WO2020164161A1

WO2020164161A1 - Systems and methods for estimated time of arrival (eta) determination

Info

Publication number: WO2020164161A1
Application number: PCT/CN2019/075403
Authority: WO
Inventors: Kun Fu; Zheng Wang
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2019-02-14
Filing date: 2019-02-18
Publication date: 2020-08-20
Anticipated expiration: 2021-08-14
Also published as: CN111563639A

Abstract

A system and method for determining an estimated time of arrival (ETA) in a trip. The method may include obtaining an origin, a destination, time information, and driver information regarding the trip. The method may include obtaining a trained machine learning model. The method may further include determining an ETA of the trip based on the trained machine learning model and the origin, the destination, the time information, and the driver information regarding the trip, wherein the ETA of the trip is independent of routes between the origin and the destination.

Description

SYSTEMS AND METHODS FOR ESTIMATED TIME OF ARRIVAL (ETA) DETERMINATION

CROSS-REFERENCE TO TELATED APPLICATIO

This application claims priority to Chinese Patent Application No. 201910114924.7, filed on February 14, 2019, the contents of which are incorporated herein by reference in their entirety.

TECHNICAL FIELD

The present disclosure generally relates to digital navigation, and more particularly, relates to systems and methods for determining an estimated time of arrival (ETA) in digital navigation.

BACKGROUND

Map-based digital navigation has become increasingly popular. Current map-based navigation applications generally determine an estimated time of arrival (ETA) for a trip based on one or more factors including routes between an origin and a destination of the trip. However, the determination of the ETA needs a large amount of computation, and is proved to be time consuming if the routes between the origin and the destination are considered. Therefore, it is desirable to develop a system or a method for determining an ETA in a trip more efficiently.

SUMMARY

According to an aspect of the present disclosure, a method for determining an estimated time of arrival (ETA) in a trip is provided. The method may be implemented on a computing device having at least one processor and at least one computer-readable storage medium. The method may comprise obtaining an origin, a destination, time information, and driver information regarding the trip; obtaining a trained machine learning model; and determining an ETA of the trip based on the trained machine learning model and the origin, the destination, the time information, and the driver information regarding the trip, wherein the ETA of the trip is independent of routes between the origin and the destination.

According to another aspect of the present disclosure, a system for determining an estimated time of arrival (ETA) in a trip is provided. The system may comprise at least one storage medium including a set of instructions; and at least one processor configured to communicate with the at least one storage medium, wherein when executing the set of instructions, the at least one processor is directed to obtain an origin, a destination, time information, and driver information regarding the trip; obtain a trained machine learning model; and determine an ETA of the trip based on the trained machine learning model and the origin, the destination, the time information, and the driver information regarding the trip, wherein the ETA of the trip is independent of routes between the origin and the destination.

According to an aspect of the present disclosure, a non-transitory computer readable medium is provided. The non-transitory computer readable medium may comprise at least one set of instructions for determining an estimated time of arrival (ETA) in a trip, wherein when executed by at least one processor of a computing device, the at least one set of instructions causes the computing device to perform a method. The method may comprise obtaining an origin, a destination, time information, and driver information regarding the trip; obtaining a trained machine learning model; and determining an ETA of the trip based on the trained machine learning model and the origin, the destination, the time information, and the driver information regarding the trip, wherein the ETA of the trip is independent of routes between the origin and the destination.

In some embodiments, the determining the ETA of the trip based on the trained machine learning model and the origin, the destination, the time information, and the driver information regarding the trip may include determining one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information; and determining the ETA of the trip based on the trained machine learning model and the determined features of the trip.

In some embodiments, the determining the one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information includes determining a longitude of the origin, a latitude of the origin, a longitude of the destination, a latitude of the destination, a link identification (ID) of the origin, a link ID of the destination, and a spherical distance between the origin and the destination based on the origin and the destination regarding the trip.

In some embodiments, the determining the one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information includes determining a day and a time of the trip based on the time information regarding the trip.

In some embodiments, the determining the one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information includes determining a driver ID based on the driver information regarding the trip.

In some embodiments, the determining the longitude of the origin, the latitude of the origin, the longitude of the destination, and the latitude of the destination based on the origin and the destination regarding the trip includes:

dividing each of meridian lines and latitude lines on a map including the origin and the destination into a plurality of bins; encoding each bin with an integer; and determining the longitude of the origin, the latitude of the origin, the longitude of the destination, and the latitude of the destination based on the encoded bins and the origin and the destination regarding the trip.

In some embodiments, the determining the link ID of the origin and the link ID of the destination based on the origin and the destination regarding the trip includes determining links on a map including the origin and the destination; encoding each of the links with an ID number; and determining the link ID of the origin and the link ID of the destination based on the origin and the destination regarding the trip and the ID number of each link.

In some embodiments, the determining the time of the trip based on the time information regarding the trip includes dividing a day into a plurality of time durations; and determining the time of the trip based on the plurality of durations and the time information regarding the trip.

In some embodiments, the one or more features includes discrete features and continuous features.

In some embodiments, the method may further include processing the discrete features using an embedding technique; and processing the continuous features based on mean values and standard deviations associated with the continuous features.

In some embodiments, the trained machine learning model is selected from the group consisting of a multiple layer perceptron (MLP) model, a gradient boosting decision tree (GBDT) model, an extreme gradient boosting (XGB) model, a logistic regression model, and a factorization machine (FM) model.

In some embodiments, the trained machine learning model is trained using a model training method, the model training method including obtaining a machine learning model and training datasets; and training the machine learning model based on the training datasets and a loss function.

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

BRIEF DESCRIPTION OF THE DRAWINGS

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

Fig. 1 is a schematic diagram illustrating an exemplary ETA determination system according to some embodiments of the present disclosure;

Fig. 2 is a schematic diagram illustrating exemplary components of a computing apparatus according to some embodiments of the present disclosure;

Fig. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary user terminal 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 block diagram illustrating an exemplary processing module according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for determining an ETA for a trip according to some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for determining one or more features of the trip according to some embodiments of the present disclosure;

FIG. 8 is a schematic diagram illustrating the determination of an ETA for a trip according to some embodiments of the present disclosure; and

FIG. 9 illustrates an exemplary structure of an MLP 900 according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

In order to illustrate the technical solutions related to the embodiments of the present disclosure, brief introduction of the drawings referred to in the description of the embodiments is provided below. Obviously, drawings described below are only some examples or embodiments of the present disclosure. Those having ordinary skills in the art, without further creative efforts, may apply the present disclosure to other similar scenarios according to these drawings. Unless stated otherwise or obvious from the context, the same reference numeral in the drawings refers to the same structure and operation.

As used in the disclosure and the appended claims, the singular forms “a, ” “an, ” and “the” include plural referents unless the content clearly dictates otherwise. It will be further understood that the terms “comprises, ” “comprising, ” “includes, ” and/or “including” when used in the disclosure, specify the presence of stated steps and elements, but do not preclude the presence or addition of one or more other steps and elements.

Some modules of the system may be referred to in various ways according to some embodiments of the present disclosure, however, any number of different modules may be used and operated in a client terminal and/or a server. These modules are intended to be illustrative, not intended to limit the scope of the present disclosure. Different modules may be used in different aspects of the system and method.

According to some embodiments of the present disclosure, flow charts are used to illustrate the operations performed by the system. It is to be expressly understood, the operations above or below may or may not be implemented in order. Conversely, the operations may be performed in inverted order, or simultaneously. Besides, one or more other operations may be added to the flowcharts, or one or more operations may be omitted from the flowchart.

Technical solutions of the embodiments of the present disclosure be described with reference to the drawings as described below. It is obvious that the described embodiments are not exhaustive and are not limiting. Other embodiments obtained, based on the embodiments set forth in the present disclosure, by those with ordinary skill in the art without any creative works are within the scope of the present disclosure.

Some embodiments of the present disclosure are directed to systems and methods for determining an estimated time of arrival (ETA) for a trip. The system may obtain an origin, a destination, time information, and driver information regarding the trip. The system may obtain a trained machine learning model. The system may determine an ETA of the trip based on the trained machine learning model and the origin, the destination, the time information, and the driver information regarding the trip. The ETA refers to a time point when a user on a vehicle arrivals at the destination of his/her trip. The ETA of the trip may be independent of routes between the origin and the destination.

Some embodiments of the present disclosure are directed to a real-time or online ETA determination function applicable in, e.g., on-demand services, which is a newly emerged service or demand rooted only in the post-Internet era. It provides the technical solutions to service requesters that could rise only in the post-Internet era. In the pre-Internet era, it is impossible to determine an ETA for a trip without considering routes between an origin and a destination of the trip. Therefore, the present solution is deeply rooted in and aimed to solve a problem only occurred in the post-Internet era.

FIG. 1 illustrates an exemplary network environment of an ETA determination system according to some embodiments of the present disclosure. The ETA determination system 100 may be an online service platform for providing travelling related services. The ETA determination system 100 may include a server 110, a network 120, a user terminal 130, a driver device 140, and a storage 150. In some embodiments, the ETA determination system 100 may further include a positioning device 160 (not shown in FIG. 1) .

The ETA determination system 100 may be applicable in a plurality of services. Exemplary services may include a travel plan service, a navigation service, an on-demand service (e.g., a taxi hailing service, a chauffeur service, an express car service, a carpool service, a bus service, or a driver hire service) , or the like, or a combination thereof.

The server 110 may process data and/or information from one or more components of the ETA determination system 100 or an external data source (e.g., a cloud data center) . The server 110 may communicate with the user terminal 130 and/or the driver device 140 to provide various functionality of online services, for example, an ETA from a start point to a destination in a trip. In some embodiments, the server 110 may be a single server, or a server group. The server group may be a centralized server group connected to the network 120 via an access point, or a distributed server group connected to the network 120 via one or more access points, respectively. In some embodiments, the server 110 may be locally connected to the network 120 or in remote connection with the network 120. For example, the server 110 may access information and/or data stored in the user terminal 130, the driver device 140, and/or the storage 150 via the network 120. As another example, the storage 150 may serve as backend data storage of the server 110. 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 in a computing device 200 having one or more components illustrated in FIG. 2 in the present disclosure.

In some embodiments, the server 110 may include a processing device 112. The processing device 112 may process information and/or data related to one or more functions described in the present disclosure. In some embodiments, the processing device 112 may perform main functions of the ETA determination system 100. In some embodiments, the processing device 112 may process trip related information to estimate a time when a driver or a passenger on a vehicle arrives a certain place. In some embodiments, the processing device 112 may perform other functions related to the method and system described in the present disclosure.

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

The network 120 may facilitate exchange of information and/or data. In some embodiments, one or more components in the ETA determination system 100 (e.g., the server 110, the user terminal 130, the driver device 140, the storage 150) may send information and/or data to other component (s) in the ETA determination system 100 via the network 120. For example, the processing device 112 may obtain a machine learning model from the storage 150 via the network 120. As another example, the processing device 112 may transmit an ETA for a trip determined using the machine learning model to the user terminal 130 via the network 120. In some embodiments, the network 120 may be any type of wired or wireless network, or combination thereof. Merely by way of example, the network 120 may include a cable network, a wireline network, an optical fiber network, a tele communications network, an intranet, an Internet, a local area network (LAN) , a wide area network (WAN) , a wireless local area network (WLAN) , a metropolitan area network (MAN) , a wide area network (WAN) , a public telephone switched network (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 ETA determination system 100 may be connected to the network 120 to exchange data and/or information.

The user terminal 130 and/or the driver device 140 may communicate with the server 110 via the network 120. In some embodiments, a passenger may be an owner of the user terminal 130. In some embodiments, the owner of the user terminal 130 may be someone other than the passenger. For example, an owner A of the user terminal 130 may use the user terminal 130 to send a service request for a passenger B, and/or receive a service confirmation and/or information or instructions from the server 110. In some embodiments, a driver may be a user of the driver device 140. In some embodiments, the user of the driver device 140 may be someone other than the driver. For example, a user C of the driver device 140 may use the driver device 140 to receive a service request for a driver D, and/or information or instructions from the server 110. In some embodiments, a driver may be assigned to use one of the driver device 140 for at least a certain period of time. For example, when a driver is available to provide an on-demand service, he/she may be assigned to use a driver terminal that receives an earliest request and a vehicle that is recommended to perform the type of on-demand service. In some embodiments, "passenger" and "terminal device" may be used interchangeably, and "driver" and "driver device" may be used interchangeably. In some embodiments, the driver device 140 may be associated with one or more drivers (e.g., a night-shift driver, a day-shift driver, or a driver pool by a random shifting) .

A passenger may receive a service response for a trip via the user terminal 130. In some embodiments, the user terminal 130 may obtain an ETA of the trip from the processing device 112 via the network 120. The user 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, 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 a built-in computer, an onboard built-in television, a built-in tablet, etc. In some embodiments, the user terminal 130 may include a signal transmitter and a signal receiver configured to communicate with the positioning device 170 for locating the position of the passenger and/or the user terminal 130, and determining a relative distance from his/her position to a road.

The driver may receive a service request via the driver device 140. The driver device 140 may obtain an ETA of the trip from the processing device 112 via the network 120. The driver device 140 may include a plurality of driver devices 140-1, 140-2, …, 140-n. In some embodiments, the driver device 140 may be similar to, or same as the user terminal 130. In some embodiments, the driver device 140 may be customized to implement online services based on travel related information obtained from the processing device 112.

The storage 150 may store data and/or instructions. The data may include geographic location information, time information, driver information, and/or external environment. Merely for illustration purposes, data related to geographic location information may include an origin, a destination, a longitude of the origin, a latitude of the origin, a longitude of the destination, a latitude of the destination, a link identification (ID) of the origin, a link ID of the destination, and a spherical distance between the origin and the destination, etc. Data related to time information may include a day and a time of the trip. Data related to driver information may include a driver identification (ID) . In some embodiments, the storage 150 may store data obtained from the user terminal 130 and/or the driver device 140. For example, the storage 150 may store logs associated with the user terminal 130.

In some embodiments, the storage 150 may store data and/or instructions that the processing device 112 may execute to determine an ETA for a trip as described in the present disclosure. In some embodiments, the storage 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 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, one or more components in the ETA determination system 100 may access the data or instructions stored in the storage 150 via the network 120. In some embodiments, the storage 150 may be directly connected to the server 110 as a backend storage.

The positioning device 170 may determine information associated with an object, for example, one or more of the user terminal 130, the driver device 140, etc. For example, the positioning device 170 may determine a current location of the user terminal 130. In some embodiments, the positioning device 170 may be a global positioning system (GPS) , a global navigation satellite system (GLONASS) , a compass navigation system (COMPASS) , a BeiDou navigation satellite system, a Galileo positioning system, a quasi-zenith satellite system (QZSS) , etc. The information provided by the positioning device 170 may include a location, an elevation, a velocity, or an acceleration of the object, and/or a current time. The location may be in the form of coordinates, such as, a latitude coordinate and a longitude coordinate, etc. The positioning device 170 may include or associate with one or more satellites. The satellites may determine the information mentioned above independently or jointly. The positioning device 170 may send the information mentioned above to the user terminal 130, or the driver device 140 via the network 120.

One of ordinary skill in the art would understand that when an element of the ETA determination system 100 performs, the element may perform through electrical signals and/or electromagnetic signals. For example, when a user terminal 130 processes a task, such as planning a trip from one place to another, the user terminal 130 may operate logical circuits in its processor to process such task. When the user terminal 130 sends out an instruction to the server 110, a processor of the user terminal 130 may generate electrical signals encoding the instruction. The processor of the user terminal 130 may then send the electrical signals to an output port. If the user terminal 130 communicates with the server 110 via a wired network, the output port may be physically connected to a cable, which further transmit the electrical signal to an input port of the server 110. If the user terminal 130 communicates with the server 110 via a wireless network, the output port of the user terminal 130 may be one or more antennas, which convert the electrical signals to electromagnetic signals. Similarly, a driver device 140 may process a task through operation of logical circuits in its processor, and receive an instruction and/or information from the server 110 via electrical signals or electromagnet signals. Within an electronic device, such as the user terminal 130, the driver device 140, and/or the server 110, when a processor thereof processes an instruction, sends out an instruction, and/or performs an action, the instruction and/or action is conducted via electrical signals. For example, when the processor retrieves data (e.g., road networks) from a storage medium (e.g., the storage 150) , it may send out electrical signals to a read device of the storage medium, which may read 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.

Fig. 2 is a schematic diagram illustrating exemplary components of a computing apparatus according to some embodiments of the present disclosure. The server 110, the storage device 120, the information provider 130, and/or the communication platform 140 may be implemented on the computing apparatus 200 according to some embodiments of the present disclosure. The particular system may use a functional block diagram to explain the hardware platform containing one or more user interfaces. The computer may be a computer with general or specific functions. Both types of the computers may be configured to implement any particular system according to some embodiments of the present disclosure. Computing apparatus 200 may be configured to implement any components that perform one or more functions disclosed in the present disclosure. For example, the computing apparatus 200 may implement any component of the ETA determination system 100 as described herein. In FIGs. 1-2, only one such computer device is shown purely for convenience purposes. One of ordinary skill in the art would understand at the time of filing of this application that the computer functions relating to the service as described herein may be implemented in a distributed fashion on a number of similar platforms, to distribute the processing load.

The computing apparatus 200, for example, may include COM ports 250 connected to and from a network connected thereto to facilitate data communications. The computing apparatus 200 may also include a processor (e.g., the processor 220) , in the form of one or more processors (e.g., logic circuits) , for executing program instructions. For example, the processor 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 exemplary computing apparatus may include the internal communication bus 210, program storage and data storage of different forms including, for example, a disk 270, and a read only memory (ROM) 230, or a random access memory (RAM) 240, for various data files to be processed and/or transmitted by the computing apparatus. The exemplary computing apparatus 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 apparatus 200 also includes an I/O component 260, supporting input/output between the computer and other components. The computing apparatus 200 may also receive programming and data via network communications.

Merely for illustration, only one processor and/or processor is illustrated in FIG. 2. Multiple CPUs and/or processors are also contemplated; thus operations and/or method steps performed by one CPU and/or processor as described in the present disclosure may also be jointly or separately performed by the multiple CPUs and/or processors. For example, if in the present disclosure the CPU and/or processor of the computing apparatus 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 apparatus 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 block diagram illustrating exemplary hardware and/or software components of an exemplary requestor terminal according to some embodiments of the present disclosure. The information provider 130 or the communication platform 140 may be implemented on the mobile device 300 according to some embodiments of the present disclosure. As illustrated in FIG. 3, the mobile device 300 may include a communication module 310, a display 320, a graphic processing unit (GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory 360, and a storage 390. The CPU 340 may include interface circuits and processing circuits similar to the processor 220. 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, a mobile operating system 370 (e.g., iOS ^TM, Android ^TM, Windows Phone ^TM, etc. ) 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 apps for receiving and rendering information relating to a service request or other information from the ETA determination system on the mobile device 300. User interactions with the information stream may be achieved via the I/O devices 350 and provided to the processing device112 and/or other components of the ETA determination system 100 via the network 150.

In order to implement various modules, units and their functions described above, a computer hardware platform may be used as hardware platforms of one or more elements (e.g., a component of the server 110 described in FIG. 1) . Since these hardware elements, operating systems, and program languages are common, it may be assumed that persons skilled in the art may be familiar with these techniques and they may be able to provide information required in the data classification according to the techniques described in the present disclosure. A computer with a user interface may be used as a personal computer (PC) , or other types of workstations or terminal devices. After being properly programmed, a computer with a user interface may be used as a server. It may be considered that those skilled in the art may also be familiar with such structures, programs, or general operations of this type of computer device. Thus, additional explanations are not described for the figures.

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 an acquisition module 410, a processing module 420, an I/O module 430, and a communication module 440. The modules may be hardware circuits of at least part of the processing device 112. The modules may also be implemented as an application or set of instructions read and executed by the processing device 112. Further, the modules may be any combination of the hardware circuits and the application/instructions. For example, the modules may be the part of the processing device 112 when the processing device 112 is executing the application/set of instructions.

The acquisition module 410 may acquire data from one or more components in the ETA determination system 100 (e.g., the user terminal 130, the driver terminal 140, the storage device 150, etc. ) . In some embodiments, the acquired data may relate to a trip. Merely by ways of example, the acquired data may include geographic location information, time information, driver information, services requests, machine learning models, user instructions, programs, algorithms, or the like, or a combination thereof. Merely for illustration purposes, data related to geographic location information may include an origin and a destination. Data related to time information may include a day and a time of the trip. Data related to driver information may include a driver identification (ID) . For example, the acquisition module 410 may acquire data, such as a current request location, a destination from a requestor via the user terminal 130. A detailed illustration of the use of such data will be presented hereinafter.

The processing module 420 may process data related to ETA determination. The processing module 420 may obtain or receive data and/or information from the acquisition module 410, the I/O module 430, and/or any storage devices capable of storing data (e.g., the storage device 130, or an external data source) . In some embodiments, the processing module 420 may obtain travel related information including the geographic location information, the time information, and/or the driver information associated with a trip, and determine an ETA for the trip based on the travel related information.

The processing module 420 may include a hardware processor, such as a microcontroller, a microprocessor, a reduced instruction set computer (RISC) , an application specific integrated circuits (ASICs) , an application-specific instruction-set processor (ASIP) , a central processing unit (CPU) , a graphics processing unit (GPU) , a physics processing unit (PPU) , a microcontroller unit, a digital signal processor (DSP) , a field programmable gate array (FPGA) , an advanced RISC machine (ARM) , a programmable logic device (PLD) , any circuit or processor capable of executing one or more functions, or the like, or any combinations thereof.

The I/O module 430 may input or output signals, data or information. For example, the I/O module 430 may output an ETA to a user (e.g., a passenger) . In some embodiments, the I/O module 430 may include an input device and an output device. Exemplary input device may include a keyboard, a mouse, a touch screen, a microphone, or the like, or a combination thereof. Exemplary output device may include a display device, a loudspeaker, a printer, a projector, or the like, or a combination thereof. Exemplary display device may include a liquid crystal display (LCD) , a light-emitting diode (LED) -based display, a flat panel display, a curved screen, a television device, a cathode ray tube (CRT) , or the like, or a combination thereof.

The communication module 440 may be connected to a network (e.g., the network 120) to facilitate data communications. The communication module 440 may establish connections between the processing engine 112 and the user terminal 130, the driver terminal 140, and/or the storage device 150. For example, the communication module 440 may send the determined ETA of a trip to the user terminal 130 and the driver terminal 140. The connection may be a wired connection, a wireless connection, any other communication connection that can enable data transmission and/or reception, and/or any combination of these connections. The wired connection may include, for example, an electrical cable, an optical cable, a telephone wire, or the like, or any combination thereof. The wireless connection may include, for example, a Bluetooth ^TM link, a Wi-Fi ^TM link, a WiMax ^TM link, a WLAN link, a ZigBee ^TM link, a mobile network link (e.g., 3G, 4G, 5G, etc. ) , or the like, or any combination thereof. In some embodiments, the communication module 440 may be and/or include a standardized communication port, such as RS232, RS485, etc.

It should be noted that the above description of the processing device 112 is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. For example, the processing device 112 may further include a storage module facilitating data storage. However, those variations and modifications do not depart from the scope of the present disclosure.

FIG. 5 is block diagram illustrating an exemplary processing module according to some embodiments of the present disclosure. The processing device 112 may include feature determination unit 510, a model training unit 520, and an ETA determination unit 530. The units may be hardware circuits of at least part of the processing device 112. The units may also be implemented as an application or set of instructions read and executed by the processing device 112. Further, the units may be any combination of the hardware circuits and the application/instructions. For example, the units may be the part of the processing device 112 when the processing device 112 is executing the application/set of instructions.

The feature determination unit 510 may determine one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information regarding the trip, and determine the ETA of the trip based on the trained machine learning model and the determined features. Merely for illustration purposes, the feature determination unit 510 may determine features including a longitude of the origin, a latitude of the origin, a longitude of the destination, a latitude of the destination, a link identification (ID) of the origin, a link ID of the destination, and a spherical distance between the origin and the destination based on the origin and the destination. The feature determination unit 510 may also determine features including a day and a time of the trip based on the time information. The feature determination unit 510 may further determine features including a driver ID based on the driver information.

The model training unit 520 may train a model for determining an ETA of a trip. The model may be or include a machine learning model. Exemplary machine learning models may include multiple layer perceptron (MLP) model, a gradient boosting decision tree (GBDT) model, an extreme gradient boosting (XGB) model, a logistic regression model, and a factorization machine (FM) model, or the like, or any combination thereof. In some embodiments, the model may be selected from the group consisting of a multiple layer perceptron (MLP) model, a gradient boosting decision tree (GBDT) model, an extreme gradient boosting (XGB) model, a logistic regression model, and a factorization machine (FM) model. The model training unit 520 may train the model with training datasets related to the ETA in historical trips of one or more users. During the training process of the model, one or more parameters (e.g., weights, a bias, a regularization item, a mini-batch size, etc. ) of the model may be updated. The training process (e.g., an iterative process) may terminate when a preset condition is satisfied. For example, the training process may terminate when a convergence of a loss function (e.g., a mean absolute percentage error (MAPE) loss) of the model is reached (e.g., the loss being smaller than equal to 0) .

The ETA determination unit 530 may determine an ETA of the trip based on the trained model and the origin, the destination, the time information, and the driver information regarding the trip. In some embodiments, the ETA determination unit 530 may determine the ETA of a trip by inputting the determined features into the trained model. In some embodiments, at least one of the features may be pre-processed (e.g., to generate a vector with a preset dimension) before it is input into the trained model. In some embodiments, the determined features may include discrete features and continuous features. As used herein, the discrete features refer to features with discretized values. For example, the longitude and the latitude of the origin may be discretized by dividing each of meridian lines and latitude lines on a map including the origin and the destination into a plurality of bins. The continuous features refer to features with continuous values. For example, the spherical distance between the origin and the destination may be a continuous feature. In some embodiments, the ETA determination unit 530 may pre-process the discrete features using an embedding technique. In some embodiments, the ETA determination unit 530 may pre-process the continuous features based on mean values and standard deviations associated with the continuous features.

FIG. 6 is a flowchart illustrating an exemplary process for determining an ETA for a trip according to some embodiments of the present disclosure. In some embodiments, the process 600 shown in FIG. 6 may be implemented in the ETA determination system 100 illustrated in FIG. 1. For example, at least a part of the process 600 may be stored in a storage device (e.g., the DISK 270 of the computing device 200) as a form of instructions, and invoked and/or executed by the server 110 (e.g., the processor 220 of the computing device 200, one or more modules illustrated in FIG. 4, or one or more units illustrated in FIG. 5) . In some embodiments, a part of the process 600 may be implemented on a terminal device. The operations of the illustrated process 600 presented below are intended to be illustrative. In some embodiments, the process 600 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process 600 as illustrated in FIG. 6 and described below is not intended to be limiting.

In 602, an origin, a destination, time information, and driver information regarding a trip may be obtained. In some embodiments, the origin, the destination, the time information, and the driver information regarding the trip may be obtained by the acquisition module 410. For example, the acquisition module 410 may obtain the origin, the destination, and the time information from the user terminal 130 over the network 120. As another example, the acquisition module 410 may obtain the driver information from a storage device (e.g., the storage device 150) or the driver terminal 140.

The origin regarding the trip refers to a start location of the trip. In some embodiments, the origin may be a current location of a user (e.g., a driver or a passenger on a vehicle) . In some embodiments, the current location of the user may be determined by the user terminal 130 or the driver terminal 140 using the positioning device 170. In some embodiments, the user terminal 130 or the driver terminal 140 may be mobile phones with built-in global positioning system (GPS) modules, and the current location of the user may be determined by the built-in GPS modules. The destination regarding the trip refers to an end location of the trip. In some embodiments, the origin and/or the destination regarding the trip may be determined based on a digital map. For example, a user may plan his/her trip from an origin to a destination through operations like inputting, searching, and/or selecting on a map.

The time information refers to a departure time of the user from the origin to the destination. In some embodiments, the time information may include a day (e.g., Sunday, Tuesday, etc. ) and a specific time in a day. The driver information refers to information of a driver of a vehicle that serves as a means of transportation in the trip. In some embodiments, the driver information may include a driver ID. In some embodiments, the driver ID may be a series of numbers. In some embodiments, the driver ID may be a combination of numbers and alphabets. In some embodiments, the driver ID may correspond to registration information and/or historical driving data of the driver.

In 604, a trained model may be obtained. The model may be trained by, for example, the model training unit 520. In some embodiments, the model used to determine the ETA for the trip may be or include a machine learning model. Exemplary machine learning models may include multiple layer perceptron (MLP) model, a gradient boosting decision tree (GBDT) model, an extreme gradient boosting (XGB) model, a logistic regression model, and a factorization machine (FM) model, or the like, or any combination thereof. In some embodiments, the model may be selected from the group consisting of a multiple layer perceptron (MLP) model, a gradient boosting decision tree (GBDT) model, an extreme gradient boosting (XGB) model, a logistic regression model, and a factorization machine (FM) model. In some embodiments, the model may be an MLP model.

Merely for illustration purposes, the model used to determine an ETA may be trained with training datasets related to the ETA in historical trips of one or more users. During the training process of the model, one or more parameters (e.g., weights, a bias, a regularization item, a mini-batch size, etc. ) of the model may be updated. The training process (e.g., an iterative process) may terminate when a preset condition is satisfied. For example, the training process may terminate when a convergence of a loss function (e.g., a mean absolute percentage error (MAPE) loss) of the model is reached (e.g., the loss being smaller than or equal to 0) . As another example, the training process may terminate when a certain number or count (e.g., 100, 300, etc. ) of iterations are performed.

In 606, an ETA of the trip may be determined based on the trained model and the origin, the destination, the time information, and the driver information regarding the trip. In some embodiments, the ETA may be determined by the ETA determination unit 530.

In some embodiments, the feature determination unit 510 may determine one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information regarding the trip, and determine the ETA of the trip based on the trained machine learning model and the determined features. Merely for illustration purposes, the feature determination unit 510 may determine features including a longitude of the origin, a latitude of the origin, a longitude of the destination, a latitude of the destination, a link identification (ID) of the origin, a link ID of the destination, and a spherical distance between the origin and the destination based on the origin and the destination. The feature determination unit 510 may also determine features including a day and a time of the trip based on the time information. The feature determination unit 510 may further determine features including a driver ID based on the driver information. Details regarding the determination of the one or more features may be described elsewhere in the present disclosure, for example, FIG. 7 and the descriptions thereof.

In some embodiments, the ETA determination unit 530 may determine the ETA of a trip by inputting the determined features into the trained model. In some embodiments, at least one of the features may be pre-processed (e.g., to generate a vector with a preset dimension) before it is input into the trained model. In some embodiments, the determined features may include discrete features and continuous features. As used herein, the discrete features refer to features with discretized values. For example, the longitude and the latitude of the origin may be discretized by dividing each of meridian lines and latitude lines on a map including the origin and the destination into a plurality of bins. The continuous features refer to features with continuous values. For example, the spherical distance between the origin and the destination may be a continuous feature. In some embodiments, the discrete features may be pre-processed using an embedding technique. For example, a feature represented by a vector with a certain dimension smaller than the preset dimension may be expanded (e.g., by filling 0 to each vacant dimension) to generate a vector with the preset dimension.

In some embodiments, the continuous features may be pre-processed based on mean values and standard deviations associated with the continuous features. For example, the spherical distance between the origin and the destination may be pre-processed by determining a difference between the spherical distance and a mean value associated with the spherical distance, then determining a quotient of the difference to a standard deviation associated with the spherical distance.

Since the process for determining the ETA does not include any operation for determining routes between the origin and the destination of the trip, the determined ETA may be independent of routes between the origin and the destination. Thus, the amount of computation and the time for determining the ETA according to the process 600 may be greatly reduced comparing with methods for determining an ETA based on routes between the origin and the destination.

It should be noted that the above description is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. For example, the determined ETA may be transmitted to a terminal device (e.g., the user terminal 130 or the driver terminal) for display. However, these variations and modifications fall in the scope of the present disclosure.

FIG. 7 is a flowchart illustrating an exemplary process for determining one or more features of the trip according to some embodiments of the present disclosure. In some embodiments, at least a portion of the operation 606 of the process 600 may be performed according to the process 700 illustrated in FIG. 7. In some embodiments, the process 700 shown in FIG. 7 may be implemented in the ETA determination system 100 illustrated in FIG. 1. For example, at least a part of the process 700 may be stored in a storage medium (e.g., the DISK 270 of the computing device 200) as a form of instructions, and invoked and/or executed by the server 110 (e.g., the processor 220 of the computing device 200, or one or more modules illustrated in FIG. 4) . In some embodiments, a part of the process 700 may be implemented on a terminal device. The operations of the illustrated process 700 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 discussed. Additionally, the order in which the operations of the process 700 as illustrated in FIG. 7 and described below is not intended to be limiting.

In 702, a longitude of the origin, a latitude of the origin, a longitude of the destination, a latitude of the destination, a link identification (ID) of the origin, a link ID of the destination, and a spherical distance between the origin and the destination may be determined based on the origin and the destination regarding the trip. In some embodiments, the longitude of the origin, the latitude of the origin, the longitude of the destination, the latitude of the destination, the link identification (ID) of the origin, the link ID of the destination, and the spherical distance between the origin and the destination may be determined by the feature determination unit 510.

The feature determination unit 510 may determine geographical coordinates of the origin and the destination after the ETA determination system 100 obtains the origin and the destination of the trip. In some embodiments, the geographical coordinates of the origin may include the longitude of the origin and the latitude of the origin. The geographical coordinates of the destination may include the longitude of the destination and the latitude of the destination. The longitude of the origin, the latitude of the origin, the longitude of the destination, and/or the latitude of the destination may be real number. In some embodiments, the feature determination unit 510 may discretize the longitude of the origin, the latitude of the origin, the longitude of the destination, and the latitude of the destination by dividing each of meridian lines and latitude lines on a map including the origin and the destination into a plurality of bins. As used herein, a bin refers a segment of a meridian line or a segment of a latitude line in the map. In some embodiments, each bin may be encoded with a number (e.g., an integer) . The feature determination unit 510 may determine the longitude of the origin, the latitude of the origin, the longitude of the destination, and the latitude of the destination based on the encoded bins and the origin and the destination regarding the trip. Taking the longitude of the origin as an example, if the longitude of the origin is in a bin of a meridian line, the feature determination unit 510 may determine the longitude the origin as the number encoding the bin of the meridian line. In some embodiments, the feature determination unit 510 may determine the latitude of the origin, the longitude of the destination, and/or the latitude of the destination by performing the same operations or similar operations as the operation for determining the longitude of the origin.

In some embodiments, the digital map may represent a road network including the origin and the destination. The road network may include a plurality of nodes. As used herein, a node refers to an intersection or a connection of roads in the road network. A road segment that connects two adjacent nodes in a road network may be defined as a link. In some embodiments, each of the links in the road network may be labeled or encoded, for example, with a certain number (also referred to as “link identification (ID) ” ) . In some embodiments, the feature determination unit 510 may determine a link ID of the origin and a link ID of the destination. Both the link ID the origin and the link ID of the destination may be discrete features.

In 704, a day and a time of the trip may be determined based on the time information regarding the trip. In some embodiments, the day and the time of the trip may be determined by the feature determination unit 510.

The day of the trip may be a discrete feature. In some embodiments, the day of the trip may be determined in terms of a week. For example, the day of the trip may be one of Monday Tuesday, Wednesday, Thursday, Friday, Saturday, and Sunday. In some embodiments, each day in a week (from Monday to Sunday) may be encoded with a specific number (e.g., an integer from 1 to 7) . In some embodiments, the feature determination unit 510 may determine the day of the trip being a certain day in a week as a specific number corresponding to the certain day in the week. Merely for illustration purposes, if the day of the trip is Friday, the feature determination unit 510 may determine the day of the trip as 5.

The time of the trip refers to a specific time point in the day of the trip when the user starts his/her trip. In some embodiments, the time of the trip may be discretized by dividing the day from 0 o’clock to 24 0’clock into a plurality of time durations. For example, the day of the trip from 0 o’clock to 24 0’clock may be divided into five time durations like 0 o’clock to 8 0’clock, 8 o’clock to 12 0’clock, 12 o’clock to 17 0’clock, 17 o’clock to 20 0’clock, and 20 o’clock to 24 0’clock. As another example, the day of the trip from 0 o’clock to 24 0’clock may be divided into a plurality of time durations by setting an empirical length (e.g., 5 minutes, 10 minutes, etc. ) of each time duration. In some embodiments, each of the plurality of time durations may be encoded with a specific number. In some embodiments, the time of the trip in a certain time duration may be determined as a specific number corresponding to the certain time duration.

Merely for illustration purposes, the day of the trip from 0 o’clock to 24 0’clock may be divided into 288 time durations by setting the length of each time duration as 5 minutes. The 288 time durations may be encoded with integers from 1 to 288. If the time of the trip falls in a time duration encoded as 160, the feature determination unit 510 may determine the time of the trip as 160.

In 706, a driver ID may be determined based on the driver information regarding the trip. In some embodiments, the ETA determination system 100 may determine a plurality of drivers based on the driver information, and encode each driver associated with the ETA determination system 100 with a driver ID. The driver ID may be a series of numbers (e.g., an integer) . The driver ID may be a discrete feature. In some embodiments, the driver ID may relate to registration information and/or historical driving data of the driver. The registration information may include basic information of the driver (e.g., the age of the driver, the gender of the driver, etc. ) , basic information of the vehicle (e.g., an actual service life of the vehicle, a total driving distance displayed on an odograph of the vehicle, information of an annual inspection) , or the like, or a combination thereof. The historical driving data of the driver may include an accident rate, an overspeed rate, etc. In some embodiment, the registration information and the historical driving data of the driver may be stored in a storage device (e.g., the storage device 150, a cloud data center, etc. ) . The ETA determination system 100 may retrieve the registration information and/or historical driving data corresponding to the driver ID from the storage device.

FIG. 8 is a schematic diagram illustrating the determination of an ETA for a trip according to some embodiments of the present disclosure. The ETA determination system 100 may obtain data, for example, from the user terminal 130, the driver terminal 140, or a storage device (e.g., a cloud data center) . The obtained data may include an origin, a destination, time information, and driver information regarding a trip. The feature determination unit 510 may determine one or more features of the trip based on at least one of the origin, the destination, the time information, and/or the driver information regarding the trip. The one or more features may include a longitude of the origin, a latitude of the origin, a longitude of the destination, a latitude of the destination, a link ID of the origin, a link ID of the destination, a spherical distance between the origin and the destination, a day and a time of the trip, and a driver ID. Then feature determination unit 510 may determine the ETA of the trip by inputting the features into a trained model (e.g., an MLP model) . The MLP model may output the ETA of the trip according to a loss function, for example, a mean absolute percentage error (MAPE) loss. The determined ETA of the trip may be transmitted to the user terminal 130 and/or the driver terminal 140 for display.

FIG. 9 illustrates an exemplary structure of an MLP model 900 according to some embodiments of the present disclosure. The illustrated MLP model 900 may include an input layer 901, an output layer 904, and at least one hidden layer (e.g., hidden layers 902 and 903) between the input layer 901 and the output layer 903. Each of the at least one hidden layer may include a plurality of nodes. Each node may be a neuron including an activation function. In some embodiments, the activation function may be a linear function or a non-linear function (e.g., a logistic sigmoid function) . In some embodiments, the MLP model 900 may input the one or more features determined based on the origin, the destination, the time information, and the driver information into the at least one hidden layer via the input layer 901. Each of the features may be input into the activation function of a hidden layer, after the features are processed according to one or more processing operations (e.g., multiplied by a weight and/or added with a bias) . The output of the activation function of the hidden layer may be input into a subsequent hidden layer. In some embodiments, the MLP model 900 may output, via the output layer 904, the ETA of the trip after a last hidden layer of the at least one hidden layer completes the one or more processing operations.

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

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

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

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

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET, Python or the like, conventional procedural programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN) , or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) or in a cloud computing environment or offered as a service such as a Software as a Service (SaaS) .

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

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

Claims

A method implemented on a computing device having at least one processor and at least one computer-readable storage medium for determining an estimated time of arrival (ETA) in a trip, the method comprising:

obtaining an origin, a destination, time information, and driver information regarding the trip;

obtaining a trained machine learning model; and

determining an ETA of the trip based on the trained machine learning model and the origin, the destination, the time information, and the driver information regarding the trip, wherein the ETA of the trip is independent of routes between the origin and the destination.
The method of claim 1, wherein determining the ETA of the trip based on the trained machine learning model and the origin, the destination, the time information, and the driver information regarding the trip includes:

determining one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information; and

determining the ETA of the trip based on the trained machine learning model and the determined features of the trip.
The method of claim 2, wherein determining the one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information includes:

determining a longitude of the origin, a latitude of the origin, a longitude of the destination, a latitude of the destination, a link identification (ID) of the origin, a link ID of the destination, and a spherical distance between the origin and the destination based on the origin and the destination regarding the trip.
The method of claim 2, wherein determining the one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information includes:

determining a day and a time of the trip based on the time information regarding the trip.
The method of claim 2, wherein determining the one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information includes:

determining a driver ID based on the driver information regarding the trip.
The method of claim 3, wherein determining the longitude of the origin, the latitude of the origin, the longitude of the destination, and the latitude of the destination based on the origin and the destination regarding the trip includes:

dividing each of meridian lines and latitude lines on a map including the origin and the destination into a plurality of bins;

encoding each bin with an integer; and

determining the longitude of the origin, the latitude of the origin, the longitude of the destination, and the latitude of the destination based on the encoded bins and the origin and the destination regarding the trip.
The method of claim 3, wherein determining the link ID of the origin and the link ID of the destination based on the origin and the destination regarding the trip includes:

determining links on a map including the origin and the destination;

encoding each of the links with an ID number; and

determining the link ID of the origin and the link ID of the destination based on the origin and the destination regarding the trip and the ID number of each link.
The method of claim 4, wherein determining the time of the trip based on the time information regarding the trip includes:

dividing a day into a plurality of time durations; and

determining the time of the trip based on the plurality of durations and the time information regarding the trip.
The method of claim 2, wherein the one or more features includes discrete features and continuous features.
The method of claim 9, further including:

processing the discrete features using an embedding technique; and

processing the continuous features based on mean values and standard deviations associated with the continuous features.
The method of claim 1, wherein the trained machine learning model is selected from the group consisting of a multiple layer perceptron (MLP) model, a gradient boosting decision tree (GBDT) model, an extreme gradient boosting (XGB) model, a logistic regression model, and a factorization machine (FM) model.
The method of claim 1, wherein the trained machine learning model is trained using a model training method, the model training method including:

obtaining a machine learning model and training datasets; and

training the machine learning model based on the training datasets and a loss function.
A system for determining an estimated time of arrival (ETA) in a trip, comprising:

at least one storage medium including a set of instructions; and

at least one processor configured to communicate with the at least one storage medium, wherein when executing the set of instructions, the at least one processor is directed to:

obtain an origin, a destination, time information, and driver information regarding the trip;

obtain a trained machine learning model; and

determine an ETA of the trip based on the trained machine learning model and the origin, the destination, the time information, and the driver information regarding the trip, wherein the ETA of the trip is independent of routes between the origin and the destination.
The system of claim 13, wherein to determine the ETA of the trip based on the trained machine learning model and the origin, the destination, the time information, and the driver information regarding the trip, the at least one processor is directed to:

determine one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information; and

determine the ETA of the trip based on the trained machine learning model and the determined features of the trip.
The system of claim 14, wherein to determine the one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information, the at least one processor is directed to:

determine a longitude of the origin, a latitude of the origin, a longitude of the destination, a latitude of the destination, a link identification (ID) of the origin, a link ID of the destination, and a spherical distance between the origin and the destination based on the origin and the destination regarding the trip.
The system of claim 14, wherein to determine the one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information, the at least one processor is directed to:

determine a day and a time of the trip based on the time information regarding the trip.
The system of claim 14, wherein to determine the one or more features of the trip based on at least one of the origin, the destination, the time information, and the driver information, the at least one processor is directed to:

determine a driver ID based on the driver information regarding the trip.
The system of claim 15, wherein to determine the longitude of the origin, the latitude of the origin, the longitude of the destination, and the latitude of the destination based on the origin and the destination regarding the trip, the at least one processor is directed to:

divide each of meridian lines and latitude lines on a map including the origin and the destination into a plurality of bins;

encode each bin with an integer; and

determine the longitude of the origin, the latitude of the origin, the longitude of the destination, and the latitude of the destination based on the encoded bins and the origin and the destination regarding the trip.
The system of claim 15, wherein to determine the link ID of the origin and the link ID of the destination based on the origin and the destination regarding the trip, the at least one processor is directed to:

determine links on a map including the origin and the destination;

encode each of the links with an ID number; and

determine the link ID of the origin and the link ID of the destination based on the origin and the destination regarding the trip and the ID number of each link.
The system of claim 16, wherein to determine the time of the trip based on the time information regarding the trip, the at least one processor is directed to:

divide a day into a plurality of time durations; and

determine the time of the trip based on the plurality of durations and the time information regarding the trip.
The system of claim 14, wherein the one or more features includes discrete features and continuous features.
The system of claim 21, the at least one processor is further directed to:

process the discrete features using an embedding technique; and

process the continuous features based on mean values and standard deviations associated with the continuous features.
The system of claim 13, wherein the trained machine learning model is selected from the group consisting of a multiple layer perceptron (MLP) model, a gradient boosting decision tree (GBDT) model, an extreme gradient boosting (XGB) model, a logistic regression model, and a factorization machine (FM) model.
The system of claim 13, wherein the trained machine learning model is trained using a model training method, the model training method including:

obtaining a machine learning model and training datasets; and

training the machine learning model based on the training datasets and a loss function.
A non-transitory computer readable medium, comprising at least one set of instructions for determining an estimated time of arrival (ETA) in a trip, wherein when executed by at least one processor of a computing device, the at least one set of instructions causes the computing device to perform a method, the method comprising:

obtaining an origin, a destination, time information, and driver information regarding the trip;

obtaining a trained machine learning model; and

determining an ETA of the trip based on the trained machine learning model and the origin, the destination, the time information, and the driver information regarding the trip, wherein the ETA of the trip is independent of routes between the origin and the destination.