Recommended route using active transportation based on usage levels

Abstract

Route recommendation for active mobility based on usage levels. The invention relates to a route recommendation method for an active mobility user in which the selection of a maximum number of segments composing the route is performed if an estimated usage level for the segment in question exceeds a threshold. The invention also relates to a terminal or server implementing the route recommendation method. Abstract figure: Figure 2

Description

Title of the invention: Recommendation of a route for active mobility based on a usage rate technical field

[0001] The present invention relates to the field of route research and recommendation for a user of active transportation. It relates in particular to a route recommendation method and an associated terminal. Previous technique

[0002] With the development of smart mobile devices such as smartphones or tablets, it is common for users of such devices to use applications that determine a route between a starting point and a destination and guide the user along the chosen route. Numerous route planning systems are available, particularly for drivers seeking routes with the lightest possible traffic flow.

[0003] In the case of a pedestrian or cyclist, who can be described as using active transportation, the search criteria may be different. In particular, in urban areas, active transportation users may want a route where they feel safe. For this reason, they may prefer to use busy streets with a large number of people rather than quiet streets. However, current route planning systems do not offer this type of recommendation. There are applications that allow users to request routes along main streets rather than secondary streets, which might give the impression that these streets are busier than others, but this is not always the case. Indeed, some secondary streets may be busier than main streets, and some main streets may not provide the feeling of safety sought by the user.

[0004] There is therefore a need to improve state-of-the-art methods in this type of situation. Description of the invention

[0005] The invention improves upon the state of the art.

[0006] To this end, the invention aims at a route recommendation method for a user in soft mobility in which the selection of a maximum of segments composing the route is carried out if an estimated frequency rate for the segment considered exceeds a threshold.

[0007] Thus the recommended route includes segments for which the traffic rate is high enough that the user on soft mobility is not isolated during their journey and thus feels safe.

[0008] Advantageously, the estimated attendance rate is an attendance rate of people using soft mobility.

[0009] Thus, the feeling of security is reinforced by the presence of people who move in the same way as the user and who can therefore be more easily accessible.

[0010] In a particular embodiment, the estimated usage rate for a route segment is calculated from a database containing network signaling data from mobile terminals present in that segment.

[0011] Network data such as signaling data emitted by mobile terminals in the segment makes it possible to obtain an estimate of the presence of people carrying these terminals and their speed of movement and thus to determine a rate of attendance on this segment.

[0012] In a particular embodiment, a historical database is constructed from network signaling data collected on predefined portions of a geographical area, this historical data including data on the frequency of people using soft mobility per predefined portion, at given times.

[0013] This database therefore contains data which can be collected over a long period of time, for example over several months or several years in order to accurately and reliably estimate a rate of attendance at a given time.

[0014] In one embodiment, a predictive model of the attendance rate of people using soft mobility for a given portion is obtained from historical data and context data.

[0015] Thus, taking into account contextual data, such as weather data or data on particular events, makes it possible to define a prediction model adapted to different situations and thus to estimate the traffic rates of requested routes even more reliably.

[0016] In an adapted embodiment, the prediction model is obtained by training a neural network, an inference of the neural network giving an estimate of the attendance rate for a given portion from a route request and associated context data.

[0017] An architecture based on a neural network makes it possible, in fact, to determine a relevant prediction model and to use it for future queries.

[0018] In an advantageous embodiment, an estimate of the usage rate for a route segment is obtained from the estimate of the rate of traffic to the geographical portion including this segment and mapping data of the geographical portion.

[0019] Thus, the estimation of the attendance rate is obtained with greater geographical precision so as to be able to select the segments for which the attendance rate has a sufficient value.

[0020] In an advantageous embodiment, attendance rates are estimated according to a time range.

[0021] Thus, depending on the time of day, traffic levels may vary and the recommended routes may differ depending on the time of day. This allows the user to choose a route and a time of day where the estimated traffic level is higher.

[0022] In a particular embodiment, the route segments are proposed if their estimated lighting rate also exceeds a brightness threshold and / or if their estimated crime rate is also lower than a crime threshold.

[0023] Thus, other criteria that enhance the user's sense of security along their route can be taken into account. Indeed, a well-lit area improves visibility and reinforces this sense of security for a user of active transportation. Similarly, information on crime statistics for a geographical area can enhance the feeling of security in that area if these statistics are low.

[0024] In one embodiment, the estimated lighting rate for a route segment is obtained from data captured by environmental cameras.

[0025] Data captured by environmental cameras, particularly in the segment under consideration, allows for the measurement of brightness levels through image analysis. These images also enable the estimation of daylight hours in these areas for a more accurate assessment of brightness levels.

[0026] Advantageously, a step of displaying the segments and their attendance rate is carried out.

[0027] Thus, the user can choose the route that best suits him according to the frequency of the segments he wants.

[0028] The user can also select routes for which there are segments which do not have a sufficient rate of traffic but which may nevertheless be of interest to the user if, for example, the rate is not far from the threshold or if the segment in question is very short and / or if the rate of light is known and reassuring.

[0029] In a particular embodiment, where a segment of a recommended route includes at least one segment for which the estimated occupancy rate is below a threshold, the following steps are implemented: - searching for at least one person located in a segment close to the segment for which the estimated occupancy rate is below the threshold and for whose contact details are recorded in a database of a support service; - sending a notification of a request for support to at least one located person; - in the event of a positive response to the request for assistance, an appointment notification is sent to both the person located and the user.

[0030] Thus, the assistance service makes it possible to compensate for the existence of segments with insufficient usage in the recommended route. User safety is therefore enhanced by this assistance service, particularly for these segments.

[0031] The invention relates to a communication terminal comprising a processing circuit for implementing the recommendation process as described.

[0032] It also relates to a server comprising a processing circuit for implementing the recommendation process as described.

[0033] The terminal and the server have the same advantages as the process they implement.

[0034] The invention relates to a computer program comprising instructions for implementing the recommendation process as described above, when executed by a processor.

[0035] Finally, the invention relates to a recording medium, readable by a processor, on which is recorded a computer program containing instructions for the execution of the recommendation process described above. Brief description of the drawings

[0036] Other features and advantages of the invention will become more apparent upon reading the following description of particular embodiments, given by way of simple illustrative and non-limiting examples, and the accompanying drawings, among which:

[0037] [Fig.1] illustrates, schematically, a terminal and a server according to an example of an embodiment of the invention;

[0038] [Fig.2] illustrates in flowchart form the main steps of the process of route recommendation according to an embodiment of the invention;

[0039] [Fig.3] illustrates in the form of a flowchart the steps of a particular embodiment of the recommendation process according to the invention;

[0040] [Fig.4a] illustrates in the form of a flowchart a construction implementation method from a historical database based on collected network signaling data;

[0041] [Fig.4b] illustrates in flowchart form the steps to obtain a rate of estimated traffic for a given segment and a given time;

[0042] [Fig.5a] and [Fig.5b] illustrate examples of representation of a recommended route and the segments constituting it associated with an estimated traffic rate, for two different time slots;

[0043] [Fig. 6] illustrates a representation of a recommended route and its constituent segments, associated with their respective usage rates; and

[0044] [Fig.7] illustrates, in the form of a flowchart, the steps of an embodiment particularly when at least one of the segments of a recommended route is associated with an estimated insufficient usage rate. Description of the implementation methods

[0045] The [Fig. 1] represents on the one hand, a terminal T, for example a mobile terminal such as a mobile phone, for example of the "smartphone" type, a digital tablet, or a personal computer, on the other hand a server S.

[0046] Terminal T comprises the following functional modules:

[0047] - a communication module (COM-T), input / output intended to communicate with a communication network R;

[0048] - a processing module (pP-T) comprising a processor and a system operating system to control the interactions between the different modules of the terminal;

[0049] - a memory module (MEM-T) in which an application or module is stored software (APP) including program instructions for implementing the steps of the recommendation process according to an embodiment of the invention and as described for example with reference to [Fig.2];

[0050] - an interface module (INT.) allowing the user carrying the terminal to interact;

[0051] - a localization module (LOC.) for implementing a localization function of the terminal. This module is optional; the terminal's location can be determined from network signaling data.

[0052] Terminal T can implement the recommendation process as described with reference to [Fig. 2] according to a first embodiment. According to a second embodiment, terminal T sends a route recommendation request via a software module or a mobile application to a server S of the communication network R. This request is received by this server S, which is capable of collecting data or obtaining data from a database DB1, DB2, or DB3. This data will be described later; it can be network signaling data from mobile terminals in a defined area, video data taken in a specific area, or GPS (Global Positioning System) positioning data.

[0053] Mapping data of the locations in which terminal T is located or of locations specified in the route recommendation request are also available in a database.

[0054] The S server and the database can be integrated, for example, into a service platform of an operator or the provider of the mobile application APP.

[0055] This server S is illustrated in [Fig. 1] and comprises the following functional modules:

[0056] - an input / output communication module (COM-S) intended to communicate with the R communication network;

[0057] - a processing module (pP-S) comprising a processor for driving the interactions between the different modules of the server;

[0058] - a memory module (MEM-S) in which a computer program is stored (Pg) including instructions for carrying out the steps of the route recommendation process according to an embodiment of the invention and as described for example with reference to [Fig.2].

[0059] Figure 2 represents, in the form of a flowchart, the steps of the route recommendation process when implemented, according to a first embodiment, by a terminal, for example the terminal T described in Figure 1, or when implemented, according to a second embodiment, by a server S of the communication network, for example as described in Figure 1.

[0060] In step E20, in the first embodiment, the terminal receives, via its user interface, a user request for a route recommendation between a starting point A and an arrival point B. These starting and arrival points are specified, for example, by an address, a geographic coordinate, a point of interest, or any other location identifier. The starting point can also be deduced from the location of the terminal initiating the request. In the second embodiment, the server S receives this request, for example, via a communication from a user's terminal T.

[0061] This request may also contain a desired time range for this route.

[0062] Other information may also be contained in this request, for example a desired attendance rate threshold, a maximum number of route segments with an attendance rate below the desired threshold or a percentage of distance to be covered in addition to the shortest route.

[0063] A segmentation is then performed in E21 to determine the possible route segments between point A and point B defined in the query. These segments are segments that can be traveled by a user in active mobility, for example a pedestrian, cyclist, scooter rider, or other low-speed individual vehicle rider. These route segments are, for example, portions of streets or paths. These segments of the route or portions can be of equal length, for example on the order of a few hundred meters.

[0064] Determining these segments can be done using existing route calculation applications. This route calculation can, for example, use a Dijkstra-type algorithm to find the shortest path between two points, as well as digital maps to visualize roads and points of interest. For this purpose, the map of the geographical area concerned, for example, in an urban area, is represented as a graph where intersections are nodes and street segments are edges. Several sequences of segments can thus be determined for a route between points A and B. The sequences may have different distances, but priority is given to finding the segments that allow for the shortest possible route.

[0065] In step E22, a usage rate of people using active transportation is estimated for each of the segments determined in the previous step. This usage rate is determined from data collected and recorded in a database. In a first embodiment, the data consists of network signaling data collected and recorded (for example, in the BD1 database illustrated in [Fig. 1]) anonymously over several time periods and for a given area. The advantage of this type of data is that it provides a usage estimate very close to reality, due to the large number of people carrying mobile devices who thus exchange signaling information.

[0066] In a second embodiment, this data consists of GPS data captured and recorded for several time periods and for a given area. This data can be stored in a database (for example, in the BD2 database illustrated in [Fig. 1]). This data may not be sufficient since it is subject to the consent of the individuals carrying the devices. The estimated attendance may therefore not be sufficiently accurate.

[0067] In a third embodiment, this data consists of video data captured via cameras installed in certain areas, for example, surveillance cameras. This data can be recorded in a database (for example, in the BD3 database illustrated in [Fig. 1]). This data depends on the presence of cameras in the environment, which can vary from one urban area to another.

[0068] One embodiment of the estimation of the attendance rate per segment is detailed with reference to Figures 4a and 4b described later.

[0069] Using digital maps that list the various roads or paths a user of active transportation can take, it is possible to compile the data recorded for a segment under consideration. This data is then analyzed. based on a desired time range to estimate the number of people using active transportation in a given segment.

[0070] Only terminals with a slow speed, for example less than 15 km / h, are considered. Thus, for a given time period, a day of the week, or even depending on the season or a calendar of events in that area, the number of people present and using active transportation is estimated for the segment in question.

[0071] Contextual data, described later, enrich the estimate of the attendance rate obtained.

[0072] An attendance rate for this segment can then be defined. This can be simply the estimated number of people in this segment or the number of people normalized to the size of the segment if the segment sizes are different.

[0073] Thus, in step E23, the estimated attendance rate for the determined segments is compared to a threshold S which can be defined by the user himself, for example in his query or which can be defined by default.

[0074] This rate could, for example, be 10 people, in one embodiment. Of course, other threshold values ​​can be defined. Segments with a occupancy rate exceeding this threshold are retained. The routes with the most segments exceeding this threshold are then determined in step E24. The Dijkstra algorithm is used here, for example, to find the shortest path that maximizes the sum of the occupancy rates on the segments that make up the route. Indeed, there may be several routes with segments that have an occupancy rate exceeding the threshold S. The occupancy rate value can be higher or lower and exceed the threshold, or it can be below the threshold but for a short segment.

[0075] The resulting routes may lengthen the travel distance compared to the shortest route between point A and point B. Indeed, the shortest route may include segments with a low traffic volume that does not exceed the required threshold. Thus, to obtain a maximum number of segments with an estimated traffic volume above the threshold S, the resulting route is very often longer. A compromise between travel length and a sufficient traffic volume must then be found. A threshold between the shortest path and the recommended route is then taken into account.

[0076] For example, a maximum distance difference between the shortest route and the recommended route can be requested and given in the initial request.

[0077] These routes and an estimate of the traffic volume associated with the segments of the route are, for example, presented to the user on the interface of their mobile device, for a given time period. They can then decide which route they will take. borrow according to one's preferences and according to estimated values ​​of attendance rates, or according to distances to be covered.

[0078] The same route can also be offered but for different time slots with different estimated traffic volumes associated with the segments. The user can then decide on the most suitable time slot to travel the route.

[0079] Figure 3 illustrates a particular embodiment of the recommendation process in which other parameters are taken into account for selecting the segments composing the route. In one example, step E23, as explained with reference to Figure 2, selects the segments (Si) for which the estimated usage rates are greater than a threshold S (FSi > S). A maximum number of segments meeting this criterion are thus selected to constitute the route to be proposed to the user. A plurality of routes can be recommended. There may be some route recommendations in which one or more of the constituent segments do not have a sufficient estimated usage rate. In this case, it is possible to refer to another criterion in step E26, for example, the brightness level (LSi) associated with the segment in question.This brightness level can be obtained via video data from cameras installed in an area encompassing the segment in question. This video data allows for the measurement of the brightness level. The user can specify a desired brightness level and therefore a threshold to be exceeded, or this threshold can be set as a default. Thus, if the brightness level for the segment in question is below this threshold (Si), then the segment is not selected. Conversely, if the brightness level is above the threshold (Si), then this segment can be selected, even if its estimated occupancy level is lower than the desired minimum occupancy level.

[0080] This brightness criterion can also be an additional criterion for segment selection. In this case, both the estimated traffic volume and the brightness level are taken into account for selecting the segment in the recommended route.

[0081] Similarly, another selection criterion can be taken into account. In E27, crime or delinquency rate data by area can be obtained via a database collecting this type of information. In this case, for a given segment, if that segment is in an area with a high crime rate (TCSi), for example, above a certain threshold Sc, then that segment is not selected for the recommended route. This selection criterion can, like the crime criterion, be a condition in addition to the ridership rate, to be met. or a criterion to be evaluated only if the attendance rate for this segment is insufficient.

[0082] Figures 4a and 4b illustrate, in the form of a flowchart, an example of the implementation of step E22 of [Fig.2]. In a first embodiment, the data used to estimate a usage rate for a segment are network signaling data from the terminals present in the segment under consideration.

[0083] A first phase, illustrated in [Fig. 4a], allows for the creation of a historical database. A geographical area is considered; in this example, this could be an urban area or a district within that urban area. In step E41, signaling data from terminals on the mobile network is collected over this geographical area. The terminals connect regularly to the telecommunications antennas covering this area (Collect.Sign.(t0, ...tb ...tp)-Ants). This signaling data allows for the identification of an approximate, anonymous position of the terminals within this geographical area. The data is collected for several time periods and for different days of the week.Thus, by analyzing successive connections to the antennas, the time elapsed between these connections, and the distance between the antennas, it is possible to determine the speed of movement of the terminals in the geographical area.

[0084] A method such as that described for example in published patent application EP4325903 can for example be implemented for this determination.

[0085] On the other hand, the geographical area is divided into E42, into portions of predefined size (Pq for example in squares with sides of 150m, thus creating an analysis grid.

[0086] In step E43, each portion thus obtained is analyzed by cross-referencing the position and speed data of the terminals to estimate the number of people using active mobility (V <Vm) dans une telle portion géographique et à un instant donné. Un déplacement en mobilité douce est par exemple un déplacement à moins de 15 km / h (ici Vm=15m / h).

[0087] This data is continuously updated over a period of time that can range from a few months to even a few years, allowing for dynamic and precise collection of visitor numbers in each section at a specific date and time. This makes it possible to create a complete and reliable historical database (BD-HIST).

[0088] From this historical and contextual database (DB-CONTEXT), a model for estimating the attendance rate per geographical area is determined, as illustrated in [Fig.4b].

[0089] Context data can be of different types. It can be data of:

[0090] - weather: Indeed, rainy or freezing weather can reduce the number of people present in a given area in relation to sunny weather and pleasant temperatures. This weather data is obtained via a weather forecasting application server, or via a communication network database;

[0091] - seasonality: seasons or months or days of the week;

[0092] - hour and minutes of the day;

[0093] - distance from places of interest: proximity to attractions, shops or public events;

[0094] - other contextual factors such as special events like concerts, demonstrations, public holidays, etc...

[0095] The prediction of visitor numbers on each segment Pj is, for example, based on a neural network (NS) architecture. Time is considered as a dimension on which convolutions are performed to extract the relationships between the variables (contextual data) at different times. A UNET-type architecture can be used.

[0096] In an example embodiment, for a short-term prediction (15 min to 2 h), the output time sequence is discretized at 15 min and has a length of 16 (4 hours in total). The explanatory variables (input) are hybrid:

[0097] - time series of observations from the last few hours on other portions geographical distances that allow for greater explainability in predicting the target portion.

[0098] - exogenous variables such as the contextual data described above.

[0099] This same architecture can be adapted with larger time steps for longer-term predictions.

[0100] A learning (or training) phase in E45, using this architecture, allows us to define a model for predicting the attendance rate per geographical area (Mod-est-F / Pj).

[0101] This model is then applied during an inference step of this neural network (E46), to the input data from step E21 which indicates route segments between a point A and a point B given in the user request for route recommendation and to the context data (D. Context.) which relates to the request and the requested time range.

[0102] An estimate of the traffic rate is then obtained on the geographical portions concerned by the route recommendation request (East-F / Pj).

[0103] At step E47, an estimate of the traffic volume per segment of the route(s) obtained at step E21 of [Fig. 2] is determined. At this step, a precision is therefore brought in to bring the estimate back to the level of segments rather than to the level of geographical portions as defined above.

[0104] Thus, for a given portion, the associated route segments are identified according to a given mapping of the portion considered.

[0105] If there is only one segment in this portion, the estimate of the number of people in soft mobility, given for the portion considered, is therefore attributed to the only segment of this portion.

[0106] If there are several segments in this portion, then a rule of proportionality can be applied: the number of people in soft mobility on the portion considered corresponds to the number of people (frequency rate) on all the segments (fragments of road) on this portion whose total length is calculated.

[0107] Let PI be the portion considered, FPi the estimated number of people (attendance rate) for this portion, and L1 the length of the set of segments identified for this portion PI. The number of people on a segment i of length li, (FSi), is obtained according to the following formula: FSi = li * FPi / L1

[0108] It is possible to refine this calculation by taking into account the type of road (segment), for example if the segment considered is a shopping street, a pedestrian street, an expressway, a cycle track, etc... In this case, a weighting can be applied according to the type of segment, a greater weighting could for example be given to pedestrian streets or cycle tracks where the number of people in soft mobility is potentially greater than for expressways.

[0109] Thus, at the end of step E47, an estimate of the usage rate on the segments of the routes given in step E21 is obtained. These routes are then reviewed in step E23, as described with reference to [Fig.2] to take into account these estimated usage rates and thus maximize the segments of the route to be recommended which have a usage rate above a threshold.

[0110] In a second embodiment, the data collected in step E41 of [Fig. 4a] are GPS location data from mobile devices in the geographical area concerned. However, retrieving this data requires widespread consent from the individuals carrying these devices, including authorization to transmit this location information and to use this data. An insufficient number of authorizations would make the method less reliable. The collected data then provides a precise position that allows for a faster estimation of the movement speed of the device users.

[0111] In a third embodiment, the data collected in step E41 of [Fig. 4a] are video data captured via cameras installed in certain areas, for example, surveillance cameras. Analysis of this data can make it possible to estimate the number of people present in a segment where a camera is indeed planned, the analysis at different times allowing to determine a speed of movement of the individuals.

[0112] However, this embodiment requires a sufficient presence of cameras for all segments to be analyzed, which is not currently the case for all agglomerations.

[0113] Figures 5a and 5b illustrate route representations and their constituent segments. Figure 5a illustrates a recommended route between point A and point B for a given date JO and time, here 23h (H=23). This route comprises 15 segments, 11 of which have an estimated usage rate FSi greater than the threshold S defined either by default or by the user. In this example, the segments are of equal length. A risk rate can be easily calculated by taking into account the number of segments for which the usage rate is below the threshold relative to the total number of segments in the route. This risk rate therefore has a value of R=4 / 15=0.27.

[0114] Figure 5b illustrates the same route for a different time period. The date here is J1 (the day after J0) at 2:00 AM (H=2). For this time period, the number of segments for which the estimated attendance rate is below the threshold S has increased. This number is now 8. The risk rate is therefore R = 8 / 15 = 0.54. This risk rate is thus higher.

[0115] Thus, a user planning to leave a party, for example, might prefer to leave at 11 p.m. rather than 2 a.m. to benefit from busier streets on their route from point A to point B and feel safer. This risk assessment can be calculated over several time periods, for example hour by hour, to allow the user to have all the necessary information and make an informed decision about their departure time.

[0116] Figure 6 illustrates another representation of a route recommendation that can be displayed on the user's terminal screen. In this representation, several routes are proposed with segments that are not all the same size. These segments can, for example, represent sections of streets without intersections.

[0117] Each segment is associated with an estimated traffic rate. Obviously, a representation illustrating the topology of the roads and paths can be provided for better visibility.

[0118] In this illustration, a first route IT1 comprises 6 segments (S1, S2, S3, S4, S5 and S6) of different lengths, for example S1 = 200 m, S2 = 50 m, S3 = 300 m, S4 = 300 m, S5 = 100 m and S6 = 100 m. The total route is therefore 1050 m. For each segment, an estimated usage rate of people using active transportation is associated, for example by the method described with reference to Figures 4a and 4b. For this For the first route, the usage rates (FSi, with i from 1 to 6) are all greater than or equal to the threshold S of value 10. Thus, we have FSi=12, FS2=10, FS3=15, FS4=14, FS5=11, and FS6=10. An average usage rate can be determined for this route to provide a basis for comparison between several routes. The average for this route IT1 is therefore (12+10+15+14+11+10) / 6=12.

[0119] Thus, another IT2 route is also proposed. This one comprises 5 segments (S1', S2', S3', S4' and S5') of different lengths, for example S1'=100m, S2'=100m, S3'=300m, S4'=350m and S5'=50m. The total distance is therefore 900m on this route, i.e. a shorter journey than the ITL route. For this route as well, each segment is associated with an estimated usage rate, for example by the method described with reference to Figures 4a and 4b. The usage rates (FS1, with i from 1 to 4) are greater than or equal to the threshold S of value 10; only one segment has a rate below this threshold. Thus, Fs1=14, FS2=12, FS3=30, Fs4=15 and FS5=8. The average usage rate for this route is (14+12+30+15+8) / 5=15.8.

[0120] Thus, the user has the choice between a slightly longer route (+150m) but with an estimated sufficient attendance rate for all segments of the route and an average of this rate over the whole route slightly above the chosen threshold, here of 10, and a shorter route but one of whose segments has an estimated insufficient attendance rate and an average of this rate over the whole route, higher than that of route 1.

[0121] The user may therefore still be interested in choosing the second route since it offers an attractive average attendance rate, a shorter route and only a short segment for which the attendance rate is not satisfactory.

[0122] In a particular embodiment, an option to request assistance may be provided for segments whose estimated occupancy rate is below the threshold S. Figure 7 illustrates the main steps of this embodiment. Once a route has been proposed in E24 (described with reference to Figure 2), it is checked whether a segment Si of the route is associated with an estimated occupancy rate for a given time period that is below the threshold S. If segment Si is below the threshold S (O to E71), then it is checked in E72 that there are people present in the segment preceding this segment Si (Sm) who are registered with an assistance service. This service offers assistance from at least one person registered as an assistant with that service. The contact details of the assistant are recorded in a dedicated database for this service, so that they can be contacted when the opportunity arises.Conditions. Usage guidelines for these coordinates can be associated with specific conditions, such as a possible time slot, prohibited areas, or any other constraints. If at least one person is present in segment Sm (O at step E72), then a notification is sent to that person at E73. This notification is a request for an escort from a user wishing to travel along segment Si, within a given time slot, provided the person receiving the notification is present in a segment preceding or near segment Si and is available during the requested time slot. If the person accepts this escort (O at step E74), then a meeting point is notified to both the escort and the person requesting the escort at E75.This meeting place could, for example, be at an address on segment Sm of the route taken by the user, this segment having a sufficient level of traffic for the user to feel safe.

[0123] If the person registered as an escort does not accept the proposed escort service, then E77 checks whether there are other people registered as escorts in an area near Si. If so (O in step E77), then the contact details of this new person are retrieved in E76 to send them a notification in E73. Otherwise, this escort service cannot be offered.

[0124] With this service option, a user wishing to travel from point A to point B can choose a route on which they will feel safe, based on knowledge of the traffic levels of the segments of the route they will take. If the route includes areas with a traffic level that is too low for the user to feel safe, then other criteria can be taken into account to measure other parameters, as illustrated in [Fig. 3], or the escort service as described with reference to [Fig. 7] can be implemented.

[0125] Thus, the user can choose a safer route for him / herself, according to the recommendations from the recommendation process as described.

Claims

Demands

1. Route recommendation method for a user in soft mobility in which the selection (E23) of a maximum of segments composing the route is carried out if an estimated usage rate (E22) for the segment in question exceeds a threshold.

2. A method according to claim 1, wherein the estimated attendance rate is an attendance rate of people using soft mobility.

3. A method according to any one of the preceding claims, wherein the estimated usage rate for a route segment is calculated from a database containing network signaling data from mobile terminals present in that segment.

4. A method according to claim 3, wherein a historical database is constructed from network signaling data collected on predefined portions of a geographical area, this historical data including data on the frequency of people using soft mobility per predefined portion, at given times.

5. Method according to claim 4, wherein a predictive model of active mobility ridership rates for a given portion is obtained from historical and contextual data.

6. A method according to claim 5, wherein the prediction model is obtained by training a neural network, an inference of the neural network giving an estimate of the attendance rate for a given portion from a route request and associated context data.

7. A method according to claim 6, wherein an estimate of the usage rate for a route segment is obtained from the estimate of the usage rate of the geographical portion including that segment and mapping data of the geographical portion.

8. A method according to any one of the preceding claims, wherein attendance rates are estimated as a function of a time range.

9. A method according to any one of the preceding claims, wherein the route segments are proposed if their lighting rate estimated also exceeds a brightness threshold and / or if their estimated crime rate is also lower than a crime threshold.

10. A method according to claim 5, wherein the estimated lighting rate for a route segment is obtained from data captured by environmental cameras.

11. A method according to any one of the preceding claims, wherein a step of displaying the segments and their attendance rate is carried out.

12. A method according to any one of the preceding claims, wherein, in the case where a segment of a recommended route includes at least one segment for which the estimated attendance rate is less than a threshold, the following steps are implemented: - searching for at least one person located in a segment close to the segment for which the estimated attendance rate is less than the threshold and for which their contact details are recorded in a database of an assistance service; - sending a notification of a request for assistance to at least one person located; - in the case of a positive response to the request for assistance, sending an appointment notification to both the person located and the user.

13. Communication terminal comprising a processing circuit for implementing the recommendation method according to any one of claims 1 to 12.

14. Server comprising a processing circuit for implementing the route recommendation method according to any one of claims 1 to 12.

15. Processor-readable recording medium on which is recorded a computer program containing instructions for carrying out the recommendation process according to any one of claims 1 to 12.

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