WO2020243963A1 - Systèmes et procédés de détermination d'informations recommandées de demande de service - Google Patents

Systèmes et procédés de détermination d'informations recommandées de demande de service Download PDF

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Publication number
WO2020243963A1
WO2020243963A1 PCT/CN2019/090398 CN2019090398W WO2020243963A1 WO 2020243963 A1 WO2020243963 A1 WO 2020243963A1 CN 2019090398 W CN2019090398 W CN 2019090398W WO 2020243963 A1 WO2020243963 A1 WO 2020243963A1
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WIPO (PCT)
Prior art keywords
sample
link
correlation
samples
determination model
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PCT/CN2019/090398
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English (en)
Inventor
Xiaowei ZHONG
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Beijing Didi Infinity Technology And Development Co., Ltd.
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Application filed by Beijing Didi Infinity Technology And Development Co., Ltd. filed Critical Beijing Didi Infinity Technology And Development Co., Ltd.
Priority to CN201980097233.9A priority Critical patent/CN113924460B/zh
Priority to PCT/CN2019/090398 priority patent/WO2020243963A1/fr
Publication of WO2020243963A1 publication Critical patent/WO2020243963A1/fr

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
    • G08G1/202—Dispatching vehicles on the basis of a location, e.g. taxi dispatching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/02—Reservations, e.g. for tickets, services or events
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services

Definitions

  • the present disclosure generally relates to systems and methods for online to offline services, and in particular, to systems and methods for determining recommended information associated with a service request for an online to offline service.
  • a system providing online to offline transportation services may obtain a service request including a service location (e.g., a start location, a destination) from a requestor and determine recommended information (e.g., a recommended driving route that starts or ends at the service location) for the requestor.
  • a service location e.g., a start location, a destination
  • recommended information e.g., a recommended driving route that starts or ends at the service location
  • the service location may be a location where a vehicle cannot stop, in order to determine the recommended driving route, the system should determine a suitable location or a suitable link where a vehicle can stop corresponding to the service location. Therefore, it is desirable to provide systems and methods for determining suitable location or suitable link corresponding to a service location of a service request, thereby determining recommended information associated with the service request efficiently and accurately.
  • An aspect of the present disclosure relates to a system for determining recommended information of a service request.
  • the system may include a storage medium to store a set of instructions and a processor communicatively coupled to the storage medium.
  • the system may receive a service request from a terminal device, the service request including a target location; determine a plurality of candidate links based on the target location; determine a plurality of correlation degrees between the plurality of candidate links and the target location by using a trained correlation determination model; and identify a target link from the plurality of candidate links based on the plurality of correlation degrees.
  • the target location may include at least one of a start location and/or a destination.
  • the target link may correspond to a road section associated with the target location.
  • the system may determine a first vector representation of the target location using a first model. For each of the plurality of candidate links, the system may determine a second vector representation of the candidate link using a second model and determine a corresponding correlation degree between the second representation and the first vector representation using the trained correlation determination model.
  • the system may determine recommended information associated with the service request based on the target link.
  • the recommended information may include a recommended driving route that starts or ends at the road section corresponding to the target link.
  • the trained correlation determination model may be determined with a training process.
  • the training process may include obtaining a plurality of historical trip records, each of the plurality of historical trip records including a sample point and one or more sample links; obtaining a plurality of samples based on the plurality of historical trip records, each of the plurality of samples including the sample point and one of the one or more sample links; extracting feature information of each of the plurality of samples; obtaining a preliminary correlation determination model; for each of the plurality of samples, determining a sample correlation degree between the sample point and the sample link based on the feature information by using the preliminary correlation determination model; determining whether a plurality of sample correlation degrees corresponding to the plurality of samples satisfy a preset condition; and designating the preliminary correlation determination model as the trained correlation determination model in response to determining that the plurality of sample correlation degrees satisfy the preset condition.
  • the training process may further may include updating the preliminary correlation determination model in response to determining that the plurality of sample correlation degrees do not satisfy the preset condition and repeating the step of determining whether the plurality of sample correlation degrees satisfy the preset condition until the plurality of sample correlation degrees satisfy the preset condition.
  • the plurality of samples may include a plurality of positive samples and a plurality of negative samples. For each of the plurality of positive samples, the sample link is correlated with the sample point in a corresponding historical trip record; and for each of the plurality of negative samples, the sample link is not correlated with the sample point in a corresponding historical trip record.
  • the feature information of each of the plurality of samples may include first feature information of the sample point and second feature information of the sample link.
  • the first feature information may include at least one of location information of the sample point, an identity of a passenger, an identity of a driver, and/or time information of the sample point.
  • the second feature information may include at least one of an identity of the sample link, a link type, a link velocity, a link angle, and/or a usage frequency of the sample link.
  • the trained correlation determination model may include a twin tower Deep Structured Semantic Model (DSSM) .
  • DSSM Deep Structured Semantic Model
  • the method may include receiving a service request from a terminal device, the service request including a target location; determining a plurality of candidate links based on the target location; determining a plurality of correlation degrees between the plurality of candidate links and the target location by using a trained correlation determination model; and identifying a target link from the plurality of candidate links based on the plurality of correlation degrees.
  • the target location may include at least one of a start location and/or a destination.
  • the target link may correspond to a road section associated with the target location.
  • the method may also include determining a first vector representation of the target location using a first model. For each of the plurality of candidate links, the method may include determining a second vector representation of the candidate link using a second model and determining a corresponding correlation degree between the second representation and the first vector representation using the trained correlation determination model.
  • the method may also include determining recommended information associated with the service request based on the target link.
  • the recommended information may include a recommended driving route that starts or ends at the road section corresponding to the target link.
  • the trained correlation determination model may be determined with a training process.
  • the training process may include obtaining a plurality of historical trip records, each of the plurality of historical trip records including a sample point and one or more sample links; obtaining a plurality of samples based on the plurality of historical trip records, each of the plurality of samples including the sample point and one of the one or more sample links; extracting feature information of each of the plurality of samples; obtaining a preliminary correlation determination model; for each of the plurality of samples, determining a sample correlation degree between the sample point and the sample link based on the feature information by using the preliminary correlation determination model; determining whether a plurality of sample correlation degrees corresponding to the plurality of samples satisfy a preset condition; and designating the preliminary correlation determination model as the trained correlation determination model in response to determining that the plurality of sample correlation degrees satisfy the preset condition.
  • the training process may further include updating the preliminary correlation determination model in response to determining that the plurality of sample correlation degrees do not satisfy the preset condition and repeating the step of determining whether the plurality of sample correlation degrees satisfy the preset condition until the plurality of sample correlation degrees satisfy the preset condition.
  • the plurality of samples may include a plurality of positive samples and a plurality of negative samples. For each of the plurality of positive samples, the sample link is correlated with the sample point in a corresponding historical trip record; and for each of the plurality of negative samples, the sample link is not correlated with the sample point in a corresponding historical trip record.
  • the feature information of each of the plurality of samples may include first feature information of the sample point and second feature information of the sample link.
  • the first feature information may include at least one of location information of the sample point, an identity of a passenger, an identity of a driver, and/or time information of the sample point.
  • the second feature information may include at least one of an identity of the sample link, a link type, a link velocity, a link angle, and/or a usage frequency of the sample link.
  • the trained correlation determination model may include a twin tower Deep Structured Semantic Model (DSSM) .
  • DSSM Deep Structured Semantic Model
  • a further aspect of the present disclosure relates to a system for determining recommended information of a service request.
  • the system may include a receiving module, a candidate link determination module, a correlation determination module, and an identification module.
  • the receiving module may be configured to receive a service request from a terminal device, the service request including a target location.
  • the candidate link determination module may be configured to determine a plurality of candidate links based on the target location.
  • the correlation determination module may be configured to determine a plurality of correlation degrees between the plurality of candidate links and the target location by using a trained correlation determination model.
  • the identification module may be configured to identify a target link from the plurality of candidate links based on the plurality of correlation degrees.
  • the target location may include at least one of a start location and/or a destination.
  • the target link may correspond to a road section associated with the target location.
  • the correlation determination module may be configured to determine a first vector representation of the target location using a first model. For each of the plurality of candidate links, the correlation determination module may further be configured to determine a second vector representation of the candidate link using a second model and determine a corresponding correlation degree between the second representation and the first vector representation using the trained correlation determination model.
  • the identification module may further be configured to determine recommended information associated with the service request based on the target link.
  • the recommended information may include a recommended driving route that starts or ends at the road section corresponding to the target link.
  • the system may further include a training module.
  • the training module may be configured to obtain a plurality of historical trip records, each of the plurality of historical trip records including a sample point and one or more sample links; obtain a plurality of samples based on the plurality of historical trip records, each of the plurality of samples including the sample point and one of the one or more sample links; extract feature information of each of the plurality of samples; obtain a preliminary correlation determination model; for each of the plurality of samples, determine a sample correlation degree between the sample point and the sample link based on the feature information by using the preliminary correlation determination model; determine whether a plurality of sample correlation degrees corresponding to the plurality of samples satisfy a preset condition; and designate the preliminary correlation determination model as the trained correlation determination model in response to determining that the plurality of sample correlation degrees satisfy the preset condition.
  • the training module may further be configured to update the preliminary correlation determination model in response to determining that the plurality of sample correlation degrees do not satisfy the preset condition and repeat the step of determining whether the plurality of sample correlation degrees satisfy the preset condition until the plurality of sample correlation degrees satisfy the preset condition.
  • the plurality of samples may include a plurality of positive samples and a plurality of negative samples. For each of the plurality of positive samples, the sample link is correlated with the sample point in a corresponding historical trip record; and for each of the plurality of negative samples, the sample link is not correlated with the sample point in a corresponding historical trip record.
  • the feature information of each of the plurality of samples may include first feature information of the sample point and second feature information of the sample link.
  • the first feature information may include at least one of location information of the sample point, an identity of a passenger, an identity of a driver, and/or time information of the sample point.
  • the second feature information may include at least one of an identity of the sample link, a link type, a link velocity, a link angle, and/or a usage frequency of the sample link.
  • the trained correlation determination model may include a twin tower Deep Structured Semantic Model (DSSM) .
  • DSSM Deep Structured Semantic Model
  • a still further aspect of the present disclosure relates to a non-transitory computer readable medium including executable instructions.
  • the executable instructions When the executable instructions are executed by at least one processor, the executable instructions may direct the at least one processor to perform a method.
  • the method may include receiving a service request from a terminal device, the service request including a target location; determining a plurality of candidate links based on the target location; determining a plurality of correlation degrees between the plurality of candidate links and the target location by using a trained correlation determination model; and identifying a target link from the plurality of candidate links based on the plurality of correlation degrees.
  • FIG. 1 is a schematic diagram illustrating an exemplary online to offline 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 device according to some embodiments of the present disclosure.
  • FIG. 5 is a flowchart illustrating an exemplary process for determining recommended information associated with a service request according to some embodiments of the present disclosure
  • FIG. 6 is a schematic diagram illustrating an exemplary process for determining a recommended driving route associated with a service request according to some embodiments of the present disclosure
  • FIG. 7 is a flowchart illustrating an exemplary process for determining a trained correlation determination model according to some embodiment of the present disclosure.
  • FIG. 8 is a schematic diagram illustrating exemplary structure of a correlation determination model according to some embodiments of the present disclosure.
  • 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.
  • systems and methods disclosed in the present disclosure are described primarily regarding online to offline 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 on demand service.
  • 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.
  • 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.
  • 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.
  • 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.
  • terms “requester” and “requester terminal” may be used interchangeably
  • 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.
  • 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.
  • 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.
  • GPS global positioning system
  • GLONASS global navigation satellite system
  • COMPASS compass navigation system
  • Galileo positioning system Galileo positioning system
  • QZSS quasi-zenith satellite system
  • WiFi wireless fidelity positioning technology
  • An aspect of the present disclosure relates to systems and methods for determining recommended information (e.g., a recommended driving route, an estimated time of arrival) associated with a service request for an online to offline service (e.g., a taxi hailing service) .
  • the system may receive the service request from the passenger’s terminal device, including a target location (e.g., a start location, a destination) of an intended service.
  • a target location e.g., a start location, a destination
  • the system may determine a plurality of candidate links (e.g., links within a predetermined range of the target location) .
  • the system may also determine a plurality of correlation degrees between the plurality of candidate links and the target location by using a trained correlation determination model (e.g., a twin tower Deep Structured Semantic Model) . Further, the system may identify a target link from the plurality of candidate links based on the plurality of correlation degrees. Also, the system may determine a recommended driving route that starts or ends at a road section corresponding to the target link. According to the systems and methods of the present disclosure, the target link is identified from a plurality of candidate links based on correlation degrees between the candidate links and the target location, which are determined based on a trained correlation determination model, thereby improving the accuracy and efficiency of the determination of recommended information associated with a service request.
  • a trained correlation determination model e.g., a twin tower Deep Structured Semantic Model
  • online to offline transportation service such as online taxi-hailing including taxi hailing combination services
  • 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.
  • 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) .
  • service provider e.g., one taxi company or agent
  • Online taxi 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.
  • the online to offline 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 to offline service system according to some embodiments of the present disclosure.
  • the online to offline 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 to offline service system 100 may include a server 110, a network 120, a requester terminal 130, a provider terminal 140, and a storage device 150.
  • the server 110 may be a single server or a server group.
  • the server group may be centralized or distributed (e.g., server 110 may be a distributed system) .
  • the server 110 may be local or remote.
  • the server 110 may access information and/or data stored in the requester terminal 130, the provider terminal 140, and/or the storage device 150 via the network 120.
  • the server 110 may be directly connected to the requester terminal130, the provider terminal 140, and/or the storage device 150 to access stored information and/or data.
  • the server 110 may be implemented on a cloud platform.
  • 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.
  • the server 110 may be implemented on a computing device 200 including one or more components illustrated in FIG. 2.
  • the server 110 may include a processing device 112.
  • the processing device 112 may process information and/or data relating to a service request to perform one or more functions described in the present disclosure. For example, the processing device 112 may identify a target link associated with a service request by using a trained correlation determination model and determine recommended information (e.g., a recommended driving route, an estimated time of arrival) associated with the service request based on the target link.
  • the processing device 112 may include one or more processing devices (e.g., single-core processing device (s) or multi-core processor (s) ) .
  • the processing device 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 processing device 112 may be integrated in the requester terminal 130 or the provider terminal 140.
  • the network 120 may facilitate exchange of information and/or data.
  • one or more components e.g., the server 110, the requester terminal 130, the provider terminal 140, or the storage device 150
  • the server 110 may transmit information and/or data to other component (s) of the online to offline service system 100 via the network 120.
  • the server 110 may obtain a service request from the requester terminal 130 via the network 120.
  • the network 120 may be any type of wired or wireless network, or any combination thereof.
  • 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 public telephone switched network (PSTN) , a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or the like, or any combination thereof.
  • the network 120 may include one or more network access points.
  • 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 to offline service system 100 may be connected to the network 120 to exchange data and/or information.
  • a service requester may be a user of the requester terminal 130.
  • the user of the requester terminal 130 may be someone other than the service requester.
  • 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.
  • a service provider may be a user of the provider terminal 140.
  • the user of the provider terminal 140 may be someone other than the service provider.
  • 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.
  • the requester 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 vehicle 130-4, or the like, or any combination thereof.
  • 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.
  • 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.
  • 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.
  • 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 thereof.
  • 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.
  • the virtual reality device and/or the augmented reality device may include a Google GlassTM, an Oculus RiftTM, a HololensTM, a Gear VRTM, etc.
  • a built-in device in the vehicle 130-4 may include an onboard computer, an onboard television, etc.
  • the requester terminal 130 may be a device with positioning technology for locating the location of the service requester and/or the requester terminal 130.
  • the provider terminal 140 may be similar to, or the same device as the requester terminal 130. In some embodiments, the provider terminal 140 may be a device with positioning technology for locating the location of the service provider and/or the provider terminal 140. In some embodiments, the requester terminal 130 and/or the provider terminal 140 may communicate with other positioning device to determine the location of the service requester, the requester terminal 130, the service provider, and/or the provider terminal 140. In some embodiments, the requester 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 relating to the service request. In some embodiments, the storage device 150 may store data obtained from the requester terminal 130 and/or the provider terminal 140. 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. 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 drive, 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.
  • DRAM dynamic RAM
  • DDR SDRAM double date rate synchronous dynamic RAM
  • SRAM static RAM
  • T-RAM thyristor RAM
  • Z-RAM zero-capacitor RAM
  • 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.
  • the storage device 150 may be implemented on a cloud platform.
  • 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.
  • the storage device 150 may be connected to the network 120 to communicate with one or more components (e.g., the server 110, the requester terminal 130, the provider terminal 140) of the online to offline service system 100.
  • One or more components of the online to offline service system 100 may access the data and/or instructions stored in the storage device 150 via the network 120.
  • the storage device 150 may be directly connected to or communicate with one or more components (e.g., the server 110, the requester terminal 130, the provider terminal 140) of the online to offline service system 100.
  • the storage device 150 may be part of the server 110.
  • one or more components (e.g., the server 110, the requester terminal 130, the provider terminal 140) of the online to offline service system 100 may have permissions to access the storage device 150.
  • one or more components of the online to offline 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.
  • the server 110 may read and/or modify one or more service requesters’ information after a service is completed.
  • 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.
  • information exchanging of one or more components of the online to offline service system 100 may be achieved by way of requesting a service.
  • the object of the service request may be any product.
  • 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.
  • PDA personal digital assistance
  • POS point of sale
  • 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.
  • 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.
  • 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.
  • a traveling 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.
  • an element or component of the online to offline service system 100 performs, the element may perform through electrical signals and/or electromagnetic signals.
  • 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.
  • 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.
  • the output port of the requester terminal 130 may be one or more antennas, which convert the electrical signal to electromagnetic signal.
  • 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.
  • 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.
  • 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.
  • an electrical signal may refer to one electrical signal, a series of electrical signals, and/or a plurality of discrete electrical signals.
  • the online to offline service 100 may be used as a navigation system.
  • the navigation system may include a user terminal (e.g., the requestor terminal 130 or the provider terminal 140) and a server (e.g., the server 110) .
  • a user may input a target location (e.g., a start location, a destination) via the user terminal.
  • the navigation system may accordingly determine recommended information (e.g., a recommended driving route, an ETA) based on the target location according to the process and/or method described in this 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.
  • the server 110, the requester terminal 130, and/or the provider terminal 140 may be implemented on the computing device 200.
  • the processing device 112 may be implemented on the computing device 200 and configured to perform functions of the processing device 112 disclosed in this disclosure.
  • the computing device 200 may be used to implement any component of the online to offline service system 100 as described herein.
  • the processing device 112 may be implemented on the computing device 200, via its hardware, software program, firmware, or a combination thereof.
  • only one such computer is shown, for convenience, the computer functions relating to the online to offline 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 processors (e.g., logic circuits) , for executing program instructions.
  • 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 other 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.
  • step A and step 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 step A and the second processor executes step B, or the first and second processors jointly execute steps 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.
  • the requester terminal 130 or the provider terminal 140 may be implemented on the mobile device 300.
  • 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.
  • 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.
  • the mobile operating system 370 e.g., iOS TM , Android TM , Windows Phone TM
  • the applications 380 may include a browser or any other suitable mobile apps for receiving and rendering information relating to online to offline services or other information from the online to offline service system 100.
  • User interactions with the information stream may be achieved via the I/O 350 and provided to the processing device 112 and/or other components of the online to offline service system 100 via the network 120.
  • FIG. 4 is a block diagram illustrating an exemplary processing device according to some embodiments of the present disclosure.
  • the processing device 112 may include a receiving module 410, a candidate link determination module 420, a correlation determination module 430, an identification module 440, and a training module 450.
  • the receiving module 410 may be configured to receive a service request from a terminal device (e.g., the requestor terminal 130) via the network 120.
  • the service request may be a request for a transportation service (e.g., a taxi service, a delivery service, a vehicle hailing service) .
  • the service request may include a target location, for example, a start location, a destination, etc.
  • the candidate link determination module 420 may be configured to determine a plurality of candidate links based on the target location.
  • the candidate link determination module 420 may identify a plurality of available links within a predetermined range of the target location and determine a plurality of distances (e.g., a linear distance, a road distance) between the plurality of available links and the target location respectively. Further, the candidate link determination module 420 may determine available links with a distance from the target location less than a distance threshold as the plurality of candidate links.
  • the candidate link determination module 420 may obtain a plurality of historically used links associated with the target location based on historical trip records within a predetermined period (e.g., the last three months) and determine a plurality of usage frequencies of the plurality of historically used links respectively. Further, the candidate link determination module 420 may determine historically used links with a usage frequency higher than a frequency threshold as the plurality of candidate links.
  • the correlation determination module 430 may be configured to determine a plurality of correlation degrees between the plurality of candidate links and the target location by using a trained correlation determination model.
  • the correlation determination module 430 may extract first features (e.g., location information) of the target location and second features (e.g., a link type, a distance between the candidate link and the target location) of the candidate link.
  • the correlation determination module 430 may also determine a first feature vector corresponding to the target location based on the first features and a second feature vector corresponding to the candidate link based on the second features.
  • the correlation determination module 430 may determine the correlation degree between the candidate link and the target location based on the first feature vector and the second feature vector by using the trained correlation determination model. In some embodiments, the correlation determination module 430 may obtain the trained correlation determination model from the training module 450 or a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure.
  • the identification module 440 may be configured to identify a target link from the plurality of candidate links based on the plurality of correlation degrees. In some embodiments, the identification module 440 may select the target link from the plurality of candidate links based on a predetermined rule. For example, the identification module 440 may rank the plurality of candidate links from high to low based on the plurality of correlation degrees. Further, the identification module 440 may select a candidate link with a highest correlation degree as the target link. In some embodiments, after identifying the target link, the identification module 440 may further determine recommended information associated with the service request based on the target link. In some embodiments, the recommended information may include a recommended driving route that starts or ends at the road section corresponding to the target link, an estimated time of arrival (ETA) of the service request, etc.
  • ETA estimated time of arrival
  • the training module 450 may be configured to determine the trained correlation determination model based on a plurality of samples according to a training process.
  • the training module 450 may obtain a plurality of historical trip records, each of the plurality of historical trip records including a sample point and one or more sample links. Further, the training module 450 may obtain the plurality of samples based on the plurality of historical trip records, each of the plurality of samples including the sample point and one of the one or more sample links. More descriptions of the training process may be found elsewhere in the present disclosure (e.g., FIG. 7 and the description thereof) .
  • the processing device 112 may further include a transmission module (not shown) which may be configured to transmit the recommended information to the requestor terminal 130 and/or the provider terminal 140 via the network 120 or save the recommended information into a storage device (e.g., the storage device 150) as disclosed elsewhere in the present disclosure.
  • a transmission module (not shown) which may be configured to transmit the recommended information to the requestor terminal 130 and/or the provider terminal 140 via the network 120 or save the recommended information into a storage device (e.g., the storage device 150) as disclosed elsewhere in the present disclosure.
  • the modules in the processing device 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.
  • LAN Local Area Network
  • WAN Wide Area Network
  • NFC Near Field Communication
  • 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.
  • the receiving module 410 and the candidate link determination module 420 may be combined as a single module which may both receive the service request and determine the plurality of candidate links.
  • the processing device 112 may include a storage module (not shown) used to store information and/or data (e.g., the plurality of candidate links, the plurality of correlation degrees, the target link, the recommended information) associated with service request.
  • the training module 450 may be unnecessary and the trained correlation determination module may be obtained from a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure or may be determined by an independent training device in the online to offline service 100.
  • FIG. 5 is a flowchart illustrating an exemplary process for determining recommended information associated with a service request according to some embodiments of the present disclosure.
  • 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.
  • the processing device 112 e.g., the receiving module 410) (e.g., the interface circuits of the processor 220) may receive a service request from a terminal device (e.g., the requestor terminal 130) via the network 120.
  • a terminal device e.g., the requestor terminal 130
  • the service request may be a request for any location based service.
  • the service request may be a request for a transportation service (e.g., a taxi service, a delivery service, a vehicle hailing service) .
  • the service request may be a real-time request, a reservation request, or the like, or any combination thereof.
  • the real-time request may include a service that a requestor expects to receive at the present moment or at a defined time close to the present moment.
  • a service request may be a real-time request if the defined time is within a time period from the present moment which is less than a time threshold, such as 5 minutes from the present moment, 10 minutes from the present moment, 20 minutes from the present moment, etc.
  • the reservation request may include a service that the requestor expects to receive at a defined time far from the present moment.
  • a service request may be a reservation request if the defined time is within a time period from the present moment which is larger than the time threshold, such 25 minutes from the present moment, 2 hours from the present moment, 1 day from the present moment, etc.
  • the time threshold may be a default setting of the online to offline service system 100 or may be adjustable according to different situations.
  • the time threshold may be relatively small (e.g., 10 minutes)
  • the time threshold may be relatively large (e.g., 1 hour) .
  • the service request may include a target location, for example, a start location, a destination, etc.
  • the start location generally refers to a location where the requestor wishes to start the service (e.g., a location where the requestor wishes to be picked up by a service provider) .
  • the destination generally refers to a location where the requestor wishes to end the service (e.g., a location where the requestor wishes to be dropped off by the service provider) .
  • the start location may be a current location of the requestor terminal 130 or any location defined by the requestor.
  • the start location and/or the destination may be obtained in various ways including but not limited to manual inputting through the requestor terminal 130, selecting from historical inputting records, selecting from system recommendations, using positioning technology, or the like, or any combination thereof.
  • the start location and/or the destination may be denoted as a description of a location, an address of the location, longitude and latitude coordinates of the location, a point corresponding to the location in a map, or the like, or any combination thereof.
  • the processing device 112 e.g., the candidate link determination module 420
  • the processing circuits of the processor 220 may determine a plurality of candidate links (e.g., 5, 10, 15) based on the target location.
  • the term “link” may refer to an element (e.g., a road section) of a road or a street in a map.
  • the target location may include a start location or a destination; accordingly, the candidate link may be a candidate start link corresponding to the start location or a candidate end link corresponding to the destination.
  • start link refers to a link where a driving route of a service request starts, which includes a pick-up location corresponding to the start location
  • end link refers to a link where the driving route of the service request ends, which includes a drop-off location corresponding to the destination.
  • the pick-up location generally refers to a location where a vehicle can stop to pick up a subject of the service, such as a requestor or goods.
  • the pick-up location may be the same as or different with the start location. For example, when the start location is a location where a vehicle cannot stop, the online to offline service system 100 may determine a suitable location near the start location as a pick-up location.
  • the drop-off location generally refers to a location where a vehicle can stop to drop off the subject of the transportation service, such as the requestor and/or the goods.
  • the drop-off location may be the same as or different with the destination. For example, when the destination is a location where a vehicle cannot stop, the online to offline service system 100 may determine a suitable location near the destination as a drop-off location.
  • the processing device 112 may identify a plurality of available links within a predetermined range (e.g., 50 meters, 100 meters, 200 meters) of the target location and determine a plurality of distances (e.g., a linear distance, a road distance) between the plurality of available links and the target location respectively. Further, the processing device 112 may determine available links with a distance from the target location less than a distance threshold as the plurality of candidate links.
  • the distance threshold may be a default setting of the online to offline service system 100 or may be adjustable under different situations.
  • the processing device 112 may obtain a plurality of historically used links associated with the target location based on historical trip records within a predetermined period (e.g., the last three months) and determine a plurality of usage frequencies of the plurality of historically used links respectively.
  • a historically used link refers to a link where a historical driving route of a historical service order associated with the target location (e.g., a historical service with a historical start location or a historical destination being the same as the target location) started or ended;
  • a usage frequency of a specific historically used link refers to a number count of historical driving routes (which correspond to historical service orders associated with the target location) that started or ended at the specific historically used link or a ratio of the number count of the historical driving routes that started or ended at the specific historically used link to a total number count of historical driving routes of historical service orders associated with the target location.
  • the processing device 112 may determine historically used links with a usage frequency higher than a frequency threshold as the plurality of candidate links.
  • the frequency threshold may be a default setting of the online to offline service system 100 or may be adjustable under different situations.
  • the processing device 112 may also take reference information (e.g., traffic information associated with the target location, preference information of the requestor) into consideration. For example, the processing device 112 may filter out available links or historically used links with bad traffic condition. As another example, the processing device 112 may give priority to links that meet the requestor’s preference (e.g., without crossing a road) .
  • reference information e.g., traffic information associated with the target location, preference information of the requestor
  • the processing device 112 may filter out available links or historically used links with bad traffic condition.
  • the processing device 112 may give priority to links that meet the requestor’s preference (e.g., without crossing a road) .
  • the processing device 112 e.g., the correlation determination module 430
  • the processing circuits of the processor 220 may determine a plurality of correlation degrees between the plurality of candidate links and the target location by using a trained correlation determination model.
  • the correlation degree may indicate a probability that the specific candidate link (or a specific candidate pick-up location included in the specific candidate link) may be selected as a pick-up location of the service request by the requestor.
  • the processing device 112 may extract first features of the target location and second features of the candidate link. The processing device 112 may also determine a first feature vector corresponding to the target location based on the first features and a second feature vector corresponding to the candidate link based on the second features. Further, the processing device 112 may determine the correlation degree between the candidate link and the target location based on the first feature vector and the second feature vector by using the trained correlation determination model.
  • the first features of the target location may include location information (e.g., a market, a school, an office building, a hospital) of the target location, an identity of the requestor (e.g., a passenger) , an identity of a service provider (e.g., a driver) who has accepted the service request, GPS information associated with the requestor (e.g., GPS information uploaded by the requestor terminal 130) , GPS information associated with the service provider (e.g., GPS information uploaded by the provider terminal 140) , time information (e.g., a time point when the service request is initiated, a weekday or a weekend) , or the like, or a combination thereof.
  • location information e.g., a market, a school, an office building, a hospital
  • an identity of the requestor e.g., a passenger
  • an identity of a service provider e.g., a driver
  • GPS information associated with the requestor e.g., GPS information uploaded by the requestor terminal 130
  • the second features of the candidate link may include an identity of the candidate link, a link type (e.g., an expressway, an overpass, a tunnel) , a link velocity (also can be referred to as “link traffic condition” ) , a link angle, GPS information associated with the candidate link (e.g., GPS information uploaded by provider terminals 140 located on the candidate link) , a distance between the candidate link and the target location, a usage frequency of the candidate link, or the like, or a combination thereof.
  • a link type e.g., an expressway, an overpass, a tunnel
  • a link velocity also can be referred to as “link traffic condition”
  • link angle also can be referred to as “link traffic condition”
  • GPS information associated with the candidate link e.g., GPS information uploaded by provider terminals 140 located on the candidate link
  • a distance between the candidate link and the target location e.g., a distance between the candidate link and the target location
  • a usage frequency of the candidate link e.
  • the processing device 112 may obtain the trained correlation determination model from the training module 450 or a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure.
  • the trained correlation determination model may be determined based on a plurality of samples associated with a plurality of historical trip records.
  • the trained correlation determination model may be a twin tower Deep Structured Semantic Model (DSSM) . More descriptions of the trained correlation determination model may be found elsewhere in the present disclosure (e.g., FIG. 7 and the description thereof) .
  • DSSM Deep Structured Semantic Model
  • the processing device 112 may determine a first vector representation (also referred to as a “point vector” ) of the target location based on the first features (or the first feature vector) by using a first model and a second vector representation (also referred to as a “link vector” ) of the candidate link based on the second features (or the second feature vector) by using a second model. Further, the processing device 112 may determine a corresponding correlation degree between the second vector representation and the first vector representation by using the trained correlation determination model.
  • the first model and the second model may be separate parts (e.g., a point network and a link network illustrated in FIG. 8) included in the trained correlation determination model (e.g., a twin tower DSSM illustrated in FIG.
  • the first model, the second model, and the trained correlation model are independent models which may be trained respectively.
  • the first model may be a first DSSM
  • the second model may be a second DSSM
  • the trained correlation determination model may be a classifier (e.g., a binary classifier) .
  • the processing device 112 e.g., the identification module 440
  • the processing circuits of the processor 220 may identify a target link from the plurality of candidate links based on the plurality of correlation degrees.
  • the candidate link may be a candidate start link corresponding to the start location or a candidate end link corresponding to the destination.
  • the target link may be a target start link corresponding to the start location which includes a target pick-up location or a target end link corresponding to the destination which includes a target drop-off location.
  • the processing device 112 may select the target link from the plurality of candidate links based on a predetermined rule. For example, the processing device 112 may rank the plurality of candidate links from high to low based on the plurality of correlation degrees. Further, the processing device 112 may select a candidate link with a highest correlation degree as the target link.
  • the processing device 112 may further determine recommended information associated with the service request based on the target link.
  • the recommended information may include a recommended driving route that starts or ends at the road section corresponding to the target link, an estimated time of arrival (ETA) of the service request, etc.
  • the processing device 112 may determine the recommended driving route based on the start location, the target start link, the target end link, and the destination.
  • the processing device 112 may determine the ETA based on the recommended driving route and/or traffic information (e.g., traffic speed, traffic flow, traffic density) associated with the service request.
  • traffic information e.g., traffic speed, traffic flow, traffic density
  • the processing device 112 may transmit the recommended information to the requestor terminal 130 and/or the provider terminal 140 via the network 120. In some embodiments, the processing device 112 may save the recommended information into a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure.
  • a storage device e.g., the storage device 150
  • one or more other optional operations may be added elsewhere in the process 500.
  • the processing device 112 may store information and/or data (e.g., the plurality of candidate links, the plurality of correlation degrees, the target link, the recommended information) associated with service request in a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure.
  • operation 510 and operation 520 may be combined into a single operation in which the processing device 112 may both receive the service request and determine the plurality of candidate links.
  • FIG. 6 is a schematic diagram illustrating an exemplary process for determining a recommended driving route associated with a service request according to some embodiments of the present disclosure.
  • a service request includes a start location S and a destination D.
  • the processing device 112 may determine a plurality of candidate start links based on the start location S and a plurality of candidate end links based on the destination D.
  • the processing device 112 may identify a target start link L S corresponding to the start location S based on a plurality of correlation degrees between the plurality of candidate start links and the start location S; and a target end link L D corresponding to the destination D based on a plurality of correlation degrees between the plurality of candidate end links and the destination D.
  • the processing device 112 may further determine a recommended driving route based on the start location S, the target start link L s , the target end link L D , and the destination D.
  • FIG. 7 is a flowchart illustrating an exemplary process for determining a trained correlation determination model according to some embodiment of the present disclosure.
  • 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 training module 450 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the training module 450 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.
  • the processing device 112 may obtain a plurality of historical trip records.
  • the processing device 112 may obtain the plurality of historical trip records from a storage device (e.g., the storage device 150, a storage module (not shown) in the processing device 112) disclosed elsewhere in the present disclosure.
  • each of the plurality of historical trip records may include a service request that has been completed (referred to as a “historical service order” ) and the information associated therein.
  • a requestor may send a service request including a point (e.g., a start location, a destination) for a transportation service to the online to offline service system 100.
  • the online to offline service system 100 may receive the service request and determine a plurality of candidate links (e.g., a plurality of candidate start links, a plurality of candidate end links) associated the point.
  • the online to offline service system 100 may further identify a start link (which includes a pick-up location) from the plurality of candidate start links and an end link (which includes a drop-off location) from the plurality of candidate end links.
  • the online to offline service system 100 may further determine a driving route based on the start link and the end link.
  • a service provider may accept the service request and provide the transportation service along the driving route that travels from the pick-up location to the drop-off location.
  • the online to offline service system 100 may store information associated with the service request (e.g., the start location, the destination, the plurality of candidate links, the start link, the end link, the driving route, the pick-up location, the drop-off location) in a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure.
  • a storage device e.g., the storage device 150
  • the plurality of historical trip records may be selected based on a temporal criteria. For example, the plurality of historical trip records may be selected within a predetermined time period, for example, the last day, the last three days, the last week, the last two weeks, the last month, the last six months, from 8: 00 a. m. to 9: 00 a. m. every day for six months, etc.
  • the plurality of historical trip records may be selected based on a spatial criteria. For example, the plurality of historical trip records may be selected within a predetermined geographic region (e.g., a city, a district) .
  • the plurality of historical trip records may be selected with respect to one or more parameters, for example, “requestor identity, ” “provider identity, ” “start location, ” “destination, ” “link identity, ” “link type, ” “link velocity, ” “link angle, ” “link usage frequency, ” etc.
  • each of the plurality of historical trip records may include a sample point and one or more sample links.
  • the sample point refers to a historical start location or a historical destination of a historical service order in a corresponding historical trip record and the one or more sample links refer to historical candidate start links corresponding to the historical start location or historical candidate end links corresponding to the historical destination in the corresponding historical trip record.
  • the processing device 112 e.g., the training module 450
  • the processing circuits of the processor 220 may obtain a plurality of samples based on the plurality of historical trip records, wherein each of the plurality of samples includes the sample point and one of the one or more sample links.
  • the plurality of samples may include a plurality of positive samples (which may be labelled as “1” ) and a plurality of negative samples (which may be labelled as “0” ) .
  • the sample link is correlated with the sample point in a corresponding historical trip record; for the negative sample, the sample link is not correlated with the sample point in a corresponding historical trip record.
  • “asample link is correlated with a sample point” refers to that a historical driving route of a historical service order associated with the sample point (i.e., a historical service order with a historical start location or a historical destination being the same as the sample point) actually started or ended at the sample link in a historical trip record.
  • the processing device 112 may divide the plurality of samples into a training set and a test set.
  • the training set may be used to train the model and the test set may be used to determine whether the training process has been completed.
  • the processing device 112 e.g., the training module 450
  • the processing circuits of the processor 220 may extract sample features (also referred to as “feature information” ) of each of the plurality of samples.
  • the sample features of each of the plurality of samples may include first sample features of the sample point and second sample features of the sample link.
  • the first sample features of the sample point may include sample location information (e.g., a market, a school, an office building, a hospital) of the sample point, an identity of a historical requestor (e.g., a passenger) who initiated a historical service request associated with the sample point, an identity of a historical service provider (e.g., a driver) who provided a historical service for the historical requestor, time information (e.g., a historical time point when the historical service request was initiated, a weekday or a weekend) , or the like, or a combination thereof.
  • sample location information e.g., a market, a school, an office building, a hospital
  • an identity of a historical requestor e.g., a passenger
  • a historical service provider e.g., a driver
  • time information e.g., a historical time point when the historical service
  • the second features of the sample link may include an identity of the sample link, a link type (e.g., an expressway, an overpass, a tunnel) , a historical link velocity (also can be referred to as “link traffic condition” ) , a link angle, a distance between the sample link and the sample point, a usage frequency of the sample link, or the like, or a combination thereof.
  • a link type e.g., an expressway, an overpass, a tunnel
  • a historical link velocity also can be referred to as “link traffic condition”
  • link angle also can be referred to as “link traffic condition”
  • the processing device 112 may obtain a preliminary correlation determination model.
  • the preliminary correlation determination model may include one or more preliminary parameters which may be default settings of the online to offline service system 100 or may be adjustable under different situations.
  • the preliminary correlation determination model may be a twin tower Deep Structured Semantic Model (DSSM) .
  • the twin tower DSSM may include a Fully Connected (FC) layer, a Batch Normalization (BN) layer, and a BELU layer. More descriptions of the correlation determination model may be found elsewhere in the present disclosure (e.g., FIG. 8 and the description thereof) .
  • the processing device 112 e.g., the training module 450
  • the processing circuits of the processor 220 may determine a sample correlation degree between the sample point and the sample link based on the first sample features and the second sample features by using the preliminary correlation determination model.
  • the processing device 112 may determine a first sample feature vector corresponding to the sample point by encoding the first sample features and a second sample feature vector corresponding to the sample link by encoding the second sample features. Further, according to the preliminary correlation determination model (e.g., through the processing by the FC layer, the BN layer, and the RELU layer) , the processing device 112 may determine a sample point vector based on the first sample feature vector and a sample link vector based on the second sample feature vector.
  • a dimension of the first sample feature vector and a dimension the second sample feature vector may be different and the first sample feature vector and the second sample feature vector are incomparable; instead, a dimension of the sample point vector and a dimension of the sample link vector are the same and the sample point vector and the sample link vector are comparable.
  • the processing device 112 may determine a dot product (an inner product) of the sample point vector and the sample link vector according to formula (1) below:
  • x refers to the dot product of the sample point vector and the sample link vector
  • sample point vector refers to the sample link vector
  • ⁇ refers to an angle between the sample point vector and the sample link vector.
  • the processing device 112 may determine the sample correlation degree based on the dot product of the sample point vector and the sample link vector according to a classification method.
  • the processing device 112 may determine the sample correlation degree according to a sigmoid function illustrated below:
  • C (x) refers to the sample correlation degree between the sample point and the sample link.
  • the processing device 112 may determine the sample correlation degree according to a threshold method illustrated below:
  • C (x) refers to the sample correlation degree between the sample point and the sample link and t refers to a threshold which may be a default setting (e.g., 0.5) of the online to offline service system 100 or may be adjustable under different situations.
  • a threshold may be a default setting (e.g., 0.5) of the online to offline service system 100 or may be adjustable under different situations.
  • the sigmoid function or the threshold method is provided for illustration purposes, other classification methods (e.g., a softmax function) also can be used in the present disclosure.
  • the processing device 112 e.g., the training module 450
  • the processing circuits of the processor 220 may determine whether a plurality of sample correlation degrees corresponding to the plurality of samples satisfy a preset condition.
  • the processing device 112 may determine a first accuracy rate of the preliminary correlation determination model corresponding to the training set and a second accuracy rate of the preliminary correlation determination model corresponding to the test set. Further, the processing device 112 may determine whether the first accuracy rate has been stable ( “stable” refers to that a first accuracy in a current iteration is substantially same as (i.e., less than a threshold) a first accuracy in a previous adjacent iteration or multiple first accuracies in multiple previous iterations) and whether the second accuracy has reached a maximum value.
  • stable refers to that a first accuracy in a current iteration is substantially same as (i.e., less than a threshold) a first accuracy in a previous adjacent iteration or multiple first accuracies in multiple previous iterations
  • the first accuracy rate and/or the second accuracy rate may be determined based on one or more parameters (e.g., a proximity degree) associated with the plurality of sample correlation degrees and the plurality of labels (i.e., “1” or “0” ) corresponding to the plurality of samples.
  • the processing device 112 may determine that the plurality of sample correlation degrees satisfy the preset condition.
  • the processing device 112 may determine that the plurality of sample correlation degrees do not satisfy the preset condition.
  • the processing device 112 may determine a loss function of the preliminary correlation determination model based on the plurality of sample correlation degrees and the plurality of labels and determine a value of the loss function based on the plurality of sample correlation degrees. Further, the processing device 112 may determine whether the value of the loss function is less than a loss threshold.
  • the loss threshold may be a default setting of the online to offline service 100 or may be adjustable under different situations.
  • the processing device 112 may determine that the plurality of sample correlation degrees satisfy the preset condition.
  • the processing device 112 may determine that the plurality of sample correlation degrees do not satisfy the preset condition.
  • the processing device 112 may determine whether a number count of iterations is larger than a count threshold. In response to determining that the number count of iterations is larger than the count threshold, the processing device 112 may determine that the plurality of sample correlation degrees satisfy the preset condition. In response to determining that the number count of iterations is less than or equal to the count threshold, the processing device 112 may determine that the plurality of sample correlation degrees do not satisfy the preset condition.
  • the processing device 112 e.g., the training module 450
  • the processing circuits of the processor 220 may designate the preliminary correlation determination model as the trained correlation determination model, which means that the training process has been completed.
  • the processing device 112 may execute the process 700 to return to operation 740 to update the preliminary correlation determination model. For example, the processing device 112 may update the one or more preliminary parameters to produce an updated preliminary correlation determination model. Further, the processing device 112 (e.g., the training module 450) (e.g., the processing circuits of the processor 220) may repeat the step of determining whether the plurality of sample correlation degrees satisfy the preset condition until the plurality of sample correlation degrees satisfy the preset condition. In response to determining that the plurality of updated sample correlation degrees under the updated correlation determination model satisfy the preset condition, the processing device 112 may designate the updated correlation determination model as the trained correlation determination model.
  • the processing device 112 may designate the updated correlation determination model as the trained correlation determination model.
  • the processing device 112 may update the trained correlation determination model at a certain time interval (e.g., per month, per two months) based on a plurality of newly obtained samples.
  • the positive samples and the negative samples may be determined manually by an operator or by the online to offline service 100 according to a predetermined rule.
  • FIG. 8 is a schematic diagram illustrating an exemplary structure of a correlation determination model according to some embodiments of the present disclosure.
  • the correlation determination model may include a point network and a link network.
  • the point network may be configured to obtain point information and determine a point vector based on the point information.
  • the processing device 112 may extract point features (i.e., the first features, the first sample features) from the point information and determine a point feature vector (i.e., the first feature vector, the first sample feature vector) by encoding the point features. Further, the processing device 112 may determine the point vector based on the point feature vector by using the point network.
  • the link network may be configured to obtain link information and determine a link vector based on the link information.
  • the processing device 112 may extract link features (i.e., the second features, the second sample features) from the link information and determine a link feature vector (i.e., the second feature vector, the second sample feature vector) by encoding the link features. Further, the processing device 112 may determine the link vector based on the link feature vector by using the link network.
  • the processing device 112 may classify the features (e.g., the point features, the link features) into embedding features and dense features and determine the point feature vector and/or the link feature vector by encoding the features.
  • the embedding features may include location information of a point or a link, an identity of a requestor, an identity of a service provider, time information, etc.
  • the dense features may include a usage frequency of a link, GPS information associated with a point or a link, a distance between the point and the link, etc. More descriptions of the features may be found elsewhere in the present disclosure (e.g., FIG. 5, FIG. 7, and the descriptions thereof) .
  • both the point network and the link network may include a Fully Connected (FC) layer, a Batch Normalization (BN) layer, and a BELU layer.
  • FC Fully Connected
  • BN Batch Normalization
  • BELU BELU layer
  • the FC layer may be configured to perform a linear transformation on the point feature vector or the link feature vector.
  • the BN layer may be configured to perform a normalization on an intermediate result of the FC layer.
  • the BELU layer may be configured to perform a nonlinear transformation on an intermediate result of BN layer.
  • a dimension of the point feature vector and a dimension of the link feature vector are different and the point feature vector and the link feature vector are incomparable.
  • a dimension of the point vector and a dimension of the link vector are the same and the point vector and the link vector are comparable.
  • the processing device 112 may determine a correlation degree between the point and the link based on the point vector and the link vector. For example, as described in connection with operation 750, the processing device 112 may determine the correlation degree between the point and the link based on a dot product between the point vector and the link vector and a classification method.
  • 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 “unit, ” “module, ” 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.
  • 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) .
  • LAN local area network
  • WAN wide area network
  • SaaS Software as a Service

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Abstract

La présente invention concerne des systèmes et procédés permettant de déterminer des informations recommandées d'une demande de service. Le système peut recevoir une demande de service en provenance d'un dispositif terminal, la demande de service comprenant un emplacement cible. Le système peut déterminer une pluralité de liens candidats en fonction de l'emplacement cible. Le système peut également déterminer une pluralité de degrés de corrélation entre la pluralité de liens candidats et l'emplacement cible à l'aide d'un modèle de détermination de corrélation formé. Le système peut en outre identifier un lien cible parmi la pluralité de liens candidats en fonction de la pluralité de degrés de corrélation.
PCT/CN2019/090398 2019-06-06 2019-06-06 Systèmes et procédés de détermination d'informations recommandées de demande de service WO2020243963A1 (fr)

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PCT/CN2019/090398 WO2020243963A1 (fr) 2019-06-06 2019-06-06 Systèmes et procédés de détermination d'informations recommandées de demande de service

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