System and method for predicting destinations in online-to-offline service
阅读说明:本技术 预测线上到线下服务中目的地的系统和方法 (System and method for predicting destinations in online-to-offline service ) 是由 陈然 陈欢 宋奇 于 2018-06-28 设计创作,主要内容包括:一种方法可以包括确定服务请求者打算在第一时间点从第一地点请求服务。所述方法还可以包括根据所述确定结果获取所述服务请求者在所述第一时间点之前的第一时间段内的多个历史订单。所述方法还可以包括基于所述多个历史订单确定一个或多个候选目的地。所述方法还可以包括,对于所述一个或多个候选目的地中的每个候选目的地,基于所述第一时间点,从所述多个历史订单中选择至少一个与所述候选目的地相关的历史订单。所述方法还可以包括基于所选择的至少一个历史订单的数量以及与所述候选目的地相关的历史订单的数量,确定与所述候选目的地相关的概率。所述方法还可以包括分别基于与所述一个或多个候选目的地相关的所述一个或多个概率,从所述一个或多个候选目的地中确定推荐目的地。(A method may include determining that a service requestor intends to request a service from a first location at a first point in time. The method may further include obtaining a plurality of historical orders of the service requester over a first time period prior to the first time point according to the determination. The method may also include determining one or more candidate destinations based on the plurality of historical orders. The method may also include, for each of the one or more candidate destinations, selecting at least one historical order from the plurality of historical orders that is relevant to the candidate destination based on the first point in time. The method may also include determining a probability associated with the candidate destination based on the selected at least one quantity of historical orders and the quantity of historical orders associated with the candidate destination. The method may also include determining a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively.)
1. A system for predicting a destination in an online-to-offline service system, comprising:
one or more storage devices storing a set of instructions; and
one or more processors configured to communicate with the one or more storage devices, the one or more processors configured to, when executing the set of instructions, cause the system to:
determining that a service requester intends to request a service from a first location at a first point in time;
obtaining a plurality of historical orders of the service requester in a first time period before the first time point according to the determination result;
determining one or more candidate destinations based on the plurality of historical orders;
for each of the one or more candidate destinations,
selecting at least one historical order from the plurality of historical orders that is relevant to the candidate destination based on the first point in time; and
determining a probability associated with the candidate destination based on the selected at least one quantity of historical orders and the quantity of historical orders associated with the candidate destination, wherein the probability represents a likelihood that the service requestor intends to travel to the candidate destination at the first point in time; and
determining a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively.
2. The system of claim 1, wherein the one or more processors are further configured to cause the system to:
and sending the recommended destination to a requester terminal related to the service requester, and displaying the recommended destination on a user interface of the requester terminal.
3. The system of claim 1 or 2, wherein the destination of the selected at least one historical order matches the candidate destination and a departure time associated with the selected at least one historical order is within a second time period that includes the first time point.
4. The system of any one of claims 1 to 3, wherein a departure location associated with the selected at least one historical order is within a distance range that includes the first location.
5. The system of any of claims 1 to 4, wherein determining the quantity of the selected at least one historical order comprises:
determining a weight for each of the selected at least one historical order based on an interval between a departure time of each of the selected at least one historical order and the first point in time; and
determining a quantity of the selected at least one historical order based on a sum of the weights of the selected at least one historical order.
6. The system of claim 5, wherein to determine a weight for each of the selected at least one historical order, the one or more processors are configured to cause the system to:
determining a half-life based on the plurality of historical orders; and
determining a weight for each of the selected at least one historical order based on the half-life and an interval between a departure time of each of the selected at least one historical order and the first point in time.
7. The system of any one of claims 1-6, wherein to determine a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively, the one or more processors are configured to cause the system to:
selecting one candidate destination with the highest probability from the one or more candidate destinations; determining whether the maximum probability exceeds a probability threshold; and
in response to a determination that the maximum probability exceeds the probability threshold, determining that the candidate destination with the maximum probability is a recommendation destination to be recommended to the service requester.
8. A method for predicting a destination in an online-to-offline service system, implemented on a computing device comprising one or more storage devices and one or more processors, the method comprising:
determining that a service requester intends to request a service from a first location at a first point in time;
obtaining a plurality of historical orders of the service requester in a first time period before the first time point according to the determination result;
determining one or more candidate destinations based on the plurality of historical orders;
for each of the one or more candidate destinations,
selecting at least one historical order from the plurality of historical orders that is relevant to the candidate destination based on the first point in time; and
determining a probability associated with the candidate destination based on the selected at least one quantity of historical orders and the quantity of historical orders associated with the candidate destination, wherein the probability represents a likelihood that the service requestor intends to travel to the candidate destination at the first point in time; and
determining a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively.
9. The method of claim 8, further comprising:
and sending the recommended destination to a requester terminal related to the service requester, and displaying the recommended destination on a user interface of the requester terminal.
10. The method of claim 8 or 9, wherein the destination of the selected at least one historical order matches the candidate destination, and a departure time associated with the selected at least one historical order is within a second time period that includes the first time point.
11. The method of any one of claims 8 to 10, wherein a departure location associated with the selected at least one historical order is within a distance range that includes the first location.
12. The method of any of claims 8 to 11, wherein determining the quantity of the selected at least one historical order comprises:
determining a weight for each of the selected at least one historical order based on an interval between a departure time of each of the selected at least one historical order and the first point in time; and
determining a quantity of the selected at least one historical order based on a sum of the weights of the selected at least one historical order.
13. The method of claim 12, wherein said determining a weight for each of said selected at least one historical order comprises:
determining a half-life based on the plurality of historical orders; and
determining a weight for each of the selected at least one historical order based on the half-life and an interval between a departure time of each of the selected at least one historical order and the first point in time.
14. The method of any of claims 8 to 13, wherein the determining a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively, comprises:
selecting one candidate destination with the highest probability from the one or more candidate destinations; determining whether the maximum probability exceeds a probability threshold; and
in response to a determination that the maximum probability exceeds the probability threshold, determining that the candidate destination with the maximum probability is a recommendation destination to be recommended to the service requester.
15. A system for predicting a destination in an online-to-offline service system includes an order acquisition module to acquire an order for the destination
Determining that a service requester intends to request a service from a first location at a first point in time; and
obtaining a plurality of historical orders of the service requester in a first time period before the first time point according to the determination result;
a candidate destination determination module to determine one or more candidate destinations based on the plurality of historical orders;
a probability determination module for
For each of the one or more candidate destinations,
selecting at least one historical order from the plurality of historical orders that is relevant to the candidate destination based on the first point in time; and
determining a probability associated with the candidate destination based on the selected at least one quantity of historical orders and the quantity of historical orders associated with the candidate destination, wherein the probability represents a likelihood that the service requestor intends to travel to the candidate destination at the first point in time; and
a destination determination module to determine a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively.
16. The system of claim 15, further comprising:
and the transmission module is used for transmitting the recommended destination to a requester terminal related to the service requester so that the recommended destination is displayed on a user interface of the requester terminal.
17. The system of claim 15 or 16, wherein the destination of the selected at least one historical order matches the candidate destination and a departure time associated with the selected at least one historical order is within a second time period that includes the first time point.
18. The system of any one of claims 15 to 17, wherein a departure location associated with the selected at least one historical order is within a distance range that includes the first location.
19. The system of any of claims 15 to 18, wherein determining the quantity of the selected at least one historical order comprises:
determining a weight for each of the selected at least one historical order based on an interval between a departure time of each of the selected at least one historical order and the first point in time; and
determining a quantity of the selected at least one historical order based on a sum of the weights of the selected at least one historical order.
20. The system of claim 19, wherein said determining a weight for each of said selected at least one historical order comprises:
determining a half-life based on the plurality of historical orders; and
determining a weight for each of the selected at least one historical order based on the half-life and an interval between a departure time of each of the selected at least one historical order and the first point in time.
21. The system of any of claims 15 to 20, wherein the determining a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively, comprises:
selecting one candidate destination with the highest probability from the one or more candidate destinations;
determining whether the maximum probability exceeds a probability threshold; and
in response to a determination that the maximum probability exceeds the probability threshold, determining that the candidate destination with the maximum probability is a recommendation destination to be recommended to the service requester.
22. A computer-readable medium comprising at least one set of instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising:
determining that a service requester intends to request a service from a first location at a first point in time;
obtaining a plurality of historical orders of the service requester in a first time period before the first time point according to the determination result;
determining one or more candidate destinations based on the plurality of historical orders;
for each of the one or more candidate destinations,
selecting at least one historical order from the plurality of historical orders that is relevant to the candidate destination based on the first point in time; and
determining a probability associated with the candidate destination based on the selected at least one quantity of historical orders and the quantity of historical orders associated with the candidate destination, wherein the probability represents a likelihood that the service requestor intends to travel to the candidate destination at the first point in time; and
determining a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively.
23. The computer-readable medium of claim 22, wherein the method further comprises:
and sending the recommended destination to a requester terminal related to the service requester, and displaying the recommended destination on a user interface of the requester terminal.
24. The computer-readable medium of claim 22 or 23, wherein the destination of the selected at least one historical order matches the candidate destination, and a departure time associated with the selected at least one historical order is within a second time period that includes the first time point.
25. The computer-readable medium of any of claims 22 to 24, wherein a departure location associated with the selected at least one historical order is within a distance range that includes the first location.
26. The computer-readable medium of any of claims 22 to 25, wherein determining the quantity of the selected at least one historical order comprises:
determining a weight for each of the selected at least one historical order based on an interval between a departure time of each of the selected at least one historical order and the first point in time; and
determining a quantity of the selected at least one historical order based on a sum of the weights of the selected at least one historical order.
27. The computer-readable medium of claim 26, wherein said determining a weight for each of said selected at least one historical order comprises:
determining a half-life based on the plurality of historical orders; and
determining a weight for each of the selected at least one historical order based on the half-life and an interval between a departure time of each of the selected at least one historical order and the first point in time.
28. The computer-readable medium of any one of claims 22 to 27, wherein the determining a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively, comprises:
selecting one candidate destination with the highest probability from the one or more candidate destinations;
determining whether the maximum probability exceeds a probability threshold; and
in response to a determination that the maximum probability exceeds the probability threshold, determining that the candidate destination with the maximum probability is a recommendation destination to be recommended to the service requester.
Technical Field
The present application relates generally to systems and methods for predicting destinations in online-to-offline services, and in particular, to systems and methods for predicting destinations in online-to-offline services using non-parametric statistics.
Background
A taxi-taking program on a user terminal (e.g., a smartphone) may periodically communicate over a network with an online-to-offline service platform on a server terminal to obtain service and/or location information. When it is determined through these communications that the passenger intends to call the car (e.g., turn on a taxi-taking program installed in the passenger's smartphone), the online-to-offline service platform may send and display the recommended destination to the passenger's smartphone. If the recommended destination matches the passenger's desired destination, the passenger may quickly enter the destination by selecting the recommended destination. In the related art, the recommendation destination may be determined using a statistical method such as a normal distribution and a Beta distribution. However, the destinations recommended using these statistical methods often do not meet the expectations of the passengers. Therefore, there is a need for a system and method for predicting destinations in an online-to-offline service that improves the accuracy of the recommendation of destinations to passengers.
Disclosure of Invention
According to one aspect of the present application, the system may include one or more processors, and a storage device configured to communicate with the one or more processors. The storage device may include a set of instructions. When the one or more processors execute the set of instructions, the one or more processors may be instructed to perform one or more of the following operations. The one or more processors may determine that a service requestor intends to request service from a first location at a first point in time. The one or more processors may obtain, according to the determination, a plurality of historical orders for the service requester over a first time period prior to the first time point. The one or more processors may determine one or more candidate destinations based on the plurality of historical orders. The one or more processors may, for each of the one or more candidate destinations, select at least one historical order from the plurality of historical orders that is relevant to the candidate destination based on the first point in time. The one or more processors may determine a probability associated with the candidate destination based on the selected at least one quantity of historical orders and the quantity of historical orders associated with the candidate destination, wherein the probability represents a likelihood that the service requester intends to travel to the candidate destination at the first point in time. The one or more processors may determine a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively.
In some embodiments, the one or more processors may send the recommended destination to a requestor terminal associated with the service requestor, causing the recommended destination to be displayed on a user interface of the requestor terminal.
In some embodiments, the destination of the selected at least one historical order matches the candidate destination, and the departure time associated with the selected at least one historical order is within a second time period that includes the first time point.
In some embodiments, the departure location associated with the selected at least one historical order is within a distance range that includes the first location.
In some embodiments, determining the quantity of the selected at least one historical order comprises determining a weight for each of the selected at least one historical orders based on an interval between a departure time of each of the selected at least one historical orders and the first point in time; and determining a quantity of the selected at least one historical order based on a sum of the weights of the selected at least one historical order.
In some embodiments, to determine a weight for each of the selected at least one historical order, the one or more processors may determine a half-life based on the plurality of historical orders. The one or more processors may determine a weight for each of the selected at least one historical order based on the half-life and an interval between a departure time of each of the selected at least one historical order and the first point in time.
In some embodiments, to determine a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively, the one or more processors may select a candidate destination having a greatest probability from the one or more candidate destinations. The one or more processors may determine whether the maximum probability exceeds a probability threshold. The one or more processors may determine, in response to a determination that the maximum probability exceeds the probability threshold, that the candidate destination with the maximum probability is a recommendation destination to recommend to the service requester.
According to another aspect of the present application, a method for predicting a destination in an online-to-offline service system may include one or more of the following operations. The one or more processors may determine that a service requestor intends to request service from a first location at a first point in time. The one or more processors may obtain, according to the determination, a plurality of historical orders for the service requester over a first time period prior to the first time point. The one or more processors may determine one or more candidate destinations based on the plurality of historical orders. The one or more processors may, for each of the one or more candidate destinations, select at least one historical order from the plurality of historical orders that is relevant to the candidate destination based on the first point in time. The one or more processors may determine a probability associated with the candidate destination based on the selected at least one quantity of historical orders and the quantity of historical orders associated with the candidate destination, wherein the probability represents a likelihood that the service requester intends to travel to the candidate destination at the first point in time. The one or more processors may determine a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively.
According to another aspect of the present application, a system for predicting a destination in an online-to-offline service system may include an order acquisition module to determine that a service requester intends to request service from a first location at a first point in time and acquire a plurality of historical orders of the service requester over a first time period prior to the first point in time based on the determination. The system may also include a candidate destination determination module to determine one or more candidate destinations based on the plurality of historical orders. The system may also include a probability determination module to, for each of the one or more candidate destinations, select at least one historical order from the plurality of historical orders that is related to the candidate destination based on the first point in time, and determine a probability related to the candidate destination based on a quantity of the selected at least one historical order and a quantity of historical orders related to the candidate destination, wherein the probability represents a likelihood that the service requester intends to travel to the candidate destination at the first point in time. The system may also include a destination determination module to determine a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively.
According to yet another aspect of the present application, a non-transitory computer-readable medium may include at least one set of instructions for predicting a destination in an online-to-offline service system. The at least one set of instructions may be executable by one or more processors of the computing device. The one or more processors may determine that a service requestor intends to request service from a first location at a first point in time. The one or more processors may obtain, according to the determination, a plurality of historical orders for the service requester over a first time period prior to the first time point. The one or more processors may determine one or more candidate destinations based on the plurality of historical orders. The one or more processors may, for each of the one or more candidate destinations, select at least one historical order from the plurality of historical orders that is relevant to the candidate destination based on the first point in time. The one or more processors may determine a probability associated with the candidate destination based on the selected at least one quantity of historical orders and the quantity of historical orders associated with the candidate destination, wherein the probability represents a likelihood that the service requester intends to travel to the candidate destination at the first point in time. The one or more processors may determine a recommended destination from the one or more candidate destinations based on the one or more probabilities associated with the one or more candidate destinations, respectively.
Additional features will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following and the accompanying drawings or may be learned from the manufacture and operation of the examples. The features of the present application may be achieved by practice or use of various aspects of the methods, instrumentalities and combinations discussed in detail in the following examples.
Drawings
The present application will be further described in conjunction with the exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These embodiments are non-limiting exemplary embodiments, in which like numerals represent like structures throughout the several views, and in which:
FIG. 1 is a schematic illustration of an online-to-offline service system, shown in accordance with some embodiments of the present application;
FIG. 2 is a schematic diagram of exemplary hardware and/or software components of a computing device implementing a processing engine, shown in accordance with some embodiments of the present application;
FIG. 3 is a diagram of exemplary hardware and/or software components of a mobile device implementing one or more terminals according to some embodiments of the present application;
FIG. 4 is a block diagram of an exemplary processing engine shown in accordance with some embodiments of the present application;
FIG. 5 is an exemplary flow diagram illustrating the determination of a destination for a service requester according to some embodiments of the present application; and
FIG. 6 is an exemplary flow chart illustrating the determination of a first quantity of at least one historical order selected according to some embodiments of the present application.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the application and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present application. Thus, the present application is not limited to the embodiments shown, but is to be accorded the widest scope consistent with the claims.
The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to limit the scope of the present application. As used herein, the singular forms "a", "an" and "the" may include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, components, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, and/or groups thereof.
The foregoing and other features, aspects of the operation, and functions of the related elements of the present application, as well as the related elements of the present application, will become more apparent from the following description of the drawings, which are to be read in connection with the accompanying drawings. It is to be understood, however, that the drawings are designed solely for the purposes of illustration and description and are not intended as a definition of the limits of the application. It should be understood that the drawings are not to scale.
Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be expressly understood that the operations in the flowcharts may be performed out of order. Rather, various steps may be processed in reverse order or simultaneously. One or more other operations may also be added to, or one or more steps may be removed from, these flowcharts.
Further, while the systems and methods herein are described primarily with respect to recommending destinations to passengers intending to call a car in a taxi service, it should be understood that this is merely one exemplary embodiment. The system and the method can be applied to any application scene that a user needs to search for a position. In some embodiments, the systems and methods of the present application may be applied to different transportation systems, including terrestrial, marine, aerospace, and the like, or any combination thereof. The vehicles of the transportation system may include taxis, private cars, trams, buses, trains, motor cars, high-speed rails, subways, boats, planes, spacecraft, hot air balloons, unmanned vehicles, bicycles, tricycles, motorcycles, and the like, or any combination thereof. The systems and methods of the present application may be applied to taxi taking, driver services, distribution services, carpooling, bus services, take-out services, driver hiring, vehicle renting, bicycle sharing services, train services, subway services, regular bus services, location services, map services, and the like. For example, the systems and methods of the present application may be applied to scenarios where a user wants to search for an advance position in a navigation service. As another example, the systems and methods of the present application may be applied in scenarios where a user wants to search for a location in a delivery service for letter or package deliveries. As another example, the systems and methods of the present application may be applied to scenarios where a user wants to search for take-away food delivery locations in a take-away service.
In some embodiments, when it is determined that the passenger intends to call a car (e.g., using a taxi-taking program installed in the passenger's smartphone), an online-to-offline service platform in communication with the taxi-taking program may send and display a recommended destination to the passenger's smartphone. The recommended destination matches the passenger's desired destination so that the location is quickly entered in the taxi-taking program. To this end, the systems and methods of the present application may determine a plurality of candidate destinations based on historical orders requested by past passengers. For each candidate destination, the systems and methods of the present application may use non-parametric statistics to determine a probability that represents a likelihood that the passenger intends to travel to the candidate destination. In non-parametric statistics, the departure time, departure location and destination of historical orders may be considered. The systems and methods of the present application may recommend to the passenger the candidate destination with the greatest probability of the plurality of candidate destinations.
Fig. 1 is a schematic diagram of an online-to-
In some embodiments, the
In some embodiments, the
In some embodiments, the service requester may be a user of the
In some embodiments, the
In some embodiments, the
In some embodiments,
The
Fig. 2 is a schematic diagram of exemplary hardware and/or software components of a computing device implementing
The processor 210 (e.g., logic circuitry) may execute computer instructions (e.g., program code) and perform the functions of the
The computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions that perform particular functions described herein. For example, the
For illustration only, only one processor is depicted in
The input/
The
Fig. 3 is a diagram illustrating exemplary hardware and/or software components of a mobile device implementing a user terminal (e.g.,
To implement the various modules, units and their functionality described in this application, a computing device or mobile device may serve as a hardware platform for one or more of the components described in this application. A computer with user interface elements may be used to implement a Personal Computer (PC) or other type of workstation or terminal device, and if suitably programmed, may also act as a server.
One of ordinary skill in the art will appreciate that when elements of the inline-to-
Fig. 4 is a block diagram of an
The
In some embodiments, the
In some embodiments, when the service requester opens the application in
The first location may be a departure location of a service requester associated with a transportation service. In some embodiments, the departure location may be a designated location input by the service requestor through the requestor terminal 130 (e.g., input/
The first point in time may refer to a departure time associated with a transportation service. In some embodiments, the transport service may be a real-time transport service. The real-time transport service indicates that the service requester desires to receive transport service at the present time or within a defined time (e.g., 1 minute, 5 minutes, or 10 minutes) that is reasonably close to the present time for one of ordinary skill in the art to the present time for the service requester to desire to receive transport service at the present time or at the present time. The service provider needs to go up immediately or substantially immediately after the online-to-
In some embodiments, reserving a transport service may indicate that the service requester desires to receive transport service at a time that is significantly longer than the current time for one of ordinary skill in the art, and that the service provider need not go online until immediately or substantially immediately after the service request is received by the
In some embodiments, the reservation departure time may be a specified point in time that the service requester inputs through requester terminal 130 (e.g., input/
Alternatively or additionally, the
In some embodiments, the departure location of the historical order may be a departure location sent when the service requester requests the historical order or a location when the service provider accepting the historical order embarks the service requester. The destination of the historical order may be the destination sent when the service requester requested the historical order or the location where the service requester disembarked from the vehicle of the service provider that accepted the historical order. The departure time of the real-time historical order may be a request time of the real-time historical order, or may be a time point when a service provider that receives the real-time historical order carries the service requester. The starting time of the reservation history order may be a reservation starting time transmitted when the service requester requests the reservation history order or a time point when a service provider receiving the real-time history order mounts the service requester.
The candidate
For each of the one or more candidate destinations, the
In some embodiments, the destination of the selected at least one historical order may match the candidate destination. For example, the one or more candidate destinations may include location 1, location 2, and location 3. For location 1, the
Alternatively or additionally, the departure location of the selected at least one historical order may be within a distance range that includes the first location. For example, the departure location of the selected at least one historical order may be within a circular area centered on the first location and having a radius that may be a particular distance (e.g., 1000 meters).
Alternatively or additionally, the
For example, for a candidate destination, the
In some embodiments, the longer the time interval between the departure time and the first point in time of a historical order, the lower the reliability and the lower the accuracy of using the historical order to determine the probability. Thus, the
The
In some embodiments, if the number of candidate destinations having the maximum probability exceeding the probability threshold is more than one, all candidate destinations having the maximum probability exceeding the probability threshold may be recommended to the service requester. Alternatively, the
The
The modules in the
It should be noted that the foregoing is provided for illustrative purposes only and is not intended to limit the scope of the present application. Many variations and modifications will be apparent to those of ordinary skill in the art in light of the teachings herein. However, such changes and modifications do not depart from the scope of the present application. For example, the
FIG. 5 is an exemplary flow diagram illustrating the determination of a destination for a service requester according to some embodiments of the present application. In some embodiments, process 500 may be implemented in an online-to-
At step 502, the order taking module 402 (or the
In some embodiments, the
In some embodiments, when the service requester opens the application in
The first location may be a departure location of a service requester associated with a transportation service. In some embodiments, the departure location may be a designated location input by the service requestor through the requestor terminal 130 (e.g., input/
The first point in time may refer to a departure time associated with a transportation service. In some embodiments, the transport service may be a real-time transport service. The real-time transport service indicates that the service requester desires to receive transport service at the present time or within a defined time (e.g., 1 minute, 5 minutes, or 10 minutes) that is reasonably close to the present time for one of ordinary skill in the art. The service provider needs to go up immediately or substantially immediately after the online-to-
In some embodiments, reserving a transport service may indicate that the service requester desires to receive transport service at a time that is significantly longer than the current time for one of ordinary skill in the art, and that the service provider need not go online until immediately or substantially immediately after the service request is received by the
In some embodiments, the reservation departure time may be a specified point in time that the service requester inputs through requester terminal 130 (e.g., input/
At step 504, the order acquisition module 402 (e.g., the
In some embodiments, the departure location of the historical order may be a departure location sent when the service requester requests a historical order or a location when a service provider accepting the historical order embarks the service requester. The destination of the historical order may be the destination sent when the service requester requested the historical order or the location where the service requester disembarked from the vehicle of the service provider that accepted the historical order. The departure time of the real-time historical order may be a request time of the real-time historical order, or may be a time point when a service provider that receives the real-time historical order carries the service requester. The starting time of the reservation history order may be a reservation starting time transmitted when the service requester requests the reservation history order or a time point when a service provider receiving the real-time history order mounts the service requester.
At step 506, the candidate destination determination module 404 (or the
For each of the one or more candidate destinations, the probability determination module 406 (or the
In some embodiments, the destination of the selected at least one historical order may match the candidate destination. For example, the one or more candidate destinations may include location 1, location 2, and location 3. For location 1, the
Alternatively or additionally, the departure location of the selected at least one historical order may be within a distance range that includes the first location. For example, the departure location of the selected at least one historical order may be within a circular area centered on the first location and having a radius that may be a particular distance (e.g., 1000 meters).
At step 510, for each of the one or more candidate destinations, probability determination module 406 (or
For example, for a candidate destination, the
In some embodiments, the longer the time interval between the departure time and the first point in time of a historical order, the lower the reliability and the lower the accuracy of using the historical order to determine the probability. Thus, the
At step 512, the destination determination module 408 (or the
In some embodiments, if the number of candidate destinations having the maximum probability exceeding the probability threshold is more than one, all candidate destinations having the maximum probability exceeding the probability threshold may be recommended to the service requester. Alternatively, the
At step 514, the
FIG. 6 is an exemplary flow chart illustrating the determination of a first quantity of at least one historical order selected according to some embodiments of the present application. In some embodiments,
At
In some embodiments, to determine the weight for each historical order, the half-life may be determined based on the plurality of historical orders. The half-life may be related to a departure time of at least one of the plurality of historical orders, a departure location of at least one of the plurality of historical orders, a destination of at least one of the plurality of historical orders, a distance (e.g., a straight-line distance, a trip distance) between the departure location and the destination of at least one of the plurality of historical orders, a quantity of the plurality of historical orders, or the like, or any combination thereof. The
For example, the
where wi refers to the weight of the selected historical order, Δ t refers to the interval between the departure time of the selected historical order and the first time point, and τ refers to the half-life.
At
For example, the
wherein N refers to a first quantity of the selected at least one historical order.
For example only, the selected at least one historical order includes order 1, order 2, and order 3. The weights associated with order 1, order 2, and order 3 are 0.5, 0.6, and 0.8, respectively. The first quantity of the selected at least one historical order may be 3 without regard to the weight of order 1, order 2, and order 3. When considering the weights of order 1, order 2, and order 3, the first quantity of the at least one historical order selected may be 1.9(0.5+0.6+0.8 ═ 1.9).
Having thus described the basic concepts, it will be apparent to those of ordinary skill in the art having read this application that the foregoing disclosure is to be construed as illustrative only and is not limiting of the application. Various alterations, improvements, and modifications may be suggested to one skilled in the art, though not expressly stated herein. Such alterations, modifications, and improvements are intended to be suggested herein and are intended to be within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific language to describe embodiments of the application. For example, this application uses specific terminology to describe embodiments of the application. Such as "one embodiment," "an embodiment," and/or "some embodiments" means a feature, structure, or characteristic described in connection with at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics may be combined as suitable in one or more embodiments of the application.
Moreover, those of ordinary skill in the art will understand that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, articles, or materials, or any new and useful modification thereof. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "module", or "system". Furthermore, aspects of the present application may be presented as a computer product, having computer-readable program code, in one or more computer-readable media.
A computer readable signal medium may include a propagated data signal with computer program code embodied therewith, for example, on baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, and the like, or any suitable combination. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable signal medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any suitable combination of the foregoing.
Computer program code required for operation of aspects of the present application may be written in any combination of one or more programming languages, including object oriented programming, such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, or similar conventional programming languages, such as the "C" programming language, Visual Basic, Fortran2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages, such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter case, the remote calculator may be connected to the user calculator through any form of network, for example, a Local Area Network (LAN) or a Wide Area Network (WAN), or connected to an external calculator (for example, through the internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).
Furthermore, unless explicitly stated in the claims, the order of processing elements or sequences, use of numbers or letters, or use of other names is not intended to limit the order of the processes and methods described herein. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by being installed in a hardware device, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile carrier.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Rather, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
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