Site determination method, device, electronic device, and computer-readable storage medium

Abstract

An embodiment of the present invention discloses a site determination method, device, electronic device and computer-readable storage medium, the method comprising obtaining historical order data of online ride-hailing services; determining a plurality of aggregation point sets based on the distribution of pick-up point locations and / or alighting point locations in the historical order data of online ride-hailing services; screening the points in the aggregation point set based on a preset screening strategy to determine a corresponding candidate point set; determining a reference point based on the candidate points in the candidate point set; and determining a fixed site based on the positional relationship between the reference point and the route points on a fixed route. Thus, the above method can set fixed sites for fixed routes based on the actual ride needs of passengers, thereby gathering carpooling needs at fixed sites and improving the carpooling rate of the trip.

Description

Technical Field

[0001] The present invention relates to the field of transportation technology, and in particular to a site determination method, device, electronic device and computer-readable storage medium. Background Art

[0002] In standard carpooling models, platforms often recommend multiple dynamic stops based on their proximity to the passenger's departure and destination points in real time. While this stop recommendation method can improve the efficiency of carpooling trips to a certain extent, it has a certain ceiling on improving the completion rate of carpooling trips when order demand is relatively dispersed. Summary of the Invention

[0003] In view of this, an object of embodiments of the present invention is to provide a site determination method, device, electronic device, and computer-readable storage medium to improve the completion rate of an itinerary.

[0004] In a first aspect, an embodiment of the present invention provides a method for determining a station, which is applied to online bus booking. The method includes:

[0005] Get historical ride-hailing order data;

[0006] Determining a plurality of aggregation point sets based on the distribution of pickup point locations and / or drop-off point locations in the historical ride-hailing order data;

[0007] Screening the points in the clustered point set based on a preset screening strategy to determine a corresponding candidate point set;

[0008] Determine a reference point according to the candidate points in the candidate point set;

[0009] The fixed site is determined according to the positional relationship between the reference point and the route points on the fixed route.

[0010] Optionally, screening the points in the clustered point set based on a preset screening strategy to determine the corresponding candidate point set includes:

[0011] Extracting points whose heat values reach a preset heat value from the aggregation point set to determine a preselected point set;

[0012] The points in the preselected point set are clustered based on the distances between the points to determine a candidate point set corresponding to the preselected point set.

[0013] Optionally, extracting points whose heat values reach a preset heat value from the gathering point set to determine the preselected point set includes:

[0014] Extracting points whose heat values reach a preset heat value from the aggregation point set to determine an initial point set;

[0015] The points in the initial point set are thinned out based on distance to determine a preselected point set.

[0016] Optionally, determining a reference point according to candidate points in the candidate point set includes:

[0017] Sort the candidate points in the candidate point set according to their heat values;

[0018] The candidate point with the highest heat value is determined as the reference point.

[0019] Optionally, determining a reference point according to candidate points in the candidate point set includes:

[0020] Determine at least one recommended point corresponding to the candidate point set, where the recommended point is a point whose total distance from each candidate point in the candidate point set is less than a preset distance value;

[0021] The reference point position corresponding to the candidate point set is determined according to each of the recommended points.

[0022] Optionally, determining the reference point corresponding to the candidate point set according to each of the recommended points includes:

[0023] A reference point is determined from the recommended points based on the sum of distances and / or heat values corresponding to the recommended points.

[0024] Optionally, determining the fixed site according to the positional relationship between the reference point and the route point on the fixed route includes:

[0025] Determine the route point closest to the reference point on the fixed route as an available site;

[0026] Determining matching parameters between the available site and each candidate point in the candidate point set;

[0027] evaluating the available sites according to the matching parameters;

[0028] In response to the available site meeting a preset condition, the available site is determined to be a fixed site.

[0029] Optionally, the matching parameter includes at least one of a distance parameter, a convenience parameter, and a security parameter;

[0030] The preset condition includes at least one of a distance parameter being less than a preset distance, a convenience parameter being greater than a preset convenience parameter value, and a safety factor being greater than a preset safety factor value;

[0031] The distance parameter is used to characterize the distance between the candidate point and the available site, the convenience parameter is used to characterize the convenience of the candidate point reaching the available site, and the safety parameter is used to characterize the safety factor of the candidate point reaching the available site.

[0032] Optionally, determining at least one fixed stop in the fixed route according to each candidate point set further includes:

[0033] The position of each of the fixed sites is adjusted based on a preset constraint condition, where the constraint condition is determined according to at least one of the distance between the fixed sites, the flow of people at the fixed sites, and a threshold value for the number of fixed sites.

[0034] In a second aspect, an embodiment of the present invention is intended to provide a site recommendation method for online bus booking, the method comprising:

[0035] Get historical ride-hailing order data;

[0036] Generate fixed routes based on historical ride-hailing order data;

[0037] Determining a plurality of aggregation point sets based on the distribution of pickup point locations and / or drop-off point locations in the historical ride-hailing order data;

[0038] Screening the points in the clustered point set based on a preset screening strategy to determine a corresponding candidate point set;

[0039] Determine a reference point according to the candidate points in the candidate point set;

[0040] Determining a fixed site based on a positional relationship between the reference point and a route point on the fixed route;

[0041] Storing the fixed sites corresponding to each candidate point set in a site database;

[0042] In response to receiving a ride request, a fixed stop in a stop library is recommended to a passenger based on a ride point in the ride request.

[0043] In a third aspect, an embodiment of the present invention is directed to providing a station determination device, which is applied to online bus booking, and the device includes:

[0044] A data acquisition unit, used to obtain historical order data of online ride-hailing services;

[0045] A point analysis unit is configured to determine a plurality of clustered point sets based on the distribution of pickup point locations and / or drop-off point locations in the historical ride-hailing order data; and to filter the points in the clustered point sets based on a preset filtering strategy to determine a corresponding candidate point set;

[0046] The site determination unit is configured to determine a reference point according to the candidate points in the candidate point set; and determine a fixed site according to a positional relationship between the reference point and a route point on a fixed route.

[0047] In a fourth aspect, an embodiment of the present invention is to provide a station recommendation system for online bus booking, the system comprising:

[0048] Data acquisition module, used to obtain historical order data of online ride-hailing services;

[0049] A route determination module, configured to generate a fixed route based on the historical order data of the online ride-hailing service;

[0050] A station determination module is configured to determine a plurality of clustered point sets based on the distribution of pickup point locations and / or drop-off point locations in the historical ride-hailing order data; screen the points in the clustered point set based on a preset screening strategy to determine a corresponding candidate point set; determine a reference point based on the candidate points in the candidate point set; and determine a fixed station based on the positional relationship between the reference point and the route points on the fixed route;

[0051] The station recommendation module is used to store the fixed stations corresponding to each candidate point set in a station library; in response to receiving a ride request, recommend the fixed stations in the station library to the passenger based on the ride point in the ride request.

[0052] In a fifth aspect, an embodiment of the present invention aims to provide a computer program product, which includes a computer program / instructions, and when the computer program / instructions are executed by a processor, implements the method as described in any one of the above items.

[0053] In a sixth aspect, an embodiment of the present invention aims to provide an electronic device comprising a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method as described in any one of the above items.

[0054] In a seventh aspect, an embodiment of the present invention aims to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the method described in any one of the above items.

[0055] The technical solution of the embodiment of the present invention is to obtain historical order data of online ride-hailing; determine multiple aggregation point sets based on the distribution of pick-up point locations and / or alighting point locations in the historical order data of online ride-hailing; filter the points in the aggregation point set based on a preset filtering strategy to determine the corresponding candidate point set; determine reference points based on the candidate points in the candidate point set; determine fixed stations based on the positional relationship between the reference points and the route points on the fixed route, and set fixed stations for fixed routes based on the actual passenger demand, thereby gathering carpooling demand at fixed stations and improving the carpooling rate of the trip. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0057] Figure 1 Schematic diagram of an online bus-hailing service system according to an embodiment of the present invention;

[0058] Figure 2 is a flow chart of a site determination method according to an embodiment of the present invention;

[0059] Figure 3 is a flowchart of determining a candidate point set according to an embodiment of the present invention;

[0060] Figure 4 is a flow chart of determining a reference point position according to an embodiment of the present invention;

[0061] Figure 5 is a flowchart of determining a fixed site according to an embodiment of the present invention;

[0062] Figure 6 is a schematic diagram of determining fixed sites corresponding to a candidate point set according to an embodiment of the present invention;

[0063] Figure 7 is a schematic diagram of determining fixed sites on a fixed route according to an embodiment of the present invention;

[0064] Figure 8 is a schematic diagram of a site recommendation method according to an embodiment of the present invention;

[0065] Figure 9 is a schematic diagram of a site determination device according to an embodiment of the present invention;

[0066] Figure 10 is a schematic diagram of a site recommendation system according to an embodiment of the present invention;

[0067] Figure 11 is a schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0068] The present application is described below based on the following embodiments, but the present application is not limited to these embodiments. In the detailed description of the present application below, certain specific details are described in detail. Those skilled in the art can fully understand the present application without the description of these details. To avoid obscuring the essence of the present application, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0069] Furthermore, persons of ordinary skill in the art will appreciate that the figures provided herein are for illustration purposes only and are not necessarily drawn to scale.

[0070] Unless the context clearly requires otherwise, words like “include”, “comprising” and the like throughout this application should be interpreted as including rather than exclusive or exhaustive; that is, as meaning “including but not limited to”.

[0071] In the description of this application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance. In addition, in the description of this application, unless otherwise specified, "plurality" means two or more.

[0072] The solutions described in this specification and the embodiments, if they involve information acquisition, will collect data under the premise of legality and compliance, ensure the legitimacy of the data source, and take appropriate technical and management measures to ensure data security. If it involves the processing of personal information, it will be processed on the premise of having a legal basis (such as obtaining the consent of the subject of personal information, or it is necessary for the performance of a contract, etc.), and will only be processed within the scope of regulations or agreements. The user (who can be a passenger, driver or any other type of user) refuses to process personal information other than the necessary information required for basic functions, which will not affect the user's use of basic functions.

[0073] In the standard carpooling model, the platform often recommends multiple dynamic stops in real time based on the passenger's order / destination points. The platform then selects the stops that maximize the trip's efficiency based on their proximity. However, while this system's recommendation of multiple dynamic stops for carpooling can improve the efficiency of pick-up and drop-off, it has a certain ceiling on the improvement in the trip's completion rate when order demand is relatively dispersed and transportation capacity is insufficient. Therefore, it is desirable to find high-frequency routes, similar to bus routes, that can aggregate as much demand as possible in time and space. This can further improve route efficiency, reduce operating costs, and ensure a reliable passenger travel experience. At the same time, it is necessary to set reasonable fixed stops for high-frequency routes based on passenger demand to improve the trip's completion rate.

[0074] In view of this, the present embodiment aims to provide a method for determining bus stops for an online-hailing bus, so as to set reasonable fixed stops for the fixed routes of the online-hailing bus that performs carpooling tasks, thereby improving the carpooling rate of the trip.

[0075] Figure 1 Schematic diagram of the online bus booking service system according to an embodiment of the present invention. Figure 1 As shown, the online bus booking service system of this embodiment includes a server 1, a passenger terminal 2, and a driver terminal 3. The server 1, the passenger terminal 2, and the driver terminal 3 are connected to each other through a network 4 to realize the interaction of information and data. It should be understood that although Figure 1 Only a certain number of online car-hailing servers 1, passenger terminals 2 and driver terminals 3 are shown, but this does not mean that their respective numbers are limited. This system can include multiple servers, passenger terminals and driver terminals.

[0076] Server 1 should be understood as a device that provides data processing, database, and communication facilities. For example, server 1 may refer to a single physical server with associated communication and data storage and database facilities, or may refer to a collection of networked or clustered processors, associated network and storage devices, and operates software and one or more database systems and application software that supports the services provided by the server. Server 1 can be a monolithic server or a distributed server across multiple computers or computer data centers, or it can be various types of cloud servers. In some embodiments, each server may include hardware, software, or embedded logic components for performing appropriate functions supported or implemented by the server, or a combination of two or more such components.

[0077] The passenger terminal 2 and the driver terminal 3 are communication terminals capable of running computer programs. These communication terminals can be terminal devices such as mobile phones, tablet computers, PDAs, wearable devices, and vehicle-mounted terminals integrated with vehicles. The passenger client 21 can be run on the passenger terminal 2. The driver client 31 can be run on the driver terminal 3. At the same time, the server 1 runs the server application 11. Both the passenger terminal 2 and the driver terminal 3 have a communication module that can perform wired or wireless communication. In some embodiments, the passenger terminal 2 and the driver terminal 3 include at least one remote communication module, such as a communication circuit for WLAN, GPRS, 2G / 3G / 4G / 5G remote communication. The passenger terminal 2 and the driver terminal 3 also have a display device and an input device. The display device can be a liquid crystal display, an LED display, or a projection device. The input device can include, for example, a touch screen, a button, a pressure sensor, etc. The passenger terminal 2 and the driver terminal 3 receive the passenger's ride request through the input device and interact with the passenger or the driver through the display device.

[0078] The online ride-hailing bus service in this embodiment is developed based on the online ride-hailing carpooling service. Compared to existing conventional carpooling models, using online ride-hailing buses to perform carpooling tasks can accommodate more passengers with similar ride needs simultaneously, allowing passengers to enjoy similar services to conventional carpooling at a lower price, thereby improving vehicle utilization efficiency. Furthermore, online ride-hailing buses have fixed routes and fixed stops, integrating the logic of existing carpooling services. On the one hand, online ride-hailing buses generate ride orders based on passenger requests, inheriting the carpooling service's mechanism of initiating rides online based on passenger requests. On the other hand, online ride-hailing buses limit boarding and alighting locations to fixed stops along fixed routes whenever possible. Fixed routes facilitate quick arrival of passengers along these routes, improving the operational efficiency of carpooling services and the passenger travel experience. The advantage of fixed stops is that they increase the probability of picking up and dropping off multiple passengers at a time, improving the chances of picking up and dropping off multiple passengers at a time, and increasing trip efficiency. Furthermore, when fixed stops are set up, passengers can initiate ride requests directly at fixed stops by scanning a QR code, in addition to initiating requests online. At the same time, the online bus-hailing business still has a high degree of flexibility. The server can match ride orders based on the location of the passenger who initiated the request and the location of the online bus driver, using a logic similar to carpooling.

[0079] exist Figure 1 In the online bus-hailing service system shown, the passenger terminal 2 can initiate a ride request to the server 1 through the passenger client 21. After receiving the ride request, the server 1 matches the appropriate fixed route and the boarding and alighting stations of the fixed route based on the location information in the ride request, and sends the ride request to the driver terminal 3 that is heading to the boarding station on the fixed route, thereby forming a ride order. The driver terminal 3 guides the online bus to the boarding station through the driver client 31, and sends the passenger to the alighting station along the predetermined route through multiple fixed stations. During this process, the driver terminal 3 of the online bus is still in the order-accepting state, and can continuously match new ride requests, pick up passengers at the corresponding boarding station and send them to the alighting station. The server 1 matches ride requests near the fixed route with the online bus on the fixed route based on the actual distribution of the received ride requests, so that the online bus runs between different stations according to the planned route to pick up passengers and provide services to passengers along the route.

[0080] Figure 2 FIG. 1 is a flow chart of a site determination method according to an embodiment of the present invention. Figure 2 As shown, in this embodiment, fixed stops are set for fixed routes by the following method.

[0081] In step S210, historical order data of online ride-hailing services is obtained.

[0082] In this embodiment, online ride-hailing order data may include one or more service types, such as ride-sharing, express, and private car services. This online ride-hailing order data can cover ride demands generated at different times and locations. By obtaining historical online ride-hailing order data, a comprehensive understanding of passenger ride demands can be achieved.

[0083] In step S220, multiple aggregation point sets are determined based on the distribution of pick-up point locations and / or alighting point locations in the historical order data of the online car-hailing service.

[0084] In this embodiment, by analyzing the distribution of pickup and / or drop-off locations in historical ride-hailing order data, multiple demand grids and the clustered locations corresponding to each demand grid can be determined. A demand grid typically corresponds to a certain geographical area, such as a business district or a residential community. Within each demand grid, there are multiple clustered pickup and / or drop-off locations.

[0085] Optionally, in this embodiment, knowledge and techniques from multiple fields such as geographic information systems (GIS), statistics, spatial analysis, and data visualization can be combined to analyze the location distribution of pick-up points and / or drop-off points in the historical order data of online ride-hailing vehicles through various specific methods such as point density analysis, kernel density estimation, spatial fit test, heat map analysis, and spatial interpolation, so as to determine the demand grids where ride demand is concentrated and the set of aggregation points corresponding to each demand grid.

[0086] In step S230, the points in the clustered point set are screened based on a preset screening strategy to determine a corresponding candidate point set.

[0087] In this embodiment, considering that there are many points in the clustered point set and the demands corresponding to different points are different, in order to facilitate the setting of sites based on each clustered point set and improve the efficiency of site determination, the points in the clustered point set will be screened based on the point heat, so as to determine the candidate point set corresponding to the clustered point set.

[0088] Optionally, in this embodiment, Figure 3 The method shown determines the set of candidate points, specifically including:

[0089] In step S310, points whose heat values reach a preset heat value in the clustered point set are extracted to determine a preselected point set.

[0090] In this embodiment, the preset heat value can be set based on factors such as the capacity and density of the points in the clustered point set. For example, the greater the point capacity and the greater the point density, the greater the preset heat value. However, it should be understood that the setting of the preset heat value can be set according to the specific application scenario and is not limited to this.

[0091] Optionally, in scenarios where the number of points in the clustered point set is large or where rapid site determination is required, this embodiment can combine point popularity with a pre-set thinning method to filter the points in the clustered point set to determine the pre-selected point set. The purpose of thinning is to eliminate some points from the clustered point set, thereby improving site determination efficiency.

[0092] Furthermore, in this embodiment, when determining the preselected point set, points whose heat values reach a preset heat value can be first extracted from the clustered point set to determine the initial point set; then, the points in the initial point set are subjected to distance thinning to determine the preselected point set. Distance thinning between points (also called point thinning or point simplification) is a technique for reducing the number of data points. It can reduce the data volume while maintaining the overall characteristics of the data, thereby saving storage space or improving processing speed.

[0093] In step S320, the points in the pre-selected point set are clustered based on the distances between the points to determine a candidate point set corresponding to the pre-selected point set.

[0094] Optionally, in this embodiment, the points in the pre-selected point set can be clustered based on the point positions and the distances between the points through the K-Means clustering algorithm, the Mean Shift clustering algorithm, the DBSCAN clustering algorithm, the Gaussian mixture model (GMMs) or other clustering methods to determine the candidate point set corresponding to the pre-selected point set.

[0095] In step S240, a reference point is determined based on the candidate points in the candidate point set.

[0096] Optionally, the reference point in this embodiment can be a candidate point in the candidate point set, or it can be a non-candidate point determined based on the attribute information of each candidate point in the candidate point set, wherein the attribute information includes at least one of the factors affecting the arrival at the fixed route, such as the heat value and the distance from the fixed route.

[0097] In an optional implementation, in this embodiment, when determining the reference point, each candidate point in the candidate point set can be sorted according to the heat value, and the sorting order can be in ascending or descending order of heat value; after the sorting is completed, the candidate point with the highest heat value is determined as the reference point.

[0098] In another optional implementation method, in this embodiment, the distances between each candidate point in the candidate point set can be first determined, and then the total distances between each candidate point and other candidate points are calculated, and the candidate points are sorted in order from the smallest to the largest total distances, and then the candidate point with the smallest total distance is determined as the reference point.

[0099] In another optional implementation, in this embodiment, the Figure 4 The method shown in the figure determines the reference point position, which specifically includes the following steps.

[0100] In step S410, at least one recommended point corresponding to the candidate point set is determined.

[0101] Optionally, the recommended point in this embodiment is a point whose total distance from each candidate point in the candidate point set is less than a preset distance value. The recommended point can be a point in the candidate point set or a point outside the candidate point set.

[0102] Furthermore, when determining the recommended points, in this embodiment, preset algorithms such as KD tree (K-Dimensional Tree), approximate nearest neighbor search, ball tree algorithm, space partitioning method, etc. can be used to determine at least one recommended point whose distance from each candidate point in the candidate point set is less than a preset distance value. At this time, the recommended point determined can be a point outside the candidate point set, or a point that coincides with the position of a candidate point in the candidate point set.

[0103] Further optionally, when determining at least one recommended point from the candidate point set, in this embodiment, the distances between each candidate point in the candidate point set may be first determined, and then the sum of the distances between each candidate point and the other candidate points may be calculated. The candidate points are then sorted in order of the sum of the distances from smallest to largest, and a preset number of candidate points at the top of the sorted results are then determined as recommended points. Alternatively, in this embodiment, after determining the sum of the distances between each candidate point in the candidate point set and the other candidate points, the candidate points whose sum of distances is less than the preset distance may be determined as recommended points.

[0104] In step S420, the reference point position corresponding to the candidate point set is determined according to each recommended point.

[0105] Optionally, after determining at least one recommended point corresponding to the candidate point set, this embodiment determines a reference point from each recommended point based on the sum of distances and / or popularity values corresponding to each recommended point.

[0106] In an optional implementation, this embodiment determines a reference point from the recommended points based solely on the sum of the distances corresponding to each recommended point. The recommended point with the smallest sum of distances is then determined as the reference point. Specifically, the recommended point with the smallest sum of distances from each candidate point in the candidate point set is determined as the reference point. In this case, this embodiment can also employ a method for determining a Fermat point to determine the recommended point. A Fermat point is the point with the smallest sum of distances from all points in the candidate point set. When solving for the Fermat point, this embodiment can employ iterative methods, numerical methods, geometric construction methods, and other methods. For example, when employing an iterative method, an optimization algorithm (such as gradient descent or Newton's method) is used to iteratively search for the point that minimizes the sum of distances to all candidate points. When employing a numerical method, an optimization toolbox in mathematical software (such as MATLAB or Python's SciPy library) is used to solve for the Fermat point corresponding to all candidate points. The determined Fermat point is then determined as the recommended point corresponding to the candidate point set, and the recommended point is then designated as the reference point for the corresponding candidate point set. Therefore, in this embodiment, the above method can determine the reference point with the smallest total distance from each candidate point in the candidate point set.

[0107] In another optional implementation, in this embodiment, a reference point is determined from each recommended point based only on the heat value corresponding to each recommended point, and the recommended point with the highest heat value is selected as the reference point. Optionally, when the location of the recommended point is not a point in the historical data of the online car-hailing service, the heat value of the recommended point can be represented by the heat average of the points within a certain range of the recommended point. Afterwards, the heat values of different recommended points are compared, and the recommended point with the largest heat value is determined as the reference point of the corresponding candidate point set. Therefore, in this embodiment, the above method can determine a reference point with a reasonable total distance from each candidate point in the candidate point set and the highest heat.

[0108] In another optional implementation, this embodiment determines a reference point from each recommended point based on the distance sum and heat value corresponding to each recommended point. When determining the reference point, this embodiment pre-sets a distance weight for the distance sum and a heat weight for the heat value; and sets a corresponding distance score for each distance sum based on the numerical distribution of each distance sum, and sets a corresponding heat score for each heat value based on the numerical distribution of the heat value; then, the distance sum and heat value of each recommended point are weighted and summed according to the distance weight, distance score, heat weight, and heat score to obtain a reference score for each recommended point; finally, the recommended point with the highest reference score is selected as the reference point for the corresponding candidate point set. Therefore, in this embodiment, the above method can determine the reference point with the best comprehensive sum of the distance to each candidate point in the candidate point set and the heat value.

[0109] In step S250 , a fixed site is determined based on a positional relationship between the reference point and the route points on the fixed route.

[0110] In this embodiment, a fixed route is a pre-determined route for an online ride-hailing service. This route can be determined by analyzing and processing the hottest routes corresponding to the hottest grid pairs in the online ride-hailing service's historical order data. The starting grid and the ending grid in a hottest grid pair are the geographic areas of the starting and ending points in the historical orders, respectively, and there is a high demand for rides between the starting grid and the ending grid. A hottest route is a route with a high popularity from the starting grid to the ending grid.

[0111] Optionally, considering that the locations where ride demand is concentrated may be different in different time periods, in order to fully improve the utilization efficiency of online-hailing buses, the fixed routes for different time periods in this embodiment may be different. The fixed routes corresponding to each time period may be determined based on the above method according to the online-hailing historical data for the corresponding time period.

[0112] Furthermore, after the reference points and known fixed routes corresponding to the candidate point set are determined based on the aforementioned method, the fixed sites corresponding to the candidate point set are determined based on the positional relationship between the reference points and the route points on the fixed route.

[0113] Figure 5 FIG. 1 is a flow chart of determining a fixed site according to an embodiment of the present invention. Figure 5 As shown, in this embodiment, the fixed site is determined by the following method.

[0114] In step S510 , the route point closest to the reference point on the fixed route is determined as an available site.

[0115] Optionally, in this embodiment, the route point on the fixed route with the smallest straight-line distance or reach distance to the reference point can be determined as an available site. Preferably, to improve the availability of available sites, in this embodiment, the route point on the fixed route with the smallest reach distance to the reference point can be determined as an available site.

[0116] In step S520, matching parameters between the available sites and each candidate point in the candidate point set are determined.

[0117] In this embodiment, the matching parameters include at least one of a distance parameter, a convenience parameter, and a safety parameter; wherein the distance parameter is used to characterize the distance between the candidate point and the available site, and the distance can be a walking distance, a cycling distance, a public transportation distance, etc. The convenience parameter is used to characterize the convenience of the candidate point in reaching the available site, and the convenience parameter can be determined based on factors that affect the convenience of travel, such as the road width, flatness, and other road conditions of the road that the candidate point takes to reach the available site, the traffic congestion coefficient, the number of public transportation facilities, etc. The safety parameter is used to characterize the safety factor of the candidate point in reaching the available site, and the safety factor can be determined based on safety-related factors such as the road traffic accident rate of the road that the candidate point takes to reach the available site and the number of monitoring facilities on the road.

[0118] In step S530, available sites are evaluated according to the matching parameters.

[0119] In this embodiment, available sites are evaluated by comparing matching parameters with preset conditions, wherein the preset conditions include at least one of a distance parameter being less than a preset distance, a convenience parameter being greater than a preset convenience parameter value, and a safety factor being greater than a preset safety factor value.

[0120] In step S540, it is determined whether the available site meets the preset conditions.

[0121] In this embodiment, when it is determined that the available site meets the preset condition, step S550 is continued to be executed. When it is determined that the available site does not meet the preset condition, step S560 is continued to be executed.

[0122] In step S550 , in response to the available site meeting a preset condition, the available site is determined to be a fixed site.

[0123] In this embodiment, when the available site meets the preset conditions, it indicates that the distance, convenience and / or safety of each candidate point in the candidate point set corresponding to the current available site to the available site can meet the passengers' travel needs. At this time, the available site can be determined as a fixed site corresponding to each candidate point in the candidate point set.

[0124] In step S560, in response to the available site not meeting the preset condition, the available site is modified.

[0125] In this embodiment, when the available site does not meet the preset conditions, it indicates that the current available site does not meet the passenger's riding needs, and the available site needs to be modified.

[0126] When correcting the available stations, the positions of the available stations can be adjusted according to the location distribution and / or popularity distribution of each candidate point in the candidate point set. For example, the available stations can be moved in a direction closer to the area where the candidate points are concentrated and / or more popular, and the moved available stations can be used to replace the original available stations to meet the travel needs of more different locations as much as possible.

[0127] Figure 6 FIG is a schematic diagram of determining a fixed site corresponding to a candidate point set according to an embodiment of the present invention. Figure 6 As shown, in this embodiment, after obtaining historical online ride-hailing order data 61, a heat map analysis is performed on the pickup and / or drop-off points in the historical online ride-hailing order data 61 to determine multiple demand grids with concentrated ride-hailing demand in a target geographic area (e.g., a city) and a cluster point set 62 corresponding to each demand grid. Then, for each cluster point set 62, the points in the cluster point set 62 are sequentially subjected to heat value screening, distance thinning, and clustering to obtain a candidate point set 63 corresponding to the cluster point set. Then, for each candidate point set 63, a reference point 64 corresponding to the candidate point set is determined based on each candidate point in the candidate point set 63. Then, based on the positional relationship between the reference point 64 and the route points on the fixed route 65, available points 651 are determined and evaluated. Finally, available points 651 that meet preset conditions are determined as fixed stops corresponding to the candidate point set. Among them, the method of determining the cluster point set, candidate point set, reference point, available point, evaluating the available point and determining the fixed site corresponding to the candidate point set based on the evaluation results has been introduced in detail in the above content and will not be repeated here.

[0128] Optionally, in this embodiment, after determining the fixed stations corresponding to each candidate point set, the fixed stations corresponding to all candidate point sets may be used as fixed stations on the fixed route and applied to the fixed route.

[0129] Alternatively, to improve the availability of fixed stations, in this embodiment, after determining the fixed stations corresponding to each candidate point set, the positions of each fixed station may be adjusted based on preset constraints. The constraints are determined based on at least one of the distance between fixed stations, the passenger flow at the fixed stations, and a threshold for the number of fixed stations (i.e., the maximum number of fixed stations that can be set on a fixed route). When adjusting the positions of each fixed station based on the preset conditions, the fixed stations corresponding to each candidate point set may be adjusted by merging fixed stations whose distance is less than the distance threshold, determining the location between fixed stations as the final fixed station, and merging the fixed stations corresponding to candidate point sets with low passenger flow into the nearest fixed station with higher passenger flow. This can help reduce operating costs while meeting more passenger demand.

[0130] Figure 7 Schematic diagram of a fixed station for determining a fixed route according to an embodiment of the present invention. Figure 7 As shown, in this embodiment, after determining the fixed stations 71, 72, 73, and 74 corresponding to each candidate point set, since the distance between fixed stations 72 and 73 is less than the distance threshold, fixed stations 72 and 73 are merged to the middle position between the two points, resulting in a new fixed station 72'. Finally, fixed stations 71, 72', and 74 are used as fixed stations on a fixed route. Thus, setting fixed stations on a fixed route using the above method can not only increase the carpooling rate of a trip and meet the travel needs of different passengers, but also avoid the problem of increased operating costs caused by frequent vehicle starts between closely spaced points.

[0131] Furthermore, after determining the fixed stops on a fixed route based on the above method, each fixed stop is stored in a stop library for subsequent recommendation to passengers. Furthermore, in response to receiving a passenger's ride request, this embodiment recommends fixed stops from the stop library based on the boarding point in the ride request.

[0132] Optionally, when recommending fixed stops to passengers, the nearest fixed stop can be recommended to the passengers in real time based on the boarding point in the passenger's boarding request, so that the passengers can quickly reach the fixed stop to board the bus, which is conducive to improving the utilization efficiency of fixed stops and enhancing the passenger travel experience.

[0133] The technical solution of this embodiment is to obtain historical order data of online ride-hailing services; determine multiple aggregation point sets based on the distribution of pick-up point locations and / or alighting point locations in the historical order data of online ride-hailing services; filter the points in the aggregation point set based on a preset filtering strategy to determine the corresponding candidate point set; determine reference points based on the candidate points in the candidate point set; determine fixed stations based on the positional relationship between the reference points and the route points on the fixed route, and set fixed stations for fixed routes based on the actual ride needs of passengers, thereby aggregating carpooling needs at fixed stations and improving the carpooling rate of the trip.

[0134] Figure 8 FIG is a schematic diagram of a site recommendation method according to an embodiment of the present invention. Figure 8 As shown, the site recommendation method in this embodiment is applied to online bus booking, and site recommendation is achieved in the following way.

[0135] In step S810, historical order data of online ride-hailing services is obtained.

[0136] In step S820, a fixed route is generated based on the historical order data of the online car-hailing service.

[0137] In step S830, multiple gathering point sets are determined based on the distribution of pick-up point locations and / or alighting point locations in the historical order data of the online car-hailing service.

[0138] In step S840, the points in the clustered point set are screened based on a preset screening strategy to determine a corresponding candidate point set.

[0139] In step S850, a reference point is determined based on the candidate points in the candidate point set.

[0140] In step S860, a fixed site is determined based on the positional relationship between the reference point and the route points on the fixed route.

[0141] In step S870, the fixed sites corresponding to each candidate point set are stored in a site database.

[0142] In step S880, in response to receiving a ride request, a fixed station in a station library is recommended to the passenger based on the ride point in the ride request.

[0143] The processing methods in steps S810-S880 of this embodiment have been introduced in the above content and will not be repeated here.

[0144] Thus, in this embodiment, the above method can set fixed routes and fixed stops based on passengers' actual ride requests, aggregating different ride-sharing requests at fixed stops and improving the success rate of rides. Furthermore, by recommending fixed stops to users when they request a ride, passengers can be guided to quickly board and disembark at appropriate stops, providing convenience for travel and further enhancing the passenger experience.

[0145] Figure 9 Schematic diagram of a site determination device according to an embodiment of the present invention. Figure 9 As shown, the station determination device in this embodiment is applied to online-hailing buses, and the station determination device includes a data acquisition unit 91, a point analysis unit 92, and a station determination unit 93. Among them, the data acquisition unit 91 is used to obtain online-hailing historical order data. The point analysis unit 92 is used to determine multiple clustered point sets based on the distribution of pick-up point locations and / or alighting point locations in the online-hailing historical order data; filter the points in the clustered point set based on a preset filtering strategy to determine the corresponding candidate point set. The station determination unit 93 is used to determine a reference point based on the candidate points in the candidate point set; and determine a fixed station based on the positional relationship between the reference point and the route points on a fixed route.

[0146] Figure 10 Schematic diagram of a site recommendation system according to an embodiment of the present invention. Figure 10 As shown, the station recommendation system in this embodiment is applied to online ride-hailing services and includes a data acquisition module 101, a route determination module 102, a station determination module 103, and a station recommendation module 104. The data acquisition module 101 is used to acquire historical order data for online ride-hailing services. The route determination module 102 is used to generate fixed routes based on the historical order data for online ride-hailing services. The station determination module 103 is used to determine multiple clustered point sets based on the distribution of pickup and / or drop-off point locations in the historical order data for online ride-hailing services; filter the points in the clustered point sets based on a preset filtering strategy to determine corresponding candidate point sets; determine reference points based on the candidate points in the candidate point sets; and determine fixed stations based on the positional relationship between the reference points and route points on the fixed route. The station recommendation module 104 is used to store the fixed stations corresponding to each candidate point set in a station library; and in response to receiving a ride request, recommend fixed stations from the station library to passengers based on the boarding point in the ride request.

[0147] Figure 11 Schematic diagram of an electronic device according to an embodiment of the present invention. Figure 11 As shown, Figure 11The electronic device shown is a general-purpose data processing device, which includes a general-purpose computer hardware structure, including at least a processor 111 and a memory 112. The processor 111 and the memory 112 are connected via a bus 113. The memory 112 is suitable for storing instructions or programs executable by the processor 111. The processor 111 can be a standalone microprocessor or a collection of one or more microprocessors. Thus, the processor 111 executes the instructions stored in the memory 112, thereby performing the method flow of the embodiment of the present invention described above to process data and control other devices. The bus 113 connects the above-mentioned multiple components together and also connects these components to the display controller 114 and the display device as well as the input / output (I / O) device 115. The input / output (I / O) device 115 can be a mouse, keyboard, modem, network interface, touch input device, somatosensory input device, printer, and other devices known in the art. Typically, the input / output device 115 is connected to the system via an input / output (I / O) controller 116.

[0148] It will be understood by those skilled in the art that the embodiments of the present application may be provided as methods, devices (equipment), or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0149] The present application is described with reference to flowcharts of methods, apparatuses (devices), and computer program products according to embodiments of the present application. It should be understood that each process in the flowcharts can be implemented by computer program instructions.

[0150] These computer program instructions may be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device that implements the process Figure 1 A function specified in a process or multiple processes.

[0151] These computer program instructions can also be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, 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 produce the instructions for implementing the process Figure 1 A device that specifies functions in a process or multiple processes.

[0152] Another embodiment of the present invention relates to a non-volatile storage medium for storing a computer-readable program, wherein the computer-readable program is used to enable a computer to execute part or all of the above method embodiments.

[0153] That is, those skilled in the art will understand that all or part of the steps in the above-mentioned embodiments can be implemented by specifying relevant hardware through a program, which is stored in a storage medium and includes a number of instructions for causing a device (which may be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the methods described in the embodiments of the present application. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc., various media that can store program code.

[0154] The foregoing is merely a preferred embodiment of the present application and is not intended to limit the present application. Persons skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application are intended to be within the scope of protection of the present application.

Claims

1. A method for determining a station, applied to online bus booking, characterized in that: The method comprises: Get historical ride-hailing order data; Determining a plurality of aggregation point sets based on the distribution of pickup point locations and / or drop-off point locations in the historical ride-hailing order data; Screening the points in the clustered point set based on a preset screening strategy to determine a corresponding candidate point set; Determine a reference point according to the candidate points in the candidate point set; Determining a fixed site based on a positional relationship between the reference point and a route point on the fixed route; The determining of the fixed site according to the positional relationship between the reference point and the route point on the fixed route includes: Determine the route point closest to the reference point on the fixed route as an available site; Determining matching parameters between the available site and each candidate point in the candidate point set; evaluating the available sites according to the matching parameters; In response to the available site meeting a preset condition, the available site is determined to be a fixed site.

2. The method according to claim 1, characterized in that The screening of points in the clustered point set based on a preset screening strategy to determine a corresponding candidate point set includes: Extracting points whose heat values reach a preset heat value from the aggregation point set to determine a preselected point set; The points in the preselected point set are clustered based on the distances between the points to determine a candidate point set corresponding to the preselected point set.

3. The method according to claim 2, characterized in that The extracting points whose heat values reach a preset heat value from the gathering point set and determining the preselected point set includes: Extracting points whose heat values reach a preset heat value from the aggregation point set to determine an initial point set; The points in the initial point set are thinned out based on distance to determine a preselected point set.

4. The method according to claim 1, wherein Determining the reference point according to the candidate points in the candidate point set includes: Sort the candidate points in the candidate point set according to their heat values; The candidate point with the highest heat value is determined as the reference point.

5. The method according to claim 1, wherein Determining the reference point according to the candidate points in the candidate point set includes: Determine at least one recommended point corresponding to the candidate point set, where the recommended point is a point whose total distance from each candidate point in the candidate point set is less than a preset distance value; The reference point position corresponding to the candidate point set is determined according to each of the recommended points.

6. The method according to claim 5, characterized in that Determining the reference point corresponding to the candidate point set according to each of the recommended points includes: A reference point is determined from the recommended points based on the sum of distances and / or heat values corresponding to the recommended points.

7. The method according to claim 1, characterized in that The matching parameter includes at least one of a distance parameter, a convenience parameter, and a security parameter; The preset condition includes at least one of a distance parameter being less than a preset distance, a convenience parameter being greater than a preset convenience parameter value, and a safety factor being greater than a preset safety factor value; The distance parameter is used to characterize the distance between the candidate point and the available site, the convenience parameter is used to characterize the convenience of the candidate point reaching the available site, and the safety parameter is used to characterize the safety factor of the candidate point reaching the available site.

8. The method according to claim 1, characterized in that The determining of at least one fixed site in the fixed route according to each of the candidate point sets further comprises: The position of each of the fixed sites is adjusted based on a preset constraint condition, where the constraint condition is determined according to at least one of the distance between the fixed sites, the flow of people at the fixed sites, and a threshold value for the number of fixed sites.

9. A site recommendation method, applied to online bus booking, characterized in that: The method comprises: Get historical ride-hailing order data; Generate a fixed route based on the historical online car-hailing order data; Determining a plurality of aggregation point sets based on the distribution of pickup point locations and / or drop-off point locations in the historical ride-hailing order data; Screening the points in the clustered point set based on a preset screening strategy to determine a corresponding candidate point set; Determine a reference point according to the candidate points in the candidate point set; Determining a fixed site based on a positional relationship between the reference point and a route point on the fixed route; Storing the fixed sites corresponding to each candidate point set in a site database; In response to receiving a ride request, recommending a fixed stop in a stop library to a passenger based on a ride point in the ride request; The determining of the fixed site according to the positional relationship between the reference point and the route point on the fixed route includes: Determine the route point closest to the reference point on the fixed route as an available site; Determining matching parameters between the available site and each candidate point in the candidate point set; evaluating the available sites according to the matching parameters; In response to the available site meeting a preset condition, the available site is determined to be a fixed site.

10. A station determination device, applied to online bus booking, characterized in that: The device comprises: A data acquisition unit, used to obtain historical order data of online ride-hailing services; A point analysis unit is configured to determine a plurality of clustered point sets based on the distribution of pickup point locations and / or drop-off point locations in the historical ride-hailing order data; and to filter the points in the clustered point sets based on a preset filtering strategy to determine a corresponding candidate point set; a station determination unit, configured to determine a reference point according to candidate points in the candidate point set; and determine a fixed station according to a positional relationship between the reference point and a route point on a fixed route; Among them, the site determination unit is also used to determine the route point on the fixed route that is closest to the reference point as an available site; determine the matching parameters of the available site and each candidate point in the candidate point set; evaluate the available site according to the matching parameters; and in response to the available site meeting the preset conditions, determine the available site as a fixed site.

11. A station recommendation system, applied to online bus booking, characterized in that: The system comprises: Data acquisition module, used to obtain historical order data of online ride-hailing services; Route determination module, used to generate fixed routes based on historical ride-hailing order data; A station determination module is configured to determine a plurality of clustered point sets based on the distribution of pickup point locations and / or drop-off point locations in the historical ride-hailing order data; screen the points in the clustered point set based on a preset screening strategy to determine a corresponding candidate point set; determine a reference point based on the candidate points in the candidate point set; and determine a fixed station based on the positional relationship between the reference point and the route points on the fixed route; a station recommendation module, configured to store the fixed stations corresponding to each of the candidate point sets in a station library; and in response to receiving a ride request, recommending the fixed stations in the station library to the passenger based on the ride point in the ride request; Among them, the site determination module is also used to determine the route point on the fixed route that is closest to the reference point as an available site; determine the matching parameters of the available site and each candidate point in the candidate point set; evaluate the available site according to the matching parameters; and in response to the available site meeting the preset conditions, determine the available site as a fixed site.

12. A computer program product, characterized in that The computer program product comprises a computer program / instruction, and when the computer program / instruction is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

13. An electronic device comprising a memory and a processor, characterized in that: The memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method according to any one of claims 1 to 9.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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