WO2021114279A1

WO2021114279A1 - Systems and methods for determining restriction attribute of area of interset

Info

Publication number: WO2021114279A1
Application number: PCT/CN2019/125356
Authority: WO
Inventors: Fenghao ZHU
Original assignee: Beijing Didi Infinity Technology And Development Co., Ltd.
Priority date: 2019-12-13
Filing date: 2019-12-13
Publication date: 2021-06-17

Abstract

Methods for determining a restriction attribute of an area of interest includes obtaining feature information associated with the AOI; associating the AOI with a plurality of selected historical behavior points and determining, based on the plurality of selected historical behavior points associated with the AOI and the feature information associated with the AOI, a restriction attribute of the AOI. Systems for determining a restriction attribute of an area of interest are also disclosed.

Description

SYSTEMS AND METHODS FOR DETERMINING RESTRICTION ATTRIBUTE OF AREA OF INTERSET

TECHNICAL FIELD

This disclosure generally relates to an online service platform, and more particularly, relates to systems and methods for determining a restriction attribute of an area of interest (AOI) .

BACKGROUND

With the development of Internet technology, online services, such as online taxi hailing services, are starting to play a significant role in people’s daily lives. When a service requestor (e.g., a passenger) initiates a service request via a taxi hailing online service platform, the service request usually includes a starting location and a destination. However, in some cases, the starting location and/or the destination can be located in an area of interest ( “AOI” ; e.g., a school, a park, or a residential district) . Oftentimes such AOI does not allow a service provider (e.g., a driver) to enter or has certain restrictions that result in poor internal traffic, leading to a diminished user experience. Thus, it is desirable to provide systems and methods to determine the type and level of restrictions imposed on an AOI and make recommendations (e.g., a boarding point) to the service requestor and/or service provider to facilitate the service and improve user experience.

SUMMARY

According to an aspect of the present disclosure, a method may include one or more of the following operations performed by at least one processor. The method may include obtaining feature information associated with the AOI. The method may also include associating the AOI with a plurality of selected historical behavior points. The selected historical points may be from a plurality of historical behavior points, which are based on a plurality of historical orders. The method may further include determining, based on the plurality of selected historical behavior points associated with the AOI and the feature information associated with the AOI, a restriction attribute of the AOI.

In some embodiments, the feature information associated with the AOI may include at least one of a contour feature, a classification feature, and a name feature.

In some embodiments, each historical behavior point may include at least one of a pick-up location, a drop-off location, a start billing location, and a stop billing location.

In some embodiments, the method may also include performing a gridding operation on the AOI and the plurality of historical behavior points. The method may also include identifying a selected historical behavior point from the plurality of historical behavior points in response to a determination that the selected historical behavior point and the AOI are located in a same grid. The method may also include associating the AOI with the selected historical behavior point.

In some embodiments, the gridding operation may be performed according to a geohash algorithm.

In some embodiments, the method may also include determining a first count of selected historical behavior points from the plurality of selected historical behavior points associated with the AOI, wherein the first count of selected historical behavior points are for behavior points in the AOI. The method may also include determining a second count of selected historical behavior points from the plurality of historical behavior points associated with the AOI, wherein the second count of selected historical behavior points are for behavior points out of the AOI.

In some embodiments, the method may also include determining the restriction attribute of the AOI based on the first count, the second count, and the feature information associated with the AOI.

In some embodiments, the restriction attribute of the AOI may include a closed and restricted AOI, a closed and time limited restricted AOI, a closed and unrestricted AOI, and an open AOI.

In some embodiments, the method may also include obtaining a service request, wherein the service request includes a point of interest (POI) located in the AOI.

In some embodiments, the method may also include determining a behavior point based on the restriction attribute of the AOI and the POI.

According to another aspect of the present disclosure, a system for determining a restriction attribute of an area of interest (AOI) may include at least one storage medium storing a set of instructions, and at least one processor in communication with the at least one storage medium. When executing the stored set of instructions, the at least one processor causes the system to obtain feature information associated with the AOI. The at least one processor may also cause the system to associate the AOI with a plurality of selected historical behavior points, wherein the selected historical points are from a plurality of historical behavior points, which are based on a plurality of historical orders. The at least one processor may also cause the system to determine, based on the plurality of selected historical behavior points associated with the AOI and the feature information associated with the AOI, a restriction attribute of the AOI.

According to still another aspect of the present disclosure, a non-transitory computer readable medium storing instructions, the instructions, when executed by at least one processor, causing the at least one processor to implement a method. The method may include obtaining feature information associated with the AOI. The method may include associating the AOI with a plurality of selected historical behavior points. The selected historical points may be from a plurality of historical behavior points, which are based on a plurality of historical orders. The method may include determining, based on the plurality of selected historical behavior points associated with the AOI and the feature information associated with the AOI, a restriction attribute of the AOI.

Additional features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The features of the present disclosure may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplary embodiments. These exemplary embodiments are described in detail with reference to the drawings. The drawings are not to scale. These embodiments are non-limiting exemplary embodiments, in which like reference numerals represent similar structures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary online service system according to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device according to some embodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing engine according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for determining a restriction attribute of an AOI according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for associating an AOI with a selected historical behavior point according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for determining a behavior point based on a restriction attribute of an AOI according to some embodiments of the present disclosure; and

FIG. 8 is schematic diagram illustrating exemplary selected historical behavior points associated with an AOI according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled in the art to make and use the present disclosure 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 disclosure. Thus, the present disclosure 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 be limiting. As used herein, the singular forms “a, ” “an, ” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprise, ” “comprises, ” and/or “comprising, ” “include, ” “includes, ” and/or “including, ” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

These and other features, and characteristics of the present disclosure, as well as the methods of operation, various components of the stated system, functions of the related elements of structure, and the combination of parts and economies of manufacture, may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form part of this disclosure. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended to limit the scope of the present disclosure. It is understood that the drawings are not to scale.

The flowcharts used in the present disclosure illustrate operations that systems implemented according to some embodiments of the present disclosure. It is to be expressly understood that the operations of the flowchart may be implemented not in order. Conversely, the operations may be implemented in inverted order, or simultaneously. Moreover, one or more other operations may be added to the flowcharts. One or more operations may be removed from the flowcharts.

Moreover, while the systems and methods disclosed in the present disclosure are described primarily regarding online transportation service, it should also be understood that this is only one exemplary embodiment. The systems and methods of the present disclosure may be applied to any other kind of online service. For example, the systems and methods of the present disclosure may be applied to transportation systems of different environments including land (e.g. roads or off-road) , water (e.g. river, lake, or ocean) , air, aerospace, or the like, or any combination thereof. The vehicle of the transportation systems may include a taxi, a private car, a hitch, a bus, a train, a bullet train, a high-speed rail, a subway, a boat, a vessel, an aircraft, a spaceship, a hot-air balloon, a driverless vehicle, or the like, or any combination thereof. The transportation systems may also include any transportation system for management and/or distribution, for example, a system for sending and/or receiving an express. The application of the systems and methods of the present disclosure may include a mobile device (e.g. smart phone or pad) application, a webpage, a plug-in of a browser, a client terminal, a custom system, an internal analysis system, an artificial intelligence robot, or the like, or any combination thereof.

The terms “passenger, ” “requester, ” “requestor, ” “service requester, ” “service requestor, ” and “customer” in the present disclosure are used interchangeably to refer to an individual, an entity or a tool that may request or order a service. Also, the terms “driver, ” “provider, ” “service provider, ” and “supplier” in the present disclosure are used interchangeably to refer to an individual, an entity or a tool that may provide a service or facilitate the providing of the service. The term “user” in the present disclosure is used to refer to an individual, an entity or a tool that may request a service, order a service, provide a service, or facilitate the providing of the service. In the present disclosure, terms “requester” and “requester terminal” may be used interchangeably, and terms “provider” and “provider terminal” may be used interchangeably.

The terms “request, ” “service, ” “service request, ” and “order” in the present disclosure are used interchangeably to refer to a request that may be initiated by a passenger, a requester, a service requester, a customer, a driver, a provider, a service provider, a supplier, or the like, or any combination thereof. Depending on context, the service request may be accepted by any one of a passenger, a requester, a service requester, a customer, a driver, a provider, a service provider, or a supplier. In some embodiments, the service request is accepted by a driver, a provider, a service provider, or a supplier. The service request may be chargeable or free.

The positioning technology used in the present disclosure may be based on a global positioning system (GPS) , a global navigation satellite system (GLONASS) , a compass navigation system (COMPASS) , a Galileo positioning system, a quasi-zenith satellite system (QZSS) , a wireless fidelity (WiFi) positioning technology, or the like, or any combination thereof. One or more of the above positioning systems may be used interchangeably in the present disclosure.

An aspect of the present disclosure is directed to systems and methods for determining a restriction attribute of an AOI. According to some systems and methods of the present disclosure, the processing engine may obtain feature information associated with the AOI. Feature information associated with the AOI refers to any information that defines, describes, or quantifies the AOI, and in some embodiments, it may include but not be limited to a contour feature, a classification feature, and a name feature. The processing engine may associate the AOI with a plurality of selected historical behavior points. The selected historical points may be selected from a plurality of historical behavior points. The term behavior point refers to locations that are defined, influenced, or limited by the behavior of persons (e.g. service requestor or service provider) associated with a service order. In some embodiments, behavior points may include but not be limited to a pick-up location, a drop-off location, a start billing location, and a stop billing location. The plurality of historical behavior points may be determined based on a plurality of historical orders. The processing engine may determine, based on the plurality of selected historical behavior points associated with the AOI and the feature information associated with the AOI, the restriction attribute of the AOI, which refers to characteristics of the AOI that indicate how access to and/or traffic within the AOI is restricted (or not restricted) . The restriction attributes of AOI may include but not be limited to a closed and restricted AOI, a closed and time limited restricted AOI, a closed and unrestricted AOI, and an open AOI. Accordingly, the restriction attribute of the AOI may be determined based on historical behaviors associated with service requestors (e.g., passengers) and service providers (e.g., drivers) . When a service requestor initiates a service quest including a point of interest (POI) located in the AOI, the processing engine may determine a behavior point (e.g., a boarding point) based on the restriction attribute of the AOI, which may improve user experience.

It should be noted that online transportation service, such as online taxi-hailing including taxi hailing combination services, is a new form of service rooted only in post-Internet era. It provides technical solutions to users and service providers that could raise only in post-Internet era. In pre-Internet era, when a passenger hails a taxi on the street, the taxi request and acceptance occur only between the passenger and one taxi driver that sees the passenger. If the passenger hails a taxi through a telephone call, the service request and acceptance may occur only between the passenger and one service provider (e.g., one taxi company or agent) . Online taxi, however, allows a user of the service to automatically distribute a service request in real-time to a vast number of individual service providers (e.g., taxi) distance away from the user. It also allows a plurality of service providers to respond to the service request simultaneously and in real-time. Therefore, through the Internet, the online transportation systems may provide a much more efficient transaction platform for the users and the service providers that may never meet in a traditional pre-Internet transportation service system.

FIG. 1 is a schematic diagram illustrating an exemplary online service system according to some embodiments of the present disclosure. For example, the online service system 100 may be an online transportation service platform for transportation services such as taxi hailing, chauffeur services, delivery vehicles, express car, carpool, bus service, driver hiring, shuttle services, etc.

The online service system 100 may include a server 110, a network 120, one or more client terminals (e.g., one or more requestor terminals 130, one or more provider terminals 140) , and a storage device 150.

In some embodiments, the server 110 may be a single server, or a server group. The server group may be centralized, or distributed (e.g., the server 110 may be a distributed system) . In some embodiments, the server 110 may be local or remote. For example, the server 110 may access information and/or data stored in the one or more client terminals (e.g., the one or more requestor terminals 130, the one or more provider terminals 140) , and/or the storage device 150 via the network 120. As another example, the server 110 may be directly connected to the one or more client terminals (e.g., the one or more requestor terminals 130, the one or more provider terminals 140) , and/or the storage device 150 to access stored information and/or data. In some embodiments, the server 110 may be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof. In some embodiments, the server 110 may be implemented on a computing device 200 including one or more components illustrated in FIG. 2.

In some embodiments, the server 110 may include a processing engine 112. The processing engine 112 may process information and/or data to perform one or more functions described in the present disclosure. For example, the processing engine 112 may obtain feature information associated with the AOI. As another example, the processing engine 112 may determine a plurality of historical behavior points based on a plurality of historical orders. As still another example, the processing engine 112 may perform a gridding operation on an AOI and a plurality of historical behavior points. As still another example, the processing engine 112 may identify a selected historical behavior point from a plurality of historical behavior points in response to a determination that the selected historical behavior point and an AOI are located in a same grid. As still another example, the processing engine 112 may associate an AOI with a selected historical behavior point. As still another example, the processing engine 112 may determine, based on a plurality of selected historical behavior points associated with an AOI and feature information associated with the AOI, a restriction attribute of the AOI.

In some embodiments, the processing engine 112 may include one or more processing engines (e.g., signal-core processing engine (s) or multi-core processor (s) ) . Merely by way of example, the processing engine 112 may include a central processing unit (CPU) , an application-specific integrated circuit (ASIC) , an application-specific instruction-set processor (ASIP) , a graphics processing unit (GPU) , a physics processing unit (PPU) , a digital signal processor (DSP) , a field-programmable gate array (FPGA) , a programmable logic device (PLD) , a controller, a microcontroller unit, a reduced instruction-set computer (RISC) , a microprocessor, or the like, or any combination thereof.

The network 120 may facilitate exchange of information and/or data. In some embodiments, one or more components in the online service system 100 (e.g., the server 110, the one or more requestor terminals 130, the one or more provider terminal 140, or the storage device 150) may send information and/data to other component (s) in the online service system 100 via the network 120. For example, the processing engine 112 may obtain feature information associated with an AOI from the storage device 150 via the network 120. As another example, the processing engine 112 may obtain a plurality of historical behavior points from the one or more client terminals (e.g., the one or more requestor terminals 130, the one or more provider terminals 140) and/or the storage device 150 via the network 120. As another example, the processing engine 112 may obtain a service request from the one or more client terminals (e.g., the one or more requestor terminals 130, the one or more provider terminals 140) via the network 120. In some embodiments, the network 120 may be any type of wired or wireless network, or any combination thereof. Merely by way of example, the network 120 may include a cable network, a wireline network, an optical fiber network, a telecommunications network, an intranet, an internet, a local area network (LAN) , a wide area network (WAN) , a wireless local area network (WLAN) , a metropolitan area network (MAN) , a wide area network (WAN) , a public telephone switched network (PTSN) , a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or the like, or any combination thereof. In some embodiments, the network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points such as base stations and/or internet exchange points 120-1, 120-2…through which one or more components of the online service system 100 may be connected to the network 120 to exchange data and/or information.

In some embodiments, a service requester may be a user of the requester terminal 130. In some embodiments, the user of the requester terminal 130 may be someone other than the service requester. For example, a user A of the requester terminal 130 may use the requester terminal 130 to send a service request for a user B or receive a service confirmation and/or information or instructions from the server 110. In some embodiments, a service provider may be a user of the provider terminal 140. In some embodiments, the user of the provider terminal 140 may be someone other than the service provider. For example, a user C of the provider terminal 140 may use the provider terminal 140 to receive a service request for a user D, and/or information or instructions from the server 110.

In some embodiments, the requestor terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, a built-in device in a motor vehicle 130-4, or the like, or any combination thereof. In some embodiments, the mobile device 130-1 may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home device may include a smart lighting device, a control device of an intelligent electrical apparatus, a smart monitoring device, a smart television, a smart video camera, an interphone, or the like, or any combination thereof. In some embodiments, the wearable device may include a smart bracelet, a smart footgear, a smart glass, a smart helmet, a smart watch, a smart clothing, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a personal digital assistance (PDA) , a gaming device, a navigation device, a point of sale (POS) device, or the like, or any combination. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, a virtual reality glass, a virtual reality patch, an augmented reality helmet, an augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include a Google Glass, an Oculus Rift, a Hololens, a Gear VR, etc. In some embodiments, built-in device in the motor vehicle 130-4 may include an onboard computer, an onboard television, etc. In some embodiments, the requestor terminal 130 may be a device with positioning technology for locating the position of the service requester and/or the requestor terminal 130.

In some embodiments, the provider terminal 140 may be similar to, or the same device as the requestor terminal 130. In some embodiments, the provider terminal 140 may be a device with positioning technology for locating the position of the driver and/or the provider terminal 140. In some embodiments, the requestor terminal 130 and/or the provider terminal 140 may communicate with other positioning device to determine the position of the service requester, the requestor terminal 130, the service provider, and/or the provider terminal 140. In some embodiments, the requestor terminal 130 and/or the provider terminal 140 may send positioning information to the server 110.

The storage device 150 may store data and/or instructions. For example, the data may be a plurality of historical orders, feature information associated with an AOI, or the like, or any combination thereof. In some embodiments, the storage device 150 may store data obtained from the one or more client terminals (e.g., the one or more requestor terminals 130, the one or more provider terminals 140) or the processing engine 112. For example, the storage device 150 may store feature information associated with an AOI. As another example, the storage device 150 may store a plurality of historical behavior points. As another example, the storage device 150 may store a plurality of selected historical behavior points determined by the processing engine 112. As still another example, the storage device 150 may store a restriction attribute of an AOI determined by the processing engine 112. In some embodiments, the storage device 150 may store data and/or instructions that the server 110 may execute or use to perform exemplary methods described in the present disclosure. For example, the storage device 150 may store instructions that the processing engine 112 may execute or use to perform a gridding operation on an AOI and a plurality of historical behavior points. As another example, the storage device 150 may store instructions that the processing engine 112 may execute or use to identify a selected historical behavior point from a plurality of historical behavior points in response to a determination that the selected historical behavior point and an AOI are located in a same grid. As still another example, the storage device 150 may store instructions that the processing engine 112 may execute or use to associate an AOI with a selected historical behavior point. As still another example, the storage device 150 may store instructions that the processing engine 112 may execute or use to determine, based on a plurality of selected historical behavior points associated with an AOI and feature information associated with the AOI, a restriction attribute of the AOI.

In some embodiments, the storage device 150 may include a mass storage, a removable storage, a volatile read-and-write memory, a read-only memory (ROM) , or the like, or any combination thereof. Exemplary mass storage may include a magnetic disk, an optical disk, a solid-state drives, etc. Exemplary removable storage may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplary volatile read-and-write memory may include a random access memory (RAM) . Exemplary RAM may include a dynamic RAM (DRAM) , a double date rate synchronous dynamic RAM (DDR SDRAM) , a static RAM (SRAM) , a thyristor RAM (T-RAM) , and a zero-capacitor RAM (Z-RAM) , etc. Exemplary ROM may include a mask ROM (MROM) , a programmable ROM (PROM) , an erasable programmable ROM (EPROM) , an electrically-erasable programmable ROM (EEPROM) , a compact disk ROM (CD-ROM) , and a digital versatile disk ROM, etc. In some embodiments, the storage device 150 may be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof.

In some embodiments, the storage device 150 may be connected to the network 120 to communicate with one or more components in the online service system 100 (e.g., the server 110, the one or more client terminals) . One or more components in the online service system 100 may access the data and/or instructions stored in the storage device 150 via the network 120. In some embodiments, the storage device 150 may be directly connected to or communicate with one or more components in the online service system 100 (e.g., the server 110, the one or more client terminals) . In some embodiments, the storage device 150 may be part of the server 110.

In some embodiments, one or more components (e.g., the server 110, the requester terminal 130, the provider terminal 140) of the online service system 100 may have permissions to access the storage device 150. In some embodiments, one or more components of the online service system 100 may read and/or modify information relating to the service requester, the service provider, and/or the public when one or more conditions are met. For example, the server 110 may read and/or modify one or more service requesters’ information after a service is completed. As another example, the provider terminal 140 may access information relating to the service requester when receiving a service request from the requester terminal 130, but the provider terminal 140 may not modify the relevant information of the service requester.

In some embodiments, information exchanging of one or more components of the online service system 100 may be achieved by way of requesting a service. The object of the service request may be any product. In some embodiments, the product may be a tangible product or an immaterial product. The tangible product may include food, medicine, commodity, chemical product, electrical appliance, clothing, car, housing, luxury, or the like, or any combination thereof. The immaterial product may include a servicing product, a financial product, a knowledge product, an internet product, or the like, or any combination thereof. The internet product may include an individual host product, a web product, a mobile internet product, a commercial host product, an embedded product, or the like, or any combination thereof. The mobile internet product may be used in a software of a mobile terminal, a program, a system, or the like, or any combination thereof. The mobile terminal may include a tablet computer, a laptop computer, a mobile phone, a personal digital assistance (PDA) , a smart watch, a point of sale (POS) device, an onboard computer, an onboard television, a wearable device, or the like, or any combination thereof. For example, the product may be any software and/or application used in the computer or mobile phone. The software and/or application may relate to socializing, shopping, transporting, entertainment, learning, investment, or the like, or any combination thereof. In some embodiments, the software and/or application relating to transporting may include a traveling software and/or application, a vehicle scheduling software and/or application, a mapping software and/or application, etc. In the vehicle scheduling software and/or application, the vehicle may include a horse, a carriage, a rickshaw (e.g., a wheelbarrow, a bike, a tricycle) , a car (e.g., a taxi, a bus, a private car) , a train, a subway, a vessel, an aircraft (e.g., an airplane, a helicopter, a space shuttle, a rocket, a hot-air balloon) , or the like, or any combination thereof.

One of ordinary skill in the art would understand that when an element (or component) of the online service system 100 performs, the element may perform through electrical signals and/or electromagnetic signals. For example, when the requester terminal 130 transmits out a service request to the server 110, a processor of the requester terminal 130 may generate an electrical signal encoding the request. The processor of the requester terminal 130 may then transmit the electrical signal to an output port. If the requester terminal 130 communicates with the server 110 via a wired network, the output port may be physically connected to a cable, which further may transmit the electrical signal to an input port of the server 110. If the requester terminal 130 communicates with the server 110 via a wireless network, the output port of the requester terminal 130 may be one or more antennas, which convert the electrical signal to electromagnetic signal. Similarly, the provider terminal 140 may process a task through operation of logic circuits in its processor, and receive an instruction and/or a service request from the server 110 via electrical signals or electromagnet signals. Within an electronic device, such as the requester terminal 130, the provider terminal 140, and/or the server 110, when a processor thereof processes an instruction, transmits out an instruction, and/or performs an action, the instruction and/or action is conducted via electrical signals. For example, when the processor retrieves or saves data from a storage medium (e.g., the storage device 150) , it may transmit out electrical signals to a read/write device of the storage medium, which may read or write structured data in the storage medium. The structured data may be transmitted to the processor in the form of electrical signals via a bus of the electronic device. Here, an electrical signal may refer to one electrical signal, a series of electrical signals, and/or a plurality of discrete electrical signals.

It should be noted that the online service system 100 is merely provided for the purposes of illustration, and is not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations or modifications may be made under the teachings of the present disclosure. For example, the online service system 100 may further include a database, an information source, or the like. As another example, the online service system 100 may be implemented on other devices to realize similar or different functions. However, those variations and modifications do not depart from the scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure. In some embodiments, the server 110, the requester terminal 130, and/or the provider terminal 140 may be implemented on the computing device 200. For example, the processing engine 112 may be implemented on the computing device 200 and configured to perform functions of the processing engine 112 disclosed in this disclosure.

The computing device 200 may be used to implement any component of the online service system 100 as described herein. For example, the processing engine 112 may be implemented on the computing device 200, via its hardware, software program, firmware, or a combination thereof. Although only one such computer is shown, for convenience, the computer functions relating to the online service as described herein may be implemented in a distributed fashion on a number of similar platforms to distribute the processing load.

The computing device 200 may include COM ports 250 connected to and from a network connected thereto to facilitate data communications. The computing device 200 may also include a processor 220, in the form of one or more, e.g., logic circuits, for executing program instructions. For example, the processor 220 may include interface circuits and processing circuits therein. The interface circuits may be configured to receive electronic signals from a bus 210, wherein the electronic signals encode structured data and/or instructions for the processing circuits to process. The processing circuits may conduct logic calculations, and then determine a conclusion, a result, and/or an instruction encoded as electronic signals. Then the interface circuits may send out the electronic signals from the processing circuits via the bus 210.

The computing device 200 may further include program storage and data storage of different forms including, for example, a disk 270, a read only memory (ROM) 230, or a random access memory (RAM) 240, for storing various data files to be processed and/or transmitted by the computing device 200. The computing device 200 may also include program instructions stored in the ROM 230, RAM 240, and/or another type of non-transitory storage medium to be executed by the processor 220. The methods and/or processes of the present disclosure may be implemented as the program instructions. The computing device 200 may also include an I/O component 260, supporting input/output between the computer and other components. The computing device 200 may also receive programming and data via network communications.

Merely for illustration, only one processor is described in FIG. 2. Multiple processors are also contemplated, thus operations and/or steps performed by one processor as described in the present disclosure may also be jointly or separately performed by the multiple processors. For example, if in the present disclosure the processor of the computing device 200 executes both operation A and operation B, it should be understood that operation A and operation B may also be performed by two different CPUs and/or processors jointly or separately in the computing device 200 (e.g., the first processor executes operation A and the second processor executes operation B, or the first and second processors jointly execute operations A and B) .

FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device according to some embodiments of the present disclosure. In some embodiments, the requester terminal 130 or the provider terminal 140 may be implemented on the mobile device 300. As illustrated in FIG. 3, the mobile device 300 may include a communication platform 310, a display 320, a graphic processing unit (GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory 360, a mobile operating system (OS) 370, and a storage 390. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown) , may also be included in the mobile device 300.

In some embodiments, the mobile operating system 370 (e.g., iOS ^TM, Android ^TM, Windows Phone ^TM) and one or more applications 380 may be loaded into the memory 360 from the storage 390 in order to be executed by the CPU 340. The applications 380 may include a browser or any other suitable mobile app for receiving and rendering information relating to online services or other information from the online service system 100. User interactions with the information stream may be achieved via the I/O 350 and provided to the processing engine 112 and/or other components of the online service system 100 via the network 120.

To implement various modules, units, and their functionalities described in the present disclosure, computer hardware platforms may be used as the hardware platform (s) for one or more of the elements described herein. A computer with user interface elements may be used to implement a personal computer (PC) or any other type of work station or terminal device. A computer may also act as a server if appropriately programmed.

FIG. 4 is a block diagram illustrating an exemplary processing engine according to some embodiments of the present disclosure. In some embodiments, the processing engine 112 may include an obtaining module 410, an association module 420, a determination module 430, and a storage module 440.

The obtaining module 410 may be configured to obtain data and/or information associated with the online service system 100. For example, the obtaining module 410 may obtain feature information associate with an AOI. As another example, the obtaining module 410 may obtain a plurality of historical behavior points. As still another example, the obtaining module 410 may obtain a plurality of historical orders. As still another example, the obtaining module 410 may obtain a service request.

In some embodiments, the obtaining module 410 may obtain the data and/or the information from one or more components (e.g., the requestor terminal 130, the provider terminal 140, and the storage device 150) of the online service system 100 or an external storage device.

The association module 420 may be configured to associate data and/or information with an AOI. In some embodiments, the association module 420 may associate an AOI with a plurality of selected historical behavior points from a plurality of historical behavior points. For example, the association module 420 may perform a gridding operation on the AOI and the plurality of historical behavior points. The association module 420 may identify a selected historical behavior point from the plurality of historical behavior points in response to a determination that the selected historical behavior point and the AOI are located in a same grid. The association module 420 may associate the AOI with the selected historical behavior point.

The determination module 430 may be configured to determine data and/or information associated with the online service system 100. For example, the determination module 430 may determine, based on a plurality of selected historical behavior points associated with an AOI and feature information associated with the AOI, a restriction attribute of the AOI. As another example, the determination module 430 may determine a behavior point based on a restriction attribute of an AOI and a POI included in a service request.

The storage module 440 may be configured to store data and/or information associated with the online service system 100. In some embodiments, the storage module 440 may store data obtained from the one or more client terminals (e.g., the one or more requestor terminals 130, the one or more provider terminals 140) or the processing engine 112. For example, the storage module 440 may store feature information associated with an AOI. As another example, the storage module 440 may store a plurality of historical behavior points. As another example, the storage module 440 may store a plurality of selected historical behavior points determined by the processing engine 112. As still another example, the storage module 440 may store a restriction attribute of an AOI determined by the processing engine 112.

In some embodiments, the storage module 440 may store data and/or instructions that the server 110 may execute or use to perform exemplary methods described in the present disclosure. For example, the storage module 440 may store instructions that the processing engine 112 may execute or use to perform a gridding operation on an AOI and a plurality of historical behavior points. As another example, the storage module 440 may store instructions that the processing engine 112 may execute or use to identify a selected historical behavior point from a plurality of historical behavior points in response to a determination that the selected historical behavior point and an AOI are located in a same grid. As still another example, the storage module 440 may store instructions that the processing engine 112 may execute or use to associate an AOI with a selected historical behavior point. As still another example, the storage module 440 may store instructions that the processing engine 112 may execute or use to determine, based on a plurality of selected historical behavior points associated with an AOI and feature information associated with the AOI, a restriction attribute of the AOI.

The modules in the processing engine 112 may be connected to or communicated with each other via a wired connection or a wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or any combination thereof. The wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or any combination thereof. Two or more of the modules may be combined into a single module, and any one of the modules may be divided into two or more units. In some embodiments, one or more modules may be combined into a single module. For example, the association module 420 and the determination module 430 may be combined as a single module which may both associated an AOI with a plurality of selected historical behavior points and determine a restriction attribute of the AOI. In some embodiments, one or more modules may be omitted. For example, the storage module 440 may be omitted. The data and/or information associated with the online service system 100 may be stored in the storage device 150 or an external storage device.

FIG. 5 is a flowchart illustrating an exemplary process for determining a restriction attribute of an AOI according to some embodiments of the present disclosure. In some embodiments, the process 500 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240. The processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 500. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 500 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 5 and described below is not intended to be limiting.

In 510, the processing engine 112 (e.g., the obtaining module 410) may obtain feature information associated with an AOI.

As used herein, an AOI may refer to a region associated with which a region parameter (e.g., an area of the region, a largest distance between two points on a boundary of the region, an average travel time (e.g., an average walking time) between the two points) is greater than a threshold. For example, a region with an area larger than an area threshold (e.g., 1 km ², 5 km ², 10 km ², 20 km ²) may be designated as an AOI. As another example, when a region is a rectangle region and a diagonal distance of the region is larger than a distance threshold (e.g., 1 km, 2 km, 5 km, 10 km) , the region may be designated as an AOI. As still another example, when a region is a rectangle region and an average walking time along a diagonal line of the region is larger than a time threshold (e.g., 10 minutes, 20 minutes, 30 minutes, 1 hour) , the region may be designated as an AOI. The thresholds mentioned above may be default settings of the online service system 100, or may be adjustable under different situations. For example, for a city, the area threshold may be relatively small for an ordinary person in the art, whereas for a village, the area threshold may be relatively large for an ordinary person in the art.

In some embodiments, the feature information associated with the AOI may include a contour feature, a classification feature, a name feature, or the like, or any combination thereof.

The contour feature of the AOI may reflect a size and a shape of the AOI. In some embodiments, the contour feature of the AOI may be determined based on a street view map. In some embodiments, the contour feature of the AOI may be determined based on a parent POI and one or more corresponding child POIs. As used herein, “a child POI and a parent POI” may refer to that there is a dependency relationship (e.g., a physical dependency, a logical dependency) between the child POI and the parent POI. In some embodiments, the child POI may include a first type of child POI and a second type of child POI. The first type of child POI may have a physical dependency of the parent POI, and may be independently from the parent POI. For example, a store, a tea shop, and a restaurant in a shopping mall may be the first type of child POIs corresponding to the parent POI (i.e., the shopping mall) . The second type of child POI may have a logical dependency of the parent POI, and may perform an auxiliary function of the parent POI. For example, a sales office, a sales center, a service station of a residential district may be the second type of child POIs corresponding to the parent POI (i.e., the residential district) . In some embodiments, the counter feature of the AOI may be determined by connecting the one or more child POIs corresponding to the parent POI. In some embodiments, the contour feature of the AOI may be drawn manually by an operator of the online service system 100, or determined by one or more components (e.g., the processing engine 112) of the online service system 100.

The classification feature of the AOI may include a residential district, a company, a hospital, a tourist attraction, a school (e.g., a kindergarten, a primary school, a secondary school) , a hotel, a shopping mall, a bank, a park, a house, a city square, or the like, or any combination thereof. The name feature of the AOI may be a name of an area or a business name associated with the AOI. For example, the name feature of the AOI may include a Zhongshan primary school, a Huayuan residential district, a bank of china (BOC) , a Macy’s mall, a KFC, or the like, or any combination thereof.

In some embodiments, the processing engine 112 may obtain the feature information of the AOI from one or more components (e.g., the storage device 150) of the online service system 100 or an external storage device.

In 520, the processing engine 112 (e.g., the obtaining module 410) may obtain a plurality of historical behavior points.

In some embodiments, the behavior point may include a pick-up location, a drop-off location, a start billing location, a stop billing location, or the like, or any combination thereof. As used herein, “a pick-up location” may refer to a location where a service requestor starts a service. For example, the pick-up location may be the location where a passenger actually gets on a vehicle. “Adrop-off location” may refer to a location where a service requestor ends a service. For example, the drop-off location may be the location where a passenger actually gets off a vehicle.

In some embodiments, the processing engine 112 may determine the pick-up location and the drop-off location based on trajectory characteristics of the service provider and the service requestor, a starting location, a destination, and/or a behavior characteristics of the service provider. For example, the processing engine 112 may determine the location at which the speed of the service provider is zero, as the pick-up location or the drop-off location. As another example, the processing engine 112 may determine the pick-up location and the drop-off location using a neural network mode. As used herein, “a starting location” may refer to a location that a service requestor inputs/selects to start a service (e.g., an online taxi hailing service) via a terminal device (e.g., the requestor terminal 130) when the service requestor initiates a service request. “A destination” may refer to a location that a service requestor inputs/selects to end a service (e.g., an online taxi hailing service) via a terminal device (e.g., the requestor terminal 130) when the service requestor initiates a service request. “A start billing location” may refer to a location where a service provider begins to bill a service requestor. “A stop billing location” may refer to a location where a service provider stops billing a service requestor. In some embodiments, the pick-up location may be the same as or different from the starting location or the start billing location. In some embodiments, the drop-off location may be the same as or different from the destination or the stop billing location.

In some embodiments, the plurality of historical behavior points may be determined based on information associated with a plurality of historical orders. As used herein, a historical order may refer to an order that has been fulfilled. In some embodiments, information associated with the historical order may include an order number, a historical starting location, a historical destination, a historical pick-up location, a historical drop-off location, a historical start billing location, a historical stop billing location, a historical time corresponding to the historical pick-up location, a historical time corresponding to the historical drop-off location, a historical time corresponding to the historical start billing location, a historical time corresponding to the historical stop billing location, user’s identity information (e.g., an identification (ID) , a telephone number, a user’s name) , or the like, or any combination thereof. As used herein, “a time corresponding to a location (e.g., a pick-up location, a drop-off location, a start billing location, a stop billing location) may refer to a time point when a service requestor or a service provider is located at the location.

In some embodiments, the processing device 112 may obtain the information associated with the plurality of historical orders from one or more components of the online service system 100 (e.g., the requester terminal 130, the provider terminal 140, the storage device 150) , or from an external source (e.g., a database) via the network 120.

In 530, the processing engine 112 (e.g., the association module 420) may associate the AOI with a plurality of selected historical behavior points from the plurality of historical behavior points.

In some embodiments, the processing engine 112 may perform a gridding operation on the AOI and the plurality of historical behavior points. For example, the processing engine 112 may segment a map or a part of a map where the AOI and the plurality of historical behavior points are located into plurality of grids according to a geohash algorithm. For each historical behavior point of the plurality of historical behavior points, the processing engine 112 may determine whether each historical behavior point and the AOI are located in a same grid. In response to determination that a historical behavior point and the AOI are located in the same grid, the processing engine 112 may identify the historical behavior point as the selected behavior point. The processing engine 112 may associate the AOI with the selected historical behavior point. More descriptions for associating the AOI with the plurality of selected historical behavior points from the plurality of historical behavior points may be found elsewhere in the present disclosure (e.g., FIG. 6, 8, and descriptions thereof) .

In some embodiments, the processing engine 112 may associate the AOI with the plurality of selected historical behavior points from the plurality of historical behavior points based on the contour feature of the AOI and geographic coordinates of the plurality of historical behavior points. For example, for each historical behavior point of the plurality of historical behavior points, the processing engine 112 may determine whether a distance between each historical behavior point and a boundary of the AOI is less than a distance threshold. As used herein, the distance between the historical behavior point and the boundary of the AOI may be a shortest direct distance between the historical behavior point and the boundary of the AOI or a shortest route distance between the historical behavior point and the boundary of the AOI via an established route (or routes) . For an ordinary person in the art, it is known that an AOI has a certain size. Take a top view as an example, it can be considered that the boundary of the AOI includes a plurality of access points and each of the plurality of access points corresponds to a route distance to the historical behavior point. The “shortest distance” refers to the shortest route distance among the plurality of route distances corresponding to the plurality of access points. In response to determination that the distance between a historical behavior point and the boundary of the AOI is less than the distance threshold, the processing engine 112 may identify the historical behavior point as the selected behavior point. The processing engine 112 may associate the AOI with the selected historical behavior point.

In 540, the processing engine 112 (e.g., the determination module 430) may determine, based on the plurality of selected historical behavior points associated with the AOI and the feature information associated with the AOI, a restriction attribute of the AOI.

In some embodiments, the restriction attribute of the AOI may include a closed and restricted AOI, a closed and time limited restricted AOI, a closed and unrestricted AOI, and an open AOI. In some embodiments, “a closed and restricted AOI” may refer to that the AOI has a physical boundary (e.g. fixed fence) and does not allow any vehicle, or any un-authorized vehicle to enter. In some embodiments, the closed and restricted AOI may have one or more exits and entrances for an authorized vehicle to enter or leave. For example, the closed and restricted AOI may include an airport, a railway station, a military, a school, a park, a house, or the like, or part thereof. In some embodiments, “a closed and time limited restricted AOI” may refer to that the AOI has a physical boundary (e.g. fixed fence) and one or more exits and entrances for a vehicle to enter or leave at a specific time period (e.g., work time) in a day. For example, the closed and time limited restricted AOI may include a tourist attraction area, a company’s campus, or the like, or part thereof. In some embodiments, “a closed and unrestricted AOI” may refer to that the AOI has a physical boundary (e.g. fixed fence) and one or more exits and entrances for a vehicle to enter or leave at all time in a day. For example, the closed and unrestricted AOI may include a residential district, a shopping plaza, or the like. In some embodiments, “an open AOI” may refer to that the AOI does not have a physical boundary and allows any vehicle to enter or leave the AOI.

In some embodiments, the processing engine 112 may determine a first count of selected historical behavior points from the plurality of selected historical behavior points associated with the AOI. The first count of selected historical behavior points may be for behavior points in the AOI. The processing engine 112 may determine a second count of selected historical behavior points from the plurality of historical behavior points associated with the AOI. The second count of selected historical behavior points may be for behavior points out of the AOI.

In some embodiments, the processing engine 112 may determine whether the selected historical behavior point is in the AOI according to a spatial intersection algorithm (e.g., a ray casting method) . For example, the processing engine 112 may determine a ray from the selected historical behavior point to infinity in any direction. The processing engine 112 may determine a count of intersections of the AOI and the ray. If the count is odd, the selected historical behavior point may be in the AOI. If the count is even, the selected historical behavior point may be out of the AOI.

The processing engine 112 may determine the restriction attribute of the AOI based on the first count, the second count, and the feature information associated with the AOI. For example, when the first count is greater than a first count threshold, and/or a ratio of the first count and the second count is greater than a first ratio threshold (e.g., 10, 20) , the processing engine 112 may determine that the restriction attribute of the AOI is the open AOI.

As another example, when the classification feature of the AOI is a school (e.g., a kindergarten, a primary school, a secondary school) , a shopping mall, a park, a house, or a city square, the first count is less than a second count threshold (e.g., 3) , and/or the ratio of the first count and the second count is less than a second ratio threshold (e.g., 1/100) , the processing engine 112 may determine that the restriction attribute of the AOI is the closed and restricted AOI.

As still another example, when the first count is greater than a third count threshold (e.g., 10) , and/or the ratio of the first count and the second count is greater than a third ratio threshold (e.g., 3/10) , the processing engine 112 may determine that the restriction attribute of the AOI is the closed and unrestricted AOI.

As still another example, when the first count is in a first count range (e.g., [50, 100] , [80, 200] ) and/or the ratio of the first count and the second count is in a first ratio range (e.g., [10, 50] , [10, 100] ) , the processing engine 112 may determine that the restriction attribute of the AOI is the open AOI.

As still another example, when the classification feature of the AOI is a school (e.g., a kindergarten, a primary school, a secondary school) , a shopping mall, a park, a house, or a city square, the first count is in a second count range (e.g., [0, 5] , [0, 10] ) , and/or the ratio of the first count and the second count is in a second ratio range (e.g., [0, 1/100) ] , [0, 1/200] ) , the processing engine 112 may determine that the restriction attribute of the AOI is the closed and restricted AOI.

As still another example, when the first count is in a third count range (e.g., [10, 50] , [20, 80] ) , and/or the ratio of the first count and the second count is in a third ratio range (e.g., [3/10, 1/2] ) , the processing engine 112 may determine that the restriction attribute of the AOI is the closed and unrestricted AOI.

In some embodiments, the restriction attribute of the AOI may be associated with time. For example, a day may be divided into a plurality of time periods. The duration of a time period may be, for example, 1 hour, 4 hours, 6 hours, or 12 hours. For each time period of the plurality of time periods, the processing engine 112 may collect a plurality of historical behavior points based on a plurality of historical orders. The processing engine 112 may associate with the AOI with a plurality of selected historical behavior points from the plurality of historical behavior points. The processing engine 112 may determine, based on the plurality of selected historical behavior points associated with the AOI and the feature information associated with the AOI, the restriction attribute of the AOI according to the each time period. For example, when the restriction attribute of the AOI is the closed and unrestricted AOI in a first time period (e.g., 8: 00～12: 00) , and the restriction attribute of the AOI is the closed and restricted AOI in a second time period (e.g., 13: 00～18: 00) , the processing engine 112 may determine that the restriction attribute of the AOI is a closed and time limited restricted AOI.

It should be noted that the above description is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. In some embodiments, one or more operations may be combined into a single operation. For example, operation 520 and operation 530 may be combined into an operation. In some embodiments, one or more other optional operations (e.g., a storing operation) may be added elsewhere in the process 500. In the storing operation, the processing engine 112 may store information and/or data associated with the AOI (e.g., the feature information) in a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure. In some embodiments, one or more operations may be performed simultaneously. For example, operation 510 and operation 520 may be performed simultaneously. It should also be noted that that is no requirement that the operations shown in Fig. 5 need to be performed according to the sequence herein presented. For example, it would be possible to perform operation 520 before 510 so that the historical data is collected and analyzed before it is necessary to analyze a particular AOI. In essence, a model based on historical data can be constructed before or simultaneously when obtaining feature information of an AOI.

FIG. 6 is a flowchart illustrating an exemplary process for associating an AOI with a selected historical behavior point according to some embodiments of the present disclosure. In some embodiments, the process 600 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240. The processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 600. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 600 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 6 and described below is not intended to be limiting.

In 610, the processing engine 112 (e.g., the association module 420) may perform a gridding operation on an AOI and a plurality of historical behavior points.

In some embodiments, the processing engine 112 may segment a plurality of grids on a map where the AOI and the plurality of historical behavior points located. In some embodiments, the processing engine 112 may segment the plurality of grids based on a segmentation parameter. The segmentation parameter may be default settings of the online service system 100, or may be adjustable under different situations. The segmentation parameter may include a size of a grid, a population density in a region (e.g., a city, a district) corresponding to the map, a building density in the region, etc. The sizes of the plurality of grids may be the same with or different from each other. The shape of each of the plurality of grids may include a rectangle, a triangle, a circle, an irregular polygon, etc. For example, the processing engine 112 may segment a plurality of grids with a size of 100 m × 100 m, 200 m × 200 m, 500 m × 500 m, etc.

In some embodiments, the processing engine 112 may perform the gridding operation on the AOI and the plurality of historical behavior points according to a geohash algorithm. As used herein, the geohash may refer to a public domain geocoding system which encodes a geographic location into a short string of letters and digits. The geohash may be a hierarchical spatial data structure which subdivides space into buckets of grid shape. Merely by way of example, the processing engine 112 may segment a plurality of regular rectangles on a map of an area within Beijing’s 5 ^th ring and encode each rectangle.

In 620, the processing engine 112 (e.g., the association module 420) may identify a selected historical behavior point from the plurality of historical behavior points in response to a determination that the selected historical behavior point and the AOI are located in a same grid.

For each historical behavior point of the plurality of historical behavior points, the processing engine 112 may determine whether the each historical behavior point and the AOI are located in the same grid. In response to determination that a historical behavior point and the AOI are located in the same grid, the processing engine 112 may identify the historical behavior point as the selected behavior point.

In 630, the processing engine 112 (e.g., the association module 420) may associate the AOI with the selected historical behavior point.

In some embodiments, the AOI may occupy one or more grids. The processing engine 112 may determine one or more historical behavior points in each grid of the one or more grids occupied by the AOI, as the one or more selected historical behavior points. The processing engine 112 may associate the AOI with the one or more selected historical behavior points. More descriptions for associating the AOI with the plurality of selected historical behavior points may be found elsewhere in the present disclosure (e.g., FIG. 8, and descriptions thereof) .

It should be noted that the above description is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure.

FIG. 7 is a flowchart illustrating an exemplary process for determining a behavior point based on a restriction attribute of an AOI according to some embodiments of the present disclosure. In some embodiments, the process 700 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240. The processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 700. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 700 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 7 and described below is not intended to be limiting.

In 710, the processing engine 112 (e.g., the obtaining module 410) may obtain a service request.

As used herein, a service request may be a request for any location based services. In some embodiments, the service request may be a request for a transportation service (e.g., a taxi service, a delivery service, a vehicle hailing service) . In some embodiments, the service request may include a starting location, a destination, a starting time, an estimated time of arrival, identity information (e.g., an identification (ID) , a telephone number, a user’s name) , or the like, or any combination thereof. As used herein, “a starting time” may refer to a time point when a service requestor wants to start a service. “An estimated time of arrival (ETA) ” may refer to an estimated time point when a service requestor arrives at a destination. In some embodiments, the service request may include a point of interest (POI) located in an AOI. For example, a starting location and/or a destination may be a POI (e.g., a child POI, a parent POI) located in the AOI.

In some embodiments, the processing engine 112 may obtain the service request from the storage device 150, the client terminal (e.g., the requestor terminal 130, the provider terminal 140) of one or more users via the network 120. In some embodiments, the client terminal (e.g., the requestor terminal 130) may establish a communication (e.g., a wireless communication) with the server 110, for example, through an application (e.g., the application 380 in FIG. 3) installed in the client terminal. In some embodiments, the application may be associated with a service platform (e.g., an online service platform) . For example, the application may be associated with a taxi-hailing service platform. In some embodiments, the service requestor may log into the application and initiate the service request. In some embodiments, the application installed in the client terminal may direct the client terminal to monitor the service request from the service requestor continuously or periodically, and automatically transmit the service request to the processing engine 112 via the network 120.

In 720, the processing engine 112 (e.g., the determination module 430) may determine a behavior point based on a restriction attribute of the AOI and the POI.

In some embodiments, when the restriction attribute of the AOI is a closed and restricted AOI, and the starting location is the POI (e.g., a child POI, a parent POI) located in the AOI, the processing engine 112 may determine a boarding point for the service provider to pick up the service requestor based on the starting location. For example, a preset boarding point may be set every 100 meters along a road. The processing engine 112 may determine the preset boarding point located less than a predetermined distance (e.g., 20 meters, 50 meters) from the AOI, as the boarding point.

In some embodiments, when the restriction attribute of the AOI is the closed and restricted AOI, and the destination is the POI (e.g., a child POI) located in the AOI, the processing engine 112 may determine a getting-off point for the service provider to drop off the service requestor based on a parent POI corresponding to the child POI. For example, the processing engine 112 may determine the getting-off point of the parent POI as the getting-off point of the child POI.

In some embodiments, when the restriction attribute of the AOI is a closed and unrestricted AOI, and the starting location is the POI (e.g., a child POI) located in the AOI, the processing engine 112 may determine the boarding point for the service provider to pick up the service requestor based on the type of the child POI. For example, if the starting location is a first type of child POI located in the AOI, the processing engine 112 may determine a preset boarding point located less than a predetermined distance (e.g., 20 meters, 50 meters) from a parent POI corresponding to the child POI, as the boarding point. As another example, if the starting location is a second type of child POI located in the AOI, the processing engine 112 may determine a preset boarding point located less than a predetermined distance (e.g., 20 meters, 50 meters) from the child POI, as the boarding point.

In some embodiments, when the restriction attribute of the AOI is a closed and unrestricted AOI, and the destination is the POI (e.g., a child POI) located in the AOI, the processing engine 112 may determine the getting-off point for the service provider to drop off the service requestor based on the type of the child POI, a service demand condition and/or a service supply condition. In some embodiments, if the destination is the first type of child POI located in the AOI, the processing engine 112 may determine a location located less than a predetermined distance (e.g., 20 meters, 50 meters) from the parent POI corresponding to the child POI, as the getting-off location. In some embodiments, if the destination is the second type of child POI located in the AOI, the processing engine 112 may determine the getting-off point based on the service demand condition and/or the service supply condition. For example, if there is a surplus supply and/or an insufficient demand, the processing engine 112 may determine a location located less than a predetermined distance (e.g., 20 meters, 50 meters) from the child POI, as the getting-off location. As another example, if there is a high demand and/or a short supply, the processing engine 112 may determine a location located less than a predetermined distance (e.g., 20 meters, 50 meters) from the parent POI corresponding to the child POI, as the getting-off location.

As illustrated, the processing engine 112 may segment an AOI 801 and a plurality of historical behavior points into 3×3 grids with the same size and the same shape. Black dots refer to the historical behavior points. The AOI 801 may be located in a plurality of grids, for example, a grid A, a grid B, a grid C, and a grid D. The processing engine 112 may determine, from the plurality of historical behavior points, a plurality of selected historical behavior points located in a same grid as the AOI 801. For example, the processing engine 112 may determine a historical behavior point a in the grid A, historical behavior points b, c, d, e in the grid B, historical behavior points f, g, h, i in the grid C, and historical behavior points j, k in the grid D, as the plurality of selected historical behavior points. The processing engine 112 may associated the AOI 801 with the plurality of selected historical behavior points.

In some embodiments, the processing engine 112 may determine a first count of selected historical behavior points (e.g., the selected historical behavior points a, b, f, g, j) in the AOI 801. The processing engine 112 may determine a second count of selected historical behavior points (e.g., the selected historical behavior points c, d, e, h, i, k) out of the AOI 801. The processing engine 112 may determine a restriction attribute of the AOI 801 based on the first count, the second count, and feature information associated with the AOI 801 as described in connection with operation 540.

Having thus described the basic concepts, it may be rather apparent to those skilled in the art after reading this detailed disclosure that the foregoing detailed disclosure is intended to be presented by way of example only and is not limiting. Various alterations, improvements, and modifications may occur and are intended to those skilled in the art, though not expressly stated herein. These alterations, improvements, and modifications are intended to be suggested by this disclosure, and are within the spirit and scope of the exemplary embodiments of this disclosure.

Moreover, certain terminology has been used to describe embodiments of the present disclosure. For example, the terms “one embodiment, ” “an embodiment, ” and “some embodiments” mean that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. 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 portions of this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined as suitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects of the present disclosure may be illustrated and described herein in any of a number of patentable classes or context including any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof. Accordingly, aspects of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc. ) or combining software and hardware implementation that may all generally be referred to herein as a “module, ” “unit, ” “component, ” “device, ” or “system. ” Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer readable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, or the like, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that may communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including wireless, wireline, optical fiber cable, RF, or the like, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET, Python or the like, conventional procedural programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, 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 scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN) , or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) or in a cloud computing environment or offered as a service such as a Software as a Service (SaaS) .

Furthermore, the recited order of processing elements or sequences, or the use of numbers, letters, or other designations therefore, is not intended to limit the claimed processes and methods to any order except as may be specified in the claims. Although the above disclosure discusses through various examples what is currently considered to be a variety of useful embodiments of the disclosure, 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 modifications and equivalent arrangements that are within the spirit and scope of the disclosed embodiments. For example, although the implementation of various components described above may be embodied in a hardware device, it may also be implemented as a software only solution, e.g., an installation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description of embodiments of the present disclosure, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the various embodiments. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed subject matter requires more features than are expressly recited in each claim. Rather, claim subject matter lie in less than all features of a single foregoing disclosed embodiment.

Claims

A method for determining a restriction attribute of an area of interest (AOI) implemented on a computing device having at least one processor and at least one storage device, the method comprising:

obtaining feature information associated with the AOI;

associating the AOI with a plurality of selected historical behavior points, wherein the selected historical points are from a plurality of historical behavior points, which are based on a plurality of historical orders; and

determining, based on the plurality of selected historical behavior points associated with the AOI and the feature information associated with the AOI, a restriction attribute of the AOI.
The method of claim 1, wherein the feature information associated with the AOI includes at least one of a contour feature, a classification feature, and a name feature.
The method of claim 1 or 2, wherein each historical behavior point includes at least one of a pick-up location, a drop-off location, a start billing location, and a stop billing location.
The method of any one of claims 1-3, wherein associating the AOI with the plurality of selected historical behavior points of the plurality of historical behavior points, further comprises:

performing a gridding operation on the AOI and the plurality of historical behavior points;

identifying a selected historical behavior point from the plurality of historical behavior points in response to a determination that the selected historical behavior point and the AOI are located in a same grid; and

associating the AOI with the selected historical behavior point.
The method of claim 4, wherein the gridding operation is performed according to a geohash algorithm.
The method of any one of claims 4-5, further comprises:

determining a first count of selected historical behavior points from the plurality of selected historical behavior points associated with the AOI, wherein the first count of selected historical behavior points are for behavior points in the AOI; and

determining a second count of selected historical behavior points from the plurality of historical behavior points associated with the AOI, wherein the second count of selected historical behavior points are for behavior points out of the AOI.
The method of claim 6, wherein determining, based on the plurality of selected historical behavior points associated with the AOI and the feature information associated with the AOI, a restriction attribute of the AOI, further comprises:

determining the restriction attribute of the AOI based on the first count, the second count, and the feature information associated with the AOI.
The method of any one of claims 1-7, wherein the restriction attribute of the AOI includes a closed and restricted AOI, a closed and time limited restricted AOI, a closed and unrestricted AOI, and an open AOI.
The method of any one of claims 1-8, further comprises:

obtaining a service request, wherein the service request includes a point of interest (POI) located in the AOI.
The method of claim 9, further comprises:

determining a behavior point based on the restriction attribute of the AOI and the POI.
A system for determining a restriction attribute of an area of interest (AOI) , comprising:

at least one storage medium storing a set of instructions;

at least one processor in communication with the at least one storage medium, when executing the stored set of instructions, the at least one processor causes the system to:

obtain feature information associated with the AOI;

associate the AOI with a plurality of selected historical behavior points, wherein the selected historical points are from a plurality of historical behavior points, which are based on a plurality of historical orders; and

determine, based on the plurality of selected historical behavior points associated with the AOI and the feature information associated with the AOI, a restriction attribute of the AOI.
The system of claim 11, wherein the feature information associated with the AOI includes at least one of a contour feature, a classification feature, and a name feature.
The system of claim 11 or 12, wherein each historical behavior point includes at least one of a pick-up location, a drop-off location, a start billing location, and a stop billing location.
The system of any one of claims 11-13, wherein to associate the AOI with the plurality of selected historical behavior points of the plurality of historical behavior points, the at least one processor causes the system to:

perform a gridding operation on the AOI and the plurality of historical behavior points;

identify a selected historical behavior point from the plurality of historical behavior points in response to a determination that the selected historical behavior point and the AOI are located in a same grid; and

associate the AOI with the selected historical behavior point.
The system of claim 14, wherein the gridding operation is performed according to a geohash algorithm.
The system of any one of claims 14-15, the at least one processor causes the system to:

determine a first count of selected historical behavior points from the plurality of selected historical behavior points associated with the AOI, wherein the first count of selected historical behavior points are for behavior points in the AOI; and

determine a second count of selected historical behavior points from the plurality of historical behavior points associated with the AOI, wherein the second count of selected historical behavior points are for behavior points out of the AOI.
The system of claim 16, wherein to determine, based on the plurality of selected historical behavior points associated with the AOI and the feature information associated with the AOI, a restriction attribute of the AOI, the at least one processor causes the system to:

determine the restriction attribute of the AOI based on the first count, the second count, and the feature information associated with the AOI.
The system of any one of claims 11-17, wherein the restriction attribute of the AOI includes a closed and restricted AOI, a closed and time limited restricted AOI, a closed and unrestricted AOI, and an open AOI.
The system of any one of claims 11-18, the at least one processor causes the system to:

obtain a service request, wherein the service request includes a point of interest (POI) located in the AOI.
The system of claim 19, the at least one processor causes the system to:

determine a behavior point based on the restriction attribute of the AOI and the POI.
A non-transitory computer readable medium storing instructions, the instructions, when executed by at least one processor, causing the at least one processor to implement a method comprising:

obtaining feature information associated with the AOI;

associating the AOI with a plurality of selected historical behavior points, wherein the selected historical points are from a plurality of historical behavior points, which are based on a plurality of historical orders; and

determining, based on the plurality of selected historical behavior points associated with the AOI and the feature information associated with the AOI, a restriction attribute of the AOI.