitable for receiving the data from the first and second sensor units (SE1, SE2) and for creating a predicted occupancy distribution (PB) from the received data, • a database (DB) coupled to the control unit (C), wherein experience data, structural data and/or event data are stored in the database (DB), • an output device (A) for outputting data derived from the predicted occupancy distribution (PB). 25. Occupancy distribution forecasting system (BVPS) for a public transport (OV) according to claim 24, characterized in that the first sensor unit (SE1) has a further sensor (S1.2) which differs in its functionality from the first sensor (S1.1). 26. Occupancy distribution forecasting system (BVPS) for a public transport (OV) according to claim 24 or 25, characterized in that the first sensor unit (SE1) is arranged in the public transport (OV) and/or the second sensor unit (SE2) is arranged at the stop (H1, H2). 27. Occupancy distribution forecasting system (BVPS) for a public transport (OV) according to one or more of claims 24 to 26, characterized in that the first sensor (S1.1, S1.2) is different in its functionality from the second sensor (S2.1). 28. Occupancy distribution forecasting system (BVPS) for a public transport (OV) according to claim 27, characterized in that the first sensor (S1.1) is provided and suitable for counting passengers (F) and/or the further first sensor (S1.2) is provided and suitable for determining the passenger behaviour (FV). 29. Occupancy distribution forecasting system (BVPS) for a public transport (OV) according to one or more of claims 24 to 28, characterized in that the control unit (C) is coupled to the first sensor unit (SE1), to the second sensor unit (SE2), to the database (DB) and to the output unit (A). 30. Occupancy distribution forecasting system (BVPS) for a public transport (OV) according to one or more of claims 24 to 29, characterized in that the evaluation of the data recorded by the second sensor (S2) is carried out with AI/ML support.