Patent Description:
With the increasing trend towards urban shared, distributed and on-demand mobility solutions such as present car sharing solutions and future urban air mobility solutions a key problem which needs to be addressed is in how to distribute a fleet throughout the zone of operation in order to maximize the usage of the available fleet.

To be able to maximize this usage the problem of accurately estimating local demand must be solved, independently of the modality of travel.

Prior art means perform such a demand estimation task in simple timeseries forecasting, which intends to only use the previous demand, but without considering other external conditions, which leads to inaccurate and/or not reliable forecasts, with which customers can be discouraged to use such a system.

<CIT> discloses a method and system for providing a mobility trace database, in particular to provide on-demand transportation services using an automated fleet dispatcher and the mobility trace database.

The problem to be solved is to improve the performance of demand estimation for a distributed shared mobility system. The problem of the invention is being solved by a method for determining a preferred location of a vehicle as defined in claim <NUM>.

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.

The function D(x) is not described deterministically, but though a probabilistic model (such as tree-based methods, deep neural-networks or Gaussian processes) and is learned based on the historic data, e.g. of Table <NUM>.

After the initial training phase conducted on the historical data, the demand estimator D(x) can receive new demand requests with all required information contained in the input x the output of this demand estimator is the predicted demand for that location, given by the value D̂ and e.g. displayed on the user dashboard for the operator to assess.

Later, the predicted D̂ value is fed-back into the prediction model alongside the actual value for the demand and can be used to enhance the system for future predictions.

The invention can be further developed as the situation data set comprises data related to at least one selected from the group including traffic information, weather information, event information and public transport information, each accompanying with a respective actual time information and a respective region information.

Thus, parameters having very significant impact on a traffic application can be considered with actual data values.

The advantage of the invention includes improved performance, as the system considers additional variables outside of pure historic information. In addition, the feedback system continuously improves the model over time.

Moreover, the application of a non-model based predictive system for estimating usage demand within a temporal and spatial region enables a very simple, but effective way to improve the forecast behavior.

The invention can be further developed as the historical data set comprises data related to at least one selected from the group including traffic information, weather information, event information and public transport information, each accompanying with a respective time information and a respective region information.

For instance, an integer value of user demand given by the function D(x) can be predicted, where the input x is a vector of the relevant features which are given in Table <NUM>.

The invention can be further developed as the demand estimator model is based on a machine learning principle, preferably though a probabilistic model, like a tree-based method, a deep neural-network or a Gaussian process.

Thus, a very accurate model can be considered, representing the actual traffic situation for the vehicle, and a respective accurate prediction can be made in a simple manner.

The problem of the invention is also being solved by a fleet management device for determining a preferred location of a vehicle, comprising.

wherein the fleet management device is configured to incorporate the preferred location into the demand estimator model.

The problem of the invention is also being solved by a fleet management system for determining a preferred location of a vehicle, comprising a fleet management device according to the invention and at least one vehicle with a vehicle device, and further a wireless communication means, which is configured to establish a wireless communication between the calculation device and the vehicle device to communicate the estimated location from the fleet management device to the vehicle device.

The invention can be further developed as the vehicle device is a navigation device, and the system is configured to execute a further step of setting a target location of the navigation device to a location corresponding to the location information of the preferred location.

The invention can be further developed as the at least one vehicle is part of a fleet with a multiplicity of vehicles, which the system comprises, and actual and/or historical locations of the at least one vehicle are considered in the demand estimator model.

The invention can be further developed as statistical representations of actual and/or historical locations of the at least one vehicle are/is considered in the demand estimator model.

The invention will be explained in more detail with reference to an exemplary embodiment shown in the attached drawings. In the drawings show in:.

It is clear, that further not shown parts are necessary for the operation of a system, e.g. electronic control components, data storage devices or other computing equipment. For the sake of better understanding these parts are not illustrated and described.

<FIG> shows a sketch with a schematic illustration of the principle of the invention with a fleet management system <NUM> for determining a preferred location <NUM> of a vehicle <NUM>.

The fleet management system <NUM> comprises a fleet management device <NUM> according to the invention and at least one vehicle <NUM> with a vehicle device <NUM>.

The fleet management system <NUM> captures situation data and historical data including traffic information <NUM>, weather information <NUM>, event information <NUM> and public transport information <NUM>, each accompanying with a respective actual time information and a respective region information.

Further, the fleet management system <NUM> comprises a wireless communication means <NUM>, which is configured to establish a wireless communication between the calculation device <NUM> and the vehicle device <NUM> to communicate the estimated location <NUM> from the fleet management device <NUM> to the vehicle device <NUM>.

The fleet management system <NUM> captures a demand estimation request <NUM>, e.g. manually or automatically from an operator.

<FIG> shows an embodiment of a block diagram according to the invention.

The invention provides a method for determining a preferred location <NUM> of a vehicle <NUM>, comprising following steps:.

The situation data set <NUM> comprises data related to traffic information <NUM>, weather information <NUM>, event information <NUM> and public transport information <NUM>, each accompanying with a respective actual time information <NUM> and a respective region information <NUM>-<NUM>.

Thus, actual data within the situation data set <NUM> with traffic information <NUM>(<NUM>, <NUM>), weather information <NUM>(<NUM>, <NUM>), event information <NUM>(<NUM>, <NUM>) and public transport information <NUM>(<NUM>, <NUM>) at respective, predefined locations <NUM>-<NUM> are provided.

The historical data set <NUM> comprises data related to traffic information <NUM>, weather information <NUM>, event information <NUM> and public transport information <NUM>, each accompanying with a respective time information <NUM>-<NUM> and a respective region information <NUM>-<NUM>.

Thus, historic data within the historical data set <NUM> with traffic information <NUM>(<NUM>, <NUM>), weather information <NUM>(<NUM>, <NUM>), event information <NUM>(<NUM>, <NUM>) and public transport information <NUM>(<NUM>, <NUM>) with respective timestamps <NUM>-<NUM> at respective, predefined locations <NUM>-<NUM> are provided by respective information collection devices <NUM>-<NUM>.

It is clear, that the term "actual time" <NUM> covers a certain time period just before the actual moment e.g. in order to enable computer calculations at the execution of the method of the invention or to cover transmission delays of respective situation data. For instance, the actual time <NUM> can be e.g. the actual local time with reference to UTC (Universal Time Coordinated), including the past five or ten or <NUM> minutes, generated by a time base device <NUM>.

The demand estimator model <NUM> is based on a machine learning principle, preferably though a probabilistic model, like a tree-based method, a deep neural-network or a Gaussian process.

<FIG> shows also the building block of a fleet management device <NUM> for determining the preferred location <NUM> of the vehicle <NUM>, comprising.

wherein the fleet management device <NUM> is configured to incorporate the preferred location <NUM> into the demand estimator model <NUM>.

The first capturing means <NUM>-<NUM> can be a data service via internet or measured data from dedicated sensors, e.g. temperature sensors, video sensors for sensing traffic flow or data from electronic toll collection systems. Location and actual time information is added to each data sample.

The second capturing means <NUM> can be a data aggregator from historic data, using previous actual data e.g. manipulated by statistical methods. Location and related time information is added to each data sample.

The estimation model processing means <NUM> can be for instance a computing device or a service in the cloud.

The third capturing means <NUM> can manually by an operator <NUM> or automatically capture a request <NUM> and adds the actual time information <NUM> and location information <NUM> to define a demand estimation request <NUM>.

The fleet management system <NUM>, e.g. for a car rental fleet, for determining the preferred location <NUM> of the vehicle <NUM> can be defined, comprising a fleet management device <NUM> and at least one vehicle <NUM> with a vehicle device <NUM>, and further a wireless communication means <NUM>, which is configured to establish a wireless communication between the calculation device <NUM> and the vehicle device <NUM> to communicate the estimated location <NUM> from the fleet management device <NUM> to the vehicle device <NUM>.

The vehicle device <NUM> is a navigation device, and the system is configured to execute a further step of setting a target location of the navigation device to a location corresponding to the location information of the preferred location <NUM>. Thus, the preferred location <NUM> can be set for a driver of the vehicle <NUM> by setting a target point within the navigation device <NUM>, e.g. to manage the rental-return of the vehicle <NUM>, which is part of a rental fleet.

The system <NUM> comprises the at least one vehicle <NUM> is part of a fleet with a multiplicity of vehicles and actual and/or historical locations of the at least one vehicle <NUM> are considered in the demand estimator model <NUM>. The fleet can be a fleet of vehicles for a shared mobility system.

Claim 1:
Computer-implemented method for determining a preferred location (<NUM>) of a vehicle (<NUM>) with a navigation device (<NUM>), comprising following steps:
a) capturing a situation data set (<NUM>) at an actual time (<NUM>) and a respective location (<NUM>-<NUM>) by a first capturing means (<NUM>-<NUM>),
b) capturing a historical data set (<NUM>) for a predefined time period, excluding the actual time (<NUM>), at respective locations (<NUM>-<NUM>) and calculating from the historical data set (<NUM>) a characteristic data set (<NUM>) by a second capturing means (<NUM>),
c) setup and training of a demand estimator model (<NUM>) using the situation data set (<NUM>) and the characteristic data set (<NUM>) by an estimation model processing means (<NUM>),
d) capturing a demand estimation request (<NUM>) by a third capturing means (<NUM>),
e) calculating a demand estimator using the demand estimator model (<NUM>) and the demand estimation request (<NUM>) by a computer,
f) estimating the preferred location (<NUM>) with location information using the demand estimator by the computer,
g) incorporating the preferred location (<NUM>) into the demand estimator model (<NUM>) by the computer
h) communicate the preferred location (<NUM>) by a wireless communication means to the vehicle with the navigation device (<NUM>),
i) setting a target location of the navigation device (<NUM>) to a location corresponding to the location information of the preferred location (<NUM>).