FR3091921A1 - Mobility assistance to provide a recommendation of a preferred mode of transport for a trip - Google Patents

Abstract

The present invention relates to a computer system (100) for assistance to mobility for providing a user (U) with a recommendation of a preferred mode of transport (V1, V2, V3) in order to make a journey, said system ( 100) comprising: - a first module (10) for acquiring path data; - a second module (20) for acquiring contextual data; - a computer learning module (30) configured to generate from the journey data and the contextual data a mobility model representative of at least one mobility habit; and - an intelligent recommendation module (40) of the preferred mode of transport configured to determine, as a function of the mobility model and of the contextual data collected at the determined time, a recommendation of the mode of transport (V1, V2, V3) to be preferred in order to achieve said path at the determined instant. Figure for the abstract: Figure 1

Description

Mobility assistance to provide a recommendation of a preferred mode of transport for a trip

The present invention relates to the field of mobility assistance to assist users in their journeys.

One of the objects of the present invention is to provide a multimodal mobility service offering its user a recommendation of a preferred mode of transport for making a journey.

The present invention will thus find numerous advantageous applications in the industry of intelligent and connected vehicles, and in particular in connected automobiles.

The present invention will also find an advantageous application in the development of mobility services.

Technology background

Many navigation systems are known today which make it possible to assist a user in his travels.

Among these systems, we mainly distinguish:

- navigation systems which are specifically dedicated solely to automobile and individual travel; among these systems, there are in particular systems such as for example W aze ®, G oogle Navigation ® or even T omtom ® ; and

- navigation systems which are specifically dedicated to modes of travel using public or shared transport, such as the City Mapper ® system, for example.

Navigation systems focused on a single mode of transport (car or public transport) remain however unsatisfactory and have limitations in daily use.

Indeed, several modes of transport are generally available to the same person to make the same journey; Among the possible modes of transport, we find the car but also public transport (tramway, metro, bus, etc.), bicycles or even shared vehicles, etc.

We are talking about a multimodal system.

The document US 20140278086 A1 describes a multimodal navigation system which makes it possible to predict the departure time by taking into consideration information relating to the context on the journey.

The document EP 3254060 A1 describes a multimodal trip planning system which takes into account public transport and so-called ordered transport. In this document, it is intended to provide a user with multimodal transport routes that incorporate public transport systems and ordered transport services that do not operate according to a fixed schedule, but are ordered by a user in advance of the time. use of the service. The transport services ordered are, for example, services of the taxi service type, car rental services or even limousine services.

The Applicant submits, however, that the systems proposed so far in the state of the art do not take into account the lifestyle of their users.

A person can indeed favor public transport or the bicycle on certain weekdays to get to his place of work and use the car on Wednesday afternoon to drive his children to a sporting or cultural activity.

Weather conditions can also affect the use of one mode of transport or another. Thus, this same person can favor the use of the car to the bicycle in bad weather. In the same way, during the week, she can prefer public transport to cycling during a pollution peak (with the exception of Wednesdays when the car is preferred to drive the children).

The different applications known until now remain compartmentalized in their areas of competence (individual car, alternative transport) and it is up to the user to find and choose the mode of transport he prefers according to his habits to achieve a trip.

Object and summary of the present invention

The present invention aims to improve the situation described above.

The present invention therefore aims to remedy the various drawbacks mentioned above by proposing a mobility assistance solution making it possible to provide a user with a personalized recommendation of a mode of transport to be favored according to the habits of said user and the context. on the road.

To this end, the object of the present invention relates, according to a first aspect, to a mobility assistance method for providing a user with a recommendation of a preferred mode of transport in order to make a journey.

Advantageously, the method according to the present invention is implemented by computer means and comprises a preliminary learning phase followed by an active phase called the use phase.

Advantageously, the learning phase comprises a first acquisition of at least one path datum containing information relating to a path taken.

Advantageously, the learning phase further comprises a second acquisition of at least one contextual datum containing information relating to the context on said route taken.

Advantageously, the learning phase further comprises a generation by a computer learning module of a mobility model representative of a mobility habit as a function of the journey data and the contextual data.

Preferably, the mobility model is capable of determining a preferred mode of transport from a list of at least one available mode of transport.

Advantageously, the use phase includes a recommendation of a mode of transport to be favored for carrying out a journey at a determined moment by using the mobility model and at least one contextual data item associated with said journey to be carried out at the determined moment.

Thus, the present invention by the implementation of its various technical steps makes it possible to assist a user in a journey by being able to take into account both several modes of transport but also the preferences and habits of the user as well than the context of the journey. The implementation of this learning phase makes it possible to record the habits and preferences of a user's journey and to combine these with contextual situations to build a mobility model making it possible to adapt the instructions of a multimodal navigation system.

Advantageously, the use phase includes a transmission to the user U of a notification of said recommendation of the mode of transport to be favored for carrying out said journey and/or a display of said recommendation via a graphical interface of the human interface type /machine.

Advantageously, the use phase includes a third acquisition of all the modes of transport available near the user from geolocation data associated with a position of the user at the determined instant.

Preferably, the contextual data comprises at least one piece of information relating to the conditions of travel such as in particular:

- the time and/or day of the journey; and or

- weather conditions on the route; and or

- the traffic conditions on the route; and or

- the level of ambient air pollution on the journey.

Preferably, the journey data includes at least one piece of information relating to the habits of the user such as in particular:

- the type of journey; and or

- the mode of transport used; and or

- the person(s) transported during the trip.

Advantageously, the use phase includes, prior to the recommendation step, a determination of the route to be taken based on the mobility model and the determined instant.

Advantageously, the determination of the route to be taken at the determined instant includes an interrogation of an electronic diary of said user.

Correlatively, the object of the present invention relates, according to a second aspect, to a computer program comprising instructions for the execution of the steps of a method such as that described above, this in particular when the computer program is executed by at least one processor

Such a computer program can use any programming language, and be in the form of source code, object code, or intermediate code between source code and object code, such as in a partially compiled form, or in any other desirable form.

The object of the present invention also relates according to a third aspect to a recording medium readable by a computer on which is recorded a computer program comprising instructions for the execution of the steps of the method according to the first aspect of the invention. .

On the one hand, the recording medium can be any entity or device capable of storing the program. For example, the medium may comprise a storage means, such as a ROM memory, a CD-ROM or a ROM memory of the microelectronic circuit type, or even a magnetic recording means or a hard disk.

On the other hand, this recording medium can also be a transmissible medium such as an electrical or optical signal, such a signal being able to be conveyed via an electrical or optical cable, by conventional or hertzian radio or by self-directed laser beam or by other ways. The computer program according to the invention can in particular be downloaded from an Internet-type network.

Alternatively, the recording medium may be an integrated circuit in which the computer program is incorporated, the integrated circuit being adapted to execute or to be used in the execution of the method in question.

The object of the present invention relates, according to a fourth aspect, to a mobility assistance computer system for providing a user with a recommendation of a preferred mode of transport in order to make a journey, said system comprising computer means for implementation of the steps of the method such as that described above.

Advantageously, such a system according to the present invention comprises a first acquisition module configured to collect at least one path datum containing information relating to a path.

Advantageously, the system according to the present invention further comprises a second acquisition module configured to collect at least one contextual data item containing information relating to the context on said path.

Advantageously, the system according to the present invention further comprises a learning computer module configured to generate from the collected journey data(s) and the collected contextual data(s) a mobility model representative of a mobility habit; and

Advantageously, the system according to the present invention further comprises a module for intelligent recommendation of the mode of transport to be favored configured to determine, according to the mobility model and at least one contextual data item collected at the determined instant, a recommendation of the preferred mode of transport from among a list of at least one mode of transport to carry out said journey at the determined instant.

The object of the present invention finally relates, according to a fifth aspect, to a vehicle on which is embedded a mobility assistance computer system as described above.

Thus, through its various functional and structural technical characteristics described above, the user has a personal mobility assistant comprising on-board intelligence providing the latter with a recommendation of the best mode of transport to take, taking into account the habits of this last and the context of the journey to be made.

Brief description of the appended figures

Other characteristics and advantages of the present invention will emerge from the description below, with reference to the appended figures which illustrate an exemplary embodiment thereof without any limiting character and in which:

schematically represents the architecture of a mobility assistance system according to an exemplary embodiment of the present invention to provide a user with a recommendation of a preferred mode of transport;

represents a flowchart of a mobility assistance method for providing a user with a recommendation of a preferred mode of transport according to an example embodiment of the present invention; and

shows a flowchart of a mobility assistance method for providing a user with a recommendation of a preferred mode of transport according to another embodiment of the present invention.

Detailed description according to an advantageous embodiment

A mobility assistance method for providing a user with a recommendation of a preferred mode of transport for making a journey as well as the computer system associated with it will now be described in the following with reference in conjunction to the figures. 1, 2 and 3.

In the example described here, provision is made for the implementation of a mobility assistance computer system 100 making it possible to accompany said user U on an upcoming journey, such a system being able to be integrated into a multimodal navigation device .

For the record, the concept underlying the present invention is to provide the user U with a recommendation of a preferred mode of transport V1, V2 and/or V3 for making a journey, taking into consideration the available modes of transport. near the user, the preferences and habits of the latter as well as the context relating to the journey to be made.

In this example, the approach implemented in the context of the present invention is to provide a learning phase P0 during which a mobility model MM representative of the habits of the user U is constructed.

During this phase P0, the first acquisition module 10 of the system 100 collects during a step S1 the data D1, called journey data, which relate to the different journeys made by the user U during this learning phase P0.

Here, the journey data D1 contain information relating to the various journeys made such as for example the type of journey (going to work, returning home, driving the children to school or to a sporting activity or cultural, etc).

The journey data D1 contain other information such as for example the mode of transport V1, V2 and/or V3 taken on the journey in question: it may be for example a journey made by car, in a shared car , by bike, by public transport (metro, bus, etc.). Of course, it will be understood here that this list is not exhaustive and that all means of transport can be considered here.

The journey data D1 may also contain information relating to the person or persons transported during the journey (for example: a journey made alone, with the children or else for example with a colleague).

Other relevant information relating to the path can also be acquired during this first acquisition step S1.

It will be understood here that these data D1 can be acquired via one or more sensors on board the vehicle.

It may be for example a first sensor positioned under each of the seats of the vehicle and making it possible to detect the presence of a passenger in the car and/or to measure the weight of the passengers to determine whether the detected passenger is a child or an adult. It can also be a camera implementing image processing means for facial recognition for example.

It can also be a second sensor making it possible to geolocate the vehicle to determine the type of journey by detecting the starting point and the point of arrival and thus deducing that it is a journey between the home and the workplace.

These data D1 can also be entered, declared or filled in manually or vocally by the user U during this learning phase P0. Provision is then made for a graphical interface of the IHM type or means for processing audio signals of the speech recognition type allowing the user on a communication terminal T to be able to provide information relating to his journey: the type of journey, the number and the type of passengers for example.

Still in this learning phase P0, the second acquisition module 20 of the system 100 allows during a step S2 a collection of contextual data D2 containing information relating to the context on the journeys made during this learning phase P0.

Thus, the module 20 is for example capable of interrogating one or more remote servers SE1, SE2, SE3 to obtain information relating to weather conditions, pollution or even traffic conditions encountered on the journey.

In the example, the module includes a radio frequency signal transmitter/receiver configured to extract data from one or more remote servers.

These examples of data are not limiting and it is understood that other information relating to the context of the journey can also be collected by the module 20 during this learning phase P0.

This D2 data can also be acquired via one or more sensors on board the vehicle.

It can for example be a third sensor configured to generate a signal depending on the ambient light to indicate whether it is night or day driving, depending on the presence of drops on the windshield and/or depending on an operating mode of a lighting member or of a windscreen wiper of said vehicle.

It may also be a fourth sensor capable of measuring a degree of pollution of the ambient air.

All of these data D1 and D2 are then processed by a learning algorithm of the Learning Machine type, for example, which is implemented on a learning computer module 30 to generate a mobility model MM representative of at least one mobility habit of the user.

Such a mobility model MM is therefore able to know the mobility habits of the user and to model them in order to determine the best mode of transport to take for the user during his next trip.

We can also provide that the user U declares himself via the interface his mobility habits to build himself a mobility model for a particular journey or to complete the missing information.

In any case, in this example, it is expected at the end of this learning phase that a mobility model MM has been constructed by the system and that this model MM knows that the user U has a preference for the use of the bicycle or scooter to get to work on weekdays when the weather permits, except on Wednesdays when the latter must drive his children by car to football and dance. The MM mobility model also knows that this same user prefers the car in bad weather unless the pollution rate is too high and, in this case, he uses the metro or the tram depending on departure times.

Once the mobility model MM has been constructed, a so-called use phase P1 is entered during which, during a step S4, the intelligent recommendation module 40 of the system determines from this model MM the best mode of transport to favor to make the next trip at a given time.

Optionally, the module 40 is also capable, depending on the day and the time, of determining prior to this determination step S4 the route to be taken. The path to be taken is determined on the basis of the mobility model MM. The MM model thus knows that on Monday morning around 8:00 a.m. the user leaves for work. In the evening around 6:00 p.m., he returns to his home. Finally, on Wednesday afternoon around 1:30 p.m., he must go to the football training center and the dance hall before returning to the workplace.

The system is therefore able to assist and personalize the navigation of the user U according to his habits.

Prior to this step S4, the module 40 is also capable of interrogating an electronic diary AE containing the information relating to the diary of the user U, for example to determine the meeting places of the user U and thus determine the journey(s) to come according to the time of the appointment(s) contained in the AE diary.

The system therefore has a predictive character: the intelligent recommendation module 40 is capable of exploiting in particular the information contained in the mobility model, the contextual data D2 collected at the determined instant and the data from an electronic diary AE.

The system therefore provides the user with information relating to the journey to be made. In this example, it is expected that once identified the path must be validated by the user U via a graphical interface 50.

At the same time, the module 40 interrogates an alternative mobility platform PMA to know all the modes of transport available near the user U.

To obtain this information, it is necessary to use the geolocation data of the user U. The processing of this data makes it possible to provide a list of modes of transport V1, V2 and V3 available near the user U.

Depending on all of this information and the contextual data collected during this phase of use P1, the module 40 sends a notification during a step S4 containing a recommendation of the mode of transport V1, V2 or V3 to be taken in function of said MM model. This recommendation is then notified to the user U and displayed during a step S5 in the form of a message of the PUSH or other message type which is displayed on the graphical interface 50 of the system 100.

Such a graphical interface 50 is for example the interface of the communication terminal T of the user U and/or on the graphical interface of the navigation system of the user U's vehicle.

It is thus understood that the notifications issued are based on the habits of the user.

Thus, according to a first example, for a user who usually goes to work by bike except on certain days of the week when the car is necessary to transport the children to their sports, the notifications will be of the type:

→ Notification of travel by car on the days of the week concerned with associated travel time; and

→ Notification of cycling on other days of the week with advice on equipment/clothing related to the weather (rain, temperature).

According to a second example in which the habits of the user are to usually go to work by car and occasionally by bike depending on weather conditions or traffic conditions, the notifications may be as follows:

→ Notification of travel by car with associated travel time; and

→ Notification of an alternative proposal for travel by bicycle when the weather conditions are suitable (good weather, pleasant temperatures) and/or the traffic conditions by car are poor or in the event of a pollution peak.

According to a third example, when a user's habits consist of usually going to work by bus and occasionally by bike depending on weather conditions or traffic conditions, the notifications will be of the following type:

→ Bus travel notification with associated travel time; and

→ Notification of an alternative proposal for travel by bicycle when the weather conditions are suitable (good weather, pleasant temperatures) and/or the traffic conditions by bus are poor.

According to a fourth example, when the habits of a user are to go to work usually by bicycle and occasionally by shared vehicle in very bad weather conditions, the notifications can be of the type:

→ Bicycle trip notification with associated travel time; and

→ Notification of an alternative proposal for shared vehicle travel when the weather conditions are very bad (snow, heavy rain).

Of course, it will be understood here that these different situations are only examples among others; all situations remain possible depending on the mobility habits and preferences of a user.

The present invention therefore proposes a system capable of recording the habits and preferences of a user's journey and of combining these with contextual situations to adapt the instructions of a multimodal navigation system.

The present invention thus offers an innovative multimodal mobility assistance solution which is capable of recommending the best means of transport (car, bus, tram, bicycle, etc.) as well as the travel time necessary to make a next journey according to one's habits. of mobility (type of journeys, means of transport, people transported, sporting profile, etc.) and depending on the context (day considered, climatic conditions, traffic conditions, pollution, etc.).

Such mobility assistance is made possible by an assistance computer system 100 implementing the following various technical and software bricks:

• a module 10 to record the user's routes regardless of the mode of transport used (car, bus, bicycle, etc.)
• a module 20 making it possible to collect the contexts of the journey;
• a learning module to determine mobility habits depending in particular on the context, these habits can be automatically learned or declared
• an intelligent recommendation module 40 notifying the user of the recommended mode of transport for the upcoming trip with the corresponding arrival time; and
• an HMI 50 interface to present the recommendation and interact with the user (smartphone or vehicle screen).

The present invention thus makes it possible to help the user in his mobility and more particularly to help him choose the best mode of transport on a daily basis.

By developing such a solution, it becomes possible to develop mobility services.

Optionally, provision is made in the example described here for the provision in the notification of information relating to the estimated time of arrival at the destination point taking into account the habits of the user, and in particular the speed average driving on the route depending on the context and in particular depending for example on the weather conditions.

Such an estimate of this time of arrival is made possible by the learning algorithm implemented by the learning module 30 and by the journey data D1 which contains for example the journey time to reach the destination. This information can also be correlated with the contextual data D2 in order for example to take into consideration the meteorological conditions during the journey.

In this example, the navigation step S7 as such is also provided; this navigation mainly consists of taking into consideration the mode of transport recommended and validated by the user during the recommendation step S4.

It is understood here that the navigation step S7 also includes steps for collecting journey data and contextual data to enrich the mobility model and refine the learning of the habits and preferences of the user.

At the end of the trip, all the information relating to the trip can be displayed during a step S8

It should be noted that this detailed description relates to a particular embodiment of the present invention, but that in no case this description is in any way limiting to the subject of the invention; on the contrary, it aims to remove any possible imprecision or any misinterpretation of the following claims.

Thus, it will be understood that one of the aspects of the present invention relates to a vehicle such as for example an automobile or more generally a land motor vehicle comprising a system such as that described above.

It should also be noted that the reference signs placed between parentheses in the following claims are in no way limiting; these signs have the sole purpose of improving the intelligibility and understanding of the following claims as well as the scope of the protection sought.

Claims (10)

Mobility assistance method for providing a user (U) with a recommendation of a preferred mode of transport (V1, V2, V3) in order to make a journey, said method implemented by computer means comprising:
a) a preliminary learning phase (P0) comprising the following steps:
- a first acquisition (S1) of at least one path datum (D1) containing information relating to a path taken;
- a second acquisition (S2) of at least one contextual datum (D2) containing information relating to the context on said route taken; and
- a generation (S3) by a learning computer module (30) of a mobility model (MM) representative of a mobility habit as a function of said at least one path datum (D1) and of said at least contextual data, said mobility model (MM) being able to determine a preferred mode of transport (V1, V2, V3) from a list of at least one available mode of transport (V1, V2, V3);
b) followed by a phase of use (P1) comprising a recommendation (S4) of a preferred mode of transport (V1; V2, V3) for carrying out a journey at a determined instant by using said mobility model (MM ) and at least one contextual datum (D2) associated with said path to be taken at the determined instant. Method according to claim 1, in which the phase of use (P1) comprises a transmission (S5) to said user (U) of a notification of said recommendation of the mode of transport to be favored for carrying out said journey and/or a display (S6) of said recommendation via a graphical interface (50) of the type for example man/machine interface Method according to one of Claims 1 or 2, in which the use phase (P1) comprises a third acquisition of all the modes of transport available near said user from geolocation data associated with a position said user at the determined instant. Method according to any one of the preceding claims, in which the said at least one contextual datum (D2) comprises at least one piece of information relating to:
- the time and/or day of the journey; and or
- weather conditions on the route; and or
- the traffic conditions on the route; and or
- the level of pollution of the ambient air on the journey. Method according to any one of the preceding claims, in which said at least one path datum (D1) comprises at least one item of information relating to:
- the type of journey; and or
- the mode of transport used; and or
- to the person(s) transported during the journey. Method according to any one of the preceding claims, in which the use phase (P1) comprises, prior to the recommendation step (S4), a determination of the route to be taken from the mobility model (MM) and from the time determined to carry out said journey. Method according to claim 6, in which the determination of the journey to be made at the determined instant comprises an interrogation of an electronic organizer (AE) belonging to the said user (U). Computer program comprising instructions for the execution of the steps of a method according to any one of claims 1 to 7 when the computer program is executed by at least one processor. Mobility assistance computer system (100) for providing a user (U) with a recommendation of a preferred mode of transport (V1, V2, V3) in order to make a journey, said system (100) comprising:
- a first acquisition module (10) configured to collect at least one path datum (D1) containing information relating to a path;
- a second acquisition module (20) configured to collect at least one contextual datum (D2) containing information relating to the context on the path;
- a learning computer module (30) configured to generate from said at least one collected path datum (D1) and from said at least one collected contextual datum (D2) a mobility model (MM) representative of at less a habit of mobility; and
- an intelligent recommendation module (40) of the preferred mode of transport configured to determine, as a function of the mobility model (MM) and of at least one contextual datum (D2) collected at the determined instant, a recommendation of the mode of transport (V1, V2, V3) to be preferred from a list of at least one mode of transport to carry out said journey at the determined instant. Vehicle on which is embedded a mobility assistance computer system (100) according to claim 9.