CN112632392A - Method, apparatus, device, medium and program product for determining candidate get-off location - Google Patents
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Abstract
A method, apparatus, device, medium, and program product for determining candidate get-off locations are provided according to embodiments of the present disclosure. In the method, for a trip of a user, candidate drop-off locations are determined for the trip of the user based on personalized statistics of historical drop-off locations of the user and general statistics of historical drop-off locations of group users associated with at least one of a pick-up location and a time of the trip. Therefore, when the candidate getting-off place is recommended to the user, the characteristics of the individual users and the group users can be comprehensively considered, and the multi-scale characteristics such as the space characteristics, the time characteristics, the space-time cross characteristics and the like of the getting-off place can be considered, so that the getting-off place which can be satisfied by the user can be recommended, and the user experience is improved.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technology, and more particularly, to a method, apparatus, device, medium, and program product for determining candidate get-off locations.
Background
With the development of computer technology and network technology, online vehicle service applications have been provided to users. Such an application may be used to submit a corresponding vehicle service request if a user wants to travel from a location to a destination in a ride. During operation, after the user confirms the boarding location, the application may recommend candidate disembarking locations to the user. Currently, there is a need for an efficient method that can recommend a suitable drop-off location for a user.
Disclosure of Invention
Embodiments of the present disclosure provide a method, apparatus, device, medium, and program product for determining a candidate drop-off location.
In a first aspect of the disclosure, a method of determining candidate drop-off locations for a trip of a user is provided. In the method, a boarding location for a trip is determined. Candidate drop-off locations for the trip are then determined based on personalized statistics of the historical drop-off locations for the user and generic statistics of historical drop-off locations for group users associated with at least one of a pick-up location and a time of the trip.
In a second aspect of the present disclosure, an apparatus for determining candidate drop-off locations for a trip of a user is provided. The apparatus includes a first determination module configured to determine candidate drop-off locations for a trip based on personalized statistics of historical drop-off locations for the user and general statistics of historical drop-off locations for group users associated with at least one of a pick-up location and a time of trip.
In a third aspect of the present disclosure, there is provided an electronic device comprising: a memory and a processor; wherein the memory is for storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to the first aspect of the disclosure.
In a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon one or more computer instructions, wherein the one or more computer instructions are executed by a processor to implement a method according to the first aspect of the present disclosure.
In a fifth aspect of the disclosure, a computer program product is provided, comprising computer programs/instructions which, when executed by a processor, implement the method according to the first aspect of the disclosure.
Drawings
The features, advantages and other aspects of various implementations of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, which illustrate, by way of example and not by way of limitation, several implementations of the present disclosure. In the drawings:
FIG. 1 illustrates a block diagram of a user interface in which an online vehicle service application according to an exemplary implementation of the present disclosure may be used;
fig. 2 illustrates a flow diagram of a method of determining candidate drop-off locations in accordance with certain embodiments of the present disclosure;
fig. 3 illustrates a schematic block diagram of an apparatus for determining candidate get-off locations according to some embodiments of the present disclosure; and
fig. 4 illustrates a block diagram of an electronic device in accordance with an exemplary implementation of the present disclosure.
Detailed Description
Preferred implementations of the present disclosure will be described in more detail below with reference to the accompanying drawings. While a preferred implementation of the present disclosure is shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited by the implementations set forth herein. Rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The term "recommendation engine" as used herein refers to a component having a function of recommending an alighting place, which can recommend an appropriate alighting place for a user according to the boarding place of the user. The recommendation engine may be implemented by software, hardware, firmware, or any combination thereof.
The term "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". The term "based on" means "based at least in part on". The terms "one example implementation" and "one implementation" mean "at least one example implementation". The term "another implementation" means "at least one additional implementation". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions are also possible below.
An application environment of an exemplary implementation of the present disclosure is first described with reference to fig. 1. FIG. 1 shows a block diagram 100 of a user interface in which an online vehicle service application according to an example implementation of the present disclosure may be used.
As shown in user interface 110 in fig. 1, the user may enter a pickup location (e.g., home) at block 120. Alternatively, candidate pick-up locations (not shown) may be displayed in block 120. For example, a default candidate pick-up location may be displayed in block 120, which may be determined based on the user's current location or the user's preferences. The user may select the candidate pick-up location, for example, by clicking. A plurality of candidate pick-up locations may also be displayed to the user in block 120 for selection by the user in the form of a drop-down menu.
After the user specifies the departure location, the user is recommended the drop-off location (e.g., company) at block 130. The user may select the recommended drop-off location by clicking on the recommendation box 130. Similar to the display of candidate pick-up locations described above, a plurality of candidate pick-up locations may be displayed to the user in block 130 in the form of a drop-down menu. Alternatively, the user may enter the destination point at block 140 himself.
It is desirable that a destination point of a user can be predicted, so that an appropriate get-off point is recommended to the user, and user experience is improved.
To this end, example embodiments of the present disclosure propose a drop-off point recommendation scheme. The approach determines one or more candidate drop-off locations based on personalized statistics of historical drop-off locations of the user and based on general statistics of historical drop-off locations of group users associated with at least one of a pick-up location and a time of travel of the user. As an example, personalized statistics for a user's historical drop-off location may include a probability distribution, a cross-road situation, and/or user preferences, among others, for the user's historical drop-off location. These historical departure points may likewise be associated with at least one of a departure point and a time of the user's trip.
In this way, in determining candidate drop-off locations, the distribution of drop-off locations for historical trips of individual users and group users whose departure locations are near the current pick-up location may be considered in the spatial dimension. In the time dimension, the distribution of drop-off locations for historical trips of individual users and group users over a time period within which the trip time is located (e.g., within the same time period of each day) may be considered.
Therefore, when the candidate getting-off place is recommended to the user, the characteristics of the individual users and the group users can be comprehensively considered, and the multi-scale characteristics such as the space characteristics, the time characteristics, the space-time cross characteristics and the like of the getting-off place can be considered, so that the getting-off place which can be satisfied by the user can be recommended, and the user experience is improved.
Fig. 2 illustrates a flow diagram of a method 200 of determining candidate drop-off locations in accordance with certain embodiments of the present disclosure. For ease of discussion, the method 200 will be described below in conjunction with FIG. 1.
As shown in FIG. 2, at block 205, a boarding location for the user's trip is determined. The pick-up location may be manually entered by the user, for example, in block 120 of fig. 1. Alternatively or additionally, the user may select a desired pick-up location from a plurality of candidate pick-up locations. For example, after the user clicks on box 120, a drop-down menu may be displayed to the user listing a plurality of candidate pick-up locations for selection by the user. Accordingly, the user may select one boarding location from the plurality of candidate boarding locations as the starting point of the current trip. In some embodiments, the user's pick-up location may also be a default. For example, automatically determined based on the current location of the user.
At block 210, candidate drop-off locations for the trip are determined based on personalized statistics of the historical drop-off locations of the user and general statistics of historical drop-off locations of group users associated with at least one of the pick-up location and the time of the trip. The determined candidate drop-off location may be displayed at block 130 in fig. 1. As described above, a plurality of candidate get-off locations may be recommended to the user. In this case, a plurality of candidate drop-off locations may be presented to the user via a drop-down menu for selection by the user.
The statistical data of historical drop-off locations for the group of users may relate to a spatial distribution of drop-off locations. In some embodiments, a probability distribution of drop-off locations for historical trips of individual users with the current pick-up location as the trip origin or starting point near the current pick-up location may be considered. As an example, a historical alighting point whose distance from the boarding point is within a predetermined distance range may be considered. For example, the statistical data may include a probability distribution of the drop-off location for historical trips of respective users boarding within 500, 1000, or 2000 meters of the current boarding location.
In some embodiments, the statistical data for historical drop-off locations may relate to a distribution of drop-off locations over time. For example, a probability distribution of historical drop-off locations for historical trips of respective users within a predetermined time period associated with the time of the current trip may be considered. The time of the current trip may be the current time or some future time specified by the user.
As an example, the drop-off location of the historical trips of the respective users within the same time period as the current trip may be considered. For example, if the current trip time is 15 minutes at 2 pm, the probability distribution of the drop-off location for each user's historical trips of 2 pm to 3 pm every day in the previous week or month may be considered. Alternatively or additionally, workdays and holidays may also be distinguished. For example, if the current itinerary occurs on a weekday, the historical itineraries for the various users during the same period of each weekday in the previous week or month may be considered. If the current trip occurs on a double holiday, the historical trips of each user during the same period of the double holiday for the previous month or months may be considered. In addition, the time scale of the day of the week may also be considered. For example, if the current trip occurred on mondays, the probability distribution of drop-off locations for the historical trips of the respective user during the same period of each monday for the previous month or months may be considered.
In addition to the period of the current trip, other time periods associated with the time of the current trip may be considered. For example, a probability distribution of drop-off locations for historical trips of respective users within a predetermined time period before the current trip, e.g., one hour before the current trip, may be considered. In the event that a certain trending event occurs, such as when a concert is to be held, it may be beneficial to consider the drop-off locations of the historical trips of the group of users over the most recent period of time when recommending candidate drop-off locations to the users.
In some embodiments, the cross-distribution of historical drop-off locations over space and time may be considered. For example, a probability distribution of historical drop-off locations for historical trips of respective users within a certain predetermined time period, where the distance of the start point from the current pick-up location is within a predetermined distance range and associated with the time of the current trip, may be considered. For example, a probability distribution of a point of alighting of the historical trip of each user whose starting point is within 500 meters from the current boarding point within the same period of time every day in the previous month may be considered.
The statistical data of the historical drop-off location may include a road crossing cost in addition to a probability distribution of the historical drop-off location. For example, whether or not to cross a road between a get-off point and a get-on point in a historical trip, how many roads to cross, and the like.
The historical drop-off location for the individual user considered in determining the candidate drop-off location may also be associated with at least one of a pick-up location and a time of the current trip, and may include, for example, a historical drop-off location for the historical trip of the user within a predetermined distance range from the pick-up location for the current trip, and/or a historical drop-off location for the historical trip of the user within one or more predetermined time periods associated with the trip time. The above-discussed criteria and methods for selecting relevant historical trips for group users and the specific implementation of statistical data of historical drop-off locations are also applicable to individual users, and therefore, detailed details are not repeated.
Personalized statistics of historical drop-off locations for individual users may include user preferences regarding a certain, some, or class of drop-off locations, in addition to probability distributions and/or cross-road conditions for historical drop-off locations. For example, a user may be interested in engaging in an activity, and the venue holding the activity may be recommended to the user as a candidate drop-off point. The user preferences may be determined based on the user profile information such as the user's gender, age, habits, hobbies, and the like. Alternatively or additionally, a preferred drop-off location may also be specified by the user.
In addition to the personalized statistics and the generic statistics regarding historical drop-off locations, characteristics of the respective candidate drop-off locations, such as whether they are hot locations, whether they are in a prohibited-stop section, etc., may also be considered in determining the candidate drop-off locations.
The recommendation of candidate drop-off locations may be implemented via a recommendation engine. The recommendation engine may be implemented using any suitable machine learning algorithm, currently known and developed in the future. As an example, the recommendation engine may use algorithms of the Decision Tree class, for example, Gradient Boosting Decision Tree (GBDT) algorithms.
Before a candidate drop-off location is recommended with a recommendation engine, the recommendation engine may be initialized first. For example, the recommendation engine may be trained with historical travel records of individual users and/or group users to initialize or warm-start the recommendation engine. As an example, the recommendation engine may be trained using the boarding locations and times of historical trips for individual users as well as group users as inputs, and the corresponding disembarking locations as outputs. The recommendation engine may be cold started directly without performing this initialization. For example, candidate alighting points may be randomly determined for a certain boarding point.
During operation, the recommendation engine may be trained or updated with individual user and/or group user historical trips to optimize recommendations for candidate drop-off locations. For example, the recommendation engine may be trained using as input the boarding locations and/or times for a set of historical trips for a group of users, and as output the corresponding disembarking locations. The historical trips used for training may be acquired over time. For example, a record of historical trips for each user over the past month may be obtained without regard to the origin and destination of the trips. A record of historical trips for each user may be obtained on a different time scale. For example, a record of historical trips for each user for a certain time period may be obtained on a daily, double-holiday, and/or weekday time scale.
The acquisition of historical travel records may also be directed to a certain origin or destination. Travel records may be collected according to boarding or disembarking locations. For example, a record of all historical trips of various users whose origin or destination is a place may be collected. A record of all historical trips that are within a predetermined distance range (e.g., within 500 meters, 1000 meters, or 2000 meters) from the pick-up location to the particular pick-up location may also be collected. Furthermore, the time dimension may also be taken into account together. For example, a record of historical trips of individual users with a particular location as an entry location or an exit location for a certain period of time for each day, double holiday, and/or workday may be obtained.
Near a certain boarding location, each user may have a large number of historical trips over a period of time. To further reduce computational complexity and improve computational efficiency in the historical trip collection process, the pick-up location and/or the drop-off location for each trip may be provided with a grid-based index value in the record of historical trips that corresponds to the location of the pick-up location and/or the drop-off location. In this way, when historical travel records near a certain boarding point are collected, the relevant historical travel records can be acquired based on the index value of the boarding point.
For example, when collecting a record of historical trips where the departure point and a certain boarding point are within a certain distance range, the range of difference values of index values corresponding to the distance range may be determined. A record of the corresponding historical trip may be obtained based on the difference range such that the difference between the obtained index value of the boarding location for each trip and the index value of the particular boarding location is within the difference range.
The index values may be calculated or assigned using any suitable spatial indexing algorithm, both currently known and developed in the future. As an example, the S2 indexing algorithm may be employed. In this way, when historical travel records are collected, the distance between each boarding place and a specific boarding place does not need to be calculated one by one, and corresponding travel records can be obtained only by searching according to the index values, so that the time is saved, and the efficiency is improved. By utilizing the collected historical travel records, the recommendation process of candidate get-off places of the recommendation engine can be further optimized, so that the satisfaction degree of the user is improved.
In some embodiments, the recommendation engine may also be updated with the boarding location and/or time for a set of historical trips for the current user as input, and the corresponding disembarking location as output. As an example, the recommendation engine may be updated with all historical trips of the current user. Alternatively, similar to the collection process of historical trips for a group of users, the collection of historical trips for individual users may also take into account characteristics of the temporal and/or spatial dimensions of the historical trips. The criteria and methods discussed above for collecting historical trips of group users are also applicable to collecting historical trips of individual users, and therefore, detailed details are not repeated.
In some embodiments, because the number of historical trips for individual users is relatively small, a grid-based indexing algorithm, such as the S2 indexing algorithm, may not be necessary when collecting records of historical trips for a current user based on the distance between boarding locations. Instead, a record of historical trips for which the distance from the departure location to a certain pick-up location is within a certain distance range may be collected based on the actual calculated distance between the pick-up locations. Therefore, the required historical travel can be acquired more accurately, the training of the recommendation engine is further enhanced, and the recommendation result is optimized.
Embodiments of the present disclosure also provide corresponding apparatuses for implementing the above methods or processes. Fig. 3 illustrates a schematic block diagram of an apparatus 300 for determining candidate drop-off locations according to some embodiments of the present disclosure.
As shown in fig. 3, the apparatus 300 may include a first determination module 310 and a second determination module 320. The first determination module 310 is configured to determine a boarding location for a trip of a user. The second determination module 320 is configured to determine candidate drop-off locations for the trip based on personalized statistics of historical drop-off locations for the user and generic statistics of historical drop-off locations for group users associated with at least one of a pick-up location and a time of trip.
In some embodiments, the user's historical drop-off location may be associated with at least one of a pick-up location and a time of trip of the trip. In some embodiments, the user's historical drop-off location may include at least one of: a corresponding historical getting-off location of the historical getting-on location of the user within a predetermined distance range; and a historical drop-off location for the user's historical itinerary within one or more predetermined time periods associated with the time of the itinerary.
In some embodiments, the personalized statistics of the user's historical drop-off location include at least one of a probability distribution, a cross-road situation, and a user preference of the user's historical drop-off location.
In some embodiments, the second determination module 320 is implemented via a recommendation engine. In this example, the apparatus 300 may further include a first obtaining module configured to obtain a record of a set of historical trips for the user. The apparatus 300 may further include an update module configured to update the recommendation engine using at least one of the boarding location and the time for the set of historical trips for the user as input and the disembarking location for the set of historical trips as output.
In some embodiments, the first obtaining module may be further configured to obtain a record of another set of historical trips of the group of users. The update module may be configured to update the recommendation engine using at least one of the boarding location and the time of the set of historical trips for the group of users as an input and the disembarking location of the set of historical trips as an output.
The modules included in the apparatus 300 may be implemented in a variety of ways including software, hardware, firmware, or any combination thereof. In some embodiments, one or more modules may be implemented using software and/or firmware, such as machine-executable instructions stored on a storage medium. In addition to, or in the alternative to, machine-executable instructions, some or all of the elements in apparatus 300 may be implemented at least in part by one or more hardware logic components. By way of example, and not limitation, exemplary types of hardware logic components that may be used include Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standards (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and so forth.
It should be understood that the operations and features described above in connection with fig. 1 and 2 are equally applicable to the various modules of the apparatus 300 and have the same effect, and detailed description is omitted.
FIG. 4 illustrates a block diagram of an electronic device 400 in which one or more embodiments of the disclosure may be implemented. It should be understood that the electronic device 400 illustrated in FIG. 4 is merely exemplary and should not be construed as limiting in any way the functionality and scope of the embodiments described herein.
As shown in fig. 4, electronic device 400 may be in the form of a general purpose computing device, for example, implemented by a computing device and/or server. The components of electronic device 400 may include, but are not limited to, one or more processors or processing units 410, memory 420, storage 430, one or more communication units 440, one or more input devices 450, and one or more output devices 460. The processing unit 410 may be a real or virtual processor and may be capable of performing various processes according to programs stored in the memory 420. In a multi-processor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capabilities of electronic device 400.
The electronic device 400 may further include additional removable/non-removable, volatile/nonvolatile storage media. Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to a bus (not shown) by one or more data media interfaces. Memory 420 may include a computer program product 425 having one or more program modules configured to perform the various methods or acts of the various embodiments of the disclosure.
The communication unit 440 enables communication with other computing devices over a communication medium. Additionally, the functionality of the components of the electronic device 400 may be implemented in a single computing cluster or multiple computing machines, which are capable of communicating over a communications connection. Thus, the electronic device 400 may operate in a networked environment using logical connections to one or more other servers, network Personal Computers (PCs), or another network node.
According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which one or more computer instructions are stored, wherein the one or more computer instructions are executed by a processor to implement the above-described method.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products implemented in accordance with the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various implementations of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing has described implementations of the present disclosure, and the above description is illustrative, not exhaustive, and not limited to the implementations disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The terminology used herein was chosen in order to best explain the principles of implementations, the practical application, or improvements to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the implementations disclosed herein.
Claims (10)
1. A method of determining candidate drop-off locations for a trip of a user, comprising:
determining a boarding location for the trip; and
determining the candidate drop-off locations for the trip based on personalized statistics of historical drop-off locations for the user and general statistics of historical drop-off locations for group users associated with at least one of the pick-up location and the time of the trip.
2. The method of claim 1, wherein the historical drop-off location of the user is associated with at least one of the pick-up location and the time.
3. The method of claim 2, wherein the historical drop-off location of the user comprises at least one of:
a historical getting-off point of the historical travel of the user, wherein the distance between the getting-on point and the getting-on point is within a preset distance range; and
a historical drop-off location for the user's historical itinerary within one or more predetermined time periods associated with the time of the itinerary.
4. The method of claim 1, wherein the personalized statistics of the user's historical drop-off location include at least one of a probability distribution, a cross-road situation, and a user preference of the user's historical drop-off location.
5. The method of claim 1, wherein determining the candidate drop-off location is accomplished via a recommendation engine, and the method further comprises:
obtaining a set of records of historical trips of the user; and
updating the recommendation engine using at least one of a boarding location and a time for the set of historical trips as an input and a disembarking location for the set of historical trips as an output.
6. The method of claim 5, further comprising:
obtaining a record of another group of historical trips of the group of users; and
updating the recommendation engine using at least one of a boarding location and a time of the other set of historical trips as an input and a disembarking location of the other set of historical trips as an output.
7. An apparatus for determining candidate drop-off locations for a trip of a user, comprising:
a first determination module configured to determine a boarding location for the trip; and
a second determination module configured to determine the candidate drop-off locations for the trip based on personalized statistics of historical drop-off locations for the user and general statistics of historical drop-off locations for group users associated with at least one of the pick-up location and the time of the trip.
8. An electronic device, comprising:
a memory and a processor;
wherein the memory is to store one or more computer instructions, wherein the one or more computer instructions are to be executed by the processor to implement the method of any one of claims 1 to 6.
9. A computer readable storage medium having one or more computer instructions stored thereon, wherein the one or more computer instructions are executed by a processor to implement the method of any one of claims 1 to 6.
10. A computer program product comprising computer programs/instructions, characterized in that the computer programs/instructions, when executed by a processor, implement the steps of the method of claim 1.
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