Travel scheme for rapid taxi taking of online taxi-hailing passengers

Abstract

The invention relates to a travel scheme for rapid taxi taking of online taxi-hailing passengers, and relates to the technical field of data analysis, registration information and historical order information are collected on an online taxi-hailing platform and are used for analyzing travel habits and preferences of passengers, and the registration information comprises registration information of passengers and drivers. Calculating an optimal boarding point according to the current position information of the passenger and travel habits and preferences, pushing the calculated boarding point information to a matched driver, enabling the driver to go to the point after confirmation, enabling the passenger to go to the destination of the passenger through offline communication with the driver after confirmation of boarding, and recording the driving track of the driver. The online car-hailing platform automatically generates an order according to the driving track, the getting-on and getting-off location, the driving time and the mileage information of the passenger, the generated order information is pushed to the passenger through a mobile application, and after the passenger confirms payment, the online car-hailing platform processes a payment request and stores the payment request in a database. A more convenient taxi-taking travel scheme is provided, and a wider range of people can be served.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and more specifically, to a travel solution for online ride-hailing passengers to quickly take a taxi. Background Art

[0002] With the widespread adoption of mobile internet and the rise of the sharing economy, the online ride-hailing industry has grown rapidly, addressing many of the challenges of the traditional taxi industry, such as unstable service, slow demand response, and poor user experience. However, this model also faces challenges, particularly in meeting the needs of specific demographics, such as the elderly and other users unfamiliar with smartphones. For these users, the inability to enter their starting and destination points through traditional online ride-hailing platforms can lead to service inconvenience and even prevent them from using online ride-hailing services. Therefore, a fast-hailing solution for online ride-hailing passengers that addresses this need is crucial.

[0003] With the development of technology and the diversification of user needs, providing fast and accessible ride-hailing services is crucial for improving user experience and expanding market reach. Developing a targeted solution for faster ride-hailing services tailored to the specific needs of the elderly and less technologically savvy users can ensure smooth and safe access to ride-hailing services for all. Summary of the Invention

[0004] The present invention aims to solve the technical problems existing in the prior art and provide a travel solution for online car-hailing passengers to quickly take a taxi, so as to solve the problems raised in the above background technology.

[0005] The present invention solves the above-mentioned technical problem with the following technical solution: A travel solution for online car-hailing passengers to quickly take a taxi, specifically comprising the following steps:

[0006] Step 101: Collect registration information and historical order information on the online ride-hailing platform to analyze passengers' travel habits and preferences;

[0007] Step 102: Calculate the optimal boarding point based on the passenger's current location information and travel habits and preferences, and push the calculated boarding point information to the matching driver. The driver confirms and proceeds to the location.

[0008] Step 103: After the passenger confirms boarding, the driver will communicate offline with the passenger's destination and record the driver's driving trajectory. When the passenger arrives at the destination, an order is generated and pushed to the passenger, who pays.

[0009] In a preferred embodiment, in step 101, registration information and historical order information are collected on the online ride-hailing platform to analyze passengers' travel habits and preferences. The registration information includes the registration information of passengers and drivers. The specific steps are as follows:

[0010] Step A1, Data Collection and Storage: The registration information is collected by drivers registering on the online ride-hailing platform, completing their personal and vehicle information, and reporting their geographic location in real time so that they can be matched with nearby passengers. Passengers fill in their personal information on the platform, including their name, contact information, and payment method;

[0011] The collection of historical order information refers to the automatic recording of detailed information of each transaction by the online ride-hailing platform after the passenger successfully places an order and completes the trip, including: starting and ending points, travel time and duration, coordinates of the origin and destination, the type of vehicle and service selected by the passenger, and driver information;

[0012] All registration information and historical order information will be stored in the backend database and updated regularly to facilitate subsequent data analysis and report generation. The platform must ensure data security, comply with relevant laws and regulations, and protect user privacy;

[0013] Step A2, Data Analysis and Feature Extraction: Utilizing big data analysis technology, we integrate passengers' historical order data to identify their travel frequency, common boarding and alighting locations, and preferred time periods. Based on this historical data, we build a passenger profile model, including travel habits, preferred vehicle types, payment methods, and user feedback, to facilitate personalized service delivery. Based on the passenger's historical behavior data and profile, we can predict passengers' travel needs during specific time periods in advance, recommend drivers to passengers in advance, and improve response speed.

[0014] In a preferred embodiment, in step 102, the optimal boarding point is calculated based on the passenger's current location information and travel habits and preferences, and the calculated boarding point information is pushed to the matching driver. The driver confirms and proceeds to the location. The specific steps are as follows:

[0015] Step B1: Extract the historical order data of passengers from the database, analyze their common boarding and alighting locations, travel time, vehicle type preference, and payment method, and represent the passenger's travel frequency within a period of time as F. Analyze the starting and ending points in the historical order data using a clustering algorithm to identify the boarding and alighting locations most frequently used by passengers, which is represented by L = {(x1, y1), (x2, y2), ..., (x n ,y n )}, where (x n ,y n ) are the coordinates of frequently used locations. The peak travel period of passengers is identified based on the mode of historical travel time, which is represented by T. The travel frequency, boarding and alighting locations, and time preference characteristics are combined to form a passenger profile, which is represented by I p =ω1F+ω2L+ω3T, where ω1, ω2, and ω3 are the weight coefficients of the features, reflecting the influence of each feature on the passenger profile;

[0016] Step B2: Use the passenger's device to obtain real-time location information including longitude and latitude. Combine the passenger's current location and historical preferences to generate multiple candidate boarding points, including public transportation stations, convenient locations in shopping malls, and the passenger's historical boarding points. The candidate boarding points are represented as a set: C = C1, C2, ..., C m , the coordinates of each candidate point are C m =(x m ,y m ), calculate a comprehensive score for each candidate boarding point, and select the candidate boarding point with the highest score as the final recommended boarding point based on the comprehensive score. The specific calculation formula is as follows:

[0017] S(C m )=α1·S distance (C m )+α2·S history (C m )+α3·S match (C m )

[0018]

[0019] Among them, S distance (C m ) is the distance from the passenger's current location to the candidate point C m The score is calculated based on the distance. The shorter the distance, the higher the score. history (C m ) is a score based on the walking time of the passenger to the candidate boarding point. The longer the time, the higher the score. match (C m ) is the similarity score between the candidate boarding point and the passenger's historical boarding location. The frequency of the boarding point is counted. The higher the frequency, the higher the score. recommended It recommends the best boarding point based on the passenger's real-time location and historical preferences.

[0020] In a preferred embodiment, in step 103, after the passenger confirms boarding, the passenger communicates with the driver offline to travel to the passenger's destination, records the driver's driving trajectory, and generates an order when the passenger arrives at the destination and pushes it to the passenger, who pays. The specific steps are as follows:

[0021] Step C1: The passenger clicks the "Confirm Boarding" button on the mobile app. The ride-hailing platform records the passenger's boarding location, time, and related information, and communicates with the driver about the destination. The driver's device obtains real-time location information, records the driving trajectory, and preserves the location information at every moment.

[0022] Step C2: Use GPS signals to detect whether the passenger has arrived at the destination. When the driver arrives at the destination, the passenger confirms arrival in the app, and the time of passenger disembarkation is recorded on the online ride-hailing platform, triggering the process of generating an order.

[0023] Step C3: The online ride-hailing platform automatically generates an order based on the passenger's driving trajectory, boarding location, disembarkation location, driving time, and mileage information. The order includes: passenger information, driver information, driving route, completion time, and fee calculation. The generated order information is pushed to the passenger through the mobile application. The passenger confirms the order details after seeing them in the application and checks the fee details. After the passenger confirms the payment, the online ride-hailing platform processes the payment request and interacts with the third-party payment platform to complete the transaction. After the payment is completed, a payment success notification is sent to the passenger and driver, and the order status is updated to "completed" and stored in the database.

[0024] The beneficial effects of the present invention are: collecting registration information and historical order information on the online car-hailing platform, which is used to analyze the passengers' travel habits and preferences, calculating the optimal boarding point based on the passengers' current location information and travel habits and preferences, and pushing the calculated boarding point information to the matching driver. The driver confirms and goes to the location. After the passenger confirms to get on the car, he communicates with the driver offline to go to the passenger's destination, records the driver's driving trajectory, and generates an order when the passenger arrives at the destination and pushes it to the passenger. The present invention provides passengers with a more convenient way to take a taxi. Passengers do not need to manually locate and enter the destination, which are complicated operations. They can directly click to place an order on the platform, and the driver can pick up the passenger according to the passenger's boarding point pushed by the platform. The final order is generated by the driver's actual driving trajectory and pushed to the passenger for payment, which simplifies the pre-order process for passengers and does not require passengers to enter the destination in advance, bringing a better car experience to passengers and improving the driver's service awareness and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0027] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically specified.

[0028] In the description of this application, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art will recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in this application.

[0029] Example 1

[0030] This embodiment provides Figure 1 The following is a travel plan for a ride-hailing passenger to quickly hail a ride, which specifically includes the following steps:

[0031] Step 101: Collect registration information and historical order information on the online ride-hailing platform to analyze passengers' travel habits and preferences;

[0032] Step 102: Calculate the optimal boarding point based on the passenger's current location information and travel habits and preferences, and push the calculated boarding point information to the matching driver. The driver confirms and proceeds to the location.

[0033] Step 103: After the passenger confirms boarding, the driver will communicate offline with the passenger's destination and record the driver's driving trajectory. When the passenger arrives at the destination, an order is generated and pushed to the passenger, who pays.

[0034] Preferably, in step 101, registration information and historical order information are collected on the online ride-hailing platform to analyze passengers' travel habits and preferences, which can help predict demand peaks in specific time periods and locations and allocate resources in advance. The registration information includes the registration information of passengers and drivers. The specific steps are as follows:

[0035] Step A1, Data Collection and Storage: The registration information is collected by drivers registering on the online ride-hailing platform, completing their personal and vehicle information, and reporting their geographic location in real time to match them with nearby passengers. Passengers fill in their personal information on the platform, including their name, contact information, and payment method. The platform uses their registration information and historical order data to improve the response speed to passenger needs and enhance satisfaction by quickly matching them with suitable vehicles and drivers.

[0036] The collection of historical order information refers to the automatic recording of detailed information of each transaction by the online ride-hailing platform after the passenger successfully places an order and completes the trip, including: starting and ending points, travel time and duration, coordinates of the origin and destination, the type of vehicle and service selected by the passenger, and driver information;

[0037] All registration information and historical order information will be stored in the backend database and updated regularly to facilitate subsequent data analysis and report generation. The platform must ensure data security, comply with relevant laws and regulations, and protect user privacy;

[0038] Step A2, Data Analysis and Feature Extraction: Utilizing big data analysis technology, we integrate passengers' historical order data to identify their travel frequency, common boarding and alighting locations, and preferred time periods. Based on this historical data, we build a passenger profile model, including travel habits, preferred vehicle types, payment methods, and user feedback, to facilitate personalized service delivery. Based on the passenger's historical behavior data and profile, we can predict passengers' travel needs during specific time periods in advance, recommend drivers to passengers in advance, and improve response speed.

[0039] Preferably, in step 102, the optimal boarding point is calculated based on the passenger's current location information and travel habits and preferences, and the calculated boarding point information is pushed to the matching driver. The driver confirms and then goes to the location, which can shorten the passenger's waiting time and improve passenger satisfaction. The specific steps are as follows:

[0040] Step B1: Extract the historical order data of passengers from the database, analyze their common boarding and alighting locations, travel time, vehicle type preference, and payment method, and represent the passenger's travel frequency within a period of time as F. Analyze the starting and ending points in the historical order data using a clustering algorithm to identify the boarding and alighting locations most frequently used by passengers, which is represented by L = {(x1, y1), (x2, y2), ..., (x n ,y n )}, where (x n ,y n) are the coordinates of frequently used locations. By analyzing the feedback from drivers and passengers on recommended pick-up points, we can better understand their preferences and habits to continuously improve services. Based on the mode of historical travel times, we can identify the peak travel time of passengers, represented by T. We combine the travel frequency, pick-up and drop-off locations, and time preference characteristics to form a passenger profile, represented by I p =ω1F+ω2L+ω3T, where ω1, ω2, and ω3 are the weight coefficients of the features, reflecting the influence of each feature on the passenger profile;

[0041] Step B2: Use the passenger's device to obtain real-time location information including longitude and latitude. Combine the passenger's current location and historical preferences to generate multiple candidate boarding points, including public transportation stations, convenient locations in shopping malls, and the passenger's historical boarding points. The candidate boarding points are represented as a set: C = C1, C2, ..., C m , the coordinates of each candidate point are C m =(x m ,y m ), calculate a comprehensive score for each candidate boarding point, and select the candidate boarding point with the highest score as the final recommended boarding point based on the comprehensive score. By selecting the best boarding point close to the passenger, the driver's idle driving distance is reduced, and the overall efficiency and income are improved. The specific calculation formula is as follows:

[0042] S(C m )=α1·S distance (C m )+α2·S history (C m )+α3·S match (C m )

[0043]

[0044] Among them, S distance (C m ) is the distance from the passenger's current location to the candidate point C m The score is calculated based on the distance. The shorter the distance, the higher the score. history (C m ) is a score based on the walking time of the passenger to the candidate boarding point. The longer the time, the higher the score. match (C m ) is the similarity score between the candidate boarding point and the passenger's historical boarding location. The frequency of the boarding point is counted. The higher the frequency, the higher the score. recommended It recommends the best boarding point based on the passenger's real-time location and historical preferences.

[0045] Preferably, in step 103, after the passenger confirms boarding, the passenger communicates with the driver offline to the passenger's destination, which can enhance interaction, allow the passenger to more accurately feedback their needs, optimize the travel experience, and record the driver's driving trajectory, which can ensure the accuracy of information generated by subsequent orders and avoid disputes caused by data loss. When the passenger arrives at the destination, the order is generated and pushed to the passenger, who pays. The specific steps are as follows:

[0046] Step C1: The passenger clicks the "Confirm Boarding" button on the mobile app. The ride-hailing platform records the passenger's boarding location, time, and related information, and communicates with the driver about the destination. The driver's device obtains real-time location information, records the driving trajectory, and preserves the location information at every moment.

[0047] Step C2: Use GPS signals to detect whether the passenger has arrived at the destination. When the driver arrives at the destination, the passenger confirms arrival in the app, and the ride-hailing platform records the passenger's disembarkation time, triggering the order generation process. The accumulated travel data is used to analyze passenger preferences. The platform can push personalized offers and services based on historical travel habits, increasing user return rates.

[0048] Step C3: The online ride-hailing platform automatically generates an order based on the passenger's driving trajectory, boarding location, disembarkation location, driving time, and mileage information. The order includes: passenger information, driver information, driving route, completion time, and fee calculation. The generated order information is pushed to the passenger through the mobile application. The passenger confirms the order details after seeing them in the application and checks the fee details. After the passenger confirms the payment, the online ride-hailing platform processes the payment request and interacts with the third-party payment platform to complete the transaction. After the payment is completed, a payment success notification is sent to the passenger and driver, and the order status is updated to "completed" and stored in the database. This improves the passenger's travel experience, ensures the accuracy of orders and payment security, and also helps with data analysis and service optimization, enhancing the driver's utilization efficiency.

[0049] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0050] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0051] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0052] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0053] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0054] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0055] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A travel solution for online car-hailing passengers to quickly take a taxi, characterized by: The specific steps include: Step 101: Collect registration information and historical order information on the online ride-hailing platform to analyze passengers' travel habits and preferences; Step 102: Calculate the optimal boarding point based on the passenger's current location information and travel habits and preferences, and push the calculated boarding point information to the matching driver. The driver confirms and proceeds to the location. Step 103: After the passenger confirms boarding, the driver will communicate offline with the passenger's destination and record the driver's driving trajectory. When the passenger arrives at the destination, an order is generated and pushed to the passenger, who pays.

2. A travel solution for online ride-hailing passengers to quickly hail a taxi according to claim 1, characterized in that: In step 101, registration information and historical order information are collected on the online ride-hailing platform to analyze passengers' travel habits and preferences. The registration information includes the registration information of passengers and drivers. The specific steps are as follows: Step A1, Data Collection and Storage: The registration information is collected by drivers registering on the online ride-hailing platform, completing their personal and vehicle information, and reporting their geographic location in real time so that they can be matched with nearby passengers. Passengers fill in their personal information on the platform, including their name, contact information, and payment method; The collection of historical order information refers to the automatic recording of detailed information of each transaction by the online ride-hailing platform after the passenger successfully places an order and completes the trip, including: starting and ending points, travel time and duration, coordinates of the origin and destination, the type of vehicle and service selected by the passenger, and driver information; All registration information and historical order information will be stored in the backend database and updated regularly to facilitate subsequent data analysis and report generation. The platform must ensure data security, comply with relevant laws and regulations, and protect user privacy; Step A2: Data Analysis and Feature Extraction: Utilize big data analysis technology to integrate passengers' historical order data, identify their travel frequency, common boarding and alighting locations, and time preferences, and build a passenger profile model based on this historical data.

3. A travel solution for online car-hailing passengers to quickly take a taxi according to claim 1, characterized in that: The optimal boarding point is calculated based on the passenger's current location information and travel habits and preferences, and the calculated boarding point information is pushed to the matching driver. The driver confirms and goes to the location. The specific steps are as follows: Step B1: Extract the historical order data of passengers from the database, analyze their common boarding and alighting locations, travel time, vehicle type preference, and payment method, and represent the passenger's travel frequency within a period of time as F. Use the clustering algorithm to analyze the starting and ending points in the historical order data to identify the boarding and alighting locations used by the passengers, which is represented as L = {(x1, y1), (x2, y2), ..., (x n ,y n )}, where (x n ,y n ) are the coordinates of frequently used locations. The mode of historical travel times is used to identify the peak travel period of passengers, denoted by T. The travel frequency, boarding and alighting locations, and time preference characteristics are combined to form a passenger profile. Step B2: Use the passenger's device to obtain real-time location information including longitude and latitude. Combine the passenger's current location and historical preferences to generate multiple candidate boarding points, including public transportation stations, convenient locations in shopping malls, and the passenger's historical boarding points. The candidate boarding points are represented as a set: C = C1, C2, ..., C m , the coordinates of each candidate point are C m =(x m ,y m ), calculate a comprehensive score for each candidate boarding point, and select the candidate boarding point with the highest score as the final recommended boarding point based on the comprehensive score.

4. A travel solution for online car-hailing passengers to quickly take a taxi according to claim 3, characterized in that: In step B1, the passenger portrait is represented by I p =ω1F+ω2L+ω3T, where ω1, ω2, and ω3 are the weight coefficients of the features, reflecting the degree of influence of each feature on the passenger profile. F represents the travel frequency, L represents the boarding and alighting locations used by passengers, and T represents the peak travel time of passengers.

5. A travel solution for online car-hailing passengers to quickly take a taxi according to claim 3, characterized in that: In step B2, a comprehensive score is calculated for each candidate boarding point. Based on the comprehensive score, the candidate boarding point with the highest score is selected as the final recommended boarding point. The specific calculation formula is as follows: S(C m )=α1·S distance (C m )+α2·S history (C m )+α3·S match (C m ) Among them, S distance (C m ) is the distance from the passenger's current location to the candidate point C m Rating, S history (C m ) is the score based on the walking time of the passenger to the candidate boarding point, S match (C m ) is the similarity score between the candidate boarding point and the passenger’s previous boarding location, C recommended It recommends the best boarding point based on the passenger's real-time location and historical preferences.

6. A travel solution for online car-hailing passengers to quickly take a taxi according to claim 1, characterized in that: In step 103, after the passenger confirms boarding, the passenger communicates with the driver offline to travel to the passenger's destination, records the driver's driving trajectory, and generates an order when the passenger arrives at the destination and pushes it to the passenger, who pays. The specific steps are as follows: Step C1: The passenger clicks the "Confirm Boarding" button on the mobile app. The ride-hailing platform records the passenger's boarding location and time, communicates with the driver about the destination, and obtains real-time location information through the driver's device, recording the driving trajectory and preserving the location information at every moment. Step C2: Use GPS signals to detect whether the passenger has arrived at the destination. When the driver arrives at the destination, the passenger confirms arrival in the app, and the time the passenger gets off the car is recorded on the online ride-hailing platform, triggering the process of generating an order. Step C3: The ride-hailing platform automatically generates an order based on the passenger's driving trajectory, boarding location, disembarkation location, driving time, and mileage information, and pushes the generated order information to the passenger through the mobile application. The passenger confirms the order details after viewing them in the application and views the fee details. After the passenger confirms the payment, the ride-hailing platform processes the payment request and interacts with the third-party payment platform to complete the transaction. After the payment is completed, a payment success notification is sent to the passenger and driver, and the order status is updated to "completed" and stored in the database.

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