WO2021022487A1

WO2021022487A1 - Systems and methods for determining an estimated time of arrival

Info

Publication number: WO2021022487A1
Application number: PCT/CN2019/099479
Authority: WO
Inventors: Qing LUO
Original assignee: Beijing Didi Infinity Technology And Development Co., Ltd.
Priority date: 2019-08-06
Filing date: 2019-08-06
Publication date: 2021-02-11

Abstract

The present disclosure relates to systems and methods for determining an estimated time of arrival. The system may receive a service request associated with a transportation service from a terminal device via a network. The system may extract one or more global features associated with the service request and one or more personalized features associated with the service request. The system may determine a preliminary estimated time of arrival (ETA) based on the one or more global features associated with the service request by using a first model. The system may determine a time deviation based on the one or more personalized features associated with the service request by using a second model. The system may determine a target ETA based on the preliminary ETA and the time deviation. The system may transmit the target ETA to the terminal device via the network.

Description

SYSTEMS AND METHODS FOR DETERMINING AN ESTIMATED TIME OF ARRIVAL

TECHNICAL FIELD

The present disclosure generally relates to systems and methods for online to offline services, and in particular, to systems and methods for determining an estimated time of arrival (ETA) associated with a transportation service request.

BACKGROUND

Online to offline (O2O) services (e.g. online to offline transportation services) utilizing Internet technology have become increasingly popular. For a request including a start location and a destination initiated by a requester, a system providing online to offline services may recommend a route that travels from the start location to the destination and determine an estimated time of arrival (ETA) based on various features associated with the recommended route. However, part of the various features associated with the recommended route may be relatively stable, whereas, the others may be relatively fluctuant, which may affect the efficiency and accuracy of the ETA. Therefore, it is desirable to provide systems and methods for determining an ETA associated with a service request efficiently and accurately.

SUMMARY

An aspect of the present disclosure relates to a system for determining an estimated time of arrival. 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 associated with a transportation service from a terminal device via a network; extract one or more global features associated with the service request and one or more personalized features associated with the service request; determine a preliminary estimated time of arrival (ETA) based on the one or more global features associated with the service request by using a first model; determine a time deviation based on the one or more personalized features associated with the service request by using a second model; determine a target ETA based on the preliminary ETA and the time deviation; and transmit the target ETA to the terminal device via the network.

In some embodiments, the one or more global features may include at least one of a distance between a start location and a destination, GPS information of the start location, GPS information of the destination, identifiers of one or more road sections associated with the service request, serial numbers of the one or more road sections, and/or speed limit information associated with the one or more road sections.

In some embodiments, the one or more personalized features may include at least one of driver information, passenger information, weather information, time information, and/or traffic information.

In some embodiments, the first model may be determined with a first training process. The first training process may include obtaining a plurality of first historical trip records; obtaining a plurality of first samples based on the plurality of first historical trip records, each of the plurality of first samples corresponding to a respective ETA; extracting one or more first global features for each of the plurality of first samples based on the plurality of first historical trip records; obtaining a preliminary first model; determining a sample ETA for each of the plurality of first samples based on the one or more first global features by using the preliminary first model; determining whether a plurality of sample ETAs and a plurality of ETAs corresponding to the plurality of first samples satisfy a first preset condition; and designating the preliminary first model as the first model in response to determining that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition.

In some embodiments, the first training process may further include updating the preliminary first model in response to determining that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples do not satisfy the first preset condition and repeating the step of determining whether the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples satisfy the first preset condition until the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples satisfy the first preset condition.

In some embodiments, each of the one or more first global features may correspond to a respective one of one or more first weighting coefficients.

In some embodiments, the second model may be determined with a second training process. The second training process may include obtaining a plurality of second historical trip records; obtaining a plurality of second samples based on the plurality of second historical trip records, each of the plurality of second samples corresponding to a reference time deviation which is determined based on an ETA and a reference ETA; extracting one or more personalized features for each of the plurality of second samples based on the plurality of second historical trip records; obtaining a preliminary second model; determining a sample time deviation for each of the plurality of second samples based on the one or more personalized features by using the preliminary second model; determining whether a plurality of sample time deviations and a plurality of reference time deviations corresponding to the plurality of second samples satisfy a second preset condition; and designating the preliminary second model as the second model in response to determining that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition.

In some embodiments, the second training process may further include updating the preliminary second model in response to determining that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples do not satisfy the second preset condition and repeating the step of determining whether the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition until the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition.

In some embodiments, for each of the plurality of second samples, the reference ETA may be determined based on one or more second global features of the second sample by using the first model.

In some embodiments, each of the one or more personalized features may correspond to a respective one of one or more second weighting coefficients.

In some embodiments, the system may further determine the target ETA by performing a linear combination on the preliminary ETA and the time deviation based on one or more coefficients, wherein the one or more coefficients may be determined based on at least one of a predetermined rule and/or a machine learning model.

In some embodiments, the first model and/or the second model may be a regression model.

Another aspect of the present disclosure relates to a method. The method may be implemented on a computing device having at least one processor, at least one storage medium, and a communication platform connected to a network. The method may include receiving a service request associated with a transportation service from a terminal device via a network; extracting one or more global features associated with the service request and one or more personalized features associated with the service request; determining a preliminary estimated time of arrival (ETA) based on the one or more global features associated with the service request by using a first model; determining a time deviation based on the one or more personalized features associated with the service request by using a second model; determining a target ETA based on the preliminary ETA and the time deviation; and transmitting the target ETA to the terminal device via the network.

In some embodiments, the method may further include determining the target ETA by performing a linear combination on the preliminary ETA and the time deviation based on one or more coefficients, wherein the one or more coefficients may be determined based on at least one of a predetermined rule and/or a machine learning model.

A further aspect of the present disclosure relates to a system for determining an estimated time of arrival. The system may include a receiving module, an extraction module, a preliminary ETA determination module, a time deviation determination module, a target ETA determination module, and a transmission module. The receiving module may be configured to receive a service request associated with a transportation service from a terminal device via a network. The extraction module may be configured to extract one or more global features associated with the service request and one or more personalized features associated with the service request. The preliminary ETA determination module may be configured to determine a preliminary estimated time of arrival (ETA) based on the one or more global features associated with the service request by using a first model. The time deviation determination module may be configured to determine a time deviation based on the one or more personalized features associated with the service request by using a second model. The target ETA determination module may be configured to determine a target ETA based on the preliminary ETA and the time deviation. The transmission module may be configured to transmit the target ETA to the terminal device via the network.

In some embodiments, the system may further include a first training module. The first training module may be configured to obtain a plurality of first historical trip records; obtain a plurality of first samples based on the plurality of first historical trip records, each of the plurality of first samples corresponding to a respective ETA; extract one or more first global features for each of the plurality of first samples based on the plurality of first historical trip records; obtain a preliminary first model; determine a sample ETA for each of the plurality of first samples based on the one or more first global features by using the preliminary first model; determine whether a plurality of sample ETAs and a plurality of ETAs corresponding to the plurality of first samples satisfy a first preset condition; and designate the preliminary first model as the first model in response to determining that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition.

In some embodiments, the first training module may be further configured to update the preliminary first model in response to determining that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples do not satisfy the first preset condition and repeat the step of determining whether the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples satisfy the first preset condition until the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples satisfy the first preset condition.

In some embodiments, the system may further include a second training module. The second training module may be configured to obtain a plurality of second historical trip records; obtain a plurality of second samples based on the plurality of second historical trip records, each of the plurality of second samples corresponding to a reference time deviation which is determined based on an ETA and a reference ETA; extract one or more personalized features for each of the plurality of second samples based on the plurality of second historical trip records; obtain a preliminary second model; determine a sample time deviation for each of the plurality of second samples based on the one or more personalized features by using the preliminary second model; determine whether a plurality of sample time deviations and a plurality of reference time deviations corresponding to the plurality of second samples satisfy a second preset condition; and designate the preliminary second model as the second model in response to determining that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition.

In some embodiments, the second training module may be further configured to update the preliminary second model in response to determining that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples do not satisfy the second preset condition and repeat the step of determining whether the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition until the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition.

In some embodiments, the target ETA determination module may be further configured to determine the target ETA by performing a linear combination on the preliminary ETA and the time deviation based on one or more coefficients, wherein the one or more coefficients may be determined based on at least one of a predetermined rule and/or a machine learning model.

A still further aspect of the present disclosure relates to a non-transitory computer readable medium including executable instructions. When 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 associated with a transportation service from a terminal device via a network; extracting one or more global features associated with the service request and one or more personalized features associated with the service request; determining a preliminary estimated time of arrival (ETA) based on the one or more global features associated with the service request by using a first model; determining a time deviation based on the one or more personalized features associated with the service request by using a second model; determining a target ETA based on the preliminary ETA and the time deviation; and transmitting the target ETA to the terminal device via the network.

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

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplary embodiments. These exemplary embodiments are described in detail with reference to the drawings. These embodiments are non-limiting exemplary embodiments, in which like reference numerals represent similar structures throughout the several views of the drawings, and wherein:

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

FIG. 6 is a schematic diagram illustrating exemplary global features and exemplary personalized features associated with a service request according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary first training process for determining a first model according to some embodiment of the present disclosure;

FIG. 8 is a flowchart illustrating an exemplary second training process for determining a second model according to some embodiment of the present disclosure; and

FIG. 9 is a schematic diagram illustrating an exemplary user interface for displaying information associated with a service request according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled in the art to make and use the present disclosure and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments shown but is to be accorded the widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms “a, ” “an, ” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprise, ” “comprises, ” and/or “comprising, ” “include, ” “includes, ” and/or “including, ” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

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

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

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

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

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

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

An aspect of the present disclosure relates to systems and methods for determining an ETA associated with a service request. The system may receive a service request associated with a transportation service from a terminal device via a network. The system may also extract one or more global features (e.g., a distance between a start location and a destination of the service request, GPS information of the start location, GPS information of the destination) associated with the service request and one or more personalized features (e.g., driver information, passenger information, weather information, time information, traffic information) associated with the service request. The system may determine a preliminary ETA based on the one or more global features by using a first model (e.g., a first regression model) and determine a time deviation based on the one or more personalized features by using a second model (e.g., a second regression model) . Then the system may determine a target ETA based on the preliminary ETA and the time deviation. Further, the system may transmit the target ETA to the terminal device via the network. According to the systems and methods of the present disclosure, the global features (which may be relatively stable) and the personalized features (which may be relatively fluctuant) associated with the service request may be separately processed, thereby improving the accuracy and efficiency of the determination of the ETA.

It should be noted that online to offline transportation service, such as online taxi-hailing including taxi hailing combination services, is a new form of service rooted only in post-Internet era. It provides technical solutions to users and service providers that could raise only in post-Internet era. In pre-Internet era, when a passenger hails a taxi on the street, the taxi request and acceptance occur only between the passenger and one taxi driver that sees the passenger. If the passenger hails a taxi through a telephone call, the service request and acceptance may occur only between the passenger and one service provider (e.g., one taxi company or agent) . Online taxi, however, allows a user of the service to automatically distribute a service request in real-time to a vast number of individual service providers (e.g., taxi) distance away from the user. It also allows a plurality of service providers to respond to the service request simultaneously and in real-time. Therefore, through the Internet, the online 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. In some embodiments, 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.

In some embodiments, the server 110 may be a single server or a server group. The server group may be centralized or distributed (e.g., server 110 may be a distributed system) . In some embodiments, the server 110 may be local or remote. For example, 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. As another example, 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. In some embodiments, the server 110 may be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof. In some embodiments, the server 110 may be implemented on a computing device 200 including one or more components illustrated in FIG. 2.

In some embodiments, the server 110 may include a processing 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 determine a preliminary ETA associated with a service request by using a first model and a time deviation by using a second model. Further, the processing device 112may determine a target ETA associated with the service request based on the preliminary ETA and the time deviation. In some embodiments, the processing device 112 may include one or more processing engines (e.g., single-core processing engine (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. In some embodiments, the processing device 112 may be integrated into the requester terminal 130 or the provider terminal 140.

The network 120 may facilitate exchange of information and/or data. In some embodiments, one or more components (e.g., the server 110, the requester terminal 130, the provider terminal 140, or the storage device 150) of the online to offline service system 100 may transmit information and/or data to other component (s) of the online to offline service system 100 via the network 120. For example, the server 110 may receive a service request from the requester terminal 130 via the network 120. In some embodiments, the network 120 may be any type of wired or wireless network, or any combination thereof. Merely by way of example, the network 120 may include a cable network, a wireline network, an optical fiber network, a telecommunications network, an intranet, an Internet, a local area network (LAN) , a wide area network (WAN) , a wireless local area network (WLAN) , a metropolitan area network (MAN) , a public telephone switched network (PSTN) , a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or the like, or any combination thereof. In some embodiments, the network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points such as base stations and/or internet exchange points 120-1, 120-2, …, through which one or more components of the online to offline service system 100 may be connected to the network 120 to exchange data and/or information.

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

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

In some embodiments, 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 devices 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. Exemplary ROM may include a mask ROM (MROM) , a programmable ROM (PROM) , an erasable programmable ROM (EPROM) , an electrically erasable programmable ROM (EEPROM) , a compact disk ROM (CD-ROM) , and a digital versatile disk ROM, etc. In some embodiments, the storage device 150 may be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof.

In some embodiments, the storage device 150 may be connected to the network 120 to communicate with one or more components (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. In some embodiments, 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. In some embodiments, the storage device 150 may be part of the server 110.

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

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

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

It should be noted that the application scenario illustrated in FIG. 1 is only provided for illustration purposes, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations or modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. For example, 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 send a service request to the server 110 via the user terminal. The navigation system may extract one or more global features and one or more personalized features associated with the service request and further determine a target ETA of the service request based on the global features and/or the personalized features 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. In some embodiments, the server 110, the requester terminal 130, and/or the provider terminal 140 may be implemented on the computing device 200. For example, the processing 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. For example, the processing device 112 may be implemented on the computing device 200, via its hardware, software program, firmware, or a combination thereof. Although only one such computer is shown, for convenience, the computer functions relating to the online 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, for example, logic circuits, for executing program instructions. For example, the processor 220 may include interface circuits and processing circuits therein. The interface circuits may be configured to receive electronic signals from a bus 210, wherein the electronic signals encode structured data and/or instructions for the processing circuits to process. The processing circuits may conduct logic calculations, and then determine a conclusion, a result, and/or an instruction encoded as electronic signals. Then the interface circuits may send out the electronic signals from the processing circuits via the bus 210.

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

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

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

In some embodiments, the mobile operating system 370 (e.g., iOS ^TM, Android ^TM, Windows Phone ^TM) and one or more applications 380 may be loaded into the memory 360 from the storage 390 in order to be executed by the CPU 340. The applications 380 may include a browser or any other suitable mobile app for receiving and rendering information relating to online 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.

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

FIG. 4 is a block diagram illustrating an exemplary processing device according to some embodiments of the present disclosure. The processing device 112 may include a receiving module 410, an extraction module 420, a preliminary ETA determination module 430, a time deviation determination module 440, a target ETA determination 450, a transmission module 460, a first training module 470, and a second training module 480.

The receiving module 410 may be configured to receive a service request associated with a transportation service from a terminal device (e.g., the requestor terminal 130) via the network 120. In some embodiments, the service request may be a real-time request, a reservation request, or the like, or any combination thereof. In some embodiments, the service request may include a start location, a destination, a start time, a passenger (e.g., the requester himself /herself, a user (e.g., a family member, a friend) other than the requester) who will receive the transportation service, or the like, or a combination thereof.

The extraction module 420 may be configured to extract one or more global features associated with the service request and one or more personalized features associated with the service request. More descriptions of the global features and the personalized features can be found elsewhere in the present disclosure (e.g., FIG. 5 and the description thereof) .

The preliminary ETA determination module 430 may be configured to determine a preliminary estimated time of arrival (ETA) of the service request based on the one or more global features associated with the service request by using a first model. In some embodiments, the preliminary ETA determination module 430 may determine a global feature vector by encoding the one or more global features associated with the service request according to an encoding method (e.g., one-hot coding, Gradient Boosting Decision Tree (GBDT) coding, binary encoding embedding, label encoding, character encoding, dummy encoding) . Further, the preliminary ETA determination module 430 may input the global feature vector into the first model and determine the preliminary ETA based on an output of the first model.

The time deviation determination module 440 may be configured to determine a time deviation based on the one or more personalized features associated with the service request by using a second model. In some embodiments, the time deviation determination module 440 may determine a personalized feature vector by encoding the one or more personalized features associated with the service request according to an encoding method (e.g., one-hot coding, GBDT coding, binary encoding embedding, label encoding, character encoding, dummy encoding, etc. ) . Further, the time deviation determination module 440 may input the personalized feature vector into the second model and determine the time deviation based on an output of the second model.

The target ETA determination 450 may be configured to determine a target ETA based on the preliminary ETA and the time deviation. In some embodiments, the target ETA determination 450 may determine the target ETA by performing a linear combination on the preliminary ETA and the time deviation based on one or more coefficients.

The transmission module 460 may be configured to transmit the target ETA to the terminal device (e.g., the requestor terminal 130 and/or the provider terminal 140) via the network 120. In some embodiments, the transmission module 460 may transmit the target ETA to the terminal device via a suitable communication protocol (e.g., the Hypertext Transfer Protocol (HTTP) , Address Resolution Protocol (ARP) , Dynamic Host Configuration Protocol (DHCP) , File Transfer Protocol (FTP) ) .

The training module 470 may be configured to determine the first model based on a plurality of first samples according to a first training process. In some embodiments, the first model may include but not be limited to a regression model (e.g., a linear regression model, a logistic regression model, a polynomial regression model, a stepwise regression model, a ridge regression model, a lasso regression model, an elastic net regression model) . In some embodiments, the training module 470 may obtain the plurality of first samples based on a plurality of first historical trip records. In some embodiments, each of the plurality of first samples may correspond to a respective ETA (e.g., an actual time of arrival (ATA) , a predetermined ETA) of a historical service order.

The second training module 480 may be configured to determine the second model based on a plurality of second samples according to a second training process. In some embodiments, the second model may include but not be limited to a regression model (e.g., a linear regression model, a logistic regression model, a polynomial regression model, a stepwise regression model, a ridge regression model, a lasso regression model, an elastic net regression model) . In some embodiments, the training module 480 may obtain the plurality of second samples based on a plurality of second historical trip records. In some embodiments, each of the plurality of second samples may correspond to a reference time deviation which is determined based on an ETA (e.g., an ATA, a predetermined ETA) and a reference ETA. In some embodiments, for each of the plurality of second samples, the reference ETA may be determined based on one or more second global features of the second sample by using the first model.

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. 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. For example, the receiving module 410 and the extraction module 420 may be combined as a single module which may both receive the service request and extract the global features and the personalized features associated with the service request. As another example, the processing device 112 may also include a storage module (not shown) used to store information and/or data (e.g., the one or more global features, the one or more personalized features, the preliminary ETA, the time deviation, the target ETA) associated with service request. As a further example, the first training module 470 or the second training module 480 may be unnecessary and the first model or the second model 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 an ETA associated with a service request according to some embodiments of the present disclosure. In some embodiments, the process 500 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240. The processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 500. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 500 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 5 and described below is not intended to be limiting.

In 510, the processing device 112 (e.g., the receiving module 410) (e.g., the interface circuits of the processor 220) may receive a service request associated with a transportation service from a terminal device (e.g., the requestor terminal 130) via the network 120.

In some embodiments, the service request may be a real-time request, a reservation request, or the like, or any combination thereof. As used herein, 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. For example, 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. For example, 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. For example, in traffic peak hours, the time threshold may be relatively small (e.g., 10 minutes) , while in off-peak hours (e.g., 10: 00-12: 00 a. m. ) , the time threshold may be relatively large (e.g., 1 hour) .

In some embodiments, the service request may include a start location, a destination, a start time, a passenger (e.g., the requester himself /herself, a user (e.g., a family member, a friend) other than the requester) who will receive the transportation service, or the like, or a combination thereof. As used herein, 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) . In some embodiments, the start location may be a current location of the requestor terminal 130 or any location defined by the requestor. In some embodiments, 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. In some embodiments, 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.

In 520, the processing device 112 (e.g., the extraction module 420) (e.g., the processing circuits of the processor 220) may extract one or more global features associated with the service request and one or more personalized features associated with the service request.

In some embodiments, the one or more global features may include a distance (e.g., a linear distance, a spatial distance (e.g., a length of a portion of a road or a street) ) between the start location and the destination of the service request, GPS information (e.g., latitude and longitude information, direction information, time information) of the start location, GPS information of the destination, identifiers of one or more road sections (e.g., road sections in a recommended driving path) associated with the service request, serial numbers of the one or more road sections, speed limit information associated with the one or more road sections, or the like, or any combination thereof. As used herein, take a specific road section as an example, the identifier and/or the serial number of the specific road section may be an expression associated with a parameter of the road section, such as a name, a category (e.g., a national road, a provincial road, a county road, a township road) , a type (e.g., a main road, a side road, a branch road) , a length, a width, etc. More descriptions of the global features may be found elsewhere in the present disclosure (e.g., FIG. 6 and the description thereof) .

In some embodiments, the one or more personalized features may include driver information (e.g., an identity of a driver who accepts the service request, a driving age or a driving experience of the driver) , passenger information (e.g., an identity of the passenger, a gender of the passenger, an age of the passenger, an occupation of the passenger, a preference of the passenger) , weather information (e.g., “sunny, ” “rainy, ” foggy, ” “snowy” ) , time information (e.g., a current time, the start time of the service request) , traffic information (e.g., smooth traffic, traffic congestion) , or the like, or any combination thereof. More descriptions of the personalized features may be found elsewhere in the present disclosure (e.g., FIG. 6 and the description thereof) .

In 530, the processing device 112 (e.g., the preliminary ETA determination module 430) (e.g., the processing circuits of the processor 220) may determine a preliminary estimated time of arrival (ETA) of the service request based on the one or more global features associated with the service request by using a first model.

In some embodiments, the processing device 112 may determine a global feature vector by encoding the one or more global features associated with the service request according to an encoding method (e.g., one-hot coding, Gradient Boosting Decision Tree (GBDT) coding, binary encoding embedding, label encoding, character encoding, dummy encoding) . Further, the processing device 112 may input the global feature vector into the first model and determine the preliminary ETA based on an output of the first model.

In some embodiments, the processing device 112 may obtain the first model from the first training module 470 or a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure. In some embodiments, the first model may be determined based on a plurality of first samples associated with a plurality of first historical trip records. In some embodiments, the first model may include but not be limited to a regression model (e.g., a linear regression model, a logistic regression model, a polynomial regression model, a stepwise regression model, a ridge regression model, a lasso regression model, an elastic net regression model) . More descriptions of the first model may be found elsewhere in the present disclosure (e.g., FIG. 7 and the description thereof) .

In 540, the processing device 112 (e.g., time deviation determination module 440) (e.g., the processing circuits of the processor 220) may determine a time deviation based on the one or more personalized features associated with the service request by using a second model. As used herein, the time deviation may refer to a deviation associated with the preliminary ETA which may be caused by the personalized features (e.g., dynamically changing traffic condition) .

In some embodiments, the processing device 112 may determine a personalized feature vector by encoding the one or more personalized features associated with the service request according to an encoding method (e.g., one-hot coding, GBDT coding, binary encoding embedding, label encoding, character encoding, dummy encoding, etc. ) . Further, the processing device 112 may input the personalized feature vector into the second model and determine the time deviation based on an output of the second model.

In some embodiments, the processing device 112 may obtain the second model from the second training module 480 or a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure. In some embodiments, the second model may be determined based on a plurality of second samples associated with a plurality of second historical trip records. In some embodiments, the second model may include but not be limited to a regression model (e.g., a linear regression model, a logistic regression model, a polynomial regression model, a stepwise regression model, a ridge regression model, a lasso regression model, an elastic net regression model, etc. ) . More descriptions of the second model may be found elsewhere in the present disclosure (e.g., FIG. 8 and the description thereof) .

In 550, the processing device 112 (e.g., the target ETA determination module 450) (e.g., the processing circuits of the processor 220) may determine a target ETA based on the preliminary ETA and the time deviation.

In some embodiments, the processing device 112 may determine the target ETA by performing a linear combination on the preliminary ETA and the time deviation based on one or more coefficients. For example, the processing device 112 may determine the target ETA according to formula (1) below:

TETA=a*PETA+b*DETA (1)

where TETA refers to the target ETA, PETA refers to the preliminary ETA, DETA refers to the time deviation, a refers to a first coefficient associated with the preliminary ETA, and b refers to a second coefficient associated with the time deviation.

In some embodiments, the processing device 112 may determine the one or more coefficients based on at least one of a predetermined rule (e.g., an experience rule) or a machine learning model (e.g., a regression model) . In some embodiments, the one or more coefficients may be default settings of the online to offline service system 100 or may be adjustable under different situations.

In 560, the processing device 112 (e.g., the transmission module 460) (e.g., the interface circuits of the processor 220) may transmit the target ETA to the terminal device (e.g., the requestor terminal 130 and/or the provider terminal 140) via the network 120.

In some embodiments, the processing device 112 may transmit the target ETA to the terminal device via a suitable communication protocol (e.g., the Hypertext Transfer Protocol (HTTP) , Address Resolution Protocol (ARP) , Dynamic Host Configuration Protocol (DHCP) , File Transfer Protocol (FTP) ) . In some embodiments, the terminal device may present the target ETA via a user interface (e.g., a user interface 900 illustrated in FIG. 9) in a form of text, image, audio, video, or the like, or any combination thereof. In some embodiments, the terminal device may broadcast the target ETA to the passenger or the driver. In some embodiments, the processing device 112 may also save the target ETA into a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure.

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

For example, the service request may be a potential request (e.g., the requester opens a transportation application via the requester terminal 130 and inputs a start location and a destination) , in this situation, the online to offline service system 100 may determine a target ETA associated with the potential request according to the process 500 and provide the target ETA to the requester terminal 130 to be displayed. As another example, one or more other optional operations (e.g., a storing operation) may be added elsewhere in the process 500. In the storing operation, the processing device 112 may store information and/or data (e.g., the one or more global features, the one or more personalized features, the preliminary ETA, the time deviation, the target ETA) associated with service request in a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure. As a further example, 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 extract the global features and the personalized features associated with the service request.

FIG. 6 is a schematic diagram illustrating exemplary global features and exemplary personalized features associated with a service request according to some embodiments of the present disclosure. As illustrated, a service request includes a start location S and a destination D. The processing device 112 determines a recommended driving path from the start location S to the destination D. It can be seen that the recommended driving path includes a plurality of road sections (e.g., road section A, road section B, road section C, road section D, road section E) .

As described in connection with FIG. 5, after receiving the service request, the processing device 112 may extract one or more global features associated with the service request, for example, a distance (e.g., “5.8 km” ) between the start location S and the destination D, GPS information (e.g., a coordinate “116: 28 E, 39: 54 N” ) of the start location S, GPS information (e.g., a coordinate “117: 14 E, 39: 04 N” ) of the destination D, identifiers (e.g., “Chaoyang road, ” “Jianguo road, ” “Changan street, ” “Tiantan road, ” “Fangqun road” ) of the road sections, serial numbers (e.g., “X025, ” “S306, ” “S203, ” “Y003, ” “S303” ) of the road sections, speed limits (e.g., “60 km/h, ” “40 km/h, ” “50 km/h, ” “80 km/h, ” “30 km/h” ) of the road sections, etc.

Further, also as described in connection with FIG. 5, the processing device 112 may also extract one or more personalized features associated with the service request, for example, driver information (e.g., an identity “002” of a driver) , passenger information (e.g., an identity “2346” of a passenger) , weather information (e.g., “rainy” ) , time information (e.g., “9: 00” ) , traffic information (e.g., “heavy congestion, ” “normal congestion, ” “mild congestion, ” “smooth traffic, ” “smooth traffic” ) of the road sections, etc.

FIG. 7 is a flowchart illustrating an exemplary first training process for determining a first model according to some embodiment of the present disclosure. In some embodiments, the process 700 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240. The processor 220 and/or the first training module 470 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the first training module 470 may be configured to perform the process 700. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 700 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 7 and described below is not intended to be limiting.

In 710, the processing device 112 (e.g., the first training module 470) (e.g., the interface circuits or the processing circuits of the processor 220) may obtain a plurality of first historical trip records. The processing device 112 may obtain the plurality of first 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.

In some embodiments, each of the plurality of first historical trip records may include a service request that has been completed (referred to as a “historical service order” ) and the information associated therein. For example, take the application scenario illustrated in FIG. 1 as an example, a requestor (e.g., a passenger) may send a transportation service request including a start location and a destination to the online to offline service system 100. After receiving the service request, the online to offline service system 100 may determine a recommended driving path based on the start location and the destination and determine an ETA associated with the service request. A service provider may accept the service request and provide the transportation service along the recommended driving path (or an actual driving path with some changes from the recommended driving path) that travels from the start location to the destination. After the service provider drops off the requestor at the destination, the online to offline service system 100 may store information associated with the service request (e.g., the start location, the destination, the recommended driving path, the actual driving path, the ETA, an actual time of arrival (ATA) ) as a historical trip record in a storage device (e.g., the storage device 150) disclosed elsewhere in the present disclosure.

In some embodiments, the plurality of first historical trip records may be selected based on a temporal criterion. For example, the plurality of first 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. In some embodiments, the plurality of first historical trip records may be selected based on a spatial criterion. For example, the plurality of first historical trip records may be selected within a predetermined geographic region (e.g., a city, a district) . In some embodiments, the plurality of first historical trip records may be selected with respect to one or more parameters, for example, “start location, ” “destination, ” “road section, ” “driver ID, ” “passenger ID, ” “traffic condition, ” etc.

In 720, the processing device 112 (e.g., the first training module 470) (e.g., the processing circuits of the processor 220) may obtain a plurality of first samples based on the plurality of first historical trip records, wherein each of the plurality of first samples may correspond to a respective one of the plurality of first historical trip records.

In some embodiments, each of the plurality of first samples may correspond to a respective ETA (e.g., an actual time of arrival (ATA) , a predetermined ETA) of a historical service order. As used herein, the ATA refers to an actual time when a historical driver of the historical service order dropped off a historical passenger of the historical service order; the predetermined ETA may be a default setting of the online to offline service system 100 or may be adjustable under different situations.

In some embodiments, the processing device 112 may divide the plurality of first samples into a first training set and a first test set. The first training set may be used to train the first model and the first test set may be used to determine whether the first training process of the first model has been completed.

In 730, for each of the plurality of first samples, the processing device 112 (e.g., the first training module 470) (e.g., the processing circuits of the processor 220) may extract one or more first global features based on the plurality of first historical trip records.

As described in connection with operation 520, for each of the plurality of first samples, the one or more first global features may include a distance between a historical start location and a historical destination of the historical service order, GPS information of the historical start location, GPS information of the historical destination, identifiers of one or more historical road sections associated with the historical service order, serial numbers of the one or more historical road sections, speed limit information associated with the one or more historical road sections, or the like, or any combination thereof.

In 740, the processing device 112 (e.g., the first training module 470) (e.g., the processing circuits of the processor 220) may obtain a preliminary first model. The preliminary first 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. In some embodiments, the preliminary first model may include but not be limited to a regression model (e.g., a linear regression model, a logistic regression model, a polynomial regression model, a stepwise regression model, a ridge regression model, a lasso regression model, an elastic net regression model) .

In 750, for each of the plurality of first samples, the processing device 112 (e.g., the first training module 470) (e.g., the processing circuits of the processor 220) may determine a sample ETA based on the one or more first global features by using the preliminary first model.

In some embodiments, for each of the plurality of first samples, the processing device 112 may determine a first global feature vector by encoding the one or more first global features according to an encoding method (e.g., one-hot coding, GBDT coding, binary encoding embedding, label encoding, character encoding, dummy encoding) . In some embodiments, each of the one or more first global features may correspond to a respective one of one or more first weighting coefficients. The one or more first weighting coefficients may be default setting of the online to offline service 100 or may be adjustable under different situations. Further, the processing device 112 may input the first global feature vector into the preliminary first model and determine the sample ETA based on an output of the preliminary first model.

In 760, the processing device 112 (e.g., the first training module 470) (e.g., the processing circuits of the processor 220) may determine whether a plurality of sample ETAs and a plurality of ETAs (e.g., ATAs, predetermined ETAs) corresponding to the plurality of first samples satisfy a first preset condition.

For example, for each of the plurality of first samples, the processing device 112 may determine a first difference between a sample ETA and an ETA (e.g., a predetermined ETA, an ATA) corresponding to the first sample. Further, the processing device 112 may determine whether most (e.g., larger than a predetermined percentage (e.g., 80%) ) or all of a plurality of first differences between the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples are less than a first difference threshold. The first difference threshold may be a default setting of the online to offline service 100 or may be adjustable under different situations. In response to determining that most or all of the plurality of first differences are less than the first difference threshold, the processing device 112 may determine that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition. In response to determining that a predetermined part (e.g., larger than a predetermined percentage (e.g., 20%) ) or all of the plurality of first differences are higher than or equal to the first difference threshold, the processing device 112 may determine that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples do not satisfy the first preset condition.

As another example, the processing device 112 may determine a first accuracy rate of the preliminary first model corresponding to the first training set and a second accuracy rate of the preliminary first model corresponding to the first test set. Further, the processing device 112 may determine whether the first accuracy rate has been stable ( “stable” refers to that a first accuracy rate in a current iteration is substantially same as (i.e., less than a threshold) a first accuracy rate in a previous adjacent iteration or multiple first accuracy rates in multiple previous iterations) and whether the second accuracy rate has reached a maximum value. As used herein, the first accuracy rate and/or the second accuracy rate may be determined based on the plurality of sample ETAs and the plurality of ETAs (e.g., the actual ETAs) corresponding to the plurality of first samples. In response to determining that the first accuracy rate has been stable and the second accuracy rate has reached the maximum value, the processing device 112 may determine that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition. In response to determining that the first accuracy rate is unstable (e.g., rising) and the second accuracy rate has not reached the maximum value, the processing device 112 may determine that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples do not satisfy the first preset condition.

As a further example, the processing device 112 may determine a first loss function of the preliminary first model based on the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples. Further, the processing device 112 may determine a value of the first loss function and determine whether the value of the first loss function is less than a first loss threshold. The first loss threshold may be a default setting of the online to offline service 100 or may be adjustable under different situations. In response to determining that the value of the first loss function is less than the first loss threshold, the processing device 112 may determine that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition. In response to determining that the value of the first loss function is higher than or equal to the first loss threshold, the processing device 112 may determine that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples do not satisfy the first preset condition.

As a still further example, the processing device 112 may determine whether a number count of iterations is larger than a first count threshold. In response to determining that the number count of iterations is larger than the first count threshold, the processing device 112 may determine that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition. In response to determining that the number count of iterations is less than or equal to the first count threshold, the processing device 112 may determine that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples do not satisfy the first preset condition.

In 770, in response to determining that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition, the processing device 112 (e.g., the first training module 470) (e.g., the processing circuits of the processor 220) may designate the preliminary first model as the first model, which means that the first training process of the first model has been completed.

On the other hand, in response to determining that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples do not satisfy the first preset condition, the processing device 112 (e.g., the first training module 470) (e.g., the processing circuits of the processor 220) may execute the process 700 to return to operation 740 to update the preliminary first model. For example, the processing device 112 may update the one or more preliminary parameters to produce an updated first model. Further, the processing device 112 (e.g., the first training module 470) (e.g., the processing circuits of the processor 220) may repeat the step of determining whether the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition until the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition. In response to determining that the plurality of updated sample ETAs under the updated first model and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition, the processing device 112 may designate the updated first model as the first model.

It should be noted that the above description is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations or modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. For example, the processing device 112 may update the first model at a certain time interval (e.g., per month, per two months) based on a plurality of newly obtained first samples.

FIG. 8 is a flowchart illustrating an exemplary process for determining a second model according to some embodiment of the present disclosure. In some embodiments, the process 800 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 second training module 480 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the second training module 480 may be configured to perform the process 800. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 800 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. 8 and described below is not intended to be limiting.

In 810, the processing device 112 (e.g., the second training module 480) (e.g., the interface circuits or the processing circuits of the processor 220) may obtain a plurality of second historical trip records. The processing device 112 may obtain the plurality of second 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. In some embodiments, the plurality of second historical trip records may be the same as, partially different from, or totally different from the plurality of first historical trip records.

In 820, the processing device 112 (e.g., the second training module 480) (e.g., the processing circuits of the processor 220) may obtain a plurality of second samples based on the plurality of second historical trip records, wherein each of the plurality of second samples may correspond to a respective one of the plurality of second historical trip records.

In some embodiments, each of the plurality of second samples may correspond to a reference time deviation which is determined based on an ETA (e.g., an ATA, a predetermined ETA) and a reference ETA. For example, the reference time deviation may be a difference between the ETA and the reference ETA. In some embodiments, for each of the plurality of second samples, the processing device 112 may determine the reference ETA based on one or more second global features of the second sample by using the first model. For example, the processing device 112 may extract the one or more second global features (e.g., a distance between a start location and a destination, GPS information of the start location, GPS information of the destination) from the second sample and determine a second global feature vector by encoding the one or more second global features. Further, the processing device 112 may input the second global feature vector into the first model and determine the reference ETA based on an output of the first model.

In some embodiments, the processing device 112 may also divide the plurality of second samples into a second training set and a second test set. The second training set may be used to train the second model and the second test set may be used to determine whether the second training process of the second model has been completed.

In 830, for each of the plurality of second samples, the processing device 112 (e.g., the second training module 480) (e.g., the processing circuits of the processor 220) may extract one or more personalized features based on the plurality of second historical trip records.

As described in connection with operation 520, for each of the plurality of second samples, the one or more personalized features may include driver information, passenger information, weather information, time information, traffic information, or the like, or any combination thereof.

In 840, the processing device 112 (e.g., the second training module 480) (e.g., the processing circuits of the processor 220) may obtain a preliminary second model. The preliminary second 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. In some embodiments, the preliminary second model may include but not be limited to a regression model (e.g., a linear regression model, a logistic regression model, a polynomial regression model, a stepwise regression model, a ridge regression model, a lasso regression model, an elastic net regression model) .

In 850, for each of the plurality of second samples, the processing device 112 (e.g., the second training module 480) (e.g., the processing circuits of the processor 220) may determine a sample time deviation based on the one or more personalized features by using the preliminary second model.

In some embodiments, for each of the plurality of second samples, the processing device 112 may determine a personalized feature vector by encoding the one or more personalized features according to an encoding method (e.g., one-hot coding, GBDT coding, binary encoding embedding, label encoding, character encoding, dummy encoding) . In some embodiments, each of the one or more personalized features may correspond to a respective one of one or more second weighting coefficients. The second weighting coefficients may be a default setting of the online to offline service 100 or may be adjustable under different situations. Further, the processing device 112 input the personalized feature vector into the preliminary second model and determine the sample time deviation based on an output of the preliminary second model.

In 860, the processing device 112 (e.g., the second training module 480) (e.g., the processing circuits of the processor 220) may determine whether a plurality of sample time deviations and a plurality of reference time deviations corresponding to the plurality of second samples satisfy a second preset condition.

For example, for each of the plurality of second samples, the processing device 112 may determine a second difference between a sample time deviation and a reference time deviation corresponding to the second sample. Further, the processing device 112 may determine whether most (e.g., larger than a predetermined percentage (e.g., 80%) ) or all of a plurality of second differences between the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples are less than a second difference threshold. The second difference threshold may be a default setting of the online to offline service 100 or may be adjustable under different situations. In response to determining that most or all of the plurality of second differences are less than the second difference threshold, the processing device 112 may determine that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition. In response to determining that a predetermined part (e.g., larger than a predetermined percentage (e.g., 20%) ) or all of the plurality of second differences are higher than or equal to the second difference threshold, the processing device 112 may determine that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples do not satisfy the second preset condition.

As another example, the processing device 112 may determine a third accuracy rate of the preliminary second model corresponding to the second training set and a fourth accuracy rate of the preliminary second model corresponding to the second test set. Further, the processing device 112 may determine whether the third accuracy rate has been stable ( “stable” refers to that a third accuracy rate in a current iteration is substantially same as (i.e., less than a threshold) a third accuracy rate in a previous adjacent iteration or multiple third accuracy rates in multiple previous iterations) and whether the fourth accuracy rate has reached a maximum value. As used herein, the third accuracy rate and/or the fourth accuracy rate may be determined based on the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples. In response to determining that the third accuracy rate has been stable and the fourth accuracy rate has reached the maximum value, the processing device 112 may determine that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition. In response to determining that the third accuracy rate is unstable (e.g., rising) and the fourth accuracy rate has not reached the maximum value, the processing device 112 may determine that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples do not satisfy the second preset condition.

As a further example, the processing device 112 may determine a second loss function of the preliminary second model based on the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples. Further, the processing device may determine a value of the second loss function and determine whether the value of the second loss function is less than a second loss threshold. The second loss threshold may be a default setting of the online to offline service 100 or may be adjustable under different situations. In response to determining that the value of the second loss function is less than the second loss threshold, the processing device 112 may determine that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition. In response to determining that the value of the second loss function is higher than or equal to the second loss threshold, the processing device 112 may determine that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples do not satisfy the second preset condition.

As still a further example, the processing device 112 may determine whether a number count of iterations is larger than a second count threshold. In response to determining that the number count of iterations is larger than the second count threshold, the processing device 112 may determine that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition. In response to determining that the number count of iterations is less than or equal to the second count threshold, the processing device 112 may determine that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples do not satisfy the second preset condition.

In 870, in response to determining that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition, the processing device 112 (e.g., the second training module 480) (e.g., the processing circuits of the processor 220) may designate the preliminary second model as the second model, which means that the training process of the second model has been completed.

On the other hand, in response to determining that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples do not satisfy the second preset condition, the processing device 112 (e.g., the second training module 480) (e.g., the processing circuits of the processor 220) may execute the process 800 to return to operation 840 to update the preliminary second model. For example, the processing device 112 may update the one or more preliminary parameters to produce an updated second model. Further, the processing device 112 (e.g., second training module 480) (e.g., the processing circuits of the processor 220) may repeat the step of determining whether the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition until the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition. In response to determining that the plurality of updated sample time deviations under the updated second model and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition, the processing device 112 may designate the updated second model as the second model.

It should be noted that the above description is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations or modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. For example, the processing device 112 may update the second model at a certain time interval (e.g., per month, per two months) based on a plurality of newly obtained second samples.

FIG. 9 is a schematic diagram illustrating an exemplary user interface for displaying information associated with a service request according to some embodiments of the present disclosure. The user interface 900 may include one or more user interface elements (also referred to as the “UI elements” ) for presenting information (e.g., ETA, path information, service fee information) associated with the service request. Each of the UI elements may be and/or include, for example, one or more buttons, icons, checkboxes, message boxes, text fields, data fields, search fields, etc.

As shown in FIG. 9, the user interface 900 may include a line 910 for presenting a recommended driving path between a start location S and a destination D associated with a service request, wherein the recommended driving path includes a plurality of road sections (e.g., Chaoyang road, Jianguo road, Changan street, Tiantan road, and Fangqun road) . The user interface 900 may also include one or more UI elements (e.g., “Bus, ” “Express, ” “Premier, ” “Taxi” ) for presenting various service types. The user interface 900 may also include a UI element 920 for presenting a selection of a start time of the service request and a UI element 940 for presenting a selection of passenger (s) of the service request. Further, the user interface 900 may also include an UI element 940 for presenting an ETA (e.g., 9: 35) associated with the service request and an UI element 950 for presenting service fee information (e.g., 25 RMB) of the service request which may be estimated based on the recommended driving path and the ETA. The user interface 900 may also include a UI element 970 (e.g., “Confirm Express” ) for presenting a confirmation of the service request. The user interface 900 may also include a UI element 920 for presenting a current time (e.g., 9: 00) .

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

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

Further, it will be appreciated by one skilled in the art, aspects of the present disclosure may be illustrated and described herein in any of a number of patentable classes or context including any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof. Accordingly, aspects of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc. ) or combining software and hardware implementation that may all generally be referred to herein as a “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. In the latter scenario, the remote computer may be connected to the user′s computer through any type of network, including a local area network (LAN) or a wide area network (WAN) , or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) or in a cloud computing environment or offered as a service such as a Software as a Service (SaaS) .

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

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

Claims

A system, comprising:

a storage medium to store a set of instructions; and

a processor, communicatively coupled to the storage medium, to execute the set of instructions to:

receive, from a terminal device via a network, a service request associated with a transportation service;

extract one or more global features associated with the service request and one or more personalized features associated with the service request;

determine, using a first model, a preliminary estimated time of arrival (ETA) based on the one or more global features associated with the service request;

determine, using a second model, a time deviation based on the one or more personalized features associated with the service request;

determine a target ETA based on the preliminary ETA and the time deviation; and

transmit, to the terminal device via the network, the target ETA.
The system of claim 1, wherein the one or more global features comprise at least one of a distance between a start location and a destination, GPS information of the start location, GPS information of the destination, identifiers of one or more road sections associated with the service request, serial numbers of the one or more road sections, or speed limit information associated with the one or more road sections.
The system of claim 1 or claim 2, wherein the one or more personalized features comprise at least one of driver information, passenger information, weather information, time information, or traffic information.
The system of any of claims 1-3, wherein the first model is determined with a first training process, the first training process comprising:

obtaining a plurality of first historical trip records;

obtaining a plurality of first samples based on the plurality of first historical trip records, each of the plurality of first samples corresponding to a respective ETA;

extracting one or more first global features for each of the plurality of first samples based on the plurality of first historical trip records;

obtaining a preliminary first model;

for each of the plurality of first samples, determining a sample ETA based on the one or more first global features by using the preliminary first model;

determining whether a plurality of sample ETAs and a plurality of ETAs corresponding to the plurality of first samples satisfy a first preset condition; and

designating the preliminary first model as the first model in response to determining that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition.
The system of claim 4, wherein the first training process further comprises:

updating the preliminary first model in response to determining that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples do not satisfy the first preset condition; and

repeating the step of determining whether the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples satisfy the first preset condition until the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples satisfy the first preset condition.
The system of claim 4 or claim 5, wherein each of the one or more first global features corresponds to a respective one of one or more first weighting coefficients.
The system of any of claims 1-6, wherein the second model is determined with a second training process, the second training process comprising:

obtaining a plurality of second historical trip records;

obtaining a plurality of second samples based on the plurality of second historical trip records, each of the plurality of second samples corresponding to a reference time deviation which is determined based on an ETA and a reference ETA;

extracting one or more personalized features for each of the plurality of second samples based on the plurality of second historical trip records;

obtaining a preliminary second model;

for each of the plurality of second samples, determining a sample time deviation based on the one or more personalized features by using the preliminary second model;

determining whether a plurality of sample time deviations and a plurality of reference time deviations corresponding to the plurality of second samples satisfy a second preset condition; and

designating the preliminary second model as the second model in response to determining that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition.
The system of claim 7, wherein the second training process further comprises:

updating the preliminary second model in response to determining that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples do not satisfy the second preset condition; and

repeating the step of determining whether the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition until the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition.
The system of claim 7 or claim 8, wherein for each of the plurality of second samples, the reference ETA is determined based on one or more second global features of the second sample by using the first model.
The system of any of claims 7-9, wherein each of the one or more personalized features corresponds to a respective one of one or more second weighting coefficients.
The system of any of claims 1-10, wherein to determine the target ETA based on the preliminary ETA and the time deviation, the processor is to:

determine the target ETA by performing a linear combination on the preliminary ETA and the time deviation based on one or more coefficients, wherein the one or more coefficients are determined based on at least one of a predetermined rule or a machine learning model.
The system of any of claims 1-11, wherein the first model or the second model is a regression model.
A method implemented on a computing device having at least one processor, at least one storage medium, and a communication platform connected to a network, the method comprising:

receiving, from a terminal device via a network, a service request associated with a transportation service;

extracting one or more global features associated with the service request and one or more personalized features associated with the service request;

determining, using a first model, a preliminary estimated time of arrival (ETA) based on the one or more global features associated with the service request;

determining, using a second model, a time deviation based on the one or more personalized features associated with the service request;

determining a target ETA based on the preliminary ETA and the time deviation; and

transmitting, to the terminal device via the network, the target ETA.
The method of claim 13, wherein the one or more global features comprise at least one of a distance between a start location and a destination, GPS information of the start location, GPS information of the destination, identifiers of one or more road sections associated with the service request, serial numbers of the one or more road sections, or speed limit information associated with the one or more road sections.
The method of claim 13 or claim 14, wherein the one or more personalized features comprise at least one of driver information, passenger information, weather information, time information, or traffic information.
The method of any of claims 13-15, wherein the first model is determined with a first training process, the first training process comprising:

obtaining a plurality of first historical trip records;

obtaining a plurality of first samples based on the plurality of first historical trip records, each of the plurality of first samples corresponding to a respective ETA;

extracting one or more first global features for each of the plurality of first samples based on the plurality of first historical trip records;

obtaining a preliminary first model;

for each of the plurality of first samples, determining a sample ETA based on the one or more first global features by using the preliminary first model;

determining whether a plurality of sample ETAs and a plurality of ETAs corresponding to the plurality of first samples satisfy a first preset condition; and

designating the preliminary first model as the first model in response to determining that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition.
The method of claim 16, wherein the first training process further comprises:

updating the preliminary first model in response to determining that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples do not satisfy the first preset condition; and

repeating the step of determining whether the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples satisfy the first preset condition until the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples satisfy the first preset condition.
The method of claim 16 or claim 17, wherein each of the one or more first global features corresponds to a respective one of one or more first weighting coefficients.
The method of any of claims 13-18, wherein the second model is determined with a second training process, the second training process comprising:

obtaining a plurality of second historical trip records;

obtaining a plurality of second samples based on the plurality of second historical trip records, each of the plurality of second samples corresponding to a reference time deviation which is determined based on an ETA and a reference ETA;

extracting one or more personalized features for each of the plurality of second samples based on the plurality of second historical trip records;

obtaining a preliminary second model;

for each of the plurality of second samples, determining a sample time deviation based on the one or more personalized features by using the preliminary second model;

determining whether a plurality of sample time deviations and a plurality of reference time deviations corresponding to the plurality of second samples satisfy a second preset condition; and

designating the preliminary second model as the second model in response to determining that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition.
The method of claim 19, wherein the second training process further comprises:

updating the preliminary second model in response to determining that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples do not satisfy the second preset condition; and

repeating the step of determining whether the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition until the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition.
The method of claim 19 or claim 20, wherein for each of the plurality of second samples, the reference ETA is determined based on one or more second global features of the second sample by using the first model.
The method of any of claims 19-21, wherein each of the one or more personalized features corresponds to a respective one of one or more second weighting coefficients.
The method of any of claims 13-22, further comprising:

determining the target ETA by performing a linear combination on the preliminary ETA and the time deviation based on one or more coefficients, wherein the one or more coefficients are determined based on at least one of a predetermined rule or a machine learning model.
The method of any of claims 13-23, wherein the first model or the second model is a regression model.
A system, comprising:

a receiving module configured to receive a service request associated with a transportation service from a terminal device via a network;

an extraction module configured to extract one or more global features associated with the service request and one or more personalized features associated with the service request;

a preliminary ETA determination module configured to determine, using a first model, a preliminary estimated time of arrival (ETA) based on the one or more global features associated with the service request;

a time deviation determination module configured to determine, using a second model, a time deviation based on the one or more personalized features associated with the service request;

a target ETA determination module configured to determine a target ETA based on the preliminary ETA and the time deviation; and

a transmission module configured to transmit, to the terminal device via the network, the target ETA.
The system of claim 25, wherein the one or more global features comprise at least one of a distance between a start location and a destination, GPS information of the start location, GPS information of the destination, identifiers of one or more road sections associated with the service request, serial numbers of the one or more road sections, or speed limit information associated with the one or more road sections.
The system of claim 25 or claim 26, wherein the one or more personalized features comprise at least one of driver information, passenger information, weather information, time information, or traffic information.
The system of any of claims 25-27, wherein the system further includes a first training module configured to:

obtain a plurality of first historical trip records;

obtain a plurality of first samples based on the plurality of first historical trip records, each of the plurality of first samples corresponding to a respective ETA;

extract one or more first global features for each of the plurality of first samples based on the plurality of first historical trip records;

obtain a preliminary first model;

for each of the plurality of first samples, determine a sample ETA based on the one or more first global features by using the preliminary first model;

determine whether a plurality of sample ETAs and a plurality of ETAs corresponding to the plurality of first samples satisfy a first preset condition; and

designate the preliminary first model as the first model in response to determining that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of first samples satisfy the first preset condition.
The system of claim 28, wherein the first training module is further configured to:

update the preliminary first model in response to determining that the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples do not satisfy the first preset condition; and

repeat the step of determining whether the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples satisfy the first preset condition until the plurality of sample ETAs and the plurality of ETAs corresponding to the plurality of samples satisfy the first preset condition.
The system of claim 28 or claim 29, wherein each of the one or more first global features corresponds to a respective one of one or more first weighting coefficients.
The system of any of claims 25-30, wherein the system further includes a second training module configured to:

obtain a plurality of second historical trip records;

obtain a plurality of second samples based on the plurality of second historical trip records, each of the plurality of second samples corresponding to a reference time deviation which is determined based on an ETA and a reference ETA;

extract one or more personalized features for each of the plurality of second samples based on the plurality of second historical trip records;

obtain a preliminary second model;

for each of the plurality of second samples, determine a sample time deviation based on the one or more personalized features by using the preliminary second model;

determine whether a plurality of sample time deviations and a plurality of reference time deviations corresponding to the plurality of second samples satisfy a second preset condition; and

designate the preliminary second model as the second model in response to determining that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition.
The system of claim 31, wherein the second training module is further configured to:

update the preliminary second model in response to determining that the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples do not satisfy the second preset condition; and

repeat the step of determining whether the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition until the plurality of sample time deviations and the plurality of reference time deviations corresponding to the plurality of second samples satisfy the second preset condition.
The system of claim 31 or claim 32, wherein for each of the plurality of second samples, the reference ETA is determined based on one or more second global features of the second sample by using the first model.
The system of any of claims 31-33, wherein each of the one or more personalized features corresponds to a respective one of one or more second weighting coefficients.
The system of any of claims 25-34, wherein the target ETA determination module is further configured to:

determine the target ETA by performing a linear combination on the preliminary ETA and the time deviation based on one or more coefficients, wherein the one or more coefficients are determined based on at least one of a predetermined rule or a machine learning model.
The system of any of claims 25-35, wherein the first model or the second model is a regression model.
A non-transitory computer readable medium, comprising executable instructions that, when executed by at least one processor, direct the at least one processor to perform a method, the method comprising:

receiving, from a terminal device via a network, a service request associated with a transportation service;

extracting one or more global features associated with the service request and one or more personalized features associated with the service request;

determining, using a first model, a preliminary estimated time of arrival (ETA) based on the one or more global features associated with the service request;

determining, using a second model, a time deviation based on the one or more personalized features associated with the service request;

determining a target ETA based on the preliminary ETA and the time deviation; and

transmitting, to the terminal device via the network, the target ETA.