A method for identifying bus route passengers based on mobile phone signaling

Abstract

The present application discloses a method for identifying bus route passengers based on mobile phone signaling, which relates to the field of data processing technology. The method includes: performing road fitting based on signaling data to obtain the movement trajectory of the passenger; obtaining bus stops, determining the distance between the bus stops and the movement trajectory, and determining the bus stops with a distance less than a threshold as the passing bus stops; obtaining bus routes, determining the bus-taking plan, and calculating the information entropy of the bus-taking plan, and determining the plan with information entropy less than the entropy threshold as the first bus route plan; identifying the accompanying passengers in the same car with the passenger, determining the first bus route plan of the accompanying passengers, and taking the intersection with the first bus route plan of the passenger to obtain the second bus route plan; determining the target bus route plan based on the second bus route plan for multiple consecutive days; and identifying bus route passengers based on the target bus route plan. The present application effectively utilizes the spatiotemporal big data of mobile operators to identify bus route passengers.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method for identifying passengers on a bus route based on mobile phone signaling. Background Art

[0002] With the rise of spatiotemporal big data (also known as geospatial big data), data has both temporal and spatial attributes, including three-dimensional information such as time, space, and thematic attributes. In real life, up to 80% of data has spatiotemporal attributes directly or indirectly. When these data accumulate to a certain scale, spatiotemporal big data is formed. Spatiotemporal big data has basic characteristics such as massive, multi-source heterogeneous, and dynamic and changeable. All data are generated in a specific time and space background and are directly or indirectly labeled with time and location. Therefore, big data in a broad sense can be regarded as having the same attributes as spatiotemporal big data in essence. It is the "sum" of data sets with quantity, quality, and time-varying characteristics in the spatial structure and spatial relationship elements of the real geographical world. In the study of spatiotemporal big data, mining the travel patterns and laws of cities or regions and identifying the travel characteristics and certain occupational characteristics of the crowd are current research hotspots. As an important part of urban transportation, the study of smart public transportation is even more important. For example, the 4G and 5G communication networks operated by various communication operators, that is, mobile phone signaling data, have all passengers and full-time signaling data. These mobile phone signaling data are all labeled with location, which is a typical spatiotemporal big data. How to use these mobile phone signaling data to effectively identify bus passengers, optimize bus schedules, balance capacity and passenger flow demand, and assist in the construction of "smart buses" is a problem that needs to be solved urgently. Summary of the invention

[0003] The main purpose of this application is to provide a method for identifying bus line passengers based on mobile phone signaling. The method is essentially a method, device, equipment, and computer program product for identifying bus line passengers based on mobile phone signaling, which aims to solve the technical problem of how to effectively use mobile phone signaling to identify bus line passengers.

[0004] To achieve the above objectives, the present application proposes a method for bus route passenger identification based on mobile phone signaling, comprising:

[0005] Generate signaling data based on the passenger's mobile phone signaling, perform road fitting through the position point sequence in the signaling data, and obtain the passenger's movement trajectory;

[0006] Obtain multiple preset bus stops, determine the distances between all bus stops and the motion trajectory, and determine the bus stops whose distances are less than a preset distance threshold as the bus stops that the passenger will pass through;

[0007] Obtain at least one preset bus route, match the bus stops along the way with the bus route, determine a bus plan, calculate the information entropy of all bus plans, and determine the bus plan with an information entropy less than a preset entropy threshold as the passenger's first bus route plan;

[0008] Identify the accompanying passenger in the same bus with the passenger according to the signaling data, determine the first bus route plan of the accompanying passenger, and take the intersection of the first bus route plan of the accompanying passenger and the first bus route plan of the passenger to obtain the second bus route plan of the passenger;

[0009] Determine the target bus route plan for the passenger according to the second bus route plan for the passenger within a preset number of consecutive days;

[0010] Identify bus route passengers according to the target bus route plan.

[0011] In one embodiment, the bus plan includes a direct bus route plan, and the steps of matching the bus stops and the bus routes to determine the bus plan include:

[0012] If there are multiple passing bus stops, the first bus stop and the last bus stop among the passing bus stops are determined as the first bus stop and the last bus stop respectively;

[0013] If there are multiple bus routes, the bus route passing through the first bus stop and the last bus stop is determined as the first bus route;

[0014] If the bus stops passed by include all the bus stops between the first bus stop and the last bus stop of the first bus line, then the plan including the first bus line is determined to be the direct bus line plan.

[0015] In one embodiment, the bus ride plan includes a one-transfer plan and a multiple-transfer plan, and the bus stops passed by are matched with the bus routes. The steps of determining the bus ride plan include:

[0016] If there are multiple passing bus stops, determine the first bus stop and the last bus stop among the multiple passing bus stops, and determine the other passing bus stops except the one between the first bus stop and the last bus stop among the passing bus stops;

[0017] Determine, in each bus route, a second bus route that passes through the first bus stop and the first other passing bus stop among the other passing bus stops, wherein the second bus route does not pass through the next passing bus stop of the first other passing bus stop;

[0018] Determine a third bus route passing through the first other passing bus stop and the last bus stop, and construct a first mixed route plan including the third bus route and the second bus route, and use the first mixed route plan as a one-transfer plan;

[0019] If there are direct bus routes between multiple pairs of passing bus stops, and the direct bus routes can be combined into a bus route plan passing through the first bus stop and the last bus stop, then the combination plan of the direct bus route plan is a multiple transfer plan, wherein adjacent direct bus route plans are different from each other, and the multiple transfer plan includes at least one combination plan of different direct bus route plans.

[0020] In one embodiment, the step of calculating the information entropy of all public transportation options further includes:

[0021] For each bus-taking plan, the information entropy of the riding distance of each direct bus line of the bus-taking plan is calculated to obtain the information entropy, wherein the lower the information entropy, the higher the probability that the bus-taking plan actually occurs.

[0022] In one embodiment, the step of identifying the accompanying passenger in the same bus with the passenger according to the signaling data and determining the first bus route plan for the accompanying passenger further includes:

[0023] Identify the accompanying passenger in the same bus with the passenger according to the signaling data, and associate the passenger, the accompanying passenger and the bus stop to determine the accompanying bus passenger pair;

[0024] The accompanying starting and ending bus stops of the accompanying bus passenger pair are determined, and a first bus route plan for the accompanying passengers is determined according to the accompanying starting and ending bus stops.

[0025] In one embodiment, the step of determining the target bus route plan according to the second bus route plan of passengers within a preset number of consecutive days includes:

[0026] Summarize the second bus route plans for multiple consecutive days and count the number of days on which each second bus route plan appears, wherein the second bus route plan includes multiple bus route plans;

[0027] The second bus route plans are sorted in descending order according to the number of days they appear, and the bus route plan ranked first is used as the target bus route plan. If there are multiple second bus route plans with the same number of days they appear, the second bus route plans with the same number of days they appear are sorted in ascending order according to information entropy.

[0028] In one embodiment, the step of obtaining the bus stop includes:

[0029] If the distance between the starting point of the motion trajectory and any bus stop is less than the distance threshold, and the distance between the ending point of the motion trajectory and any bus stop is less than the distance threshold, the step of determining the distance between all bus stops and the station of the motion trajectory is executed.

[0030] In addition, to achieve the above purpose, the present application also proposes a bus route passenger identification device based on mobile phone signaling, and the bus route passenger identification device based on mobile phone signaling includes:

[0031] The trajectory acquisition module generates signaling data based on the passenger's mobile phone signaling, performs road fitting through the position point sequence in the signaling data, and obtains the passenger's movement trajectory;

[0032] A station acquisition module is used to acquire a plurality of preset bus stations, determine the station distances between all bus stations and the motion trajectory, and determine the bus stations whose station distances are less than a preset distance threshold as the bus stations that the passengers pass by;

[0033] The route probability calculation module obtains at least one preset bus route, matches the bus stops passed by with the bus route, determines the bus plan, and calculates the information entropy of all bus plans, and determines the bus plan with an information entropy less than a preset entropy threshold as the first bus route plan for the passenger;

[0034] The route optimization module identifies the accompanying passenger in the same bus with the passenger according to the signaling data, determines the first bus route plan of the accompanying passenger, and takes the intersection of the first bus route plan of the accompanying passenger and the first bus route plan of the passenger to obtain the second bus route plan of the passenger;

[0035] A route determination module, which determines a target bus route plan for a passenger based on the passenger's second bus route plan within a preset number of consecutive days;

[0036] The passenger identification module identifies bus route passengers according to the target bus route plan.

[0037] In addition, to achieve the above-mentioned purpose, the present application also proposes a bus line passenger identification device based on mobile phone signaling, the device comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for bus line passenger identification based on mobile phone signaling as described above.

[0038] In addition, to achieve the above-mentioned purpose, the present application also proposes a medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for identifying bus line passengers based on mobile phone signaling as described above are implemented.

[0039] In addition, to achieve the above-mentioned purpose, the present application also provides a product, which is a computer program product. The computer program product includes a computer program. When the computer program is executed by a processor, it implements the steps of the method for bus line passenger identification based on mobile phone signaling as described above.

[0040] One or more technical solutions proposed in this application have at least the following technical effects:

[0041] The present application generates signaling data based on the passenger's mobile phone signaling, performs road fitting through the sequence of position points in the signaling data, and obtains the passenger's movement trajectory, thereby achieving an accurate description of the passenger's travel path and providing reliable basic data for subsequent bus route identification; obtains multiple preset bus stops, determines the station distances between all bus stops and the movement trajectory, and determines the bus stops with station distances less than a preset distance threshold as the passenger's passing bus stops. This process effectively screens out the bus stops that the passenger may take, reduces the complexity of subsequent analysis, and improves recognition efficiency; obtains at least one preset bus route, matches the passing bus stops with the bus route, determines the bus riding plan, and calculates the information entropy of all bus riding plans, and determines the bus riding plan with an information entropy less than a preset entropy threshold as the passenger's first bus route plan. By introducing information entropy, it is possible to evaluate the various parties. The uncertainty of the plan provides a basis for further optimizing the bus route selection; the accompanying passengers are identified according to the signaling data, the first bus route plan of the accompanying passengers is determined, and the intersection of the first bus route plan of the accompanying passengers and the first bus route plan of the passenger is taken to obtain the second bus route plan of the passenger. This step uses the information of the accompanying passengers to enhance the accuracy of bus route identification, and the possible bus routes are further narrowed through the intersection operation; the target bus route plan of the passenger is determined according to the second bus route plan of the passenger within a preset number of consecutive days, and the bus route passengers are identified according to the target bus route plan. Through the accumulation and analysis of data for multiple consecutive days, the regular bus routes of the passengers can be more accurately identified, and the regular passengers of the bus routes can also be identified, which can not only provide passengers with more personalized travel suggestions and services, but also contribute to urban transportation planning and optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1 This is a flow chart of a first embodiment of a method for identifying bus route passengers based on mobile phone signaling of the present application;

[0045] Figure 2A schematic diagram of a bus stop for the method of bus route passenger identification based on mobile phone signaling in this application;

[0046] Figure 3 A process diagram of a method for identifying bus route passengers based on mobile phone signaling in this application;

[0047] Figure 4 This is a schematic diagram of the module structure of a bus route passenger identification device based on mobile phone signaling in an embodiment of the present application;

[0048] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the method for bus route passenger identification based on mobile phone signaling in an embodiment of the present application.

[0049] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0050] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0051] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0052] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a terminal system, etc. The following takes the system as an example to illustrate this embodiment and the following embodiments.

[0053] Based on this, this embodiment provides a method for identifying bus line passengers based on mobile phone signaling, referring to Figure 1 , Figure 1 This is a flow chart of a method for identifying bus line passengers based on mobile phone signaling in this application. The method for identifying bus line passengers based on mobile phone signaling includes steps S10 to S60:

[0054] Step S10, generating signaling data based on the passenger's mobile phone signaling, performing road fitting through the position point sequence in the signaling data, and obtaining the passenger's movement trajectory;

[0055] It should be noted that signaling data refers to data generated by base stations or mobile devices in mobile communication networks, which are used to describe information such as passenger location, movement status, and communication behavior. Road fitting is to simulate and restore the actual road path of passengers by processing the sequence of location points (including longitude, latitude, etc.) in the signaling data of passengers in the mobile communication network through algorithms. This process can reflect the movement trajectory of passengers, including information such as the type of road they are walking on, road ID, movement direction, and movement speed. The movement trajectory refers to the path of an object (such as a person, vehicle, etc.) moving in space, usually including the starting point, the end point, and various points passed in the middle.

[0056] First, read the 4G and 5G signaling data of the passengers, which contain the sequence of location points during the passenger's movement, such as the service cell identifier ECI (NCI), timestamp, longitude, latitude, etc. Then, identify the passenger's movement state and distinguish between the resident state and the moving state. The resident state indicates that the passenger stays in a certain area for a long time, and the typical feature is that the stay time in a certain base station cell exceeds a certain threshold (such as 20 minutes). The moving state indicates that the passenger has a large range of location movement, and its typical feature is that the stay time in a single cell is short, the number of different cells that stay continuously is large, and there is an obvious movement direction. For passengers in the moving state, their location point sequence will be used for road fitting. The process of road fitting is to process the passenger's location point sequence through an algorithm to simulate and restore the road path that the passenger actually walks. This process can reflect the passenger's movement trajectory and generate a movement trajectory containing information such as road type, road ID, movement direction, and movement speed.

[0057] Step S20, obtaining a plurality of preset bus stops, determining the distances between all bus stops and the motion trajectory, and determining the bus stops whose distances are less than a preset distance threshold as the bus stops that the passenger passes by;

[0058] It should be noted that the bus stops passing by are those near the passenger's movement trajectory (the distance between the stops is less than the preset threshold), which are considered as stops that the passenger may pass by. The stop distance is the straight-line distance between the bus stop and a point on the passenger's movement trajectory or the shortest path distance based on the road network.

[0059] Obtain bus route data, which includes bus stop data and bus route data. The bus route contains the order and location information of bus stops. Then, compare the distance between the passenger's movement trajectory and all bus stops, and calculate the distance between each bus stop and the nearest point on the passenger's movement trajectory, that is, the station distance. Set a preset distance threshold (such as 500 meters), and determine the bus stops with a station distance less than the threshold as the passenger's passing station. If the bus stop cannot be found at the starting point or the end point of the movement trajectory (that is, there is no matching bus stop within the given distance threshold), the probability of the passenger taking the bus is low and does not participate in the subsequent bus route identification calculation.

[0060] For example, the data structure of the bus stops is shown in Table 1:

[0061] Table 1

[0062]

[0063] Step S30, obtaining at least one preset bus route, matching the bus stops passed by with the bus route, determining a bus plan, and calculating the information entropy of all bus plans, and determining the bus plan with an information entropy less than a preset entropy threshold as the passenger's first bus route plan;

[0064] It should be noted that bus route matching is to compare the passing bus stops with the existing bus routes to find the bus routes that include these stops. The bus ride plan is a text data, which is stored in the form of a file or text in the storage medium of bus route passenger identification based on mobile phone signaling. Information entropy is an indicator to measure the uncertainty or diversity of bus ride plans. The larger the information entropy, the more diverse the plans or the higher the uncertainty.

[0065] Obtain bus route data from bus companies or web crawlers, including bus stop names, sequences, and location information. Match the bus stops on the trajectory with the bus routes to determine the possible bus route combinations (i.e., bus travel plans) that the passenger may take. Calculate the information entropy of each bus travel plan. Information entropy is an indicator of uncertainty and is widely used in information theory. Here, by calculating the information entropy of the bus travel plan, the uncertainty of the plan can be evaluated. The lower the information entropy, the more certain the plan is. According to the preset entropy threshold, select the bus travel plan with an information entropy lower than the preset entropy threshold as the passenger's first bus route plan.

[0066] For example, refer to Figure 2 , passing by bus stops such as Figure 2 Blue dots, bus routes are Figure 2 Middle red road.

[0067] Step S40, identifying an accompanying passenger in the same bus with the passenger according to the signaling data, determining a first bus route plan of the accompanying passenger, and taking the intersection of the first bus route plan of the accompanying passenger and the first bus route plan of the passenger to obtain a second bus route plan of the passenger;

[0068] It should be noted that the accompanying passenger is another passenger who is determined to be on the same transport as the passenger in the same time period through signaling data. The second bus route plan is a possible bus route plan for the passenger and the accompanying passenger, that is, the intersection of the first bus route plans of the two.

[0069] Using mobile phone signaling data, the accompanying passengers who are on the same bus as the passenger (i.e., whose trajectories overlap) are identified. The same bus route matching and information entropy calculation process is performed on the accompanying passengers to determine the first bus route plan of the accompanying passenger. The first bus route plan of the passenger and the first bus route plan of the accompanying passenger are intersected to obtain the second bus route plan of the passenger. The presence of the accompanying passenger can provide additional information for the passenger, because the passengers on the same bus overlap in trajectory and have a typical accompanying relationship. By taking the intersection, the uncertain bus routes can be further excluded and the certainty of the plan can be improved. By taking the intersection, the second bus route plan of the more certain passenger is obtained.

[0070] For example, passenger A takes a long bus ride from bus stop 1 to bus stop 10, and the first bus route option has only one BusA; passenger B takes a short bus ride from bus stop 4 to bus stop 6, and the first bus route option has three buses (BusA, BusB, BusC). After calculation, there is a companion relationship between passenger A and passenger B from bus stop 4 to bus stop 6. After taking the intersection, the bus route that the two are most likely to take is BusA, so passenger B's second bus route option is BusA.

[0071] Step S50, determining the target bus route plan for the passenger according to the second bus route plan for the passenger within a preset number of consecutive days;

[0072] Step S60: identifying bus route passengers according to the target bus route plan.

[0073] It should be noted that the target bus route plan is the most likely bus route plan for passengers determined through observation and analysis over multiple consecutive days.

[0074] Summarize the second bus route plans of passengers within a preset number of consecutive days (such as the last 30 days). Count the number of days each bus route plan appears and sort them from high to low according to the number of days. In the case of the same number of days, sort them from low to high according to the average information entropy. Take the first bus route plan as the target bus route plan for the passenger, and the other plans as alternative plans. By summarizing and counting data for multiple consecutive days, the bus route that passengers take most frequently can be further determined. The dual sorting of days and average information entropy can ensure the certainty and accuracy of the final plan. Determine the target bus route plan for the passenger based on the data for multiple consecutive days. Finally, the passengers on any bus route can be identified and predicted based on the target bus route plans of multiple users.

[0075] Exemplary, reference Figure 3 , calculate the passenger's single-day movement trajectory, then fit the movement trajectory with the bus route to obtain the bus route, calculate the information entropy probability of the bus route, and calculate the bus route based on the accompanying passenger identification. Finally, the bus route based on the accompanying passenger identification is sorted according to the information entropy probability, and the second bus route of the single day is determined from the bus route plan that is less than the preset entropy threshold, and then the bus routes of consecutive days are collected to determine the target route plan.

[0076] This embodiment generates signaling data based on the passenger's mobile phone signaling, performs road fitting through the position point sequence in the signaling data, and obtains the passenger's movement trajectory, thereby achieving an accurate description of the passenger's travel path and providing reliable basic data for subsequent bus line identification; obtains multiple preset bus stops, determines the station distances between all bus stops and the movement trajectory, and determines the bus stops with station distances less than a preset distance threshold as the passenger's passing bus stops. This process effectively screens out the bus stops that the passenger may take, reduces the complexity of subsequent analysis, and improves recognition efficiency; obtains at least one preset bus route, matches the passing bus stops with the bus route, determines the bus riding plan, and calculates the information entropy of all bus riding plans, and determines the bus riding plan with an information entropy less than a preset entropy threshold as the passenger's first bus route plan. By introducing information entropy, it is possible to evaluate each bus route. The uncertainty of the plan provides a basis for further optimizing the bus route selection; the accompanying passengers are identified according to the signaling data, the first bus route plan of the accompanying passengers is determined, and the intersection of the first bus route plan of the accompanying passengers and the first bus route plan of the passenger is taken to obtain the second bus route plan of the passenger. This step uses the information of the accompanying passengers to enhance the accuracy of bus route identification, and the possible bus routes are further narrowed through the intersection operation; the target bus route plan of the passenger is determined according to the second bus route plan of the passenger within a preset number of consecutive days, and the bus route passengers are identified according to the target bus route plan. Through the accumulation and analysis of data for consecutive days, the passenger's regular bus route can be more accurately identified, and the regular passengers of the bus route can also be identified, which can not only provide passengers with more personalized travel suggestions and services, but also contribute to urban transportation planning and optimization.

[0077] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above introduction, and will not be repeated hereafter. On this basis, the step S20 also includes step A10:

[0078] Step A10: If the distance between the starting point of the motion trajectory and any bus stop is less than the distance threshold, and the distance between the ending point of the motion trajectory and any bus stop is less than the distance threshold, then the step of determining the distances between all bus stops and the station of the motion trajectory is executed.

[0079] It should be noted that bus stops are fixed locations in the public transportation system where buses stop and passengers get on and off. Stop distance refers to the actual distance between a point on the motion trajectory (or the starting point or the end point) and the bus stop.

[0080] First, the system needs to obtain the information of a motion trajectory, including the starting point, the end point, and possible intermediate points of the trajectory. At the same time, the system also needs to obtain the location information of all bus stops. Then, the system calculates the distance between the starting point of the motion trajectory and each bus stop. Similarly, the system also calculates the distance between the end point of the motion trajectory and each bus stop. For the distance between the starting point and each bus stop, the system checks whether there is at least one distance less than the preset distance threshold. Similarly, for the distance between the end point and each bus stop, the system also makes a similar judgment. If the following two conditions are met at the same time: the distance between the starting point and at least one bus stop is less than the distance threshold, and the distance between the end point and at least one bus stop is also less than the distance threshold, then proceed to the next step. If the above conditions are not met, the subsequent steps may not be executed, or other operations may be performed according to the specific situation. When the above conditions are met, the system executes this step, that is, calculating and determining the distance between all points on the motion trajectory (including the starting point, the end point, and the intermediate point) and each bus stop. The purpose of this step is to understand the spatial relationship between the motion trajectory and the bus stop in more detail, which may be used for subsequent analysis, optimization or decision support.

[0081] This embodiment can accurately screen out motion trajectories that may be related to bus routes by judging whether the distance between the starting point and the end point of the motion trajectory and the bus stop is less than a preset distance threshold, avoiding interference from irrelevant trajectories and improving the accuracy of subsequent analysis. Subsequent station distance calculations are only performed on motion trajectories that meet the conditions, effectively reducing unnecessary calculations and improving algorithm efficiency.

[0082] In a feasible implementation manner, step S30 further includes steps B10 to B30:

[0083] Step B10, if there are multiple passing bus stops, determine the first bus stop and the last bus stop in each passing bus stop as the first bus stop and the last bus stop respectively;

[0084] It should be noted that the passing bus stops refer to the set of bus stops passed on the movement trajectory (such as the movement path of pedestrians, vehicles, etc.). Among the passing bus stops, the two bus stops located at the beginning and end of the movement trajectory are called the first bus stop and the last bus stop, respectively.

[0085] From the set of bus stops passed through, identify the first bus stop at the starting position of the movement trajectory and the last bus stop at the ending position.

[0086] Step B20: if there are multiple bus routes, determine the bus route passing through the first bus stop and the last bus stop as the first bus route;

[0087] Use the first and last bus stops as query conditions, match them in the bus route database, and find the first bus route that passes through these two stops.

[0088] Step B30: If the bus stops passing by include all the bus stops between the first bus stop and the last bus stop of the first bus line, then the plan including the first bus line is determined to be a direct bus line plan.

[0089] It should be noted that a direct bus route plan refers to a bus route plan that allows you to reach your destination from the first bus stop to the last bus stop without having to transfer.

[0090] Check whether the passing bus stops are completely included in all stops between the first and last bus stops of the first bus line. If yes, there is a direct bus route solution, and if not, there is no direct bus route solution.

[0091] The steps of step S30 also include steps B40 to B70:

[0092] Step B40, if there are multiple passing bus stops, determine the first bus stop and the last bus stop among the multiple passing bus stops, and determine the other passing bus stops among the passing bus stops except between the first bus stop and the last bus stop;

[0093] Step B50, determining a second bus route in each bus route that passes through the first bus stop and the first other passing bus stop among the other passing bus stops, wherein the second bus route does not pass through the next passing bus stop of the first other passing bus stop;

[0094] Step B60, determining a third bus route passing through the first other passing bus station and the last bus station, and constructing a first mixed route plan including the third bus route and the second bus route, and using the first mixed route plan as a one-transfer plan;

[0095] It should be noted that a one-transfer plan refers to a bus plan that starts from the first bus stop and arrives at the last bus stop through one transfer.

[0096] Traverse the bus stops except the first and last bus stops, and make the following judgments for each stop: whether there is a second bus route from the first bus stop to the first bus stop currently traversed. Whether there is a third bus route from the first bus stop to the last bus stop. If both are satisfied, there is a transfer plan and it is a mixed route plan of the second bus route and the third bus route. A transfer plan can include multiple different plans.

[0097] Step B70, if there are direct bus routes between multiple pairs of passing bus stops, and the direct bus routes can be combined into a bus route plan passing through the first bus stop and the last bus stop, then the combination plan of the direct bus route plan is a multiple transfer plan, wherein adjacent direct bus route plans are different from each other, and the multiple transfer plan includes at least one different combination plan of direct bus route plans.

[0098] It should be noted that the multiple transfer plan refers to a bus plan that starts from the first bus stop and reaches the last bus stop through multiple transfers. The bus plan is all possible bus plans from the first bus stop to the last bus stop determined based on the movement trajectory and bus stop information, including direct, one-transfer and multiple-transfer plans.

[0099] Further check the combination relationship between the passing bus stops to determine whether there are multiple pairs of passing bus stops with direct bus routes, and these direct bus routes can be combined into a bus route plan passing through the first and last bus stops. If these conditions are met, there are multiple transfer plans. Multiple transfer plans may contain multiple different bus route combinations passing through the first and last bus stops, and it is necessary to ensure that adjacent direct bus route plans are different from each other, that is, adjacent transfer buses in the same transfer plan cannot be the same. For example, taking bus 1 from bus stop A to bus stop B, and then taking bus 1 from bus stop B to bus stop C is not allowed.

[0100] The determination of multiple transfer plans can be achieved by using traditional recursive program traversal query or by algorithms. For example, the bus routes and station information are constructed into a graph structure, where the stations are nodes, the routes are edges, and the weights of the edges can be set according to the actual situation (such as time, distance, etc.). The path search algorithm is applied in the graph structure to search all possible paths from the starting node to the end node. Among all the searched paths, the paths containing at least two transfer stations are selected, that is, the multiple transfer plans. At the same time, the plans can be sorted or filtered according to conditions such as the number of transfers and the length of the path. All the direct bus route plans, one-transfer plans, and multiple transfer plans found are summarized as the final bus plan. Furthermore, the number of stations that passengers pass through and the maximum number of transfers can be set. These two parameters can be used to filter the bus plans or reduce the workload in the process.

[0101] For example, the following code idea can be used to determine whether there are multiple transfer options. First, prepare the bus route data, including the list of stops for all bus routes, the order between the stops, and the service time of each route. Then prepare the user trajectory data, including all the bus stops that the user passes by and their timestamps.

[0102] For a given list of user's passing stations (excluding the first and last stations), write a query function to find out if there is a direct bus route between any two stations. The query function returns a list containing all possible direct bus routes. Write a recursive function to construct all possible transfer plans from the starting point to the ending point. In this function, for each non-terminal station, call the query function to find the direct bus routes from this station to each subsequent station, and add these routes to the current transfer plan. If a direct route from the current station to the ending point is found, save the current transfer plan as a sub-transfer plan. If the current station is not the last station, repeat the above process for the next station until all stations are traversed.

[0103] Define a filtering function that receives all the sub-transfer plans generated by the recursive function and filters out those that can be combined into valid plans passing through the first and last stations. Ensure that adjacent direct bus route plans are different, that is, adjacent transfer buses in the same transfer plan are different. Finally, output the results.

[0104] Exemplarily, if there are n transfers, a movement trajectory corresponds to n + 1 bus routes. The specific steps to determine the bus-taking plan are as follows:

[0105] 1. First, check if there is a direct plan. According to the first and last bus stops matched by the movement trajectory, query the bus routes, find the bus routes that pass through both the first and last bus stops of the movement trajectory, and satisfy that the passenger's passing bus stops include all other bus stops of the bus route between the first and last bus stops. Then there is a direct bus route plan; otherwise, there is no direct bus route plan.

[0106] 2. Check the one-transfer bus route plan. Sequentially traverse the list of bus stops passed by the movement trajectory, excluding the first and last bus stops. Each time, select a passing bus stop m (1 < m < M), where M is the maximum number of passenger passing stops set in advance. According to the method in the previous step, check whether there are direct plans from station 1 to m and from station m to M respectively. If there are, then this one-transfer plan is valid; otherwise, it is invalid.

[0107] 3. Check the transfer plans for two or more times. The method is similar to step 2. For the n-transfer plan, all combinations of n passing stops need to be checked in turn to see if there is a direct bus route between adjacent two stops. If there is, then this n-transfer plan is valid; otherwise, it is invalid.

[0108] Exemplarily, the data structure of the bus-taking plan is shown in Table 2:

[0109] Table 2

[0110]

[0111] In this embodiment, not only direct bus route plans are considered, but also single transfer and multiple transfer plans are covered, meeting the diverse travel needs of passengers. By traversing the bus stops along the way and matching the first and last bus stops with the bus routes, the actual travel plan of the passenger can be accurately identified, improving the accuracy of identification. Passengers are provided with a variety of travel options, which helps passengers choose the best travel plan based on their personal preferences and actual conditions and optimize the travel experience.

[0112] In a feasible implementation manner, step S30 further includes step B60:

[0113] Step B60, for each bus-taking plan, information entropy is calculated for the riding distance of each direct bus line of the bus-taking plan to obtain information entropy, wherein the lower the information entropy, the higher the probability that the bus-taking plan actually occurs.

[0114] It should be noted that in information theory, information entropy is a measure of information uncertainty. In the context of information entropy, lower information entropy means higher certainty or predictability, while higher information entropy means lower certainty or higher uncertainty.

[0115] First, we need to determine all possible bus travel options. These options may include one or more direct bus routes, or they may include a combination of routes that require transfers. But in the explanation of this step, we focus on direct bus routes. For each direct bus route in each bus travel option, we need to calculate its ride distance. The ride distance can be obtained through a geographic information system or a bus company's database, which reflects the actual distance traveled by passengers from the starting station to the terminal. Next, we substitute the ride distance of each direct bus route into the preset information entropy formula for calculation. The information entropy formula is usually a complex mathematical expression that takes into account the impact of different factors (such as ride distance, bus departure frequency, passenger travel preferences, etc.) on information uncertainty. In this embodiment, the information entropy formula is as follows.

[0116] ;

[0117] Among them, s is the information entropy, n represents n bus routes, k is a positive integer, 1 <k<n, It is the ratio of the riding distance of the kth bus line to the total riding distance.

[0118] The calculation process of information entropy may involve multiple steps and parameter adjustments, but the basic principle is: for each bus-taking plan, if the riding distance of the direct bus line it contains is closer to a certain "ideal" or "average" value, the information entropy of this plan is lower; conversely, if the riding distance varies greatly, the information entropy is higher. The calculation result of information entropy reflects the uncertainty or diversity of bus-taking plans. In this embodiment, we assume that the lower the information entropy, the higher the actual probability of occurrence of the bus-taking plan. This is because the lower information entropy indicates that the direct bus lines in the plan are more consistent or predictable in riding distance, thereby increasing the possibility of passengers choosing this plan. Finally, according to the calculated information entropy value, we can sort or filter all bus-taking plans. Select the plan with lower information entropy (i.e., higher actual probability of occurrence) as the recommended plan or priority plan to meet the travel needs of passengers.

[0119] For example, for a 10-kilometer movement trajectory, according to the direct solution, the information entropy calculated by the formula in this embodiment is 0; if there is one transfer, the distance of the two bus lines accounts for 0.5 each, and the information entropy is 0.301; if there are two transfers, the distance of the three bus lines accounts for 0.333 each, and the information entropy is 0.477. Obviously, the direct solution has the smallest information entropy and the highest possibility.

[0120] In this embodiment, by calculating the information entropy of the bus plan, the probability of each plan actually occurring can be evaluated. The lower the information entropy, the more reliable the plan, providing passengers with more reliable bus recommendations. The calculation results of the information entropy can serve as an important reference for passengers to choose bus plans, helping passengers make more informed decisions.

[0121] Based on the first or second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first or second embodiment can be referred to the above description, and will not be described in detail later. Step S40 also includes steps C10 to C20:

[0122] Step C10, identifying the accompanying passenger in the same bus with the passenger according to the signaling data, and associating the passenger, the accompanying passenger and the bus stop to determine the accompanying bus passenger pair;

[0123] It should be noted that the accompanying bus passenger pair refers to a pair consisting of a passenger and an accompanying passenger who take the same bus in the same time period. This pair is used to analyze the travel patterns and accompanying relationships of passengers.

[0124] First, the approximate location of the passenger is determined by using the base station information in the signaling data and the base station positioning technology. The timestamps in the signaling data are analyzed to find passengers who are within the coverage of similar base stations in the same time period. These passengers may be on the same bus. The accompanying relationship is further confirmed by calculating the similarity of the movement trajectories between the passengers (such as distance change, speed change, etc.). A certain similarity threshold is set. When the trajectory similarity of two passengers exceeds the threshold, they are considered to be accompanying passengers. Obtain a bus stop database, which should contain the location information of the bus stop (such as longitude and latitude), stop number, etc. Match the passenger location information in the signaling data with the bus stop database to find the closest bus stop when the passenger gets on and off the bus. According to the matching results, the passenger and the accompanying passenger are associated with the bus stop when they get on and off the bus. Traverse all the identified accompanying passengers and combine them with the main passenger to form an accompanying bus passenger pair. For each accompanying bus passenger pair, verify whether their boarding and alighting bus stops are consistent or close (considering a certain distance threshold) and whether the time windows overlap. When the above conditions are met, the validity of the accompanying bus passenger pair is confirmed.

[0125] Step C20, determining the accompanying starting and ending bus stops of the accompanying bus passenger pair, and determining the first bus route plan for the accompanying passengers according to the accompanying starting and ending bus stops.

[0126] It should be noted that the accompanying starting and ending bus stops refer to the bus stops where the accompanying bus passengers get on and off the bus. These stops are used to determine the travel paths of the accompanying passengers and possible bus route plans.

[0127] For each valid accompanying bus passenger pair, determine the accompanying starting and ending bus stops based on their boarding and alighting bus stops. Finally, determine the bus route plan that the accompanying passenger may take based on the accompanying starting and ending bus stops. This can be done by querying the bus route database to find all bus routes connecting the accompanying starting and ending bus stops. Furthermore, the optimal bus route plan can be screened out based on certain rules (such as route length, departure frequency, number of transfers, etc.). This plan can serve as the basis for further analysis of passenger travel patterns and accompanying relationships, and can also provide data support for bus service optimization.

[0128] Exemplarily, the data structure of the accompanying bus passenger pair is shown in Table 3:

[0129] Table 3

[0130]

[0131] In this embodiment, by identifying the accompanying passengers in the same bus with the passenger and determining the accompanying bus passenger pairs, the ability to identify bus route passengers can be further improved, providing passengers with more personalized services. Determining the first bus route plan for the accompanying passenger based on the accompanying starting and ending bus stops helps optimize the travel experience of the accompanying passengers and improve travel efficiency.

[0132] In a feasible implementation manner, step S40 further includes steps D10 to D20:

[0133] Step D10, summarizing the second bus route plans within a preset number of consecutive days, and counting the number of days on which each second bus route plan appears, wherein the second bus route plan includes multiple bus route plans;

[0134] It should be noted that the number of days of appearance refers to the number of days that a second bus route plan is selected as a travel plan in the statistics of multiple consecutive days. It reflects the popularity or stability of the plan.

[0135] First, collect the second bus route plans generated for the same travel demand within a preset number of consecutive days (such as a week, a month, etc.). The second bus route plan for a certain day can be determined according to the given maximum number of bus routes. Traverse the summarized data and count the number of times each second bus route plan appears in multiple consecutive days, that is, the number of days it appears. Create a dictionary or data table to associate each plan with its corresponding number of days it appears.

[0136] Step D20, sorting the second bus route plans in descending order according to the number of days they appear, and taking the bus route plan ranked first as the target bus route plan, wherein, if there are multiple second bus route plans with the same number of days they appear, then sorting the second bus route plans with the same number of days they appear in ascending order according to information entropy.

[0137] It should be noted that the target bus route plan refers to the best plan selected from multiple second bus route plans after statistics and sorting. This plan usually has the highest number of appearance days (or the lowest information entropy when the number of appearance days is the same). Sort the second bus route plans in the dictionary or data table according to the number of appearance days, from high to low. If it is found that multiple second bus route plans have the same number of appearance days during the sorting process, further processing is required. Calculate the information entropy of these plans. The calculation of information entropy can be based on factors such as the diversity of bus routes in the plan, departure frequency, and number of transfers. Sort in ascending order according to information entropy, that is, select the plan with the lowest information entropy as the better plan. Low information entropy means that the bus routes in the plan are more certain and the uncertainty of the selection is smaller. After the above sorting and processing, the bus route plan ranked first is selected as the target bus route plan.

[0138] If there are still multiple final solutions (i.e., the number of days and information entropy are the same), other factors (such as passenger satisfaction, operating costs, etc.) can be further considered to make a decision.

[0139] In this embodiment, by summarizing the second bus route plans for multiple consecutive days and counting the number of days each plan appears, the most stable and most commonly used bus route plan can be screened out, thereby improving the stability of the plan. In the case of the same number of days, sorting by information entropy can further screen out more reliable and optimized bus route plans, providing passengers with more accurate travel suggestions. The final bus route plan comprehensively considers the two factors of the number of days and information entropy, which is more in line with the actual travel needs of passengers and helps to improve the passenger experience.

[0140] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the method of bus route passenger identification based on mobile phone signaling of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0141] This application also provides a bus route passenger identification device based on mobile phone signaling, please refer to Figure 4 , a bus route passenger identification device based on mobile phone signaling includes:

[0142] The trajectory acquisition module 10 generates signaling data based on the passenger's mobile phone signaling, performs road fitting through the position point sequence in the signaling data, and obtains the passenger's movement trajectory;

[0143] The station acquisition module 20 acquires a plurality of preset bus stations, determines the station distances between all bus stations and the motion trajectory, and determines the bus stations whose station distances are less than a preset distance threshold as the bus stations that the passenger passes by;

[0144] The route probability calculation module 30 obtains at least one preset bus route, matches the bus stops passed by with the bus route, determines the bus route plan, and calculates the information entropy of all the bus routes, and determines the bus route plan with an information entropy less than a preset entropy threshold as the first bus route plan for the passenger;

[0145] The route optimization module 40 identifies the accompanying passenger in the same bus with the passenger according to the signaling data, determines the first bus route plan of the accompanying passenger, and takes the intersection of the first bus route plan of the accompanying passenger and the first bus route plan of the passenger to obtain the second bus route plan of the passenger;

[0146] A route determination module 50, which determines a target bus route plan for a passenger according to the second bus route plan of the passenger within a preset number of consecutive days;

[0147] The passenger identification module 60 performs bus route passenger identification according to the target bus route plan.

[0148] The bus route passenger identification device based on mobile phone signaling provided by the present application adopts the bus route passenger identification method based on mobile phone signaling in the above embodiment, which can solve the technical problem of how to effectively use mobile phone signaling to identify bus route passengers. Compared with the prior art, the beneficial effects of the bus route passenger identification device based on mobile phone signaling provided by the present application are the same as the beneficial effects of the bus route passenger identification method based on mobile phone signaling provided by the above embodiment, and other technical features of the bus route passenger identification device based on mobile phone signaling are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0149] The present application provides a bus route passenger identification device based on mobile phone signaling, and the bus route passenger identification device based on mobile phone signaling includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the bus route passenger identification method based on mobile phone signaling in the above-mentioned embodiment 1.

[0150] Reference below Figure 5 , which shows a schematic diagram of the structure of a bus route passenger identification device based on mobile phone signaling suitable for implementing the embodiment of the present application. The bus route passenger identification device based on mobile phone signaling in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The bus line passenger identification device based on mobile phone signaling shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0151] like Figure 5As shown, the bus route passenger identification device based on mobile phone signaling may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in a read-only memory (ROM: Read Only Memory) 1002 or the program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the bus route passenger identification device based on mobile phone signaling are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 may allow the bus line passenger identification device based on mobile phone signaling to communicate wirelessly or wired with other devices to exchange data. Although the bus line passenger identification device based on mobile phone signaling with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0152] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0153] The bus route passenger identification device based on mobile phone signaling provided by the present application adopts the bus route passenger identification method based on mobile phone signaling in the above embodiment, which can solve the technical problem of how to effectively use mobile phone signaling to identify bus route passengers. Compared with the prior art, the beneficial effects of the bus route passenger identification device based on mobile phone signaling provided by the present application are the same as the beneficial effects of the bus route passenger identification method based on mobile phone signaling provided by the above embodiment, and the other technical features of the bus route passenger identification device based on mobile phone signaling are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0154] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0155] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0156] The present application provides a medium, which is a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for bus route passenger identification based on mobile phone signaling in the above-mentioned embodiment.

[0157] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0158] The computer-readable storage medium may be included in the bus route passenger identification device based on mobile phone signaling; or may exist independently without being assembled into the bus route passenger identification device based on mobile phone signaling.

[0159] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the bus route passenger identification device based on mobile phone signaling, the bus route passenger identification device based on mobile phone signaling:

[0160] Generate signaling data based on the passenger's mobile phone signaling, perform road fitting through the position point sequence in the signaling data, and obtain the passenger's movement trajectory;

[0161] Obtain multiple preset bus stops, determine the distances between all bus stops and the motion trajectory, and determine the bus stops whose distances are less than a preset distance threshold as the bus stops that the passenger will pass through;

[0162] Obtain at least one preset bus route, match the bus stops along the way with the bus route, determine a bus plan, calculate the information entropy of all bus plans, and determine the bus plan with an information entropy less than a preset entropy threshold as the passenger's first bus route plan;

[0163] Identify the accompanying passenger in the same bus with the passenger according to the signaling data, determine the first bus route plan of the accompanying passenger, and take the intersection of the first bus route plan of the accompanying passenger and the first bus route plan of the passenger to obtain the second bus route plan of the passenger;

[0164] Determine the target bus route plan for the passenger according to the second bus route plan for the passenger within a preset number of consecutive days;

[0165] The passenger identification module identifies bus route passengers according to the target bus route plan.

[0166] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the passenger computer, partially on the passenger computer, as a separate software package, partially on the passenger computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the passenger computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0167] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0168] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0169] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for identifying bus route passengers based on mobile phone signaling, and can solve the technical problem of how to effectively use mobile phone signaling to identify bus route passengers. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the method for identifying bus route passengers based on mobile phone signaling provided in the above-mentioned embodiment, and will not be repeated here.

[0170] The present application also provides a product, which is a computer program product, including a computer program, which, when executed by a processor, implements the steps of the method for identifying bus route passengers based on mobile phone signaling as described above.

[0171] The computer program product provided by this application can solve the technical problem of how to effectively use mobile phone signaling to identify bus route passengers. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the method for identifying bus route passengers based on mobile phone signaling provided in the above embodiment, and will not be repeated here.

[0172] The above are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for identifying bus route passengers based on mobile phone signaling, characterized in that: The method for identifying bus route passengers based on mobile phone signaling includes: Generate signaling data based on the passenger's mobile phone signaling, and perform road fitting through a sequence of position points in the signaling data to obtain the passenger's movement trajectory; Acquire a plurality of preset bus stops, determine the station distances between all the bus stops and the motion trajectory, and determine the bus stops whose station distances are less than a preset distance threshold as the bus stops that the passenger passes through; Obtain at least one preset bus route, match the bus stops passed by with the bus route, determine a bus plan, calculate the information entropy of all the bus plans, and determine the bus plan with the information entropy less than a preset entropy threshold as the first bus route plan of the passenger; Identify the accompanying passenger in the same bus with the passenger according to the signaling data, determine the first bus route plan of the accompanying passenger, and take the intersection of the first bus route plan of the accompanying passenger and the first bus route plan of the passenger to obtain the second bus route plan of the passenger; Determine the target bus route plan for the passenger according to the second bus route plan for the passenger within a preset number of consecutive days; The bus route passengers are identified according to the target bus route plan.

2. The method for bus route passenger identification based on mobile phone signaling as claimed in claim 1, characterized in that: The bus ride plan includes a direct bus route plan, and the step of matching the passing bus stops with the bus routes to determine the bus ride plan includes: If there are multiple bus stops along the way, determine that the first bus stop and the last bus stop among the bus stops along the way are the first bus stop and the last bus stop respectively; If there are multiple bus routes, the bus route passing through the first bus stop and the last bus stop is determined as the first bus route; If the passing bus stops include all bus stops between the first bus stop and the last bus stop of the first bus line, the plan including the first bus line is determined to be the direct bus line plan.

3. The method for bus route passenger identification based on mobile phone signaling as claimed in claim 2, characterized in that: The bus ride plan includes a one-transfer plan and a multiple-transfer plan, and the step of matching the passing bus stops with the bus routes to determine the bus ride plan includes: If there are multiple passing bus stops, determine the first bus stop and the last bus stop among the multiple passing bus stops, and determine other passing bus stops among the passing bus stops except between the first bus stop and the last bus stop; Determine a second bus route among the bus routes that passes through the first bus stop and the first other passing bus stop among the other passing bus stops, wherein the second bus route does not pass through the next passing bus stop of the first other passing bus stop; Determine a third bus route passing through the first other passing bus station and the last bus station, and construct a first mixed route plan including the third bus route and the second bus route, and use the first mixed route plan as a one-transfer plan; If there are direct bus routes between multiple pairs of the passing bus stops, and the direct bus routes can be combined into a bus route plan passing through the first bus stop and the last bus stop, then the combination plan of the direct bus route plan is the multiple transfer plan, wherein adjacent direct bus route plans are different from each other, and the multiple transfer plan includes at least one different combination plan of the direct bus route plans.

4. The method for bus route passenger identification based on mobile phone signaling as claimed in claim 1, characterized in that: The step of calculating the information entropy of all the public transportation options also includes: For each of the bus-taking plans, information entropy is calculated for the riding distance of each direct bus line of the bus-taking plan to obtain the information entropy, wherein the lower the information entropy is, the higher the probability that the bus-taking plan actually occurs.

5. The method for bus route passenger identification based on mobile phone signaling as claimed in claim 1, characterized in that: The step of identifying the accompanying passenger in the same bus with the passenger according to the signaling data and determining the first bus route plan for the accompanying passenger also includes: Identify the accompanying passenger in the same bus with the passenger according to the signaling data, and associate the passenger, the accompanying passenger and the bus stop to determine the accompanying bus passenger pair; The accompanying starting and ending bus stops of the accompanying bus passenger pair are determined, and a first bus route plan for the accompanying passengers is determined according to the accompanying starting and ending bus stops.

6. The method for bus route passenger identification based on mobile phone signaling as claimed in claim 1, characterized in that: The step of determining the target bus route plan according to the second bus route plan of the passenger within a preset number of consecutive days comprises: Summarizing the second bus route plans within a preset number of consecutive days, and counting the number of days on which each second bus route plan appears, wherein the second bus route plan includes a plurality of bus route plans; The second bus route plans are sorted in descending order according to the number of days of appearance, and the bus route plan ranked first is used as the target bus route plan, wherein, if there are multiple second bus route plans with the same number of days of appearance, the second bus route plans with the same number of days of appearance are sorted in ascending order according to the information entropy.

7. The method for identifying bus route passengers based on mobile phone signaling as claimed in claim 1, characterized in that: The step of obtaining the bus stop then includes: If there is a distance between the starting point of the motion trajectory and any of the bus stops that is less than the distance threshold, and the distance between the end point of the motion trajectory and any of the bus stops is less than the distance threshold, then the step of determining the station distances between all the bus stops and the motion trajectory is performed.

8. A bus route passenger identification device based on mobile phone signaling, characterized in that: The bus route passenger identification device based on mobile phone signaling includes: A trajectory acquisition module generates signaling data based on the passenger's mobile phone signaling, performs road fitting through a sequence of position points in the signaling data, and obtains the passenger's movement trajectory; A station acquisition module is used to acquire a plurality of preset bus stations, determine the station distances between all the bus stations and the motion trajectory, and determine the bus stations whose station distances are less than a preset distance threshold as the bus stations that the passenger passes by; A route probability calculation module is configured to obtain at least one preset bus route, match the bus stops passed by the bus route with the bus route, determine a bus plan, and calculate the information entropy of all the bus plans, and determine the bus plan with the information entropy less than a preset entropy threshold as the first bus route plan of the passenger; a route optimization module, which identifies an accompanying passenger in the same bus with the passenger according to the signaling data, determines a first bus route plan for the accompanying passenger, and takes an intersection of the first bus route plan for the accompanying passenger and the first bus route plan for the passenger to obtain a second bus route plan for the passenger; A route determination module, which determines a target bus route plan for the passenger according to the second bus route plan for the passenger within a preset number of consecutive days; The passenger identification module identifies bus route passengers according to the target bus route plan.

9. A bus route passenger identification device based on mobile phone signaling, characterized in that: The bus route passenger identification device based on mobile phone signaling includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the bus route passenger identification method based on mobile phone signaling as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the method for identifying bus route passengers based on mobile phone signaling as claimed in any one of claims 1 to 6 are implemented.

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