Method, device, electronic device and readable storage medium for calculating estimated arrival time

Abstract

The present disclosure provides a method, device, electronic device and readable storage medium for calculating an estimated arrival time, and relates to the field of intelligent transportation technology. The method for calculating the estimated arrival time includes: obtaining navigation time information corresponding to the navigation request at the current moment, the navigation time information includes the estimated arrival time at the beginning of navigation and the actual arrival time at the end of navigation; when it is determined that the estimated arrival time and the actual arrival time meet the first preset condition, collecting multiple navigation tracks in real time; according to the multiple navigation tracks, obtaining current calculation parameters corresponding to different navigation attributes; using the current calculation parameters, calculating the estimated arrival time of subsequent navigation requests initiated at subsequent moments of the current moment. This embodiment can update the calculation parameters in a timely manner when the traffic pattern changes, thereby accelerating the iteration speed of the calculation parameters and improving the calculation accuracy of the estimated arrival time.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to the field of intelligent transportation technology. Provided are a method, device, electronic device, and readable storage medium for calculating an estimated arrival time. Background Art

[0002] An important dimension in driving navigation is the estimated arrival time. The accuracy of the estimated arrival time during navigation will affect the order of different routes, the rationality of the user's estimated departure time, and the user's driving navigation experience. The existing technology only calculates the estimated arrival time based on the historical travel time of each link in the navigation trajectory, and cannot respond in time when the traffic pattern changes, thereby reducing the calculation accuracy of the estimated arrival time. Summary of the invention

[0003] According to a first aspect of the present disclosure, a method for calculating an estimated arrival time is provided, comprising: obtaining navigation time information of a navigation request corresponding to a current moment, the navigation time information including an estimated arrival time when navigation starts and an actual arrival time when navigation ends; collecting a plurality of navigation trajectories in real time when it is determined that the estimated arrival time and the actual arrival time satisfy a first preset condition; obtaining current calculation parameters corresponding to different navigation attributes based on the plurality of navigation trajectories; and using the current calculation parameters, calculating an estimated arrival time of a subsequent navigation request initiated at a subsequent moment of the current moment.

[0004] According to a second aspect of the present disclosure, there is provided a device for calculating an estimated arrival time, comprising: an acquisition unit, for acquiring navigation time information corresponding to a navigation request at a current moment, wherein the navigation time information includes an estimated arrival time at the start of navigation and an actual arrival time at the end of navigation; a collection unit, for collecting a plurality of navigation trajectories in real time when it is determined that the estimated arrival time and the actual arrival time satisfy a first preset condition; a processing unit, for obtaining current calculation parameters corresponding to different navigation attributes based on the plurality of navigation trajectories; and a calculation unit, for calculating the estimated arrival time of a subsequent navigation request initiated at a subsequent moment of the current moment using the current calculation parameters.

[0005] According to a third aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method as described above.

[0006] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method as described above.

[0007] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method as described above when executed by a processor.

[0008] It can be seen from the above technical solution that this embodiment determines whether the traffic pattern has changed through the navigation time information of the navigation request, and when it is determined that the traffic pattern has changed, the calculation parameters for calculating the estimated arrival time are updated in a timely manner, thereby speeding up the iteration speed of the calculation parameters and improving the calculation accuracy of the estimated arrival time.

[0009] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0011] Figure 1 is a schematic diagram according to a first embodiment of the present disclosure;

[0012] Figure 2 is a schematic diagram according to a second embodiment of the present disclosure;

[0013] Figure 3 The block diagram of an electronic device used to implement the method for calculating the estimated arrival time according to the embodiment of the present disclosure. DETAILED DESCRIPTION

[0014] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and mechanisms is omitted in the following description.

[0015] The method for calculating the estimated arrival time of the present embodiment first obtains the navigation time information of the navigation request corresponding to the current moment, then after determining that the estimated arrival time in the navigation time information and the actual arrival time satisfy a first preset condition, collects multiple navigation tracks in real time, and then obtains current calculation parameters corresponding to different navigation attributes based on the navigation tracks collected in real time, and finally uses the obtained current calculation parameters to calculate the estimated arrival time of the navigation request initiated at a subsequent moment. Since the present embodiment determines whether the traffic pattern has changed through the navigation time information, when it is determined that the traffic pattern has changed, the calculation parameters for calculating the estimated arrival time are updated in a timely manner, thereby accelerating the iteration speed of the calculation parameters and improving the calculation accuracy of the estimated arrival time.

[0016] Figure 1 Schematic diagram of the first embodiment of the present disclosure. Figure 1 As shown, the method for calculating the estimated arrival time in this embodiment may specifically include the following steps:

[0017] S101. Obtain navigation time information corresponding to a navigation request at a current moment, wherein the navigation time information includes an estimated arrival time when navigation starts and an actual arrival time when navigation ends.

[0018] In this embodiment, when S101 is executed, the navigation request corresponding to the current moment may be at least one of a navigation request completed at the current moment and a navigation request being performed at the current moment.

[0019] Preferably, in order to more accurately determine whether the traffic pattern has changed based on the navigation time information, when executing S101 to obtain the navigation time information of the navigation request corresponding to the current moment, this embodiment will also obtain the navigation time information of the navigation requests completed and in progress at the current moment.

[0020] S102: When it is determined that the estimated arrival time and the actual arrival time satisfy a first preset condition, collect multiple navigation tracks in real time.

[0021] In this embodiment, after executing S101 to obtain navigation time information corresponding to the navigation request at the current moment, executing S102 to collect multiple navigation tracks in real time when it is determined that the obtained estimated arrival time and the actual arrival time meet a first preset condition.

[0022] In this embodiment, when executing S102 to determine whether the acquired estimated arrival time and actual arrival time meet the first preset condition, it can be determined that the absolute value of the difference between the estimated arrival time and the actual arrival time exceeds the first preset threshold, or it can be determined that the number of times the absolute value of the difference between the estimated arrival time and the actual arrival time exceeds the first preset threshold exceeds the second preset threshold. This embodiment is not limited to this.

[0023] That is, this embodiment determines whether the traffic pattern at the current moment has changed based on the estimated arrival time and the actual arrival time of the navigation request, and then collects multiple navigation tracks in real time after determining that the traffic pattern at the current moment has changed.

[0024] If this embodiment determines in S102 that the estimated arrival time and the actual arrival time do not meet the first preset condition, it can jump to the step of obtaining the navigation time information of the navigation request corresponding to the next moment, and continue to do so to determine whether it is necessary to collect multiple navigation trajectories in real time.

[0025] In addition, if this embodiment executes S102 to determine that the estimated arrival time and the actual arrival time do not meet the first preset condition, it may also include the following content: obtaining the update time of the existing calculation parameters; and when it is determined that the obtained update time meets the second preset condition, collecting multiple trajectory data in real time.

[0026] In this embodiment, when S102 is executed to determine that the acquired update time satisfies the second preset condition, it may be determined that the time interval between the acquired update time and the current time exceeds a third preset threshold.

[0027] That is to say, when it is determined that the traffic pattern has not changed and the calculation parameters have not been updated for a long time, this embodiment can obtain the current calculation parameters through multiple trajectory data collected in real time, thereby updating the calculation parameters and improving the calculation accuracy of the estimated arrival time.

[0028] It can be understood that after the server performs route planning based on the navigation request initiated by the user, the server will store the navigation trajectories corresponding to different navigation requests (including data such as the planned road sections and the actual travel time of each road section) in the database. Therefore, when executing S102 to determine that the estimated arrival time and the actual arrival time meet the first preset condition or the update time meets the second preset condition, this embodiment collects multiple navigation trajectories from the database in real time.

[0029] When executing S102 to collect multiple navigation tracks in real time, this embodiment may collect multiple navigation tracks located in a specific area in real time, or may collect multiple navigation tracks located in all areas in real time; wherein, the multiple navigation tracks collected in real time by executing S102 in this embodiment correspond to the ongoing navigation request.

[0030] S103: Obtain current calculation parameters corresponding to different navigation attributes according to the multiple navigation tracks.

[0031] After executing S102 to collect multiple navigation data in real time, this embodiment executes S103 to obtain current calculation parameters corresponding to different navigation attributes according to the multiple navigation tracks collected in real time.

[0032] Specifically, when executing S103 in this embodiment to obtain current calculation parameters corresponding to different navigation attributes based on multiple navigation trajectories collected in real time, an optional implementation method that can be used is: extracting navigation attribute data of preset types from the multiple navigation trajectories collected in real time respectively; and obtaining current calculation parameters corresponding to different navigation attributes based on the extracted navigation attribute data.

[0033] Among them, the preset type of navigation attribute data extracted by executing S103 in this embodiment includes different types of data such as the geographical location of the navigation track, the mileage of the navigation track, the end point type of the navigation track, the road type of each link in the navigation track, and the actual travel time of each link in the navigation track.

[0034] That is to say, this embodiment pre-sets the type of navigation attribute data extracted from the navigation trajectory, and then obtains current calculation parameters corresponding to different navigation attributes based on the preset type of navigation attribute data extracted from the navigation trajectory, which can improve the accuracy of the obtained current calculation parameters and thus improve the calculation accuracy of the estimated arrival time.

[0035] The current calculation parameters obtained by executing S103 in this embodiment include current calculation coefficients and current calculation deviations corresponding to different navigation attributes; the current calculation coefficients correspond to the road type attributes of each section in the navigation track, and the current calculation deviations correspond to the geographical location, mileage, end point type and other attributes of the navigation track.

[0036] When executing S103 in this embodiment to obtain current calculation parameters corresponding to different navigation attributes based on the extracted navigation attribute data, an optional implementation method that can be adopted is: for the navigation attribute data of each navigation trajectory, the navigation attribute data is input into a pre-trained parameter generation model; and the output result of the parameter generation model for each navigation attribute data is used as the current calculation parameter corresponding to the different navigation attributes.

[0037] It can be understood that the parameter generation model used in executing S103 of this embodiment is pre-trained, and can output the current calculation parameters of the navigation attributes corresponding to the navigation attribute data according to the input navigation attribute data.

[0038] This embodiment can combine the K-fold method and the fast-gbdt method to train the parameter generation model, which can enhance the performance of the parameter generation model and improve the accuracy of the generated calculation parameters.

[0039] S104: Using the current calculation parameters, calculate an estimated arrival time of a subsequent navigation request initiated at a subsequent time point of the current time point.

[0040] After executing S103 to obtain current calculation parameters corresponding to different navigation attributes, this embodiment executes S104 to use the obtained current calculation parameters to calculate the estimated arrival time of subsequent navigation requests initiated at a subsequent time of the current time.

[0041] That is to say, this embodiment replaces the existing calculation parameters with the obtained current calculation parameters, thereby completing the update of the calculation parameters, so that the calculation of the estimated arrival time at subsequent moments is completed using the latest updated current calculation parameters, thereby improving the calculation accuracy of the estimated arrival time.

[0042] When this embodiment uses the current calculation parameters during execution S104 to calculate the estimated arrival time of subsequent navigation requests initiated at a subsequent time after the current time, an optional implementation method that can be used is: according to the navigation trajectory planned by the navigation request, target navigation attributes are obtained, such as the road type of each section in the navigation trajectory, the geographical location of the navigation trajectory, the mileage, the terminal type and other attributes; the current calculation parameters corresponding to the obtained target navigation attributes are used as target calculation parameters, the target calculation parameters include a target calculation coefficient and a target calculation deviation; and the estimated arrival time of the navigation request is calculated according to the target calculation parameters and the navigation trajectory.

[0043] Among them, when executing S104, this embodiment calculates the estimated arrival time of the navigation request according to the target calculation parameters and the navigation trajectory, the target calculation parameters and the navigation trajectory can be directly input into a pre-trained time estimation model, and the output result of the time estimation model is used as the estimated arrival time of the navigation request.

[0044] In addition, when executing S104, the present embodiment calculates the estimated arrival time of the navigation request according to the target calculation parameters and the navigation trajectory, it can also perform calculations based on the target calculation parameters, the road type of each section in the navigation trajectory, and the historical travel time, and use the calculation result as the estimated arrival time of the navigation request.

[0045] For example, if the navigation path planned according to the navigation request includes road 1 and road 2, the road type of road 1 is type 2, and the road type of road 2 is type 1, if the historical travel time of road 1 is 10 minutes, and the historical travel time of road 2 is 20 minutes, if in the target calculation parameters determined by executing S104, the target calculation coefficient corresponding to type 1 is coefficient 1, and the target calculation coefficient corresponding to type 2 is coefficient 2, and the target calculation deviation is deviation 1, then the estimated arrival time of the navigation request is ((10×coefficient 1+20×coefficient 2)+deviation 1).

[0046] Through the above method provided in this embodiment, when it is determined that the traffic pattern at the current moment has changed, the calculation parameters used to calculate the estimated arrival time can be updated in a timely manner, thereby accelerating the iteration speed of the calculation parameters and improving the accuracy of calculating the estimated arrival time at subsequent moments.

[0047] Figure 2 is a schematic diagram according to the second embodiment of the present disclosure. Figure 2 As shown, the estimated arrival time calculation device 200 of this embodiment includes:

[0048] The acquisition unit 201 is used to acquire navigation time information corresponding to the navigation request at the current moment, wherein the navigation time information includes an estimated arrival time when navigation starts and an actual arrival time when navigation ends.

[0049] The navigation request corresponding to the current moment in the acquisition unit 201 may be at least one of a navigation request completed at the current moment and a navigation request being performed at the current moment.

[0050] Preferably, in order to more accurately determine whether the traffic pattern has changed based on the navigation time information, when acquiring the navigation time information corresponding to the navigation request at the current moment, the acquiring unit 201 also acquires the navigation time information of the navigation requests completed and in progress at the current moment.

[0051] The collection unit 202 is used to collect multiple navigation tracks in real time when it is determined that the estimated arrival time and the actual arrival time meet a first preset condition.

[0052] In this embodiment, after the acquisition unit 201 acquires the navigation time information corresponding to the navigation request at the current moment, the collection unit 202 collects multiple navigation tracks in real time when it is determined that the acquired estimated arrival time and the actual arrival time meet the first preset condition.

[0053] When the acquisition unit 202 determines that the acquired estimated arrival time and the actual arrival time meet the first preset condition, it may be determined that the absolute value of the difference between the estimated arrival time and the actual arrival time exceeds the first preset threshold, or it may be determined that the number of times the absolute value of the difference between the estimated arrival time and the actual arrival time exceeds the first preset threshold exceeds the second preset threshold. This embodiment is not limited to this.

[0054] That is, the collection unit 202 determines whether the traffic pattern at the current moment has changed according to the estimated arrival time and the actual arrival time of the navigation request, and then collects multiple navigation tracks in real time after determining that the traffic pattern at the current moment has changed.

[0055] If the collection unit 202 determines that the estimated arrival time and the actual arrival time do not meet the first preset condition, it can proceed to execute the step of obtaining navigation time information corresponding to the navigation request at the next moment, and continue to do so to determine whether it is necessary to collect multiple navigation trajectories in real time.

[0056] In addition, if the acquisition unit 202 determines that the estimated arrival time and the actual arrival time do not meet the first preset condition, it can also include the following content: obtaining the update time of the existing calculation parameters; and when it is determined that the obtained update time meets the second preset condition, collecting multiple trajectory data in real time.

[0057] When the acquisition unit 202 determines that the acquired update time satisfies the second preset condition, it may determine that the time interval between the acquired update time and the current time exceeds a third preset threshold.

[0058] That is to say, when it is determined that the traffic pattern has not changed and the calculation parameters have not been updated for a long time, the collection unit 202 can obtain the current calculation parameters through multiple trajectory data collected in real time, thereby updating the calculation parameters and improving the calculation accuracy of the estimated arrival time.

[0059] When collecting multiple navigation tracks in real time, the collection unit 202 may collect multiple navigation tracks in a specific area or multiple navigation tracks in all areas; wherein the multiple navigation tracks collected by the collection unit 202 in real time correspond to the ongoing navigation request.

[0060] The processing unit 203 is configured to obtain current calculation parameters corresponding to different navigation attributes according to the multiple navigation tracks.

[0061] In this embodiment, after the collection unit 202 collects a plurality of navigation data in real time, the processing unit 203 obtains current calculation parameters corresponding to different navigation attributes according to the plurality of navigation tracks collected in real time.

[0062] Specifically, when the processing unit 203 obtains the current calculation parameters corresponding to different navigation attributes based on the multiple navigation trajectories collected in real time, an optional implementation method that can be adopted is: extracting navigation attribute data of preset types from the multiple navigation trajectories collected in real time respectively; and obtaining the current calculation parameters corresponding to the different navigation attributes based on the extracted navigation attribute data.

[0063] Among them, the preset type of navigation attribute data extracted by the processing unit 203 includes different types of data such as the geographical location of the navigation track, the mileage of the navigation track, the end point type of the navigation track, the road type of each link in the navigation track, and the actual travel time of each link in the navigation track.

[0064] That is to say, the processing unit 203 pre-sets the type of navigation attribute data extracted from the navigation trajectory, and then obtains current calculation parameters corresponding to different navigation attributes based on the preset type of navigation attribute data extracted from the navigation trajectory, which can improve the accuracy of the obtained current calculation parameters and thus improve the calculation accuracy of the estimated arrival time.

[0065] The current calculation parameters obtained by the processing unit 203 include current calculation coefficients and current calculation deviations corresponding to different navigation attributes; the current calculation coefficients correspond to the road type attributes of each section in the navigation track, and the current calculation deviations correspond to the geographical location, mileage, terminal type and other attributes of the navigation track.

[0066] When the processing unit 203 obtains the current calculation parameters corresponding to different navigation attributes based on the extracted navigation attribute data, an optional implementation method that can be adopted is: for the navigation attribute data of each navigation trajectory, the navigation attribute data is input into a pre-trained parameter generation model; and the output result of the parameter generation model for each navigation attribute data is used as the current calculation parameter corresponding to the different navigation attributes.

[0067] It can be understood that the parameter generation model used by the processing unit 203 is pre-trained, and can output the current calculation parameters of the navigation attributes corresponding to the navigation attribute data according to the input navigation attribute data.

[0068] The calculation unit 204 is configured to calculate an estimated arrival time of a subsequent navigation request initiated at a subsequent time after the current time using the current calculation parameters.

[0069] In this embodiment, after the processing unit 203 obtains the current calculation parameters corresponding to different navigation attributes, the calculation unit 204 uses the obtained current calculation parameters to calculate the estimated arrival time of subsequent navigation requests initiated at a subsequent time of the current time.

[0070] That is, the calculation unit 204 replaces the existing calculation parameters with the obtained current calculation parameters, thereby completing the update of the calculation parameters, so that the calculation of the estimated arrival time at subsequent moments is completed using the latest updated current calculation parameters, thereby improving the calculation accuracy of the estimated arrival time.

[0071] When the calculation unit 204 uses the current calculation parameters to calculate the estimated arrival time of a subsequent navigation request initiated at a subsequent time after the current time, an optional implementation method that can be adopted is: obtaining the target navigation attribute according to the navigation trajectory planned by the navigation request; using the current calculation parameters corresponding to the obtained target navigation attribute as the target calculation parameters; and calculating the estimated arrival time of the navigation request based on the target calculation parameters and the navigation trajectory.

[0072] Among them, when the calculation unit 204 calculates the estimated arrival time of the navigation request based on the target calculation parameters and the navigation trajectory, it can directly input the target calculation parameters and the navigation trajectory into a pre-trained time estimation model, and use the output result of the time estimation model as the estimated arrival time of the navigation request.

[0073] In addition, when calculating the estimated arrival time of the navigation request based on the target calculation parameters and the navigation trajectory, the calculation unit 204 can also perform calculations based on the target calculation parameters, the road type of each section in the navigation trajectory and the historical travel time, and use the calculation result as the estimated arrival time of the navigation request.

[0074] In the technical solution disclosed herein, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0075] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0076] like Figure 3 , is a block diagram of an electronic device for calculating the estimated arrival time according to an embodiment of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0077] like Figure 3 As shown, the device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0078] A number of components in the device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the device 300 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0079] The computing unit 301 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 301 performs the various methods and processes described above, such as a method for calculating an estimated arrival time. For example, in some embodiments, the method for calculating an estimated arrival time may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 308.

[0080] In some embodiments, part or all of the computer program may be loaded and / or installed on the device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method for calculating the estimated arrival time described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the method for calculating the estimated arrival time in any other appropriate manner (e.g., by means of firmware).

[0081] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0082] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable computing device for estimating the arrival time, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0083] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0084] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0085] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0086] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server for a distributed system, or a server combined with a blockchain.

[0087] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0088] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for calculating an estimated arrival time, comprising: Obtaining navigation time information corresponding to the navigation request at the current moment, wherein the navigation time information includes an estimated arrival time when navigation starts and an actual arrival time when navigation ends; In the case where it is determined that the estimated arrival time and the actual arrival time satisfy a first preset condition, collecting a plurality of navigation tracks in real time, the collected navigation tracks corresponding to the ongoing navigation request; According to the plurality of navigation tracks, current calculation parameters corresponding to different navigation attributes are obtained, the current calculation parameters comprising a current calculation coefficient and a current calculation deviation, and the navigation attributes comprising at least two of a geographical location of the navigation track, a mileage of the navigation track, a terminal type of the navigation track, a road type of each section in the navigation track, and an actual travel time of each section in the navigation track; Using the current calculation parameters, calculating an estimated arrival time of a subsequent navigation request initiated at a subsequent time of the current time; The using the current calculation parameters to calculate the estimated arrival time of a subsequent navigation request initiated at a subsequent time of the current time includes: Obtaining a target navigation attribute of the at least one road segment according to the at least one road segment included in the navigation trajectory planned by the subsequent navigation request; using a current calculation parameter corresponding to the target navigation attribute of the at least one road segment as a target calculation parameter of the at least one road segment, the target calculation parameter including a target calculation coefficient and a target calculation deviation; The estimated arrival time of the subsequent navigation request is calculated based on the historical travel time of the at least one road segment, the target calculation coefficient, and the target calculation deviation.

2. The method according to claim 1, wherein: The navigation request corresponding to the current moment is at least one of a navigation request completed at the current moment or a navigation request being performed at the current moment.

3. The method according to claim 1, further comprising: After determining that the estimated arrival time and the actual arrival time do not satisfy the first preset condition, obtaining an update time of the existing calculation parameters; When it is determined that the update time satisfies the second preset condition, a plurality of trajectory data are collected in real time.

4. The method according to claim 1, wherein: The obtaining, according to the plurality of navigation tracks, current calculation parameters corresponding to different navigation attributes comprises: Extracting navigation attribute data of preset types from the plurality of navigation tracks respectively; According to the navigation attribute data, current calculation parameters corresponding to different navigation attributes are obtained.

5. The method according to claim 4, wherein: The obtaining, according to the navigation attribute data, current calculation parameters corresponding to different navigation attributes comprises: For each navigation track, the navigation attribute data is input into a pre-trained parameter generation model; The output result of the parameter generation model for each navigation attribute data is used as the current calculation parameter corresponding to the different navigation attributes.

6. A computing device for estimating arrival time, comprising: an acquisition unit, configured to acquire navigation time information corresponding to the navigation request at the current moment, wherein the navigation time information includes an estimated arrival time when navigation starts and an actual arrival time when navigation ends; a collection unit, configured to collect a plurality of navigation tracks in real time when it is determined that the estimated arrival time and the actual arrival time satisfy a first preset condition, wherein the collected navigation tracks correspond to the ongoing navigation request; a processing unit, configured to obtain current calculation parameters corresponding to different navigation attributes according to the plurality of navigation tracks, the current calculation parameters comprising a current calculation coefficient and a current calculation deviation, and the navigation attributes comprising at least two of a geographical location of the navigation track, a mileage of the navigation track, a terminal type of the navigation track, a road type of each section in the navigation track, and an actual travel time of each section in the navigation track; a calculation unit, configured to calculate an estimated arrival time of a subsequent navigation request initiated at a subsequent time point of the current time point using the current calculation parameter; The calculation unit, when using the current calculation parameters to calculate the estimated arrival time of a subsequent navigation request initiated at a subsequent time of the current time, specifically performs: Obtaining a target navigation attribute of the at least one road segment according to the at least one road segment included in the navigation trajectory planned by the subsequent navigation request; using a current calculation parameter corresponding to the target navigation attribute of the at least one road segment as a target calculation parameter of the at least one road segment, the target calculation parameter including a target calculation coefficient and a target calculation deviation; The estimated arrival time of the subsequent navigation request is calculated based on the historical travel time of the at least one road segment, the target calculation coefficient, and the target calculation deviation.

7. The device according to claim 6, wherein: The navigation request corresponding to the current moment is at least one of a navigation request completed at the current moment or a navigation request being performed at the current moment.

8. The device according to claim 6, wherein the acquisition unit is further configured to execute: After determining that the estimated arrival time and the actual arrival time do not satisfy the first preset condition, obtaining an update time of the existing calculation parameters; When it is determined that the update time satisfies the second preset condition, a plurality of trajectory data are collected in real time.

9. The device according to claim 6, wherein: When the processing unit obtains the current calculation parameters corresponding to different navigation attributes according to the plurality of navigation tracks, the processing unit specifically performs: Extracting navigation attribute data of preset types from the plurality of navigation tracks respectively; According to the navigation attribute data, current calculation parameters corresponding to different navigation attributes are obtained.

10. The device according to claim 9, wherein: When the processing unit obtains the current calculation parameters corresponding to different navigation attributes according to the navigation attribute data, the processing unit specifically performs: For each navigation track, the navigation attribute data is input into a pre-trained parameter generation model; The output result of the parameter generation model for each navigation attribute data is used as the current calculation parameter corresponding to the different navigation attributes.

11. An electronic device, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 5.

13. A computer program product, comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 5.

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