A method, device, electronic device and storage medium for determining boarding point diversion

Abstract

The present invention provides a method, device, electronic device and storage medium for determining pick-up point diversion, the method comprising: in response to real-time monitoring of taxi demand in various regions, determining the regions where pick-up point diversion is required; based on each region where pick-up point diversion is required, querying the set of available pick-up points within a set range around the corresponding region; estimating the maximum taxi capacity of each pick-up point in the set of available pick-up points in each region; based on the maximum taxi capacity, determining the cost of the current location of taxi users in each region and each pick-up point in the corresponding region; when the cost is minimum, determining the diversion results of all taxi users in each region at multiple pick-up points. Based on the real-time taxi demand pressure, the present invention determines the diversion results of all taxi users in each region at multiple pick-up points, dynamically diverts users to multiple pick-up points in the surrounding area to get on the bus, alleviates vehicle congestion, reduces the waiting time, and improves the overall experience of taxi users.

Description

Technical Field

[0001] The present invention relates to the field of vehicle service technology, and in particular to a method, device, electronic device and computer-readable storage medium for determining boarding point diversion. Background Art

[0002] With the development of Internet technology, online ride-hailing has become a common mode of travel. People can enter the pick-up and drop-off points on the user terminal or locate their current location, make a request for a ride, and the driver will pick up the passengers based on the user's order information.

[0003] However, in real life, when a large number of users place taxi requests in the same place within a short period of time, for example, when a concert ends, when elementary schools are out of school, at long-distance bus stations during holidays, etc. However, in the related art, if the pick-up location recommendation is still made based on the personal perspective of a single user, a large number of users will be recommended and guided to the same pick-up location (such as the main entrance of a building, etc.), which will cause traffic congestion, users will wait too long for the car, a large number of orders will be cancelled, and the passenger experience will be poor.

[0004] Therefore, there are a large number of users who actually need to take a taxi at the same location, and how to recommend the boarding location for these users is a problem that needs to be solved. Summary of the invention

[0005] The present invention provides a method, device, electronic device and computer-readable storage medium for determining boarding point diversion, so as to at least solve the problem in the related art that when a large number of users take a taxi at the same location in a short period of time, it will cause vehicle congestion, users wait too long for the taxi, a large number of orders are cancelled, and the passenger experience is poor. The technical solution of the present invention is as follows:

[0006] According to a first aspect of an embodiment of the present invention, a method for determining boarding point diversion is provided, comprising:

[0007] In response to real-time monitoring of taxi demand in various regions, determine the areas where pick-up point diversion is required;

[0008] Based on each area where pick-up point diversion is required, query the set of available pick-up points within the set range around the corresponding area;

[0009] Estimate the maximum taxi capacity of each pickup point in the set of available pickup points in each area;

[0010] Based on the maximum taxi capacity of each pick-up point, determine the cost of each pick-up point in the corresponding area and the current location of the taxi user in each area;

[0011] When the cost is minimum, the diversion results of all taxi users in each area at multiple boarding points are determined.

[0012] Optionally, the step of determining the area where pick-up point diversion is required in response to real-time monitoring of taxi demand in various areas includes:

[0013] Obtain real-time monitoring of taxi demand in various regions;

[0014] Based on the taxi demand, determine the area where taxis are concentrated;

[0015] An area where pick-up point diversion is required is selected from the areas where taxis are concentrated.

[0016] Optionally, estimating the maximum taxi capacity of each boarding point in the set of available boarding points in each area includes:

[0017] Obtain information about the road where each pickup point in the pickup point set is located, the information including at least one of the following: grade, road condition, pickup time, estimated cancellation rate, other pickup points in the vicinity, and the maximum number of completed orders within a historical standard time;

[0018] Extracting features of information of the road where each boarding point is located;

[0019] The features of the information of the road where each boarding point is located are extracted and input into the regression model for estimation to obtain the maximum taxi capacity corresponding to the boarding point.

[0020] Optionally, the determining of the cost of the current location of the taxi user in each area and each boarding point in the corresponding area based on the maximum taxi capacity of each boarding point includes:

[0021] Based on the maximum taxi capacity of each pick-up point, calculate the time it takes for taxi users in each area to walk from their current location to the pick-up point in the corresponding area; and

[0022] Estimate the time it takes for the taxi user to wait for the taxi, the probability of the taxi user's personal willingness to walk, and the probability of completing the order;

[0023] The cost of each boarding point between the current location of the taxi user and the corresponding area is calculated based on the time spent at the taxi user's boarding point, the time spent waiting for the taxi, the probability of the individual's willingness to walk and the probability of completing the order.

[0024] Optionally, the cost of each pick-up point in the corresponding area between the current location of the taxi user and the current location of the taxi user is calculated according to the following formula based on the time spent at the pick-up point, the time spent waiting for the taxi, the probability of the individual's willingness to walk, and the probability of completing the order:

[0025] The cost of each pick-up point = (the time it takes for the taxi user to walk to the pick-up point + the estimated time it takes for the taxi user to wait for the taxi) / (the taxi user's personal probability of walking willingness * predicted probability of completing the order).

[0026] Optionally, when the cost is minimum, determining the diversion results of all taxi users in each area at multiple boarding points includes:

[0027] The diversion problem is constructed as a minimum cost maximum flow problem;

[0028] Based on the constructed minimum cost maximum flow problem, all taxi users in each area are assigned to each pick-up point in the corresponding area; determine the minimum cost when satisfying the needs of as many taxi users as possible in each area;

[0029] When the cost is minimum, a standard operations research optimization algorithm is used to solve the problem and obtain the diversion results of all taxi users in each area at multiple boarding points.

[0030] Optionally, constructing the traffic diversion problem as a minimum cost maximum flow problem includes:

[0031] Each intermediate node in the weighted directed graph corresponds to a candidate boarding point, and each edge corresponds to the distance that the taxi user needs to walk to the corresponding boarding point. The capacity of each edge is the maximum capacity of the taxi, and the cost of each edge is the price, constructing a minimum cost maximum flow problem.

[0032] According to a second aspect of an embodiment of the present invention, there is provided a device for determining boarding point diversion, comprising:

[0033] A first determination module, in response to real-time monitoring of taxi demand in various regions, determines the area where pick-up point diversion is required;

[0034] A query module is used to query the set of available boarding points within a set range around the corresponding area based on each area where boarding point diversion is required;

[0035] An estimation module, used for estimating the maximum taxi capacity of each pick-up point in the set of available pick-up points in each area;

[0036] The second determination module is used to determine the cost of each pick-up point in the corresponding area and the current location of the taxi user in each area based on the maximum taxi capacity of each pick-up point;

[0037] The third determination module is used to determine the diversion results of all taxi users in each area at multiple boarding points when the cost is minimum.

[0038] Optionally, the first determining module includes:

[0039] The taxi demand acquisition module is used to obtain the taxi demand in various regions monitored in real time;

[0040] An area determination module, used to determine an area where taxis are concentrated based on the taxi demand;

[0041] The area selection module is used to select the area where pick-up point diversion is required from the taxi-hailing concentrated area.

[0042] Optionally, the estimation module includes:

[0043] An information acquisition module is used to acquire information about the road where each pick-up point in the pick-up point set is located, wherein the information includes at least one of the following: grade, road condition, pickup time, estimated cancellation rate, other surrounding pick-up points, and the maximum number of completed orders within a historical standard time;

[0044] A feature extraction module, used to extract features of information of the road where each boarding point is located;

[0045] The taxi capacity estimation module is used to input the features of the information extracted by the feature extraction module into the regression model for estimation, so as to obtain the maximum taxi capacity of the corresponding boarding point.

[0046] Optionally, the second determining module includes:

[0047] A time consumption calculation module, used to calculate the time consumption for a taxi user in each area to walk from the current location to the pickup point in the corresponding area based on the maximum taxi capacity of each pickup point; and

[0048] An estimation module is used to estimate the time it takes for the taxi user to wait for the taxi, the probability of the taxi user's personal willingness to walk, and the probability of completing the order;

[0049] The cost calculation module is used to calculate the cost of each boarding point in the corresponding area between the current location of the taxi user and the corresponding area according to the time spent at the boarding point, the waiting time, the probability of personal walking willingness and the probability of completing the order.

[0050] Optionally, the cost calculation module calculates the cost of each boarding point in the corresponding area and the current location of the taxi user according to the following formula:

[0051] The cost of each pick-up point = (the time it takes for the taxi user to walk to the pick-up point + the estimated time it takes for the taxi user to wait for the taxi) / (the taxi user's personal probability of walking willingness * predicted probability of completing the order).

[0052] Optionally, the third determining module includes:

[0053] A building block for framing the diversion problem as a minimum-cost maximum-flow problem;

[0054] The cost determination module is used to allocate all taxi users in each area to each pick-up point in the corresponding area based on the constructed minimum cost maximum flow problem; determine the minimum cost when satisfying the needs of as many taxi users as possible in each area;

[0055] The operations research optimization module is used to use a standard operations research optimization algorithm to solve when the cost is minimum, and obtain the diversion results of all taxi users in each area at multiple boarding points.

[0056] Optionally, the construction module is specifically used to correspond each intermediate node in the weighted directed graph to a candidate boarding point, and each edge corresponds to the distance that the taxi user walks to the corresponding boarding point, wherein the capacity of each edge is the maximum taxi capacity, and the cost of each edge is the cost, thereby constructing a minimum cost maximum flow problem.

[0057] According to a second aspect of an embodiment of the present invention, there is provided an electronic device, including:

[0058] processor;

[0059] a memory for storing instructions executable by the processor;

[0060] Wherein, the processor is configured to execute the instructions to implement the method for determining the boarding point diversion as described above.

[0061] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method for determining boarding point diversion as described above.

[0062] According to a fifth aspect of an embodiment of the present invention, there is provided a computer program product, comprising a computer program or instructions, which, when executed by a processor of an electronic device, implements the method for determining boarding point diversion as described above.

[0063] The technical solution provided by the embodiments of the present invention brings at least the following beneficial effects:

[0064] In the embodiment of the present invention, in response to the real-time monitoring of the demand for taxis in various regions, the regions where pick-up point diversion is required are determined; based on each region where pick-up point diversion is required, the set of available pick-up points within a set range around the corresponding region is queried; the maximum taxi capacity of each pick-up point in the set of available pick-up points in each region is estimated; for taxi users in each region, the cost between the current position of the taxi user and each pick-up point in the corresponding region is determined; when the cost is minimum, the diversion results of all taxi users in each region at multiple pick-up points are determined. That is to say, in the embodiment of the present invention, based on the real-time taxi demand pressure, the diversion results of all taxi users in each region at multiple pick-up points are determined, and users are dynamically diverted to multiple pick-up points in the surrounding area to get on the bus, so that they do not have to wait together to get on the bus, which reduces vehicle congestion, reduces the waiting time of taxi users, and improves the overall experience of all taxi users in the concentrated taxi-hailing area.

[0065] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention, and do not constitute an improper limitation of the present invention. In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art description will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0067] Figure 1 It is a flow chart of a method for determining boarding point diversion provided by an embodiment of the present invention.

[0068] Figure 2 It is a block diagram of a device for determining boarding point diversion provided by an embodiment of the present invention.

[0069] Figure 3 It is a block diagram of a first determination module provided in an embodiment of the present invention.

[0070] Figure 4 It is a block diagram of an estimation module provided by an embodiment of the present invention.

[0071] Figure 5 It is a block diagram of a second determination module provided in an embodiment of the present invention.

[0072] Figure 6 It is a block diagram of a third determination module provided in an embodiment of the present invention.

[0073] Figure 7 It is a block diagram of an electronic device provided by an embodiment of the present invention.

[0074] Figure 8 It is a block diagram of a device for determining boarding point diversion provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0075] In order to enable ordinary persons in the art to better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings.

[0076] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0077] Figure 1 is a flow chart of a method for determining boarding point diversion provided by an embodiment of the present invention, such as Figure 1 As shown, the method for determining the boarding point diversion includes the following steps:

[0078] Step 101: In response to real-time monitoring of taxi demand in various regions, determine the areas where pick-up point diversion is required.

[0079] Step 102: Based on each area where pick-up point diversion is required, query the set of available pick-up points within a set range around the corresponding area.

[0080] Step 103: Estimate the maximum taxi capacity of each boarding point in the set of available boarding points in each area.

[0081] Step 104: Based on the maximum taxi capacity of each boarding point, determine the cost of the current location of the taxi user in each area and each boarding point in the corresponding area.

[0082] Step 105: When the cost is minimum, determine the diversion results of all taxi users in each area at multiple boarding points.

[0083] The method for determining boarding point diversion described in the present invention can be applied to terminals, servers, etc., without limitation herein. The terminal implementation device can be electronic devices such as smart phones, laptops, tablet computers, desktop computers, personal digital assistants (PDAs) and wearable devices. The server can be an independent server or a server cluster, or a server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, intermediary services, online car-hailing platforms, or big data and artificial intelligence platforms, etc., without limitation herein.

[0084] Combine the following Figure 1 , the specific implementation steps of a method for determining boarding point diversion provided by an embodiment of the present invention are described in detail.

[0085] In step 101, in response to real-time monitoring of taxi demand in various regions, the regions where pick-up point diversion is required are determined.

[0086] In this step, firstly, the demand for taxis in various regions monitored in real time is obtained; based on the acquired demand for taxis in various regions, the areas where taxis are concentrated are determined; and the areas where pick-up point diversion is required are selected from the areas where taxis are concentrated.

[0087] That is to say, in this step, the platform can dynamically monitor the taxi demand in each region in real time, and obtain the taxi demand in each region on the platform in real time, where the taxi demand can include real-time taxi demand and estimated taxi demand, etc. By statistically analyzing the real-time taxi demand, the area where the taxi demand is concentrated can be obtained, and the area where the pick-up point needs to be diverted can be selected from the area where the taxi demand is concentrated. The platform can be an online car-hailing platform or other vehicle service platform, etc., which is not limited in this implementation.

[0088] In step 102, based on each area where boarding point diversion is required, a set of available boarding points within a set range around the corresponding area is queried.

[0089] In this step, for each area where pick-up point diversion is required, a set of available pick-up points within a certain range of the surrounding area is queried, where the set of pick-up points can be represented by P. There are many query methods, such as navigation platform, map matching method, etc. These query methods are well-known technologies for those skilled in the art and will not be described in detail here.

[0090] In step 103, the maximum taxi capacity of each boarding point in the set of available boarding points in each area is estimated.

[0091] In this step, first obtain the information of the road where each pick-up point Pi in the pick-up point set P is located, and the information includes at least one of the following: grade, road condition, pick-up time, estimated cancellation rate, other surrounding pick-up points, and the maximum number of completed orders within the historical standard time; extract the characteristics of the information of the road where each pick-up point Pi is located; then, input the characteristics of the information of the road where each pick-up point Pi is located into the regression model for estimation, and obtain the maximum taxi capacity Ci of the corresponding pick-up point.

[0092] That is to say, for each pick-up point Pi in the pick-up point set P, its maximum taxi capacity Ci can be estimated based on the grade, road conditions, pick-up time, estimated cancellation rate, other pick-up points in the surrounding area, the maximum number of completed orders within the historical standard time, etc. of the road where the current pick-up point is located. It should be noted that the maximum taxi capacity Ci is usually calculated by a regression model, that is, the grade, road conditions, pick-up time, estimated cancellation rate, other pick-up points in the surrounding area, the maximum number of completed orders within the historical standard time and other features of the road where the current pick-up point is located are input into the regression model for calculation to obtain the maximum taxi capacity Ci of the corresponding pick-up point.

[0093] The regression model in this embodiment can also be called a predictive regression model. Through sample information, the overall correlation between the predicted object and related factors is analyzed, and an appropriate mathematical model is set to express the type of this correlation. Then, the sample information is used to apply parameter estimation to establish a sample regression model that reflects the overall relationship between the predicted object and the main related factors, and necessary tests are performed. Finally, the future status of the research object is predicted based on the sample regression model that has been established and passed the test. Before prediction, some preparatory work is required, including: model setting, parameter estimation, and model testing, which are collectively referred to as regression analysis.

[0094] In step 104, based on the maximum taxi capacity of each pick-up point, the cost of the current location of the taxi user in each area and each pick-up point in the corresponding area is determined.

[0095] This step specifically includes: 1) calculating the time it takes for taxi users in each area to walk from their current location to the pickup point in the corresponding area based on the maximum taxi capacity of each pickup point; and estimating the time it takes for the taxi users to wait for the taxi, the probability of the taxi users' personal willingness to walk, and the probability of completing the order.

[0096] There are many ways to calculate it. One common method is to use the walking time displayed on the navigation to calculate the time it takes for the taxi user to walk from the current location to the pick-up point in the corresponding area, such as 5 minutes.

[0097] It should be noted that the maximum taxi capacity of each boarding point and the taxi users diverted to each boarding point cannot exceed the maximum taxi capacity. For example, the maximum taxi capacity at the gate of a community is six vehicles. If it exceeds the traffic pool, it will cause traffic jams, etc. Therefore, under normal circumstances, the maximum parking capacity at the gate of the community cannot exceed six vehicles.

[0098] 2) According to the time spent at the taxi user's boarding point, the time spent waiting for the taxi, the probability of personal walking willingness and the probability of completing the order, the cost Costi of each boarding point Pi in the corresponding area between the taxi user's current location is calculated.

[0099] In this step, the cost Costi of each pick-up point Pi in the corresponding area and the current location of the taxi user can be calculated by the following formula:

[0100] Costi = (the time it takes for the taxi user to walk to the pick-up point + the estimated time it takes for the taxi user to wait for the taxi) / (the taxi user's personal probability of walking willingness * predicted probability of completing the order).

[0101] Among them, the taxi users, that is, passengers, usually spend time in minutes, but not limited to this.

[0102] It should be noted that the Costi calculation in this embodiment is a custom rule that comprehensively considers walking cost, user's personal wishes, order completion probability, and other factors.

[0103] In step 105, when the cost is minimum, the diversion results of all taxi users in each area at multiple boarding points are determined.

[0104] The steps specifically include: 1) constructing the diversion problem as a minimum cost maximum flow problem;

[0105] In this step, each intermediate node in the weighted directed graph G(V, E) corresponds to a candidate boarding point Pi, and each edge corresponds to the distance that the taxi user needs to walk to the corresponding boarding point Pi. The maximum capacity of each edge is Ci, and the cost of each edge is Costi, thereby constructing a minimum cost maximum flow problem.

[0106] It should be noted that the minimum cost maximum flow problem is a typical problem in economics and management. Under the condition that each path in a network (weighted directed graph G = (V, E)) has two constraints, "capacity" and "cost", the study of this type of problem attempts to find out: when the flow goes from A to B, how to select the path and distribute the flow through the path to achieve the minimum cost requirement. This is already a well-known technology for those skilled in the art and will not be elaborated here.

[0107] 2) Based on the constructed minimum cost maximum flow problem, all taxi users in each area are assigned to each pick-up point in the corresponding area; determine the minimum cost when the needs of as many taxi users as possible are met in each area;

[0108] 3) When the cost is the smallest, a standard operations research optimization algorithm (such as the Bellman-Ford algorithm with queue optimization, etc.) is used to obtain the diversion results of all taxi users in each area at multiple boarding points.

[0109] In this step, the diversion problem is modeled as a minimum cost maximum flow problem, that is, all passengers are assigned to these boarding points. Under the premise that the needs of as many users as possible are met, the overall cost is minimized. After that, the Bellman-Ford algorithm with queue optimization is used to solve it to obtain the final diversion result of all users at multiple boarding points.

[0110] That is to say, in this step, each intermediate node in the weighted directed graph G corresponds to a candidate boarding point Pi, and each edge corresponds to the distance that the passenger walks to the corresponding boarding point Pi, that is, the capacity of each edge is Ci, and the cost of each edge is Costi. Thus, a minimum cost maximum flow problem is constructed. The solution to this problem can be obtained using a standard operations research optimization algorithm (Bellman-Ford algorithm with queue optimization), that is, the final user diversion result at the boarding point is obtained.

[0111] Among them, the Bellman-Ford algorithm can solve the shortest path problem from a single source point in a more general case (the presence of negative-weight edges). For a given weighted (directed or undirected) graph G = (V, E), its source point is s, and the weighting function w is a mapping of the edge set E. The result of running the Bellman-Ford algorithm on the graph G is a Boolean value, indicating whether there is a negative-weight loop in the graph that is reachable from the source point s. If there is no such loop, the algorithm will give the shortest path d[v] from the source point s to any vertex v in the graph G. It should be noted that the Bellman-Ford algorithm is already a well-known technology to those skilled in the art and will not be described in detail here.

[0112] In the embodiment of the present invention, in response to the real-time monitoring of the demand for taxis in various regions, the regions where pick-up point diversion is required are determined; based on each region where pick-up point diversion is required, the set of available pick-up points within a set range around the corresponding region is queried; the maximum taxi capacity of each pick-up point in the set of available pick-up points in each region is estimated; based on the maximum taxi capacity of each pick-up point, the time taken for taxi users in each region to walk from the current location to the pick-up point in the corresponding region is calculated; when the cost is minimum, the diversion results of all taxi users in each region at multiple pick-up points are determined. That is to say, in the embodiment of the present invention, based on the real-time taxi demand pressure, the diversion results of all taxi users in each region at multiple pick-up points are determined, and users are dynamically diverted to multiple pick-up points in the surrounding area to get on the bus, so that they do not have to wait together to get on the bus, which reduces vehicle congestion, reduces the waiting time of taxi users, and improves the overall experience of all taxi users in the concentrated taxi-hailing area.

[0113] It should be noted that, for the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that this implementation disclosure is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the present invention.

[0114] Figure 2 The block diagram of a device for determining boarding point diversion provided by an embodiment of the present invention includes: a first determination module 201, a query module 202, an estimation module 203, a second determination module 204 and a third determination module 205, wherein:

[0115] The first determination module 201 determines the area where pick-up point diversion is required in response to real-time monitoring of taxi demand in various areas;

[0116] A query module 202 is used to query a set of available boarding points within a set range around each area that needs to be diverted to a boarding point;

[0117] An estimation module 203, configured to estimate the maximum taxi capacity of each boarding point in the set of available boarding points in each area;

[0118] The second determination module 204 is used to calculate the time taken by the taxi user in each area to walk from the current location to the pickup point in the corresponding area based on the maximum taxi capacity of each pickup point;

[0119] The third determination module 205 is used to determine the diversion results of all taxi users in each area at multiple boarding points when the cost is minimum.

[0120] Optionally, in another embodiment, based on the above embodiment, the first determination module 201 includes: a taxi demand acquisition module 301, an area determination module 302 and an area selection module 303, and its structural block diagram is as follows: Figure 3 As shown,

[0121] The taxi demand acquisition module 301 is used to acquire the taxi demand in various regions monitored in real time;

[0122] An area determination module 302 is used to determine an area where taxis are concentrated based on the taxi demand;

[0123] The area selection module 303 is used to select an area where pick-up point diversion is required from the area where taxis are concentrated.

[0124] Optionally, in another embodiment, based on the above embodiment, the estimation module 203 includes: an information acquisition module 401, a feature extraction module 402 and a taxi capacity estimation module 403, and its structural block diagram is as follows: Figure 4 As shown,

[0125] The information acquisition module 401 is used to acquire information about the road where each pick-up point in the pick-up point set is located, and the information includes at least one of the following: grade, road condition, pickup time, estimated cancellation rate, other pick-up points in the vicinity, and the maximum number of completed orders within the historical standard time;

[0126] A feature extraction module 402 is used to extract features of information of the road where each boarding point is located;

[0127] The taxi capacity estimation module 403 is used to input the features of the information extracted by the feature extraction module into the regression model for estimation, so as to obtain the maximum taxi capacity of the corresponding boarding point.

[0128] Optionally, in another embodiment, based on the above embodiment, the second determination module 204 includes: a time consumption calculation module 501, an estimation module 502 and a cost calculation module 503, and its structural block diagram is as follows: Figure 5 As shown,

[0129] The time consumption calculation module 501 is used to calculate the time consumption for a taxi user in each area to walk from the current location to the pickup point in the corresponding area based on the maximum taxi capacity of each pickup point; and

[0130] An estimation module 502 is used to estimate the time the taxi user spends waiting for the taxi, the probability of the taxi user's personal willingness to walk, and the probability of completing the order;

[0131] The cost calculation module 503 is used to calculate the cost of each boarding point in the corresponding area between the current location of the taxi user and the corresponding area according to the time spent at the boarding point, the waiting time, the probability of personal walking willingness and the probability of completing the order of the taxi user.

[0132] Optionally, in another embodiment, based on the above embodiment, the cost calculation module calculates the cost of each boarding point in the corresponding area and the current location of the taxi user according to the following formula:

[0133] The cost of each pick-up point = (the time it takes for the taxi user to walk to the pick-up point + the estimated time it takes for the taxi user to wait for the taxi) / (the taxi user's personal probability of walking willingness * predicted probability of completing the order).

[0134] Optionally, in another embodiment, based on the above embodiment, the third determination module 205 includes: a construction module 601, a cost determination module 602 and an operations optimization module 603, and its structural block diagram is as follows: Figure 6 As shown,

[0135] A construction module 601 is used to construct the diversion problem as a minimum cost maximum flow problem;

[0136] The cost determination module 602 is used to allocate all taxi users in each area to each boarding point in the corresponding area based on the constructed minimum cost maximum flow problem; and determine that the cost is minimum when the needs of as many taxi users as possible are met in each area;

[0137] The operations research optimization module 603 is used to use a standard operations research optimization algorithm to solve when the cost is minimum, and obtain the diversion results of all taxi users in each area at multiple boarding points.

[0138] Optionally, in another embodiment, based on the above embodiment, the construction module is specifically used to correspond each intermediate node in the weighted directed graph to a candidate boarding point, and each edge corresponds to the distance that the taxi user walks to the corresponding boarding point, wherein the capacity of each edge is the maximum capacity of the taxi, and the cost of each edge is the price, so as to construct a minimum cost maximum flow problem.

[0139] Optionally, an embodiment of the present invention further provides an electronic device, including:

[0140] processor;

[0141] a memory for storing instructions executable by the processor;

[0142] Wherein, the processor is configured to execute the instructions to implement the method for determining the boarding point diversion as described above.

[0143] Optionally, an embodiment of the present invention further provides a computer-readable storage medium, which, when instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to execute the method for determining boarding point diversion as described above.

[0144] Optionally, an embodiment of the present invention further provides a computer program product, including a computer program or instructions, which, when executed by a processor of an electronic device, implements the method for determining boarding point diversion as described above.

[0145] The embodiment of the present invention adopts an operations optimization algorithm to determine the diversion results of all taxi users in each area at multiple boarding points based on real-time taxi demand pressure, and dynamically diverts users to multiple boarding points in the surrounding area to board the taxi, without having to concentrate them in one place, thereby alleviating vehicle congestion, reducing the waiting time for passengers, and improving the overall experience of all users.

[0146] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0147] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0148] Figure 7 700 is a block diagram of an electronic device 700 provided in an embodiment of the present invention. For example, the electronic device 700 may be a mobile terminal or a server, and the electronic device is a mobile terminal as an example for explanation in the embodiment of the present invention. For example, the electronic device 700 may be a mobile phone, a computer, a digital broadcast terminal, a message transceiver device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0149] Reference Figure 7 , the electronic device 700 may include one or more of the following components: a processing component 702 , a memory 704 , a power component 706 , a multimedia component 708 , an audio component 710 , an input / output (I / O) interface 712 , a sensor component 714 , and a communication component 716 .

[0150] The processing component 702 generally controls the overall operation of the electronic device 700, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 702 may include one or more processors 720 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 702 may include one or more modules to facilitate the interaction between the processing component 702 and other components. For example, the processing component 702 may include a multimedia module to facilitate the interaction between the multimedia component 708 and the processing component 702.

[0151] The memory 704 is configured to store various types of data to support operations on the device 700. Examples of such data include instructions for any application or method operating on the electronic device 700, contact data, phone book data, messages, pictures, videos, etc. The memory 704 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0152] The power supply component 706 provides power to the various components of the electronic device 700. The power supply component 706 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 700.

[0153] The multimedia component 708 includes a screen that provides an output interface between the electronic device 700 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 708 includes a front camera and / or a rear camera. When the device 700 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0154] The audio component 710 is configured to output and / or input audio signals. For example, the audio component 710 includes a microphone (MIC), and when the electronic device 700 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 704 or sent via the communication component 716. In some embodiments, the audio component 710 also includes a speaker for outputting audio signals.

[0155] I / O interface 712 provides an interface between processing component 702 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0156] The sensor assembly 714 includes one or more sensors for providing various aspects of status assessment for the electronic device 700. For example, the sensor assembly 714 can detect the open / closed state of the device 700, the relative positioning of components, such as the display and keypad of the electronic device 700, and the sensor assembly 714 can also detect the position change of the electronic device 700 or a component of the electronic device 700, the presence or absence of contact between the user and the electronic device 700, the orientation or acceleration / deceleration of the electronic device 700, and the temperature change of the electronic device 700. The sensor assembly 714 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 714 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 714 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0157] The communication component 716 is configured to facilitate wired or wireless communication between the electronic device 700 and other devices. The electronic device 700 can access a wireless network based on a communication standard, such as WiFi, a carrier network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 716 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 716 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0158] In an embodiment, the electronic device 700 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the method for determining boarding point diversion shown above.

[0159] In an embodiment, a computer-readable storage medium is also provided. When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device 700 can execute the above-mentioned method for determining the boarding point diversion. For example, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0160] In an embodiment, a computer program product is also provided, including a computer program or instructions. When the computer program or instructions are executed by the processor 720 of the electronic device 700, the electronic device 700 executes the above-mentioned method for determining the boarding point diversion.

[0161] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0162] Figure 8 800 is a block diagram of a device 800 for determining boarding point diversion provided by an embodiment of the present invention. For example, the device 800 may be provided as a server. Figure 8 The apparatus 800 includes a processing component 822, which further includes one or more processors, and a memory resource represented by a memory 832 for storing instructions executable by the processing component 822, such as an application. The application stored in the memory 832 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 822 is configured to execute instructions to perform the above-mentioned method for determining the boarding point diversion.

[0163] The device 800 may also include a power supply component 826 configured to perform power management of the device 800, a wired or wireless network interface 850 configured to connect the device 800 to a network, and an input / output (I / O) interface 858. The device 800 may operate based on an operating system stored in the memory 832, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0164] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed by the present invention. The specification and examples are to be considered exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0165] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for determining boarding point diversion, characterized in that: include: In response to real-time monitoring of taxi demand in various regions, determine the areas where pick-up point diversion is required; Based on each area where pick-up point diversion is required, query the set of available pick-up points within the set range around the corresponding area; Estimate the maximum taxi capacity of each pickup point in the set of available pickup points in each area; Based on the maximum taxi capacity of each pick-up point, determine the cost of each pick-up point in the corresponding area and the current location of the taxi user in each area; When the cost is minimum, the diversion results of all taxi users in each area at multiple boarding points are determined.

2. The method for determining boarding point diversion according to claim 1, characterized in that: The step of determining the area where pick-up point diversion is required in response to real-time monitoring of the taxi demand in various areas includes: Obtain real-time monitoring of taxi demand in various regions; Based on the taxi demand, determine the area where taxis are concentrated; An area where pick-up point diversion is required is selected from the areas where taxis are concentrated.

3. The method for determining boarding point diversion according to claim 1, characterized in that: The estimating the maximum taxi capacity of each boarding point in the set of available boarding points in each area includes: Obtain information about the road where each pickup point in the pickup point set is located, the information including at least one of the following: grade, road condition, pickup time, estimated cancellation rate, other pickup points in the vicinity, and the maximum number of completed orders within a historical standard time; Extracting features of information of the road where each boarding point is located; The features of the information of the road where each boarding point is located are extracted and input into the regression model for estimation to obtain the maximum taxi capacity corresponding to the boarding point.

4. The method for determining boarding point diversion according to claim 1, characterized in that: The method of determining the cost of each pick-up point in the corresponding area and the current location of the taxi user in each area based on the maximum taxi capacity of each pick-up point includes: Based on the maximum taxi capacity of each pick-up point, calculate the time it takes for taxi users in each area to walk from their current location to the pick-up point in the corresponding area; and Estimate the time it takes for the taxi user to wait for the taxi, the probability of the taxi user's personal willingness to walk, and the probability of completing the order; The cost of each boarding point between the current location of the taxi user and the corresponding area is calculated based on the time spent at the taxi user's boarding point, the time spent waiting for the taxi, the probability of the individual's willingness to walk and the probability of completing the order.

5. The method for determining boarding point diversion according to claim 4, characterized in that: The cost of each pick-up point in the corresponding area between the current location of the taxi user and the taxi user is calculated according to the following formula based on the time spent at the pick-up point, the time spent waiting for the taxi, the probability of personal walking willingness and the probability of completing the order: The cost of each pick-up point = (the time it takes for the taxi user to walk to the pick-up point + the estimated time it takes for the taxi user to wait for the taxi) / (the taxi user's personal probability of walking willingness * predicted probability of completing the order).

6. The method for determining boarding point diversion according to claim 1, characterized in that: When the cost is the minimum, determining the diversion results of all taxi users in each area at multiple boarding points includes: The diversion problem is constructed as a minimum cost maximum flow problem; Based on the constructed minimum cost maximum flow problem, all taxi users in each area are assigned to each pick-up point in the corresponding area; determine the minimum cost when satisfying the needs of as many taxi users as possible in each area; When the cost is minimum, a standard operations research optimization algorithm is used to solve the problem and obtain the diversion results of all taxi users in each area at multiple boarding points.

7. The method for determining boarding point diversion according to claim 6, characterized in that: The diversion problem is constructed as a minimum cost maximum flow problem, including: Each intermediate node in the weighted directed graph corresponds to a candidate boarding point, and each edge corresponds to the distance that the taxi user walks to the corresponding boarding point. The capacity of each edge is the maximum capacity of the taxi, and the cost of each edge is the price, constructing a minimum cost maximum flow problem.

8. A device for determining boarding point diversion, characterized in that: include: A first determination module, in response to real-time monitoring of taxi demand in various regions, determines the area where pick-up point diversion is required; A query module is used to query the set of available boarding points within a set range around the corresponding area based on each area where boarding point diversion is required; An estimation module, used for estimating the maximum taxi capacity of each pick-up point in the set of available pick-up points in each area; The second determination module is used to determine the cost of each pick-up point in the corresponding area and the current location of the taxi user in each area based on the maximum taxi capacity of each pick-up point; The third determination module is used to determine the diversion results of all taxi users in each area at multiple boarding points when the cost is minimum.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method for determining boarding point diversion as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method for determining boarding point diversion as described in any one of claims 1 to 7.

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