DE102024002047B3 - Computer-implemented method for determining a number of vehicles and computer program product - Google Patents

Abstract

The invention relates to a computer-implemented method for determining a number of vehicles passing through a specific geographical area within a specified time interval, wherein at least one measuring vehicle (1) continuously collects position data during operation with the aid of position-determining means, wherein the position data comprise at least the current geoposition of the measuring vehicle at the respective collection time of a respective position data point (2), the measuring vehicle (1) sends the position data to a computing unit, the computing unit enters each position data point (2) at its respective geoposition in a digital road map (3) and assigns it to a road section (4) of the digital road map (3). The method according to the invention is characterized in that the digital road map (3) is divided into grid segments (5.1, 5.2, 5.3), wherein the computing unit determines the number of vehicles by counting the position data points (2) assigned to a respective grid segment (5.1, 5.2, 5.3) in the time interval;
the computing unit enters a direct connecting line (6) for two consecutive position data points (2) in the digital road map (3) and generates at least one interpolation point (7) between the two position data points (2) on the connecting line (6);
the computing unit assigns the interpolation points (7) lying on the connecting line (6) to a road section (4) of the digital road map (3); and
the computing unit counts the interpolation points (7) to determine the number of vehicles.

Description

The invention relates to a computer-implemented method for determining a number of vehicles passing through a specific geographical area within a specified time interval according to the type defined in more detail in the preamble of claim 1, as well as to a computer program product designed to carry out such a method.

Traffic analysis can be used to determine the traffic volume or density within a specific geographical area, such as a specific road section. This involves counting the number of individual vehicles or road users within a specific road section within a specific time frame. Such traffic information can be used in a variety of ways, for example, to identify or predict traffic jams or to assess whether the infrastructure is adequate to handle traffic volumes in different situations. This can be used, for example, to determine whether a specific road section or road needs to be expanded.

The counting of vehicles passing through the said road section within the relevant time window can be done manually or automatically using machine counting devices. For example, light barriers can be installed at the entrance and exit of the said road section, with the help of which vehicles entering and leaving the road section can be automatically recorded. However, the disadvantage of this is that any information about traffic volume is limited to the monitored road section. Such a method for measuring traffic quantities is known, for example, from EP 2 009 610 A2 known.

Furthermore, it is known to record the location of vehicles in a fleet using positioning devices such as a GPS-based navigation system and to aggregate this position data over time in a digital road map. For this purpose, the corresponding vehicles regularly transmit their location at a specific transmission frequency. This allows the number of vehicles to be determined with temporal and spatial resolution using the digital road map. Such a method is known, for example, from WO 2019/158438 A1 The position data transmitted by the vehicles is aggregated by a server in the digital road map and assigned to road sections of the road network covered by the digital road map. Congested road sections are then identified, and this information is used to predict traffic volume on a non-congested road section.

The DE 102004054870 A1 Describes a method for creating digital road networks in which position data from road users is transmitted to a central location and evaluated there. The method comprises the following steps: a) storing the transmitted position data from at least one journey and one vehicle in the form of coordinates of a coordinate network in a database, b) dividing the area of the coordinate network into a predetermined number of grid areas with variable dimensions, c) determining an average of the geographical position data obtained for a grid area and considering this as the grid area center, whereby the coordinates of the associated base station are also counted as the grid area center when mobile phones are used. Alternatively, when geographical position data and cell IDs are received, a weighted average of the position data and the coordinates of the base station is used as the grid area center. d) To create an initial graph, the determined grid area centers are connected and, after checking the meaningfulness of these connections, are counted as nodes and the connecting edges as streets.

The DE 102006004130 A1 Describes a method for determining a future road route through communication between motor vehicles, independent of digital maps. The method comprises the following steps: continuously determining position data of the vehicle's own vehicle using a sensor in the geodetic coordinate system, storing the position data in a position data list based on a defined selection criterion, continuously or cyclically transmitting the current position data list via a communication medium, and comparing and/or supplementing the vehicle's own position data list upon receipt of a position data list from another road user. Furthermore, if data describing positions in front of the vehicle is available, information about the future road route is determined from this stored data. It is ensured that only position data describing a position within a predetermined distance from the vehicle's own vehicle is included in the position data list, while position data describing a position outside this distance is deleted.

The DE 102015000394 A1 describes a method for generating a digital map in which location-dependent estimated values of a physical measurement value are recorded. The method comprises the steps of: receiving measured values of the measurement value from several motor vehicles, determining a respective measurement position for each measured value, determining a respective scatter by which each measurement position is distorted, and generating a respective spatial distribution of the measured value based on the scatter, wherein the spatial distribution at different locations indicates the probability with which the respective measured value was measured there. The step additionally comprises determining an estimated value for several pixel areas of the map by combining the measured values, weighted according to the probability specified by the spatial distribution, and dividing the resulting measured value by the sum of the probabilities in order to obtain a standardized estimated value.

The present invention is based on the object of providing an improved method for determining the number of vehicles passing through a specific geographical area within a specified time interval, which method is capable of providing more meaningful results than solutions known from the prior art.

According to the invention, this object is achieved by a computer-implemented method having the features of claim 1. Advantageous embodiments and further developments as well as a computer program product for executing the method by a computing unit emerge from the dependent claims.

A computer-implemented method for determining a number of vehicles passing through a specific geographical area within a specified time interval, wherein at least one measuring vehicle continuously collects position data during operation with the aid of position-determining means, wherein the position data comprise at least the current geoposition of the measuring vehicle at the respective collection time of a respective position data point, the measuring vehicle sends the position data to a computing unit, the computing unit enters each position data point at its respective geoposition in a digital road map and assigns it to a road section of the digital road map, is further developed according to the invention in that the digital road map is divided into grid segments, wherein the computing unit determines the number of vehicles by counting the position data points assigned to a respective grid segment in the time interval;
the computing unit enters a direct connecting line for two consecutive position data points in the digital road map and generates at least one interpolation point between the two position data points on the connecting line;
the computing unit assigns the interpolation points lying on the connecting line to a road section of the digital road map; and
the computing unit counts the interpolation points to determine the number of vehicles.

Typically, measurement vehicles do not transmit their respective position data continuously, but rather at a fixed transmission frequency. For example, a measurement vehicle may transmit position data every minute, or every 20 seconds, or so. However, within this time, the measurement vehicle continues to move. Thus, the measurement vehicle's trajectory cannot be tracked seamlessly. This ultimately leads to the risk that the measurement vehicle's presence in some of the relevant geographical areas is not recorded, even though the measurement vehicle has already passed through the said geographical area. This leads to an incorrectly determined number of vehicles and thus to an incorrect result for a variable dependent on this variable, such as traffic density or traffic volume.

According to the invention, this problem is addressed by not directly considering the road section for counting a vehicle in a relevant geographical area, but rather a grid segment comprising a respective road section. Thus, at least the relevant part of the digital road map is divided into a grid comprising a plurality of grid segments. The grid segments are represented by a polygon. Individual grid segments can have a different shape and/or size, or they can have the same shape and/or size. Grid segments can be spaced apart from one another in the digital road map or directly adjacent to one another. Particularly preferably, the digital road map is evenly divided into said grid, so that all grid segments have the same shape and size and are directly adjacent to one another. Grid segments can also be distributed in the digital road map in a contact-analogous manner to a road course. The use of grid segments facilitates counting vehicles, since a vehicle or a position data point representing the geoposition of the vehicle does not lie entirely on said road section. must be counted. A grid segment is characterized by a larger area than the specified road section, thus reducing the risk of missing a vehicle. Furthermore, the processing effort required to count the vehicles is reduced. To increase the probability that a specified position data point is assigned to the actual geographical area in which the measuring vehicle was located, the position data points are assigned to road sections.

A measurement vehicle can be any vehicle in a fleet that has appropriate positioning means and is capable of transmitting corresponding position data to the computing unit. The positioning means used can be conventional state-of-the-art positioning means, such as navigation systems based on a global navigation satellite system or systems capable of determining the position of the respective measurement vehicle using triangulation. Such systems can, for example, analyze the received signal strength of several nearby mobile network base stations. Other positioning means, such as detecting the location of the measurement vehicle by visually capturing characteristic environmental features, are also conceivable.

Despite the use of a digital road map divided into grid segments, there is still a risk that, due to incomplete transmission of the position data, the respective measuring vehicle passes through grid segments but the location of the measuring vehicle in these grid segments is not recorded. To prevent this, the computing unit generates the connecting line between two consecutive position data points and generates corresponding interpolation points on the connecting line. These are then assigned to nearby road sections in the digital road map using well-known map matching methods. This makes it possible to determine the passing of grid segments that would otherwise have been ignored. This ultimately makes it possible to determine the correct number of vehicles for the respective grid segments with greater reliability, thus obtaining particularly meaningful information describing the traffic situation.

The computing unit can be a cloud server, for example. Communication with the computing unit is then possible, for example, via the internet. The respective measuring vehicles can be equipped with a suitable communication system for this purpose, such as a telecommunications unit. With the help of a telecommunications unit, the measuring vehicles can transmit data wirelessly, for example, using mobile communications, Wi-Fi, or the like, to the computing unit.

• - die Fortbewegungsgeschwindigkeit des Messfahrzeugs zum Erhebungszeitpunkt;
• - eine Aufenthaltszeit des Messfahrzeugs, in dem Rastersegment, in dem der respektive Positionsdatenpunkt liegt;
• - einem Zeitstempel, insbesondere in Form der Uhrzeit des Erhebungszeitpunkts.

An advantageous development of the method according to the invention provides that the position data points are each enriched with at least one of the following additional information:

• - the speed of movement of the measuring vehicle at the time of the survey;
• - a residence time of the measuring vehicle in the grid segment in which the respective position data point is located;
• - a timestamp, in particular in the form of the time of the survey.

With the help of this additional information, traffic conditions can be analyzed even more comprehensively. To enrich the position data, the respective measuring vehicles are equipped with corresponding computer systems. This could be, for example, a central on-board computer, the control unit of a vehicle subsystem, or the like. The computing unit entrusted with counting the vehicles, for example the aforementioned cloud server, can transfer the additional information assigned to the position data points to the self-generated interpolation points. Various mathematical and statistical methods can be used for this purpose. If, for example, the measuring vehicle is moving at the same speed at two consecutive position data points, this speed can also be assumed for the interpolation points. If, on the other hand, the speed of travel at the consecutive position data points is different, the average value of the speed at the first and second position data points can be applied to all interpolation points. For example, if the measuring vehicle is moving at 30 km/h at a first position data point and at 50 km/h at a second position data point, and three interpolation points are generated on the connecting line, these interpolation points could be assigned a speed of 35 km/h, 40 km/h, and 45 km/h, respectively. This would allow the acceleration of the measuring vehicle to be mapped accordingly.

Measuring vehicles can maintain a digital road map divided into grid segments. This allows the measuring vehicles to determine their own time spent in said grid segments and assign this time to the position data points. For example, the measuring vehicles can only assign the time spent in the grid segment to a position data point once the respective grid segment has been left. The said time spent in the grid segment can then be measured by the measuring vehicle. This time spent in the grid segment can therefore also be subsequently assigned to a position data point. The said position data point can then be retransmitted from the measuring vehicle to the computing unit. However, it is also conceivable for the measuring vehicles to estimate their time spent in a grid segment. For example, navigation guidance can be active in a respective measuring vehicle. This allows the measuring vehicle to estimate which upcoming road sections are being traveled at what speed. From this, a probable time spent in a grid segment can be derived and assigned to the position data points accordingly.

According to a further advantageous embodiment of the method according to the invention, the computing unit distributes the interpolation points equidistantly along the connecting line. In general, it would be conceivable to configure the spacing of the interpolation points along the connecting line depending on various variables, for example, depending on the speed of the measuring vehicle. If the measuring vehicle moves more slowly, for example, the interpolation points can be located closer together. However, there is a risk that interpolation points will then be assigned to the same grid segment multiple times. This could result in the same vehicle being counted twice. Such problems can be addressed by evenly distributing the interpolation points along the connecting line.

A further advantageous embodiment of the method according to the invention further provides that the computing unit determines the number of interpolation points to be generated on the connecting line proportional to the speed of the measuring vehicle. Thus, not only the distance but also the number of interpolation points can be made dependent on other variables. If the measuring vehicle moves faster, it also covers a greater distance in the same time. With a constant number of interpolation points, this would increase the risk that grid segments for counting the measuring vehicle would again be overlooked. As the speed of travel increases, more interpolation points are preferably generated on the connecting line. This ensures that each grid segment on the distance traveled by the vehicle is actually assigned an interpolation point.

According to a further advantageous embodiment of the method according to the invention, the computing unit determines the number of interpolation points to be generated on the connecting line inversely proportional to the size of the raster segments. This means that as the size of the raster segments increases, fewer interpolation points are generated on the connecting line. Conversely, if the size of the raster segments decreases, more interpolation points are generated. This ensures that an interpolation point is reliably generated for each raster segment, while simultaneously preventing multiple interpolation points from being generated for one and the same raster segment.

A further advantageous embodiment of the method according to the invention further provides that the computing unit determines such a number of interpolation points that, for a set of grid segments located on a road section extending between the two consecutive position data points, exactly one interpolation point is assigned to each grid segment of the set of grid segments. This enables a particularly meaningful determination of the number of vehicles. Double counting of one and the same measuring vehicle or overlooking the presence of the measuring vehicle in a grid segment can thus be reliably prevented. In this context, the number of interpolation points to be generated depends in particular on the size and distribution of the grid segments in the digital road map.

First, two consecutive position data points are determined. Next, the distance, i.e., the length of the connecting line, is determined. A check is then made to determine whether this distance is less than twice the resolution of the grid segments. "Resolution" in this context refers to the side length of the respective polygons. This is necessary to ensure that an interpolation point is not placed in the same grid segment as a position data point. If this condition is met, the number of required interpolation points is determined by the equation: $$\left(\text{Strecke}-2\times \text{Aufl}\ddot{\text{o}}\text{sung}\right)/\text{Aufl}\ddot{\text{o}}\text{sung}=\text{Anzahl notwendiger Interpolationspunkte}$$ certainly.

The position data points and interpolation points are linked to the information whether they are position data points or interpolation points. This allows you to subsequently switch between including interpolation points to determine the number of vehicles in the respective grid segments or not using them.

According to a further advantageous embodiment of the method according to the invention, it is further provided that the computing unit does not assign a further interpolation point to a grid segment if a position data point or an interpolation point lying on the same connecting line is already assigned to the respective grid segment. As already explained, this prevents double counting from one and the same measuring vehicle.

A further advantageous embodiment of the method according to the invention further provides that the computing unit uses artificial intelligence to assign at least one of the position data points or interpolation points to a respective road section. With the help of artificial intelligence, it is possible to easily and reliably assign position data points or interpolation points to road sections. All common machine learning algorithms can be used, such as "ball tree methods." In particular, the assignment of corresponding data points to road segments is carried out with the help of artificial neural networks.

To assign a point, the nearest street to a respective position data point is first determined on the digital road map, with a direction of travel in the same direction as that of the measuring vehicle. A road segment between the two consecutive position data points on this road is then determined. This road segment is then divided into subsegments depending on the number of interpolation points to be generated. For example, one more subsegment can be generated than the number of interpolation points provided. An interpolation point is then always placed at the end of such a subsegment.

According to a further advantageous embodiment of the method according to the invention, it is further provided that each road section in the digital road map is assigned a speed limit, and the computing unit, in the step of assigning at least one of the position data points or interpolation points to a respective road section, assigns it to a more distant road section if the travel speed of the measuring vehicle assigned to the position data point or interpolation point deviates by a specified amount from the speed limit applicable on the currently observed road section. This allows for a correct assignment of position data points or interpolation points to road sections or roads with greater reliability. Therefore, assignment to another road section on an adjacent road can also be made.

The speed limit could, for example, be the speed limit applicable under the road traffic regulations. However, it could also be a speed limit determined from historical data. For example, the average speed of measuring vehicles or other observed vehicles can be tracked over time on specific road sections, and an average speed limit can be derived from this. If the speed of the measuring vehicle deviates from this speed limit, this is an indication that the position of the measuring vehicle has been assigned to an incorrect road section or street. In this case, the said position data point or interpolation point is assigned to a road section of the nearest nearby road. Different values can be used for the specified value, such as 5 km/h, 10 km/h, or fractions or multiples thereof. The specified value can depend on the road type, so that, for example, a higher value applies on a country road than in an urban area.

A further advantageous embodiment of the method according to the invention further provides that the grid segments have the shape of a regular polygon and the distance from an interpolation point to its respective adjacent interpolation point or position data point is at most as large as the side length of one side of the grid segment. In a regular polygon, all sides are of equal length. The polygon can, for example, have the shape of a square. If the maximum distance between interpolation points on the connecting line is equal to the value of the side length of such a Polygon, it can be reliably achieved that exactly one interpolation point is found for each grid segment on the travel route of the measuring vehicle.

A computer program product according to the invention comprises machine-interpretable instructions that cause a processor of a computing unit to execute a method described above. The computer program product is accordingly stored on a computer-readable storage medium according to the invention.

Further advantageous embodiments of the computer-implemented method according to the invention and of the computer program product also emerge from the exemplary embodiments which are described in more detail below with reference to the figures.

• 1 einen schematischen Ausschnitt aus einer digitalen Straßenkarte, welcher von einer Recheneinheit zum Zählen der Anzahl von Fahrzeugen innerhalb von geografischen Arealen genutzt wird; und
• 2 einen zur 1 entsprechenden Ausschnitt aus der digitalen Straßenkarte, bei dem die Recheneinheit die Anzahl an Fahrzeugen mit Hilfe eines erfindungsgemäßen Verfahrens zählt.

Showing:

• 1 a schematic section of a digital road map used by a computing unit to count the number of vehicles within geographical areas; and
• 2 one to 1 corresponding section of the digital road map, in which the computing unit counts the number of vehicles using a method according to the invention.

By counting the number of vehicles passing through a geographical area within a specified time interval, information can be derived that can be used to characterize the traffic situation. 1 shows the process flow executed by a computing unit (not shown in detail), for example a cloud server. It shows an excerpt from a digital road map 3. It shows the course of a road comprising a plurality of road sections 4. A measuring vehicle 1 moves along this road in a direction indicated by an arrow. The measuring vehicle 1, for example a vehicle from a vehicle manufacturer's fleet, determines its current position at successive sampling times and transmits this data wirelessly to the computing unit at specified time intervals in the form of position data. The locations at which the measuring vehicle 1 is located at a respective sampling time are marked in the figures. These are position data points 2. As 1 can be seen, the measuring vehicle 1 moves forward, so that due to discontinuous data acquisition, the respective position data points 2 are distributed along the course of the road. The position data points 2 located behind the measuring vehicle 1 in the direction of travel have already been recorded and transmitted to the computing unit. The position data points 2 located in front of the measuring vehicle 1 are still to be recorded and transmitted. Depending on the positioning quality and the design of the digital road map 3, the position data points 2 can also be located next to the respective road being traveled. With the help of proven map comparison methods, the position data points 2 can be assigned to corresponding road sections 4.

The digital road map 3 is divided into a plurality of grid segments 5.1, 5.2, 5.3. The grid segments 5.1, 5.2, 5.3 can take on any desired shape and size. They are preferably uniform polygons. The fact that the grid segments 5.1, 5.2, 5.3 can be designed differently is indicated by a comparatively large square grid segment 5.1 and several smaller rectangular grid segments 5.2 and 5.3. If a position data point 2 lies within a grid segment, this has the reference symbol 5.2 (or 5.1) in the figures. A grid segment without a position data point 2 or without a 2 The interpolation point 7 shown has the reference number 5.3. The computing unit only counts those stays in grid segments 5.2 that have a corresponding position data point 2 or interpolation point 7. Although the measuring vehicle 1 is in 1 along the road, the presence of measuring vehicle 1 in said grid segments 5.3 is not detected, so that a deviating number of vehicles is determined for these geographical areas. Using grid segments 5.1 - 5.3, the presence of a vehicle in a specific geographical area can also be determined if an alternative route is taken that coincides with the respective grid segment 5.1 - 5.3, which is 1 top right is indicated by a dashed line or street.

Using a method according to the invention for counting the number of vehicles, it is possible to obtain more meaningful results. The procedure is described in 2 The computing unit first enters a direct connecting line 6 in the digital road map 3 between two consecutive position data points 2. This is shown in 2a) shown.

The computing unit then generates at least one interpolation point 7 on the connecting line 6 between the two position data points 2. This is shown in 2b) The number of interpolation points 7 to be generated by the computing unit can vary depending on various variables. For example, the number of interpolation points 7 to be generated can depend on the resolution or size of the grid segments 5.1, 5.2, 5.3 and/or the speed of travel of the measuring vehicle 1.

Preferably, the number of interpolation points 7 to be generated is selected such that exactly one interpolation point 7 is found for each grid segment 5.3 along the course of the road.

How 2b) shows, the interpolation points 7 are located far away from the road course and thus from the grid segments 5.3. It is therefore intended that the computing unit assigns the interpolation points 7 located on the connecting line 6 to the respective road sections 4 of the digital road map 3. This is shown in 2c ). Proven map matching methods, particularly those using artificial intelligence, can also be used here.

As the comparison of 1 and 2c ), the number of road sections or grid segments 5.1, 5.2, 5.3 for which no presence of the measuring vehicle 1 was detected can be reduced. Using the method according to the invention, it is thus possible to more reliably determine the actual number of vehicles for respective geographical areas. This ultimately allows for more meaningful results regarding the actual number of vehicles. Thus, a more realistic vehicle density or vehicle volume can be determined.

Claims (11)

Computer-implemented method for determining the number of vehicles passing through a specific geographical area within a specified time interval, wherein at least one measuring vehicle (1) continuously collects position data during operation with the aid of position-determining means, wherein the position data comprise at least the current geoposition of the measuring vehicle at the respective collection time of a respective position data point (2), the measuring vehicle (1) sends the position data to a computing unit, the computing unit enters each position data point (2) at its respective geoposition in a digital road map (3) and assigns it to a road section (4) of the digital road map (3), characterized in that the digital road map (3) is divided into grid segments (5.1, 5.2, 5.3), wherein the computing unit determines the number of vehicles by counting the position data points (2) assigned to a respective grid segment (5.1, 5.2, 5.3) in the time interval; the computing unit enters a direct connecting line (6) for two consecutive position data points (2) in the digital road map (3) and generates at least one interpolation point (7) between the two position data points (2) on the connecting line (6); the computing unit assigns each of the interpolation points (7) located on the connecting line (6) to a road section (4) of the digital road map (3); and the computing unit counts the interpolation points (7) to determine the number of vehicles. Procedure according to Claim 1 , characterized in that the position data points (2) are each enriched with at least one of the following additional information: - the speed of travel of the measuring vehicle (1) at the time of collection; - a residence time of the measuring vehicle (1) in the grid segment (5.1, 5.2, 5.3) in which the respective position data point (2) is located; - a time stamp, in particular in the form of the time of the collection time. Procedure according to Claim 1 or 2 , characterized in that the computing unit distributes the interpolation points (7) equidistantly on the connecting line (6). Procedure according to Claim 2 or 3 , characterized in that the computing unit determines the number of interpolation points (7) to be generated on the connecting line (6) proportional to the speed of travel of the measuring vehicle (1). Method according to one of the Claims 1 until 4 , characterized in that the computing unit determines the number of interpolation points (7) to be generated on the connecting line (6) inversely proportional to the size of the grid segments (5.1, 5.2, 5.3). Method according to one of the Claims 1 until 5 , characterized in that the computing unit determines such a number of interpolation points (7) that for a set of grid segments (5.1, 5.2, 5.3) which lie on a road section (4) extending between the two successive position data points (2), exactly one interpolation point (7) is assigned to each grid segment (5.1, 5.2, 5.3) of the set of grid segments (5.1, 5.2, 5.3). Method according to one of the Claims 1 until 6 , characterized in that the computing unit does not assign a further interpolation point (7) to a raster segment (5.1, 5.2, 5.3) if a position data point (2) or an interpolation point (7) lying on the same connecting line (6) is already assigned to the respective raster segment (5.1, 5.2, 5.3). Method according to one of the Claims 1 until 7 , characterized in that the computing unit uses artificial intelligence to assign at least one of the position data points (2) or interpolation points (7) to a respective road section (4). Method according to one of the Claims 2 until 8 , characterized in that a speed limit is each assigned to the road sections (4) in the digital road map (3) and the computing unit, in the step of assigning at least one of the position data points (2) or interpolation points (7) to a respective road section (4), carries out the assignment to a more distant road section if the speed of travel of the measuring vehicle (1) assigned to the position data point (2) or interpolation point (7) deviates by a predetermined amount from the speed limit applicable on the road section (4) currently being considered. Method according to one of the Claims 1 until 9 , characterized in that the grid segments (5.1, 5.2, 5.3) have the shape of a regular polygon and the distance from an interpolation point (7) to its respective adjacent interpolation points (7) or position data points (2) is at most as large as the side length of one side of the grid segment (5.1, 5.2, 5.3). Computer program product, characterized by machine-interpretable instructions that cause a processor of a computing unit to execute a method according to one of the Claims 1 until 10 to execute.