A reservation-based travel time recommendation method and related apparatus

Abstract

This application discloses a method and related apparatus for recommending travel time based on reservations, which not only improves the accuracy of time calculation and travel efficiency, but also enhances fulfillment efficiency and user travel experience. The method includes: acquiring origin information of the travel origin, destination information of the travel destination, and first time information, wherein the first time information indicates the expected travel time range selected by the target user for the origin and destination; determining target travel behavior information based on the origin and destination information, wherein the target travel behavior information characterizes the target user's travel behavior from the origin to the destination; determining second time information based on the target travel behavior information; adjusting the first time information based on the second time information to obtain target travel time information; and recommending the target travel time information to the target user.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to a method and apparatus for recommending travel times based on reservations. Background Technology

[0002] In modern society, transportation services have become an important part of people's daily lives. People use transportation services (such as ride-sharing and other car-sharing services) to travel to specific places. Among these transportation services, pre-booked transportation services are extremely important. For example, people often use pre-booked transportation services when they have time constraints for certain activities. For instance, if people need to get to the airport early the next morning to catch a specific flight, booking a car-sharing service to the airport is a common choice.

[0003] However, current ride-hailing service solutions typically involve the platform recommending default time slots based on the user's input of origin and destination information, or allowing the user to select the earliest or latest departure time according to their travel needs. This allows them to board a shared vehicle within the recommended or selected timeframe. However, the current solution often fails to accurately identify the user's travel situation within the booked time slot, leading to inefficient travel, such as early or late arrivals. Furthermore, most users book according to the platform's recommended time. Since ride-hailing services are reservation-based, if the recommended travel time is too long or too short, it can cause a time mismatch between the driver and passenger, leading to order cancellations, reduced fulfillment efficiency, and a negative impact on the user's travel experience. Summary of the Invention

[0004] This application provides a method and related apparatus for recommending travel time based on reservations, which not only improves the accuracy of time calculation and travel efficiency, but also enhances the efficiency of fulfilling reservations and the user's travel experience.

[0005] In view of this, this application provides a reservation-based travel time recommendation method. The method includes: obtaining origin information of the travel origin, destination information of the travel destination, and first time information, wherein the first time information indicates the expected travel time range selected by the target user for the origin and destination; determining target travel behavior information based on the origin and destination information, wherein the target travel behavior information characterizes the target user's travel behavior from the origin to the destination; determining second time information based on the target travel behavior information; adjusting the first time information based on the second time information to obtain target travel time information; and recommending the target travel time information to the target user.

[0006] This application also provides a travel time recommendation device. The travel time recommendation device includes:

[0007] The acquisition unit is used to acquire origin information of the travel origin, destination information of the travel destination, and first time information. The first time information is used to indicate the expected travel time range selected by the target object for the travel origin and destination.

[0008] The determining unit is used to determine the target travel behavior information based on the origin information and the destination information. The target travel behavior information is used to characterize the travel behavior of the target object from the origin to the destination.

[0009] The determining unit is used to determine the second time information based on the target travel behavior information;

[0010] The adjustment unit is used to adjust the first time information based on the second time information to obtain the target travel time information;

[0011] The recommendation unit is used to recommend target travel time information to the target audience.

[0012] In one possible design, in another implementation of the embodiments of this application, the determining unit is specifically used for:

[0013] Calculate the target travel distance between the origin and destination based on origin and destination information;

[0014] The type of point of interest for determining the origin of the trip is determined based on the origin information, and the type of point of interest for determining the destination is determined based on the destination information.

[0015] Based on the target travel distance, the type of point of interest at the origin and the type of point of interest at the destination, the target travel behavior information is obtained.

[0016] In one possible design, in another implementation of another aspect of the embodiments of this application, the determining unit is further used for:

[0017] Based on the first-time information, travel cycle information and travel time information are determined. Travel cycle information is used to indicate whether the target person's expected travel is on a weekday or a non-working day, and travel time information is used to indicate whether the target person's expected travel is during peak or off-peak hours.

[0018] Obtain the travel preference information of the target audience, which is used to indicate the travel services selected by the target audience during historical travel phases;

[0019] Based on at least one of the following: travel cycle information, travel time information, and travel preference information, as well as the target travel distance, the type of point of interest at the origin of the trip, and the type of point of interest at the destination of the trip, the target travel behavior information is obtained.

[0020] In one possible design, in another implementation of another aspect of the embodiments of this application, the determining unit is specifically used for:

[0021] The determining unit is configured to determine second time information based on the target travel behavior information and each type of preset travel behavior information in the preset travel behavior set, wherein the second time information is the travel time corresponding to the preset travel behavior information in the preset travel behavior set that matches the target travel behavior information.

[0022] In one possible design, in another implementation of another aspect of the embodiments of this application, the determining unit is specifically used for:

[0023] Calculate the similarity distance between the target travel behavior information and each type of preset travel behavior information in the preset travel behavior set to obtain multiple similarity scores. Each similarity score is used to characterize the degree of similarity between the target travel behavior information and each type of preset travel behavior information.

[0024] The maximum similarity is determined from multiple similarity values, and the time information corresponding to the preset travel behavior information corresponding to the maximum similarity is determined as the second time information.

[0025] In one possible design, in another implementation of another aspect of the embodiments of this application, the first behavior information includes a preset travel distance, a preset origin point type of interest, and a preset destination point type of interest, and the first behavior information is any one of multiple types of preset travel behavior information in a preset travel behavior set; the determining unit is specifically used for:

[0026] Calculate the similarity distance between the target travel distance and the preset travel distance to obtain the distance similarity.

[0027] Calculate the similarity distance between the point of interest type of the travel origin and the point of interest type of the preset origin to obtain the first type similarity;

[0028] Calculate the similarity distance between the point of interest type of the travel destination and the point of interest type of the preset destination to obtain the second type similarity;

[0029] The similarity between the target travel behavior information and the first type of behavior information is obtained by weighted summation of distance similarity, first type of similarity and second type of similarity.

[0030] In one possible design, in another implementation of another aspect of the embodiments of this application, the first behavior information further includes preset period information, preset time information, and preset preference information; the determining unit is specifically used for:

[0031] Calculate the similarity distance between the travel cycle information and the preset cycle information to obtain the cycle similarity.

[0032] Calculate the similarity distance between the travel time information and the preset time information to obtain the time similarity.

[0033] Calculate the similarity distance between travel preference information and preset preference information to obtain preference similarity;

[0034] The similarity between the target travel behavior information and the first behavior information is obtained by weighted summation of at least one of period similarity, time similarity, and preference similarity, as well as distance similarity, first type similarity, and second type similarity.

[0035] In one possible design, in another implementation of another aspect of the embodiments of this application, the adjustment unit is specifically used for:

[0036] Determine the comparison result between the target travel distance and the preset distance threshold;

[0037] Based on the comparison results, the second time information is adjusted to the first time information to obtain the target travel time information.

[0038] In one possible design, in another implementation of another aspect of the embodiments of this application, the second time information includes a first travel time and a first pre-waiting time range, wherein the first travel time is used to represent the earliest travel time corresponding to the preset travel behavior information, and the first pre-waiting time range is used to represent the willing waiting period; the adjustment unit is specifically used for:

[0039] When the comparison result shows that the target travel distance is less than the preset distance threshold, the earliest travel time in the first time information is adjusted to the first travel time, and the pre-waiting time range in the first time information is adjusted to the first pre-waiting time range to obtain the target travel time information.

[0040] In one possible design, in another implementation of another aspect of the embodiments of this application, the second time information includes a first travel time and a second travel time, wherein the first travel time is used to represent the earliest travel time corresponding to the preset travel behavior information, and the second travel time is used to represent the latest travel time corresponding to the preset travel behavior information; the adjustment unit is specifically used for:

[0041] When the comparison result shows that the target travel distance is greater than or equal to the preset distance threshold, the earliest travel time in the first time information is adjusted to the first travel time, and the earliest travel time in the first time information is adjusted to the second travel time to obtain the target travel time information.

[0042] In one possible design, in another implementation of another aspect of the embodiments of this application, the destination information includes the latitude and longitude of the travel destination; the determining unit is specifically used for:

[0043] Based on the latitude and longitude of the destination and a preset radius, the target area is determined. The target area is the range constructed with the latitude and longitude of the destination as the center and the preset radius.

[0044] Detect one or more candidate points of interest that contain the point of interest type within the target area;

[0045] Calculate the distance between the travel destination and each candidate point of interest, and determine the minimum distance from multiple distances;

[0046] The type of candidate point of interest corresponding to the minimum distance is determined as the type of point of interest at the destination.

[0047] In one possible design, in another implementation of another aspect of the embodiments of this application, the determining unit is specifically used for:

[0048] The point of interest information for the destination is determined based on the destination information, which includes a category field.

[0049] When the value of the category field is a preset point of interest, the point of interest type of the travel destination is determined to be the point of interest type corresponding to the preset point of interest.

[0050] In one possible design, in another implementation of another aspect of the embodiments of this application, the determining unit is specifically used for:

[0051] Based on the destination information, the point of interest information of the travel destination is determined, which includes keywords of the point of interest.

[0052] Based on the environmental information, comments, and keywords of the points of interest, determine the type of point of interest at the destination.

[0053] In one possible design, in another implementation of another aspect of the embodiments of this application, the travel time recommendation device further includes a prompting unit;

[0054] Specifically, the determining unit is also used to recommend the target travel time information to the target object, and then calculate the estimated travel cost based on the target travel time information, origin information and destination information.

[0055] The prompting unit is specifically used to prompt the target audience with the estimated cost of travel.

[0056] In another aspect, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the methods described above.

[0057] Another aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the methods described above.

[0058] Another aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the methods described above.

[0059] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0060] In this embodiment, the origin information of the starting point, the destination information of the destination, and the first time information are first obtained. It should be noted that the first time information reflects the expected travel time range selected by the target object for the origin and destination. After obtaining the origin and destination information, target travel behavior information is determined based on these information. This target travel behavior information characterizes the target object's travel behavior from the origin to the destination. Subsequently, after determining the target travel behavior information, second time information is determined based on it. Then, the second time information is used to adjust the first time information, thereby recommending the obtained target travel time information to the target object.

[0061] By employing the methods described above, this application determines target travel behavior information based on origin and destination information, comprehensively considering the travel scenarios the target individual may encounter during their journey. Thus, by determining matching possible travel times (i.e., second time information) based on the target travel behavior information, and adjusting the target individual's chosen travel time range based on this second time information, suitable travel times can be recommended. On one hand, considering relevant travel behavior factors to calculate and adjust the target individual's travel time range improves the accuracy of time calculations and enhances travel efficiency; on the other hand, the adjusted target travel time information better aligns with the target individual's booking needs, reducing order cancellations and improving fulfillment efficiency and user travel experience. Attached Figure Description

[0062] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 The diagram illustrates an application scenario for booking travel.

[0064] Figure 2 This diagram illustrates one possible process for booking a trip within the relevant scheme.

[0065] Figure 3 A schematic diagram of the implementation environment of the reservation-based travel time recommendation method provided in this application is shown;

[0066] Figure 4 A flowchart illustrating the reservation-based travel time recommendation method provided in this application is shown.

[0067] Figure 5 This application provides a schematic diagram of a process framework for determining target travel behavior information.

[0068] Figure 6 This application provides an optional schematic diagram illustrating the types of points of interest for determining the origin of a trip.

[0069] Figure 7 This paper illustrates another process framework diagram for determining target travel behavior information provided in this application;

[0070] Figure 8 A flowchart illustrating the process for determining second time information provided in this application is shown;

[0071] Figure 9 This paper illustrates a flowchart of a similarity calculation process provided in this application.

[0072] Figure 10 This paper illustrates another similarity calculation process provided in this application.

[0073] Figure 11 This application provides a schematic diagram of the process for adjusting first time information based on second time information.

[0074] Figure 12A A schematic diagram of an interface for the adjustment time information provided in this application is shown;

[0075] Figure 12B Another schematic diagram of the interface for the adjustment time information provided in this application is shown;

[0076] Figure 13A A schematic diagram of an interface for the adjustment time information provided in this application is shown;

[0077] Figure 13B Another schematic diagram of the interface for the adjustment time information provided in this application is shown;

[0078] Figure 14 Another flowchart illustrating the reservation-based travel time recommendation method provided in this application is shown;

[0079] Figure 15 A schematic diagram of the travel time recommendation device provided in this application is shown;

[0080] Figure 16 A schematic diagram of the computer device provided in this application is shown. Detailed Implementation

[0081] This application provides a method and related apparatus for recommending travel time based on reservations, which not only improves the accuracy of time calculation and travel efficiency, but also enhances the efficiency of fulfilling reservations and the user's travel experience.

[0082] It is understood that in the specific implementation of this application, data such as user information are involved. When the embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0083] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0084] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that implementations of the application described herein can be implemented, for example, in sequences other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0085] Mobility services are a comprehensive transportation service model designed to provide the public with efficient, convenient, comfortable, and personalized transportation solutions. It encompasses everything from traditional public transportation to emerging sharing economy models and future transportation driven by smart technology. In recent years, with the acceleration of urbanization and the increasing prominence of traffic congestion, shared mobility, as an innovative service model, has received widespread attention. Among them, ride-sharing and carpooling services, as important components of shared mobility, have become an indispensable part of modern urban life due to their convenience, economy, and environmental friendliness.

[0086] Taking ride-sharing as an example, a ride-sharing system is a shared mobility platform based on internet and mobile application technologies. It connects drivers with available seats with passengers who need to travel in the same direction, achieving a mutually beneficial travel experience. After drivers publish their trip information through the ride-sharing server platform, passengers can select and book a suitable driver based on their travel needs. The driver will then pick up the passenger from the departure point to the destination within the booked time.

[0087] Figure 1 The diagram illustrates an application scenario for booking a ride. For example... Figure 1 As shown, taking a ride-sharing scenario as an example, a person named Q plans to take a rideshare from community P in city A to high-speed rail station XX in city B on March 2nd, and then take the YY train departing at 18:00 to travel to city C. Based on this, person Q can input their origin (community P in city A) and destination (high-speed rail station XX in city B) into the ride-sharing service platform before March 2nd. Then, person Q can choose the earliest (e.g., 14:00 on March 2nd) and latest (e.g., 14:20 on March 2nd) time slots recommended by the platform and make a ride reservation, generating a ride order. Therefore, a rideshare or carpooling driver with travel needs (such as rideshare driver P001) can see the order on the travel service platform and, after accepting the travel order of the person Q, can pick up the person Q from community P in city A between 14:00 and 14:20 on March 2nd and go to XX high-speed railway station in city B.

[0088] by Figure 1 Taking the example of the booking-based travel scenario shown, Figure 2 This diagram illustrates a typical travel booking process within a relevant scheme. For example... Figure 2 As shown, in the relevant booking and travel service schemes, after the user inputs their origin and destination, the travel service platform typically determines whether the trip is an intercity trip based on the user's input. For example... Figure 1The example shown is a trip from City A to City B, which is considered intercity travel. For intercity travel, the user selects the earliest and latest departure times and the number of passengers based on their travel needs, then proceeds to the cost estimation process for shared rides. Conversely, for non-intercity travel, the travel service platform directly recommends default time slots and default settings such as traveling alone, then proceeds to the cost estimation process for shared rides.

[0089] In other words, Figure 2 The proposed ride-sharing plans require users to be able to board shared vehicles at the platform's default recommended time or their selected time. However, the current plans often fail to accurately identify the user's travel needs within the booked time slot, leading to inefficient travel, such as early or late arrivals. Furthermore, most users book according to the time recommended by the ride-hailing platform. Since ride-sharing services are reservation-based, if the recommended travel time is too long or too short, it can cause a mismatch between the supply and demand sides (driver and passenger), leading to order cancellations, reduced fulfillment efficiency, and a negative impact on the user's travel experience.

[0090] To address the aforementioned technical issues, this application provides a reservation-based travel time recommendation method. This method can be applied to reservation-based travel scenarios. By determining target travel behavior information based on origin and destination information, this application comprehensively considers the possible travel situations the target user may face during their trip. Then, by combining this with preset travel behavior information for each type, it determines the possible travel time matching the target travel behavior information. This adjusts the travel time range selected by the target user, thus recommending a suitable travel time. On one hand, by considering relevant travel behavior and combining preset travel behavior information from past users to calculate and adjust the target user's travel time range, the accuracy of time calculation is improved, and travel efficiency is enhanced. On the other hand, the adjusted target travel time information better matches the target user's reservation travel needs, reducing the likelihood of order cancellations and improving fulfillment efficiency and user travel experience.

[0091] For example, the travel time recommendation method provided in this application can be applied to one or more of the following application scenarios, as detailed below:

[0092] (I) Ride-sharing scenario

[0093] Ride-sharing typically refers to a mode of transportation based on the sharing economy, where drivers with available seats can use their vehicles to pick up passengers going in the same direction to share travel costs or earn some compensation. For example, in daily commutes, long-distance travel, holiday trips home, and intercity travel, users can choose ride-sharing services to complete their journey from origin to destination. Applying the reservation-based travel time recommendation method of this application to ride-sharing scenarios allows for the calculation and adjustment of the target user's travel time range based on their past travel behavior information. This makes the adjusted travel time information more aligned with the target user's reservation needs, improving travel efficiency, extending the user's willingness to wait, reducing order cancellations, and enhancing fulfillment efficiency and user travel experience.

[0094] (II) Ride-hailing scenario

[0095] Ride-hailing scenarios refer to shared mobility services that utilize internet platforms and mobile applications for vehicle booking and riding. Applying the travel time recommendation method of this application to ride-hailing scenarios under certain conditions allows target users to choose ride-hailing services to complete their journey from origin to destination. For example, ride-hailing scenarios under certain conditions may include, but are not limited to, the following situations: For instance, booking a ride-hailing service during extreme weather conditions such as blizzards, high temperatures, or snowstorms may require longer arrival times due to changes in road conditions and passenger demand; passengers can book in advance to ensure sufficient waiting time. Similarly, booking a ride-hailing service for extremely long distances, such as hundreds or thousands of kilometers, also allows passengers to book in advance to ensure sufficient waiting time. Furthermore, booking a ride-hailing service during peak travel periods such as morning and evening commutes, weekends, and other holidays, where large numbers of people need to travel and ride-hailing resources are relatively scarce, allows passengers to book in advance to ensure a smooth ride.

[0096] Applying the reservation-based travel time recommendation method of this application to the aforementioned ride-hailing scenarios that meet certain conditions allows for the calculation and adjustment of the target user's travel time range by considering the user's travel behavior and combining it with past preset travel behavior information. This ensures that the adjusted target travel time information better matches the user's reservation travel needs. This not only improves travel efficiency and extends the user's willingness to wait, but also reduces the cancellation rate of ride orders, improving fulfillment efficiency and the user's travel experience.

[0097] It should be noted that the above application scenarios are merely examples. The travel time recommendation method provided in this embodiment can also be applied to other shared mobility scenarios, and this application does not limit it.

[0098] For example, the method provided in this application can be applied to Figure 3 The implementation environment is shown. Figure 3 The illustrated implementation environment includes a terminal 110 and a server 120, and the terminal 110 and server 120 can communicate with each other via a communication network. The communication network uses standard communication technologies and / or protocols, typically the Internet, but can also be any network, including but not limited to Bluetooth, a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), mobile, private networks, or any combination of virtual private networks. In some embodiments, customized or dedicated data communication technologies may be used to replace or supplement the aforementioned data communication technologies. Optionally, the implementation environment may also include a database 130, wherein the server 120 may also be connected to the database 130 via a communication network.

[0099] Combination Figure 3 In the implementation environment shown, in step A1, the target object inputs the origin information of the starting point and the destination information of the destination into terminal 110. Subsequently, in step A2, the origin information of the starting point and the destination information of the destination are sent to server 120 through the communication network via terminal 110.

[0100] In step A3, server 120 can also obtain first-time information. That is, through this first-time information, it can instruct the target object on the expected travel time range selected for the origin and destination. In step A4, server 120 determines the target travel behavior information based on the origin and destination information. It should be noted that the target travel behavior information is used to characterize the target object's travel behavior from the origin to the destination. For example, as described above... Figure 1 Taking the scenario shown as an example, the corresponding target travel behavior information includes, but is not limited to: the target distance between P community in City A and XX high-speed railway station in City B, the type of point of interest of the starting point P community in City A, the type of point of interest of the destination XX high-speed railway station in City B, the travel cycle (such as peak travel period), the travel time (such as weekdays), travel preferences (such as sharing a seat), etc., which are not limited in this application.

[0101] In step A5, server 120 determines the second time information based on the target travel behavior information. For example, server 120 can obtain information on each type of preset travel behavior from the preset travel behavior set in database 130, and match the target travel behavior information with each type of travel behavior information to determine the second time information. In step A6, server 120 adjusts the first time information based on the second time information to obtain the target travel time information. In step A7, server 120 also recommends the target travel time information to the target user.

[0102] Optionally, in step A8, the server 120 can transmit the target travel time information to the terminal 110 via a communication network. The terminal 110 then displays the target travel time information to the target user through a visual display interface.

[0103] It should be noted that this application Figure 3 The description of each type of preset travel behavior information is only illustrative of the example stored in database 130. In practical applications, each type of preset travel behavior information in the preset travel behavior set of this application may also be stored in other storage media, such as a distributed storage system; or it may be stored in a server; or it may be stored in a blockchain. This application does not limit the specific storage medium.

[0104] Optionally, the aforementioned terminal 110 and server 120 can be block nodes in a blockchain network. By applying the reservation-based travel time recommendation method of this application to a blockchain network scenario, the logic of various operations can be run in the smart contract in the blockchain network, enabling both terminal 110 and server 120 to call the smart contract and participate in the business.

[0105] The terminal 110 involved in this application includes, but is not limited to, mobile phones, tablets, laptops, desktop computers, smart voice interaction devices, smart home appliances, vehicle terminals, and aircraft. The client is deployed on the terminal 110 and can run on the terminal 110 via a browser or as a standalone application (APP).

[0106] The server 120 involved in this application may include, but is not limited to, terminals and servers with data processing capabilities. The described server may be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence (AI) platforms.

[0107] The database 130 involved in this application is a collection of data stored together in a certain way, shareable by multiple users, with minimal redundancy, and independent of applications. A Database Management System (DBMS) is a computer software system designed to manage databases, generally possessing basic functions such as storage, retrieval, security, and backup. DBMSs can be classified according to the database model they support, such as relational or Extensible Markup Language (XML); or according to the type of computer they support, such as server clusters or mobile phones; or according to the query language used, such as Structured Query Language (SQL) or XQuery; or according to performance priorities, such as maximum scale or highest operating speed; or other classification methods. Regardless of the classification method used, some DBMSs can cross categories, for example, simultaneously supporting multiple query languages. In this application, database 130 can be used for each type of preset travel behavior information, etc.

[0108] Based on the above introduction, the reservation-based travel time recommendation method in this application will be described below. Figure 4 A flowchart illustrating the reservation-based travel time recommendation method provided in this application is shown. Figure 4 As shown, the travel time recommendation method in this application includes at least the following steps:

[0109] 401. Obtain the origin information of the travel origin, the destination information of the travel destination, and the first time information. The first time information is used to indicate the expected travel time range selected by the target object for the origin and destination.

[0110] In one or more embodiments, when a target user intends to travel to a certain location via a ride-sharing service, they can manually input the starting point information of their trip through the terminal's display interface on the ride-sharing service platform; alternatively, if location services are enabled, the starting point information can be automatically input via location services. This application does not limit the input of the starting point information. The described starting point can be understood as the location where the target user begins their journey. Furthermore, the target user can also input the destination information of their trip through the terminal's display interface on the ride-sharing service platform. The described destination can be understood as the location where the target user ends their journey.

[0111] For example, taking a trip from P Community in City A to XX High-speed Railway Station in City B as an example, the starting point can be P Community in City A, and the destination can be XX High-speed Railway Station in City B.

[0112] The described ride-sharing service platform connects the supply and demand sides of travel services using digital tools such as applications or web pages on a terminal. For example, the ride-sharing service platform can be an application, client, or web page deployed on a terminal to provide travel services. For example, ride-sharing service platforms include, but are not limited to: ride-sharing applications, ride-sharing mini-programs, ride-hailing applications, ride-hailing mini-programs, etc., which are not limited in this application.

[0113] Therefore, the server can obtain origin information (starting point) and destination information (endpoint). Origin information may include, but is not limited to, the location information of the origin, such as its latitude and longitude. Destination information may include, but is not limited to, the location information of the destination, such as its latitude and longitude.

[0114] In addition, the target user needs to select the first-time information on the ride-sharing service platform's display interface based on their current travel needs. That is, the target user needs to select the expected travel time range for the journey between the origin and destination on the display interface. Specifically, they need to select the earliest departure time, the latest departure time, and the expected waiting time range. The server then obtains the first-time information, i.e., it knows the target user's currently selected expected travel time range. For example, the first-time information could include: earliest departure time 16:10, latest departure time 16:20, and an expected waiting time range of 10 minutes before and after.

[0115] The earliest departure time mentioned in this application can be understood as the earliest time the target user expects the driver to pick them up after they have made a travel service request (e.g., hailing a ride). The latest departure time mentioned can be understood as the latest time the target user can accept a driver to pick them up after they have made a travel service request. The expected waiting time range mentioned can be understood as the time interval required for the target user to wait between making a travel service request and actually receiving travel service (e.g., the driver picking them up).

[0116] 402. Determine target travel behavior information based on origin and destination information. Target travel behavior information is used to characterize the travel behavior of the target object from the origin to the destination.

[0117] In one or more embodiments, the travel behavior of the target user during the pre-trip process, such as whether it is a peak travel period, whether it is a weekday or a non-working day, the target distance of the trip, the type of point of interest (POI) of the origin, the type of POI of the destination, and past travel preferences, can also affect the setting of the reservation time to some extent. For example, peak travel periods usually lead to traffic congestion, and if the target user chooses to make a travel reservation during a peak travel period, they may need more time to deal with possible traffic delays.

[0118] Therefore, in recommending travel times, in addition to considering the origin and destination, the server also needs to determine the target travel behavior information based on the origin and destination information to fully take into account the possible travel behavior of the target person from the origin to the destination.

[0119] 403. Determine the second time information based on the target travel behavior information.

[0120] In one or more embodiments, after determining the target travel behavior information in step 402, second time information can be determined based on the target travel behavior information.

[0121] As an example, the second time information can be determined by combining the information of each type of preset travel behavior in the preset travel behavior set. The preset travel behavior set involved in this application contains multiple types of preset travel behavior information. For each type of preset travel behavior information, it corresponds to a time information. The time information corresponding to each type of preset travel behavior information can be understood as the historical travel time period booked by a historical traveler with the corresponding type of preset travel behavior information.

[0122] For example, suppose there are three types of preset travel behavior information in the preset travel behavior set: preset travel behavior information A, preset travel behavior information B, and preset travel behavior information C. Preset travel behavior information A corresponds to a time information Ta. This time information Ta can be the historical travel time period booked by historical travel objects A1 to An (n≥1, n is an integer). Similarly, preset travel behavior information B corresponds to a time information Tb. This time information Tb can be the historical travel time period booked by historical travel objects B1 to Bm (m≥1, m is an integer). Preset travel behavior information C corresponds to a time information Tc. This time information Tc can be the historical travel time period booked by historical travel objects C1 to Ci (i≥1, i is an integer).

[0123] Taking the time information Ta corresponding to preset travel behavior information A as an example, it can include the earliest departure time and the expected waiting time range; or it can include the latest departure time and the latest departure time. The time information corresponding to other preset travel behavior information can also be understood by referring to the time information Ta corresponding to preset travel behavior information A, which will not be elaborated here.

[0124] In this way, after determining the target travel behavior information of the target object, the server can match the target travel behavior information with each type of preset travel behavior information in the preset travel behavior set. If the target travel behavior information matches a certain type of preset travel behavior information, the time information corresponding to the matched preset travel behavior information is used as the second time information, so that the first time information selected by the target object can be flexibly adjusted subsequently using the second time information. It should be noted that the second time information involved in this application can be understood as the travel time corresponding to the preset travel behavior information that matches the target travel behavior information in the preset travel behavior set.

[0125] For example, if the target travel behavior information matches the preset travel behavior information A, then the time information Ta corresponding to the preset travel behavior information A can be used as the second time information. In this way, the first time information is adjusted using this time information Ta. The content described regarding the first time information can be understood with reference to the content described in step 401 above, and will not be repeated here.

[0126] 404. Adjust the first time information based on the second time information to obtain the target travel time information.

[0127] In one or more embodiments, after determining the second time information, the server can flexibly adjust the first time information based on the second time information. For example, it can adjust the earliest departure time in the first time information according to the earliest departure time in the second time information, or adjust the latest departure time in the first time information according to the latest departure time in the second time information, thereby obtaining the target travel time information. This target travel time information reflects the travel time that the target user can most likely book to satisfy their travel behavior.

[0128] 405. Recommend target travel behavior information to the target audience.

[0129] In one or more embodiments, after determining the target travel behavior information, the server can recommend the target travel behavior information to the target object. As an illustrative example, the server can send the target travel behavior information to a terminal, which then displays the information in a visual interface to recommend it to the target object.

[0130] In this embodiment, the target travel behavior information is determined based on origin and destination information, comprehensively considering the travel situations the target may encounter during the trip. Thus, based on the target travel behavior information, a matching possible travel time (i.e., second time information) is determined. By adjusting the travel time range selected by the target based on this second time information, a suitable travel time can be recommended to the target. On the one hand, considering relevant travel behavior factors to calculate and adjust the target's travel time range improves the accuracy of time calculation and enhances travel efficiency; on the other hand, the adjusted target travel time information better matches the target's booking travel needs, reducing the likelihood of order cancellations and improving fulfillment efficiency and user travel experience.

[0131] In some alternative embodiments, in the above... Figure 4 Based on the described embodiments, and in relation to the foregoing Figure 4 Step 402, which involves determining the target travel behavior information based on origin and destination information, can be referenced in the following process. Figure 5 Use the described framework diagram to understand. For example... Figure 5 As shown, determining target travel behavior information includes at least the following processes:

[0132] After obtaining the origin and destination information, the target travel distance between the origin and destination is first calculated based on this information. For example, the origin and destination can be entered into a map application to automatically calculate and display the target travel distance. Alternatively, if the origin information includes the latitude and longitude of the origin and the destination information includes the latitude and longitude of the destination, the target travel distance can be calculated based on these coordinates. It should be noted that other methods for calculating the distance between the origin and destination may also be used in practical applications, and this application does not impose specific limitations on these methods.

[0133] In addition to calculating the target travel distance, the server also needs to determine the type of point of interest (POI) at the starting point based on the origin information. The POI type provides rich reference information for the target user. The described POI type typically refers to the category or functional attribute of the starting point chosen by the target user when initiating a trip. For example, the POI type includes, but is not limited to, the following: residential POIs such as houses, apartments, and villas; work POIs such as companies, enterprises, and office buildings; educational POIs such as schools, universities, and kindergartens; commercial POIs such as shopping malls, supermarkets, and restaurants; and transportation POIs such as train stations, bus stations, and public transport stops. In practical applications, other types of POIs, such as parks, hospitals, libraries, and place names / addresses, are also included, but this application does not specifically limit them.

[0134] As an illustrative example, determining the type of point of interest as the starting point for a trip can be achieved in several ways, as detailed below:

[0135] Method ①: Based on the latitude and longitude of the travel origin

[0136] In one or more embodiments, after obtaining the origin information of the travel origin, the latitude and longitude of the travel origin can be extracted from the origin information. Thus, in the process of determining the point of interest type of the travel origin based on the origin information, the server first constructs a travel area around the travel origin, using the latitude and longitude of the travel origin as the center and a preset radius as the area radius. In other words, this travel area is the range constructed using the latitude and longitude of the travel origin as the center and the preset radius.

[0137] Thus, for the given travel area, the server detects one or more candidate points of interest (POIs) within that area. After identifying the candidate POIs, the server also needs to calculate the distance between the originating point and each candidate POI. For example, this can be done automatically using map software; alternatively, the distance can be calculated based on the latitude and longitude of the originating point and the latitude and longitude of each candidate POI.

[0138] After calculating the distances between the origin and each candidate point of interest, the server determines the minimum distance from these distances. Then, the server identifies the type of the candidate point of interest corresponding to the minimum distance as the type of point of interest for the origin.

[0139] For example, Figure 6 This illustration shows an optional diagram of the type of point of interest for determining the origin of a trip, as provided in this application. For example... Figure 6 As shown, taking P community in City A as the starting point, if the latitude and longitude of P community in City A is A', and the travel area is constructed with a radius r of 100 meters (m), then four candidate points of interest can be detected within this travel area B', such as point of interest H1 (e.g., "Tianhe Shopping Mall"), point of interest H2 (e.g., "Mini Supermarket"), point of interest H3 (e.g., "City A Railway Station"), and point of interest H4 (e.g., "Y Kindergarten").

[0140] Calculations show that the distances between P residential area in City A and points of interest H1, H2, H3, and H4 are 25m, 45m, 80m, and 65m, respectively. Therefore, the point of interest type for P residential area in City A can be determined to be point of interest H1 (i.e., "Tianhe Shopping Mall"), which is a commercial point of interest.

[0141] It should be noted that the above Figure 6 The radius r described in the document is 100m, but it can take other values ​​in practical applications, which are not limited in this application.

[0142] Method ②: Values ​​of the classification field in the POI information based on the travel origin.

[0143] In one or more embodiments, after obtaining the origin information of the travel origin, the server determines the point of interest (POI) information of the travel origin based on the origin information. For example, the server can use the POI query function in the map service to search for POI information near the travel origin to obtain the POI information of the travel origin. It should be noted that the POI information of the travel origin includes a classification field. Through the classification field, the value of the POI type of the travel origin can be clearly defined.

[0144] In this way, after obtaining the point of interest information of the travel origin, the server reads the category field in the point of interest information. If the value of the category field is a preset point of interest, then the point of interest type of the travel origin is determined to be the point of interest type corresponding to the preset point of interest.

[0145] For example, if the category field in the point of interest information of the starting point of travel explicitly indicates values ​​such as "station" or "bus station," then based on these values, the point of interest type of the starting point of travel can be determined to be a station type or a transportation-related point of interest. Alternatively, if the category field in the point of interest information of the starting point of travel explicitly indicates values ​​such as "XX University" or "XXX Kindergarten," then based on these values, the point of interest type of the starting point of travel can be determined to be an education-related point of interest.

[0146] Method 3: Keywords based on POI information from the travel origin

[0147] In one or more embodiments, after obtaining the origin information of the travel origin, the server determines the point of interest (POI) information of the travel origin based on the origin information. For example, the server can use the POI query function in the map service to search for POI information near the travel origin to obtain the POI information of the travel origin. It should be noted that the POI information of the travel origin includes keywords related to the POI.

[0148] In this way, the server then obtains environmental information and comment information about the points of interest, and combines this with keywords to determine the type of point of interest at the travel destination. That is, when the point of interest information at the travel origin does not directly contain a category field, but is indicated by keywords, it is necessary to combine external environmental factors such as comments to determine the type of point of interest at the travel origin.

[0149] For example, if the point of interest information for the starting point of a trip does not explicitly indicate terms like "station" or "bus station," but instead uses keywords such as "e.g., bus stop," then based on the keyword "bus stop," and combined with comments and environmental information about the starting point, it can be determined whether the starting point is indeed a station.

[0150] Similarly, the server also needs to determine the type of point of interest (POI) at the destination based on the destination information. The POI type of the destination provides rich reference information for the target user. The described POI type typically refers to the category or functional attribute of the destination selected by the target user when starting a trip. For example, the POI type of the destination includes, but is not limited to, the following: residential POIs such as houses, apartments, and villas; work POIs such as companies, enterprises, and office buildings; educational POIs such as schools, universities, and kindergartens; commercial POIs such as shopping malls, supermarkets, and restaurants; and transportation POIs such as train stations, bus stations, and public transport stops. In practical applications, other types of POIs, such as parks, hospitals, libraries, and place names / addresses, are also included, but this application does not specifically limit them.

[0151] As an illustrative example, determining the type of point of interest for a travel destination can be achieved in several ways, as detailed below:

[0152] Method 1: Based on the latitude and longitude of the destination

[0153] In one or more embodiments, after obtaining the destination information, the latitude and longitude of the destination can be extracted from the destination information. Thus, in the process of determining the point of interest type of the destination based on the destination information, the server first constructs a travel area around the destination, using the destination's latitude and longitude as the center and a preset radius as the area radius. In other words, this travel area is the range constructed using the destination's latitude and longitude as the center and the preset radius.

[0154] Thus, for the given travel area, the server detects one or more candidate points of interest (POIs) within that area. After identifying the candidate POIs, the server also needs to calculate the distance between the travel destination and each candidate POI. For example, this can be done automatically using map software; alternatively, the distance can be calculated based on the latitude and longitude of the travel destination and the latitude and longitude of each candidate POI. After calculating the distances between the travel destination and each candidate POI, the server determines the minimum distance from these multiple distances. Then, the server identifies the type of the candidate POI corresponding to the minimum distance as the POI type for the travel destination.

[0155] It should be noted that the travel area and the type of point of interest for determining the destination described here can be understood by referring to the type of point of interest for determining the origin in method ① above, and will not be elaborated here.

[0156] Method 2: Based on the value of the classification field in the POI information of the travel destination.

[0157] In one or more embodiments, after obtaining the destination information, the server determines the point of interest (POI) information for the destination based on the destination information. For example, the server can use the POI query function in the map service to search for POI information near the destination to obtain the POI information for that destination. It should be noted that the POI information for the destination includes a classification field. The classification field clarifies the POI type value for the destination.

[0158] In this way, after obtaining the point of interest information of the travel destination, the server reads the category field in the point of interest information. If the value of the category field is a preset point of interest, then the point of interest type of the travel destination is determined to be the point of interest type corresponding to the preset point of interest.

[0159] It should be noted that the method for determining the type of point of interest for the destination described here can be understood by referring to the method for determining the type of point of interest for the origin in the aforementioned method ②, and will not be elaborated here.

[0160] Method 3: Keywords based on POI information of the travel destination

[0161] In one or more embodiments, after obtaining the destination information, the server determines the point of interest (POI) information for the destination based on the destination information. For example, the server can use the POI query function in the map service to search for POI information near the destination to obtain the POI information for that destination. It should be noted that the POI information for the destination includes keywords related to the POI.

[0162] In this way, the server obtains environmental information and comment information about the points of interest, and then combines them with keywords to determine the type of point of interest at the destination.

[0163] In other words, when the destination's point of interest information does not directly include a category field, but is indicated by keywords, it is necessary to combine external factors such as the environment and comments of the point of interest to determine the type of point of interest at the destination.

[0164] It should be noted that the travel area and the type of point of interest for determining the destination described here can be understood by referring to the type of point of interest for determining the origin in method ③ above, and will not be elaborated here.

[0165] Thus, after calculating the target travel distance, the type of point of interest at the origin, and the type of point of interest at the destination, the target travel behavior information is obtained based on these three factors. In other words, this target travel behavior information includes the target travel distance, the type of point of interest at the origin, and the type of point of interest at the destination.

[0166] By employing the methods described above, the process of determining the target travel behavior information of the target audience comprehensively considers the target travel distance, the type of interest at the origin, and the type of interest at the destination. This not only allows for a more accurate understanding of the target audience's travel needs based on travel distance and point-of-interest type, improving user travel experience and efficiency, but also enables the generation of more convenient and efficient travel plans, such as optimal departure times, based on detailed travel behavior information.

[0167] In some other alternative embodiments, in the above... Figure 5 Based on the described embodiments, this application, in the process of determining target travel behavior information, considers, in addition to Figure 5 In addition to the target travel distance, the type of point of interest at the origin, and the type of point of interest at the destination described in the text, factors such as whether it is a peak travel period, a weekday, and past travel preferences can also be considered. Specifically, Figure 7 This illustration shows another flowchart of the process framework for determining target travel behavior information provided in this application. Figure 7 As shown above, in the aforementioned Figure 5 Based on the previous implementation, determining the target travel behavior information includes at least the following process:

[0168] The server is executing Figure 5 In addition to determining the target travel distance, the type of point of interest at the starting point, and the type of point of interest at the destination, the process also requires determining the travel cycle information and travel time information based on the first-time information.

[0169] The travel cycle information described in this application can be used to indicate whether the target's expected travel date falls on a weekday or a non-working day. In other words, the travel cycle information reflects whether the date the target intends to travel at the first available time is a weekday or a public holiday. The described public holidays may include statutory holidays, Saturdays, Sundays, etc.

[0170] For example, the server can first determine the travel date corresponding to the first-time information. Then, the server can use a calendar or date lookup tool to check whether the travel date falls on a weekday or a holiday. This allows the server to determine the corresponding travel schedule information.

[0171] Furthermore, the travel time information described in this application can be used to indicate the peak and off-peak travel conditions when the target user intends to travel. In other words, the travel time information reflects whether the target user is in the morning peak, evening peak, or off-peak period when they intend to travel at the first available time. The morning peak period refers to a specific time period in the city where traffic flow increases significantly in the morning, such as 7:00 AM to 9:00 AM on a weekday. The evening peak period refers to a specific time period in the city where traffic flow increases significantly in the evening, such as 5:00 PM to 7:00 PM or 5:00 PM to 9:00 PM. Off-peak periods are understood as any time of day other than the morning and evening peak periods.

[0172] For example, the server can first determine the time period corresponding to the first time information. Then, the server can match this time period with the morning peak travel period, evening peak travel period, and off-peak travel period. In this way, the server uses the time range within which the first time information falls as the travel time information. For instance, if the time period corresponding to the first time information falls within the morning peak travel period, then the travel time information is determined to be the morning peak travel period.

[0173] Furthermore, the travel services chosen by the target individual during their past trips, such as preferences for the number of travelers and the type of travel, will influence their travel behavior. Therefore, the server also needs to obtain the target individual's travel preference information before calculating their travel behavior information. For example, the server can obtain the target individual's travel preference information from their historical booking information.

[0174] It should be noted that this travel preference information reflects the travel services selected by the target individual during their historical travel periods. For example, travel preference information includes, but is not limited to, information on the number of travelers and the type of travel. The described number of travelers information indicates the number of people selected when making a time reservation, such as 1 person, 2 people, 3 people, etc. The described travel type information indicates the travel mode selected when making a time reservation, such as sharing a seat with one other person, sharing a seat with two other people, or traveling alone.

[0175] In this way, the server can obtain target travel behavior information based on the target travel distance, the type of point of interest at the origin, and the type of point of interest at the destination by sampling in the following manner:

[0176] The server obtains target travel behavior information based on at least one of the following: travel cycle information, travel time information, and travel preference information, as well as the target travel distance, the type of point of interest at the origin of the trip, and the type of point of interest at the destination of the trip.

[0177] In other words, this application, in addition to considering the target travel distance, the type of interest at the origin, and the type of interest at the destination, also needs to comprehensively consider one or more of the following information to determine the target travel behavior information: travel cycle information, travel time information, and travel preference information. For example, the target travel behavior information can be obtained based on the target travel distance, the type of interest at the origin, the type of interest at the destination, and the travel cycle information; or, based on the target travel distance, the type of interest at the origin, the type of interest at the destination, the travel cycle information, and the travel time information; or, based on the target travel distance, the type of interest at the origin, the type of interest at the destination, the travel time information, and the travel preference information; or, based on the target travel distance, the type of interest at the origin, the type of interest at the destination, the travel cycle information, the travel time information, and the travel preference information.

[0178] It should be noted that in practical applications, other permutations and combinations of the above factors can be used to determine target travel behavior information. This application is not limited to the methods mentioned above. Furthermore, other influencing factors can be considered in the process of determining target travel behavior information, which is not limited in this application.

[0179] By using the above methods, this application, in addition to considering the target travel distance, the type of interest at the origin of the trip, and the type of interest at the destination, also needs to comprehensively consider one or more of the following information: travel cycle information, travel time information, and travel preference information. This allows for a more accurate determination of the target travel behavior information and lays a data foundation for improving the accuracy of subsequent recommendation times.

[0180] In some other alternative embodiments, in the foregoing Figure 5 or Figure 6 Based on the described one or more embodiments, the process for determining the second time information in step 403 can be referred to Figure 8 Use the flowchart shown to understand the process.

[0181] like Figure 8 As shown, it includes at least the following steps:

[0182] S4031. Calculate the similarity distance between the target travel behavior information and each type of preset travel behavior information in the preset travel behavior set to obtain multiple similarities. Each similarity is used to characterize the degree of similarity between the target travel behavior information and each type of preset travel behavior information.

[0183] In one or more embodiments, after calculating the target travel behavior information, the server calculates the similarity distance between the target travel behavior information and each type of preset travel behavior information in the preset travel behavior set, thereby obtaining multiple similarities. Each similarity can characterize the degree of similarity between the target travel behavior information and each type of preset travel behavior information.

[0184] For example, suppose there are three types of preset travel behavior information in the preset travel behavior set: preset travel behavior information A, preset travel behavior information B, and preset travel behavior information C. Then, the server calculates the similarity distance between the target travel behavior information A' and preset travel behavior information A, B, and C, respectively. Thus, the similarity *a* between the target travel behavior information A' and preset travel behavior information A, the similarity *b* between the target travel behavior information A' and preset travel behavior information B, and the similarity *c* between the target travel behavior information A' and preset travel behavior information C are calculated.

[0185] S4032. Determine the maximum similarity from multiple similarities, and determine the time information corresponding to the preset travel behavior information corresponding to the maximum similarity as the second time information.

[0186] In one or more embodiments, after calculating these multiple similarities, the server can determine the maximum similarity from these multiple similarities through similarity ranking and other processing. Then, the server determines the time information corresponding to the preset travel behavior information corresponding to the maximum similarity as the second time information.

[0187] For example, assuming similarity a > similarity b > similarity c, the server determines the maximum similarity to be similarity a. Further, the server selects the time information Ta corresponding to the preset travel behavior information A with similarity a as the second time information.

[0188] By calculating similarity using the above method, we can identify the type of information most similar to the target travel behavior from a large amount of pre-set travel behavior information. The time information corresponding to this most similar pre-set travel behavior information is then used as the second time information. Adjusting the first time information accordingly can reduce the travel time cost for the target individual and improve travel efficiency.

[0189] In some alternative embodiments, since the calculation process for each similarity is essentially similar, therefore, for the aforementioned Figure 8 This application only uses any one type of preset travel behavior information (e.g., the first behavior information) as an example to illustrate how to calculate the similarity between the target travel behavior information and various preset travel behavior information. (As mentioned above...) Figure 8 Based on the described embodiments, Figure 9 A schematic diagram of a similarity calculation process provided in this application is shown.

[0190] like Figure 9 As shown, taking any type of preset travel behavior information (e.g., first behavior information) as an example, this first behavior information includes a preset travel distance, a preset origin point type of interest, and a preset destination point type of interest. The preset travel distance can be understood as the distance between the preset origin point and the preset destination point in the first behavior information. The preset origin point can be understood as the departure point corresponding to the first behavior information, and the preset destination point can be understood as the destination point corresponding to the first behavior information.

[0191] In the process of calculating the similarity between the target travel behavior information and the first behavior information, the server can calculate the similarity distance between the target travel distance and the preset travel distance to obtain the distance similarity.

[0192] Similarly, the server also needs to calculate the similarity distance between the point of interest type at the travel origin and the point of interest type at the preset origin, obtaining the first type of similarity. Furthermore, the server also calculates the similarity distance between the point of interest type at the travel destination and the point of interest type at the preset destination, obtaining the second type of similarity. It should be noted that the first type of similarity can be understood as the degree of similarity between the point of interest types at the travel origin and the preset origin. The second type of similarity can be understood as the degree of similarity between the point of interest types at the travel destination and the preset destination.

[0193] In this way, after calculating the distance similarity, the first type of similarity, and the second type of similarity, the server performs a weighted sum of the distance similarity, the first type of similarity, and the second type of similarity to obtain the similarity between the target travel behavior information and the first behavior information.

[0194] It should be noted that the described similarity distance may include, but is not limited to, cosine similarity, Euclidean distance, etc., and is not specifically limited in this application. Furthermore, the above... Figure 9 This explanation uses the calculation of the similarity between the target travel behavior information and the first behavior information as an example. In practical applications, the calculation process for the similarity between the target travel behavior information and other preset travel behavior information can also be referred to. Figure 9 The calculation process described is for your understanding and will not be elaborated upon here.

[0195] The above method comprehensively considers multiple factors such as distance, the type of POI at the origin, and the type of POI at the destination. It calculates the similarity between the target travel behavior information and the first travel behavior information by weighted summation of the similarities among various factors in other preset travel behavior information of similar categories. This not only makes the calculation results more comprehensive and accurate but also provides travel times that better match the travel needs of the target individual, thus offering more accurate travel suggestions.

[0196] In some alternative embodiments, if one or more of the following factors are considered: travel cycle information, travel time information, and travel preference information, then in the aforementioned... Figure 9 In calculating the similarity between target travel behavior information and preset travel behavior information, one or more factors, including travel cycle information, travel time information, and travel preference information, can be comprehensively considered to influence the similarity score. Based on this, in the aforementioned... Figure 9 Based on the described embodiments, Figure 10 A schematic diagram of another similarity calculation process provided in this application is shown.

[0197] like Figure 10 As shown, taking any type of preset travel behavior information (e.g., first behavior information) as an example, in addition to the aforementioned... Figure 9 In addition to the preset travel distance, preset origin point type of interest, and preset destination point type of interest mentioned in the text, it may also include preset period information, preset time information, and preset preference information. The preset period information can be understood as the weekday or non-weekday situation corresponding to the first set of travel information. The preset time information can be understood as the peak and off-peak travel situation corresponding to the first set of travel information. The preset preference information can be understood as the travel service situation corresponding to the first set of travel information.

[0198] In calculating the similarity between the target travel behavior information and the first behavior information, the server also needs to calculate the similarity distance between the travel cycle information and the preset cycle information to obtain the cycle similarity. Similarly, the server also needs to calculate the similarity distance between the travel time information and the preset time information to obtain the time similarity. It should be noted that the cycle similarity mentioned in this application can reflect the degree of similarity between the travel cycle information and the preset cycle information. The time similarity mentioned in this application can reflect the degree of similarity between the travel time information and the preset time information.

[0199] In addition, the server also needs to calculate the similarity distance between the travel preference information and the preset preference information to obtain the preference similarity. It should be noted that the preference similarity mentioned in this application can reflect the degree of similarity between the travel preference information and the preset preference information.

[0200] As an illustrative example, when considering only travel preference information, including passenger number preference information, the similarity distance between the passenger number preference information and the passenger number preference information in the preset preference information can be calculated to obtain the preference similarity. Alternatively, when considering only travel preference information, including category preference information, the similarity distance between the category preference information and the category preference information in the preset preference information can be calculated to obtain the preference similarity. Furthermore, when comprehensively considering both passenger number preference information and category preference information, the similarity distance between the passenger number preference information and the passenger number preference information in the preset preference information can be calculated first to obtain the passenger number preference similarity; then, the similarity distance between the category preference information and the category preference information in the preset preference information can be calculated to obtain the category preference similarity; finally, the passenger number preference similarity and the category preference similarity are weighted and summed to obtain the preference similarity.

[0201] Thus, the server in the aforementioned Figure 9 The similarity between the target travel behavior information and the first behavior information is obtained by weighted summation of distance similarity, first-type similarity, and second-type similarity. This can be achieved by sampling as follows: The server performs weighted summation of at least one of periodic similarity, time-time similarity, and preference similarity, as well as distance similarity, first-type similarity, and second-type similarity, to obtain the similarity between the target travel behavior information and the first behavior information.

[0202] For example, the weighted summation methods mentioned here include, but are not limited to, the following permutations and combinations: weighted summation of distance similarity, first-type similarity, second-type similarity, and periodic similarity; or weighted summation of distance similarity, first-type similarity, second-type similarity, and temporal similarity; or weighted summation of distance similarity, first-type similarity, second-type similarity, and preference similarity; or weighted summation of distance similarity, first-type similarity, second-type similarity, periodic similarity, and temporal similarity; or weighted summation of distance similarity, first-type similarity, second-type similarity, periodic similarity, and preference similarity; or weighted summation of distance similarity, first-type similarity, second-type similarity, periodic similarity, temporal similarity, and preference similarity.

[0203] It should be noted that the concepts of distance similarity, type I similarity, and type II similarity described here can be found in the preceding text. Figure 9The content described is for your understanding and will not be elaborated upon here. Additionally, the above... Figure 10 This explanation uses the calculation of the similarity between the target travel behavior information and the first behavior information as an example. In practical applications, the calculation process for the similarity between the target travel behavior information and other preset travel behavior information can also be referred to. Figure 10 The calculation process described is for your understanding and will not be elaborated upon here.

[0204] By using the above methods, not only can the accuracy of the calculation results be improved, but the time information corresponding to other types of behavior information that are more similar to the target travel behavior information can also be selected based on the final calculated similarity. This allows for accurate and flexible adjustment of the time selected by the target object, thereby obtaining a travel time that better meets the travel needs of the target object.

[0205] In some other alternative embodiments, in the foregoing Figures 5 to 10 Based on the one or more embodiments described, the method for adjusting the first time information according to the second time information as described in step 404 can be referred to... Figure 11 Understand the described flowchart. Figure 11 As shown, it includes at least the following process, namely:

[0206] The server can first determine the comparison result between the target travel distance and a preset distance threshold. It should be noted that the described comparison result includes either the target travel distance being less than the preset distance threshold, or the target travel distance being greater than or equal to the preset distance threshold. Furthermore, the preset distance threshold can be a distance range defined by the travel service platform.

[0207] In this way, after determining the comparison result, the server adjusts the first time information based on the second time information to obtain the target travel time information.

[0208] By comprehensively considering the comparison results between the distance between the origin and destination and the preset distance threshold, and by adopting different adjustment modes under different comparison results, it is possible to achieve fine-grained adjustment of the first-time information.

[0209] As an illustrative example, different adjustment modes can be selected to adjust the processing of first-time information depending on the comparison results. For instance, if the comparison result shows that the target travel distance is less than a preset distance threshold, and the time reservation is relatively urgent, then only the earliest departure time and the willing waiting time range can be adjusted. Conversely, if the comparison result shows that the target travel distance is greater than or equal to the preset distance threshold, and the time reservation is relatively less urgent, then both the earliest and latest departure times can be adjusted to meet the reservation needs of the target group at the corresponding time.

[0210] Specifically, the second time information includes a first travel time and a first pre-waiting time range. The first travel time represents the earliest travel time corresponding to the preset travel behavior information. The first pre-waiting time range mentioned in this application represents the willing waiting period. In other words, this first pre-waiting time range reflects the time interval required for a historical object with corresponding preset travel behavior information to wait between issuing a travel service request and actually receiving travel service (e.g., a driver picking it up).

[0211] Based on this, such as Figure 11 As shown, when the comparison result is that the target travel distance is less than the preset distance threshold, the earliest travel time in the first time information is adjusted to the first travel time, and the pre-waiting time range in the first time information is adjusted to the first pre-waiting time range to obtain the target travel time information.

[0212] For example, Figure 12A , Figure 12B Each of the following is a schematic diagram of an interface providing the adjustment time information in this application. For example... Figure 12A and Figure 12B As shown, assuming the starting point is A, the destination is B, and the preset distance threshold is 30km, the calculated target travel distance is 25km. Therefore, 25km < 30km. If the earliest departure time in the initial information is T1 = 16:10, and the pre-waiting time range is W1 = 20 minutes before and after, then...

[0213] like Figure 12A As shown in section (a), if the target is a business traveler with tight deadlines, the first departure time in the second time information of this working person, determined through the aforementioned steps, is T2 = 16:00, and the first pre-waiting time range is W2 = 10 minutes before and after. Therefore, from... Figure 12A As can be seen from section (a), the earliest departure time for the target traveler can be adjusted from T1 = 16:10 to T2 = 16:00. Similarly, the pre-waiting time range can be adjusted from W1 = 20 minutes before and after to W2 = 10 minutes before and after, thus obtaining the target travel time information as "departing today from 15:50 to 16:10".

[0214] Or, such as Figure 12B As shown in section (a), if the target is someone with relatively flexible time to dine at location B, then, after the aforementioned steps, the first departure time in the second time information of this diner is determined to be "T11 = 16:20", and the first pre-waiting time range W3 = 10 minutes before and after. Therefore, from... Figure 12AAs can be seen from section (a), the earliest departure time for the diner can be adjusted from T1 = 16:10 to T11 = 16:20. Similarly, the pre-waiting time range can be adjusted from W1 = 20 minutes before and after to W3 = 10 minutes before and after, thus obtaining the target departure time information as "departure at 16:10-16:30 today".

[0215] In other examples, the second time information includes a first travel time and a second travel time. The first travel time represents the earliest travel time corresponding to the preset travel behavior information. In other words, the first travel time reflects the earliest time a historical entity with corresponding preset travel behavior information expects a driver to pick it up after issuing a travel service request. Similarly, the second travel time represents the latest travel time corresponding to the preset travel behavior information. In other words, the second travel time reflects the latest time a historical entity with corresponding preset travel behavior information can accept a driver's pick-up after issuing a travel service request.

[0216] Based on this, such as Figure 11 As shown, when the comparison result is that the target travel distance is greater than or equal to the preset distance threshold, the earliest travel time in the first time information is adjusted to the first travel time, and the earliest travel time in the first time information is adjusted to the second travel time to obtain the target travel time information.

[0217] For example, Figure 13A , Figure 13B Each of the following is a schematic diagram of an interface providing the adjustment time information in this application. For example... Figure 13A and Figure 13B As shown, assuming the starting point is A, the destination is C, and the preset distance threshold is 30km, the calculated target travel distance is 80km. Therefore, it can be determined that 80km > 30km. If the earliest departure time in the first time information is T3 = 16:15 and the latest departure time is T4 = 16:35...

[0218] like Figure 13A As shown in section (a), if the target is an office worker traveling to location C, then, after the aforementioned steps, the first departure time in the office worker's second time information is determined to be T5 = 16:10, and the second departure time is T6 = 16:20. Therefore, from... Figure 13A As can be seen from section (a), the earliest departure time for this commuter can be adjusted from T3 = 16:15 to T5 = 16:10. Similarly, the latest departure time can be adjusted from T4 = 16:35 to T6 = 16:20, resulting in the target departure time information as "earliest departure today at 16:10, latest departure today at 16:20".

[0219] Or, such as Figure 13B As shown in section (a), if the target is a student visiting location C, then, after the aforementioned steps, the student's first departure time in the second time information is determined to be T5 = 16:30, and the second departure time is T6 = 16:40. Therefore, from... Figure 13B As can be seen from section (a), the earliest departure time for the student's scheduled trip can be adjusted from T3 = 16:15 to T5 = 16:30. Similarly, the latest departure time range can be adjusted from T4 = 16:35 to T6 = 16:40, thus obtaining the target departure time information as "earliest departure today at 16:30, latest departure today at 16:40".

[0220] It should be noted that the above Figures 12A to 12B , Figures 13A to 13B The various time values ​​described herein are merely illustrative. In practical applications, other values ​​may also be included, which are not limited in this application.

[0221] By comprehensively considering the comparison results between the distance between the origin and destination and a preset distance threshold, and employing different adjustment modes under different comparison results, it is possible to achieve fine-grained adjustment of the first-time information. This allows for the automatic shortening of the estimated travel time when the target travel distance is less than the preset distance threshold, reducing unnecessary waiting and stops, and improving travel efficiency. Conversely, when the target travel distance is greater than the preset distance threshold, the estimated travel time can be appropriately increased to cope with possible traffic congestion, changes in road conditions, and other factors, ensuring the reliability of the trip. Moreover, this approach also improves the user's travel experience and travel satisfaction.

[0222] In other words, current travel booking solutions typically only recommend the same earliest and latest travel times based on the same origin and destination input by different individuals, without flexibly recommending travel times for different types of people. The travel time recommendation method proposed in this application, however, comprehensively considers various factors such as point of interest type, travel distance, travel duration, travel time, and travel preferences. It adjusts and recommends the optimal travel time for different individuals, subtly improving their booking awareness, extending their waiting time, and ultimately helping the platform improve fulfillment efficiency and user travel experience. For example, with the current time being 12:00, the existing solutions would display "Departure time between 12:20 and 12:40 today" after different individuals input their origin and destination. This application, however, will consider the impact of various travel factors on travel time and show, for different types of travel needs, travel time such as "departing at 12:10-12:20 today" for commuters during the morning rush hour, and "departing at 12:30-13:00 today" for students who regularly carpool. This application does not make specific limitations.

[0223] In some other alternative embodiments, in the foregoing Figures 4 to 11 In one or more embodiments of this application, after performing step 405 to recommend the target travel time information to the target object, in another embodiment, the server can further calculate the estimated travel cost based on the target travel time information, origin information, and destination information. Subsequently, the server displays the estimated travel cost to the target object. For example, the server can send the estimated travel cost to a terminal, which then displays the estimated travel cost to the target object through the display interface of the ride-sharing service platform.

[0224] For example, such as Figure 12A As shown in section (b), after recommending target travel time information to the business traveler, the estimated cost can be displayed on the estimated cost page, for example: A. Ride-sharing, estimated cost 50 yuan; B. Ride-sharing, estimated cost 60 yuan; C. Ride-sharing, estimated cost 70 yuan, etc.; and so on. Figure 12B As shown in section (b), after recommending target travel time information to diners, the estimated cost can be displayed on the estimated cost page, for example: A. Ride-sharing, estimated cost 40 yuan; B. Ride-sharing, estimated cost 50 yuan; C. Ride-sharing, estimated cost 70 yuan, etc. Or, as... Figure 13A As shown in section (b), after recommending target travel time information to commuters, the estimated cost can be displayed on the estimated cost page, for example: A. Ride-sharing, estimated cost 100 yuan; B. Ride-sharing, estimated cost 80 yuan; C. Ride-sharing, estimated cost 90 yuan, etc.; and so on. Figure 13BAs shown in section (b), after recommending target travel time information to students, the estimated cost can be displayed on the estimated cost page, for example: A. Ride-sharing, estimated cost 70 yuan; B. Ride-sharing, estimated cost 55 yuan; C. Ride-sharing, estimated cost 68 yuan, etc.

[0225] By providing the estimated cost of the trip to the target audience in the above manner, we can not only increase the transparency of the entire travel service and avoid disputes and other issues arising from cost issues, but also help the target audience to better manage their budget and avoid delays or interruptions to their travel plans.

[0226] In the foregoing Figures 4 to 11 Based on one or more embodiments, Figure 14 Another flowchart illustrating the reservation-based travel time recommendation method provided in this application is shown. Figure 14 As shown, after the target object inputs the starting point and destination information, the target travel distance will first be determined based on the starting point and destination information.

[0227] Subsequently, the comparison result between the target travel distance and a preset distance threshold is determined. Furthermore, if the comparison result indicates that the target travel distance is greater than or equal to the preset distance threshold, the types of points of interest at the origin and destination are determined. Additionally, travel cycle information, travel time information, and travel preference information can also be determined. Thus, the target travel behavior information for this target individual is obtained. Further, by combining the preset travel behavior information of past travelers and matching the target individual's target travel behavior information with various preset travel behavior information, the determined second time information is used to adjust the first time information. Specifically, the earliest and latest travel times in the second time information are used to adjust the earliest and latest travel times in the first time information. Finally, after obtaining the target travel time information, the user proceeds to the estimated cost page to estimate the travel cost.

[0228] Conversely, if the comparison result shows that the target travel distance is less than a preset distance threshold, the types of points of interest at the origin and destination are determined. Additionally, travel cycle information, travel time information, and travel preference information can also be determined. This yields the target travel behavior information for the target individual. Further, by combining the preset travel behavior information of past travelers and matching the target individual's target travel behavior information with various preset travel behavior information, the determined second time information is used to adjust the first time information. Specifically, the earliest departure time and pre-waiting time range in the second time information are used to adjust the earliest departure time and pre-waiting time range in the first time information. Finally, after obtaining the target travel time information, the user proceeds to the estimated cost page to estimate the travel cost.

[0229] It should be noted that the methods described here for determining point-of-interest types, travel cycle information, travel time information, travel preference information, various preset travel behavior information, second-time information, and first-time information can be found in the aforementioned text. Figures 4 to 11 The content described herein will be understood in detail here, and will not be elaborated upon further.

[0230] The foregoing primarily describes the solutions provided by the embodiments of this application from a methodological perspective. It is understood that to achieve the above functions, corresponding hardware structures and / or software modules are included to execute each function. Those skilled in the art should readily recognize that, based on the modules and algorithm steps described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0231] The travel time recommendation device in this application is described in detail below. Please refer to [link / reference]. Figure 15 , Figure 15 A schematic diagram of the travel time recommendation device provided in this application is shown. Figure 15 As shown, the travel time recommendation device includes:

[0232] The acquisition unit 1501 is used to acquire the origin information of the travel origin, the destination information of the travel destination, and the first time information. The first time information is used to indicate the expected travel time range selected by the target object for the travel origin and the travel destination.

[0233] The determining unit 1502 is used to determine the target travel behavior information based on the origin information and the destination information. The target travel behavior information is used to characterize the travel behavior of the target object from the origin to the destination.

[0234] Determining unit 1502 is used to determine second time information based on target travel behavior information;

[0235] The adjustment unit 1503 is used to adjust the first time information based on the second time information to obtain the target travel time information;

[0236] Recommendation unit 1504 is used to recommend target travel time information to the target audience.

[0237] Optionally, in the above Figure 15 Based on the corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the determining unit 1502 is specifically used for:

[0238] Calculate the target travel distance between the origin and destination based on origin and destination information;

[0239] The type of point of interest for determining the origin of the trip is determined based on the origin information, and the type of point of interest for determining the destination is determined based on the destination information.

[0240] Based on the target travel distance, the type of point of interest at the origin and the type of point of interest at the destination, the target travel behavior information is obtained.

[0241] Optionally, in the above Figure 15 Based on one or more corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the determining unit 1502 is further used for:

[0242] Based on the first-time information, travel cycle information and travel time information are determined. Travel cycle information is used to indicate whether the target person's expected travel is on a weekday or a non-working day, and travel time information is used to indicate whether the target person's expected travel is during peak or off-peak hours.

[0243] Obtain the travel preference information of the target audience, which is used to indicate the travel services selected by the target audience during historical travel phases;

[0244] Based on at least one of the following: travel cycle information, travel time information, and travel preference information, as well as the target travel distance, the type of point of interest at the origin of the trip, and the type of point of interest at the destination of the trip, the target travel behavior information is obtained.

[0245] Optionally, in the above Figure 15 Based on one or more corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the determining unit 1502 is specifically used for:

[0246] Based on the target travel behavior information and each type of preset travel behavior information in the preset travel behavior set, the second time information is determined. The second time information is the travel time corresponding to the preset travel behavior information that matches the target travel behavior information in the preset travel behavior set.

[0247] Optionally, in the above Figure 15 Based on one or more corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the determining unit 1502 is specifically used for:

[0248] Calculate the similarity distance between the target travel behavior information and each type of preset travel behavior information in the preset travel behavior set to obtain multiple similarity scores. Each similarity score is used to characterize the degree of similarity between the target travel behavior information and each type of preset travel behavior information.

[0249] The maximum similarity is determined from multiple similarity values, and the time information corresponding to the preset travel behavior information corresponding to the maximum similarity is determined as the second time information.

[0250] Optionally, in the above Figure 15 Based on one or more corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the first behavior information includes a preset travel distance, a preset origin point type of interest, and a preset destination point type of interest. The first behavior information is any one of multiple preset travel behavior information in a preset travel behavior set; the determining unit 1502 is specifically used for:

[0251] Calculate the similarity distance between the target travel distance and the preset travel distance to obtain the distance similarity.

[0252] Calculate the similarity distance between the point of interest type of the travel origin and the point of interest type of the preset origin to obtain the first type similarity;

[0253] Calculate the similarity distance between the point of interest type of the travel destination and the point of interest type of the preset destination to obtain the second type similarity;

[0254] The similarity between the target travel behavior information and the first type of behavior information is obtained by weighted summation of distance similarity, first type of similarity and second type of similarity.

[0255] Optionally, in the above Figure 15 Based on one or more corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the first behavior information further includes preset period information, preset time information, and preset preference information; the determining unit 1502 is further used for:

[0256] Calculate the similarity distance between the travel cycle information and the preset cycle information to obtain the cycle similarity.

[0257] Calculate the similarity distance between the travel time information and the preset time information to obtain the time similarity.

[0258] Calculate the similarity distance between travel preference information and preset preference information to obtain preference similarity;

[0259] The similarity between the target travel behavior information and the first behavior information is obtained by weighted summation of at least one of period similarity, time similarity, and preference similarity, as well as distance similarity, first type similarity, and second type similarity.

[0260] Optionally, in the above Figure 15Based on one or more corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the adjustment unit 1503 is specifically used for:

[0261] Determine the comparison result between the target travel distance and the preset distance threshold;

[0262] Based on the comparison results, the second time information is adjusted to the first time information to obtain the target travel time information.

[0263] Optionally, in the above Figure 15 Based on one or more corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the second time information includes a first travel time and a first pre-waiting time range, wherein the first travel time is used to represent the earliest travel time corresponding to the preset travel behavior information, and the first pre-waiting time range is used to represent the period of time for which one is willing to wait; the adjustment unit 1503 is specifically used for:

[0264] When the comparison result shows that the target travel distance is less than the preset distance threshold, the earliest travel time in the first time information is adjusted to the first travel time, and the pre-waiting time range in the first time information is adjusted to the first pre-waiting time range to obtain the target travel time information.

[0265] Optionally, in the above Figure 15 Based on one or more corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the second time information includes a first travel time and a second travel time, wherein the first travel time is used to represent the earliest travel time corresponding to the preset travel behavior information, and the second travel time is used to represent the latest travel time corresponding to the preset travel behavior information; the adjustment unit 1503 is specifically used for:

[0266] When the comparison result shows that the target travel distance is greater than or equal to the preset distance threshold, the earliest travel time in the first time information is adjusted to the first travel time, and the earliest travel time in the first time information is adjusted to the second travel time to obtain the target travel time information.

[0267] Optionally, in the above Figure 15 Based on one or more corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the destination information includes the latitude and longitude of the travel destination; the determining unit 1502 is specifically used for:

[0268] Based on the latitude and longitude of the destination and a preset radius, the target area is determined. The target area is the range constructed with the latitude and longitude of the destination as the center and the preset radius.

[0269] Detect one or more candidate points of interest that contain the point of interest type within the target area;

[0270] Calculate the distance between the travel destination and each candidate point of interest, and determine the minimum distance from multiple distances;

[0271] The type of candidate point of interest corresponding to the minimum distance is determined as the type of point of interest at the destination.

[0272] Optionally, in the above Figure 15 Based on one or more corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the determining unit 1502 is specifically used for:

[0273] The point of interest information for the destination is determined based on the destination information, which includes a category field.

[0274] When the value of the category field is a preset point of interest, the point of interest type of the travel destination is determined to be the point of interest type corresponding to the preset point of interest.

[0275] Optionally, in the above Figure 15 Based on one or more corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the determining unit 1502 is specifically used for:

[0276] Based on the destination information, the point of interest information of the travel destination is determined, which includes keywords of the point of interest.

[0277] Based on the environmental information, comments, and keywords of the points of interest, determine the type of point of interest at the destination.

[0278] Optionally, in the above Figure 15 Based on one or more of the corresponding embodiments, in another embodiment of the travel time recommendation device provided in this application, the travel time recommendation device further includes a prompting unit 1505;

[0279] Specifically, the determining unit 1502 is further used to calculate the estimated travel cost based on the target travel time information, origin information and destination information after recommending the target travel time information to the target object.

[0280] The prompting unit 1505 is specifically used to prompt the target audience with the estimated cost of travel.

[0281] The above describes the travel time recommendation device in the embodiments of this application from the perspective of modular functional entities. The following describes the computer device in the embodiments of this application from the perspective of hardware processing. Figure 16This is a schematic diagram of an optional hardware structure of the computer device provided in this application. The computer device can vary considerably due to differences in configuration or performance, including but not limited to the aforementioned... Figure 15 The travel time recommendation device described in the text, etc. For example... Figure 16 As shown, the computer device can vary significantly due to differences in configuration or performance, and may include one or more central processing units (CPUs) 322 (e.g., one or more processors) and memory 332, and one or more storage media 330 (e.g., one or more mass storage devices) for storing application programs 342 or data 344. The memory 332 and storage media 330 can be temporary or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the figure), each module including a series of instruction operations on the processing device. Furthermore, the CPU 322 may be configured to communicate with the storage media 330 and execute a series of instruction operations stored in the storage media 330 on the computer device. Exemplarily, the CPU 322 is used to execute the application program 342 stored in the storage media 330, thereby implementing the reservation-based travel time recommendation method provided in the above embodiments of this application.

[0282] The computer device may also include one or more power supplies 326, one or more wired or wireless network interfaces 350, one or more input / output interfaces 358, and / or one or more operating systems 341, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0283] For example, Figure 16 The central processing unit 322 can call computer execution instructions stored in memory 332 to cause the computer device to perform actions such as... Figures 4 to 14 The method in the corresponding method embodiment.

[0284] Specifically, Figure 15 The functions / implementation process of the determination unit 1502, adjustment unit 1503, recommendation unit 1504, and prompting unit 1505 can be achieved through... Figure 16 The central processing unit 322 in the memory calls computer execution instructions stored in the memory 332 to achieve this. Figure 15 The function / implementation process of the acquisition unit 1501 can be achieved through... Figure 16 The input / output interface 358 is used to implement this.

[0285] The steps performed by the computer device in the above embodiments can be based on this Figure 16The computer device structure shown.

[0286] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the methods described in the foregoing embodiments.

[0287] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the methods described in the foregoing embodiments.

[0288] It is understood that in the specific embodiments of this application, data such as user information are involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0289] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0290] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0291] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0292] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0293] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a server or terminal device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0294] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for recommending travel times based on reservations, characterized in that, The method includes: Obtain origin information of the travel origin, destination information of the travel destination, and first time information. The first time information is used to indicate the expected travel time range selected by the target object for the travel origin and the travel destination. Target travel behavior information is determined based on the origin information and the destination information. The target travel behavior information is used to characterize the travel behavior of the target object from the origin to the destination. The second time information is determined based on the target travel behavior information; The first time information is adjusted based on the second time information to obtain the target travel time information; The target travel time information is recommended to the target object.

2. The method according to claim 1, characterized in that, The target travel behavior information is determined based on the origin information and the destination information, including: Calculate the target travel distance between the travel origin and the travel destination based on the origin information and the destination information; The type of point of interest for the starting point is determined based on the starting point information, and the type of point of interest for the destination is determined based on the destination information. The target travel behavior information is obtained based on the target travel distance, the type of point of interest at the travel origin, and the type of point of interest at the travel destination.

3. The method according to claim 2, characterized in that, The method further includes: Based on the first time information, travel cycle information and travel time information are determined. The travel cycle information is used to indicate whether the target object is on a weekday or a non-working day when it is expected to travel, and the travel time information is used to indicate whether the target object is experiencing a travel peak or valley when it is expected to travel. Obtain the travel preference information of the target object, which is used to indicate the travel services selected by the target object during historical travel phases; Based on the target travel distance, the point of interest type of the travel origin, and the point of interest type of the travel destination, the target travel behavior information is obtained, including: The target travel behavior information is obtained based on at least one of the travel cycle information, the travel time information, and the travel preference information, as well as the target travel distance, the type of interest of the travel origin, and the type of interest of the travel destination.

4. The method according to any one of claims 2 to 3, characterized in that, The second time information is determined based on the target travel behavior information, including: Based on the target travel behavior information and each type of preset travel behavior information in the preset travel behavior set, second time information is determined. The second time information is the travel time corresponding to the preset travel behavior information in the preset travel behavior set that matches the target travel behavior information.

5. The method according to claim 4, characterized in that, Based on the target travel behavior information and the preset travel behavior information for each type in the preset travel behavior set, second time information is determined, including: Calculate the similarity distance between the target travel behavior information and each type of preset travel behavior information in the preset travel behavior set to obtain multiple similarities. Each similarity is used to characterize the degree of similarity between the target travel behavior information and each type of preset travel behavior information. The maximum similarity is determined from the multiple similarities, and the time information corresponding to the preset travel behavior information corresponding to the maximum similarity is determined as the second time information.

6. The method according to claim 5, characterized in that, The first behavioral information includes the preset travel distance, the type of interest at the preset starting point, and the type of interest at the preset destination. The first behavioral information is any one of the multiple types of preset travel behavioral information in the preset travel behavior set. Calculate the similarity distance between the target travel behavior information and each type of preset travel behavior information in the preset travel behavior set to obtain multiple similarity scores, including: Calculate the similarity distance between the target travel distance and the preset travel distance to obtain the distance similarity. Calculate the similarity distance between the point of interest type of the travel origin and the point of interest type of the preset origin to obtain the first type similarity; Calculate the similarity distance between the point of interest type of the travel destination and the point of interest type of the preset destination to obtain the second type similarity; The similarity between the target travel behavior information and the first behavior information is obtained by weighted summation of the distance similarity, the first type similarity, and the second type similarity.

7. The method according to claim 6, characterized in that, The first line of information also includes preset period information, preset time information, and preset preference information; The method further includes: Calculate the similarity distance between the travel cycle information and the preset cycle information to obtain the cycle similarity. Calculate the similarity distance between the travel time information and the preset time information to obtain the time similarity. Calculate the similarity distance between the travel preference information and the preset preference information to obtain the preference similarity. The similarity between the target travel behavior information and the first behavior information is obtained by weighted summing of the distance similarity, the first type of similarity, and the second type of similarity, including: The similarity between the target travel behavior information and the first behavior information is obtained by weighted summing of at least one of the period similarity, the time similarity, and the preference similarity, as well as the distance similarity, the first type similarity, and the second type similarity.

8. The method according to any one of claims 2 to 7, characterized in that, The first time information is adjusted based on the second time information to obtain the target travel time information, including: Determine the comparison result between the target travel distance and the preset distance threshold; Based on the comparison results, the second time information is used to adjust the first time information to obtain the target travel time information.

9. The method according to claim 8, characterized in that, The second time information includes a first travel time and a first pre-waiting time range. The first travel time is used to represent the earliest travel time corresponding to the preset travel behavior information, and the first pre-waiting time range is used to represent the time period for which one is willing to wait. Based on the comparison results, the second time information is used to adjust the first time information to obtain the target travel time information, including: When the comparison result indicates that the target travel distance is less than the preset distance threshold, the earliest travel time in the first time information is adjusted to the first travel time, and the pre-waiting time range in the first time information is adjusted to the first pre-waiting time range to obtain the target travel time information.

10. The method according to claim 8, characterized in that, The second time information includes a first travel time and a second travel time. The first travel time is used to represent the earliest travel time corresponding to the preset travel behavior information, and the second travel time is used to represent the latest travel time corresponding to the preset travel behavior information. Based on the comparison results, the second time information is used to adjust the first time information to obtain the target travel time information, including: When the comparison result indicates that the target travel distance is greater than or equal to the preset distance threshold, the earliest travel time in the first time information is adjusted to the first travel time, and the earliest travel time in the first time information is adjusted to the second travel time to obtain the target travel time information.

11. The method according to any one of claims 2 to 10, characterized in that, The destination information includes the latitude and longitude of the destination. Determining the point of interest type of the travel destination based on the destination information includes: Based on the latitude and longitude of the destination and a preset radius, a target area is determined. The target area is a range constructed with the latitude and longitude of the destination as the center and the preset radius. Detect one or more candidate points of interest that contain the point of interest type within the target area; Calculate the distance between the travel destination and each of the candidate points of interest, and determine the minimum distance from a plurality of the distances; The type of candidate point of interest corresponding to the minimum distance is determined as the type of point of interest of the travel destination.

12. The method according to any one of claims 2 to 10, characterized in that, Determining the point of interest type of the travel destination based on the destination information includes: Based on the destination information, the point of interest information of the travel destination is determined, and the point of interest information of the travel destination includes a classification field; When the value of the classification field is a preset point of interest, the point of interest type of the travel destination is determined to be the point of interest type corresponding to the preset point of interest.

13. The method according to any one of claims 2 to 10, characterized in that, Determining the point of interest type of the travel destination based on the destination information includes: Based on the destination information, the point of interest information of the travel destination is determined, and the point of interest information of the travel destination includes keywords of the point of interest. Based on the environmental information of the points of interest, the comment information of the points of interest, and the keywords, the type of point of interest for the travel destination is determined.

14. The method according to any one of claims 1 to 13, characterized in that, After recommending the target travel time information to the target object, the method further includes: Based on the target travel time information, the origin information, and the destination information, calculate the estimated travel cost; The estimated travel cost is displayed to the target audience.

15. A travel time recommendation device, characterized in that, include: The acquisition unit is used to acquire origin information of the origin, destination information of the destination, and first time information. The first time information is used to indicate the expected travel time range selected by the target object for the origin and destination. The determining unit is used to determine target travel behavior information based on the starting point information and the destination information, wherein the target travel behavior information is used to characterize the travel behavior of the target object from the starting point to the destination. The determining unit is used to determine the second time information based on the target travel behavior information; The adjustment unit is used to adjust the first time information based on the second time information to obtain the target travel time information; The recommendation unit is used to recommend the target travel time information to the target object.

16. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the reservation-based travel time recommendation method according to any one of claims 1 to 14.

17. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the reservation-based travel time recommendation method according to any one of claims 1 to 14.

18. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the reservation-based travel time recommendation method as described in any one of claims 1 to 14.

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