Public transport passenger flow evaluation method, device and equipment and storage medium

Abstract

The invention provides a public transport passenger flow evaluation method and device, equipment and a storage medium, and belongs to the field of data analysis. The method comprises the following steps: acquiring bus card data, bus track data and bus route data according to the bus running condition of a target area; generating station passenger flow data based on the bus card data, the bus track data and the bus route data; obtaining target passenger flow data based on the station passenger flow data and the multiple pieces of community vector data; determining a plurality of target influence factors corresponding to the target passenger flow data; and generating a public transport passenger flow evaluation result for the target region based on the plurality of target influence factors. In the embodiment of the invention, target passenger flow data is obtained based on station passenger flow data and multiple pieces of community vector data, and the community vector data is introduced to evaluate the public transport passenger flow; and based on the target influence factor representing the factors influencing the public transport passenger flow, a public transport passenger flow evaluation result for the target region is generated, so that the accuracy of the public transport passenger flow evaluation result is improved.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and in particular to a method, apparatus, equipment and storage medium for assessing public transportation passenger flow. Background Technology

[0002] The ever-increasing urban population is putting increasing pressure on traffic in large and medium-sized cities. Developing a rational public transport operation is not only a necessity for public travel but also essential for building low-carbon cities. Therefore, it is necessary to assess public transport passenger flow in various urban areas and then optimize public transport operation strategies based on the assessment results to improve public transport capacity.

[0003] Existing methods for assessing public transport passenger flow are mostly based on cross-sectional design. These methods are difficult to accurately identify factors that affect public transport passenger flow and do not take into account the spatial heterogeneity of public transport passenger flow. As a result, the assessment results do not match the actual passenger flow situation and are therefore inaccurate. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, and storage medium for assessing public transport passenger flow, aiming to solve the technical problem that the assessment results do not match the actual passenger flow situation, resulting in inaccurate assessment results.

[0005] In a first aspect, embodiments of this application provide a method for assessing public transport passenger flow, the method comprising:

[0006] Based on the public transportation operation status of the target area, obtain public transportation card data, bus trajectory data, and bus route data; the public transportation card data includes public transportation card swiping data of the target area, and the bus trajectory data includes public transportation vehicle data of the target area.

[0007] Based on the bus card data, the bus trajectory data, and the bus route data, station passenger flow data is generated; the station passenger flow data is used to characterize the passenger flow at each bus stop on the bus route.

[0008] Based on the station passenger flow data and multiple community vector data, target passenger flow data is obtained; the target area includes multiple communities, and each community corresponds one-to-one with community vector data.

[0009] Multiple target influencing factors are determined corresponding to the target passenger flow data; the target influencing factors are used to characterize the factors affecting public transport passenger flow.

[0010] Based on the aforementioned multiple target influencing factors, a public transport passenger flow assessment result is generated for the target region.

[0011] Optionally, generating station passenger flow data based on the bus card data, the bus trajectory data, and the bus route data includes:

[0012] Data cleaning is performed on the bus card data, the bus trajectory data, and the bus route data;

[0013] Station name data is obtained based on the station topology map and the cleaned bus route data; the station topology map is obtained based on the cleaned bus trajectory data.

[0014] Based on the station name data and the cleaned bus card data, station passenger flow data is obtained.

[0015] Optionally, obtaining the target passenger flow data based on the station passenger flow data and multiple community vector data includes:

[0016] Perform data aggregation processing on the station passenger flow data and the multiple community vector data to obtain community passenger flow data;

[0017] Cluster analysis is performed on the community passenger flow data to obtain the target passenger flow data.

[0018] Optionally, determining the multiple target influencing factors corresponding to the target passenger flow data includes:

[0019] Principal component analysis and regression analysis are performed on the target passenger flow data and multiple preset benchmark influencing factors to determine the linear relationship between the target passenger flow data and each benchmark influencing factor;

[0020] The nonlinear relationship between the target passenger flow data and each benchmark influencing factor is determined using a machine learning model.

[0021] Based on the linear and nonlinear relationships, multiple target influencing factors are determined.

[0022] Optionally, the determination of multiple target influencing factors based on the linear relationship and the nonlinear relationship includes:

[0023] Based on the linear relationship between each benchmark influence factor and the target passenger flow data, a first score corresponding to each benchmark influence factor is determined;

[0024] Based on the nonlinear relationship between each benchmark influence factor and the target passenger flow data, a second score corresponding to each benchmark influence factor is determined;

[0025] Based on the first and second scores corresponding to each benchmark impact factor, the target impact factor among the plurality of benchmark impact factors is determined.

[0026] Optionally, generating public transport passenger flow assessment results for the target area based on the multiple target influencing factors includes:

[0027] Based on the target influencing factors corresponding to each community, the public transport passenger flow assessment results for each community are determined.

[0028] Based on the public transport passenger flow assessment results for each community, the public transport passenger flow assessment results for the target area are generated.

[0029] Optionally, after generating the public transport passenger flow assessment results for the target area based on the multiple target influencing factors, the method further includes:

[0030] Extract the bus passenger flow for each community in each time period from the bus passenger flow assessment results;

[0031] Based on the bus passenger flow of each community in each time period, calculate the average bus passenger flow of each community.

[0032] The time period in which the corresponding bus passenger flow is higher than the average bus passenger flow is determined as the target time period;

[0033] Based on the target time period and the communities associated with the target time period, an early warning message is sent.

[0034] Secondly, embodiments of this application provide a public transport passenger flow assessment device, the public transport passenger flow assessment device comprising:

[0035] The acquisition module is used to acquire bus card data, bus trajectory data, and bus route data based on the bus operation status of the target area; the bus card data includes bus card swiping data of the target area, and the bus trajectory data includes bus vehicle data of the target area;

[0036] The first generation module is used to generate station passenger flow data based on the bus card data, the bus trajectory data, and the bus route data; the station passenger flow data is used to characterize the passenger flow at each bus stop on the bus route.

[0037] The second generation module is used to obtain target passenger flow data based on the station passenger flow data and multiple community vector data; the target area includes multiple communities, and each community corresponds one-to-one with community vector data;

[0038] The first determining module is used to determine multiple target influencing factors corresponding to the target passenger flow data;

[0039] The third generation module is used to generate public transport passenger flow assessment results for the target area based on the multiple target influencing factors.

[0040] Thirdly, embodiments of this application provide an electronic device, including: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, it implements the steps of the public transport passenger flow assessment method as described in the first aspect.

[0041] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the public transport passenger flow assessment method as described in the first aspect.

[0042] This application provides a method, apparatus, device, and storage medium for assessing public transport passenger flow. The method includes: acquiring public transport card data, bus trajectory data, and bus route data based on the public transport operation status of a target area; the public transport card data includes public transport card swiping data for the target area, and the bus trajectory data includes public transport vehicle data for the target area; generating station passenger flow data based on the public transport card data, bus trajectory data, and bus route data; the station passenger flow data is used to characterize the passenger flow at each bus stop on the bus route; obtaining target passenger flow data based on the station passenger flow data and multiple community vector data; the target area includes multiple communities, each corresponding one-to-one with community vector data; determining multiple target influencing factors corresponding to the target passenger flow data; and generating a public transport passenger flow assessment result for the target area based on the multiple target influencing factors. In this embodiment, the target passenger flow data is obtained based on the station passenger flow data and multiple community vector data, and community vector data is introduced to assess public transport passenger flow; and a public transport passenger flow assessment result for the target area is generated based on the target influencing factors characterizing factors affecting public transport passenger flow, thereby improving the accuracy of the public transport passenger flow assessment result. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of a public transport passenger flow assessment method provided in an embodiment of this application;

[0045] Figure 2 This is a data flow diagram illustrating the determination of a set of tracking points provided in an embodiment of this application;

[0046] Figure 3 This is a schematic diagram of the structure of a public transport passenger flow assessment device provided in an embodiment of this application;

[0047] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0048] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0049] See Figure 1 , Figure 1 This is a flowchart of a public transport passenger flow assessment method provided in an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0050] Step 101: Based on the public transportation operation status of the target area, obtain public transportation card data, bus trajectory data, and bus route data; the public transportation card data includes public transportation card swiping data of the target area, and the bus trajectory data includes public transportation vehicle data of the target area.

[0051] In this step, public transportation operation data for a target area over a specific period can be obtained. Optionally, public transportation operation data for the target area over the past year can be obtained. From this data, bus card data, bus trajectory data, and bus route data are extracted.

[0052] The aforementioned public transport card data includes, but is not limited to, card swiping time and location. The aforementioned bus trajectory data includes, but is not limited to, bus location, bus arrival time, and bus departure time. The aforementioned bus route data includes, but is not limited to, bus routes and bus stops.

[0053] Step 102: Based on the bus card data, the bus trajectory data, and the bus route data, generate station passenger flow data; the station passenger flow data is used to characterize the passenger flow at each bus stop on the bus route.

[0054] In this step, after obtaining bus card data, bus trajectory data, and bus route data, station passenger flow data can be generated based on these data. The station passenger flow data is used to characterize the passenger flow at each bus stop along the bus route.

[0055] For details on the technical solutions for generating site visitor flow data, please refer to the following embodiments.

[0056] Step 103: Based on the station passenger flow data and multiple community vector data, obtain the target passenger flow data; the target area includes multiple communities, and each community corresponds one-to-one with the community vector data.

[0057] It should be noted that the target area includes multiple communities. Optionally, the target area can be divided into multiple communities based on administrative divisions, and each community corresponds one-to-one with community vector data.

[0058] In this step, after obtaining the station's passenger flow data, multiple community vector data are acquired, and the target passenger flow data is obtained based on the station's passenger flow data and the multiple community vector data.

[0059] Step 104: Determine multiple target influencing factors corresponding to the target passenger flow data; the target influencing factors are used to characterize the factors affecting public transport passenger flow.

[0060] Step 105: Based on the multiple target influencing factors, generate a public transport passenger flow assessment result for the target area.

[0061] It should be noted that the influencing factor is used to characterize the factors affecting public transport passenger flow. For a detailed explanation, please refer to the following examples.

[0062] In this step, multiple target influencing factors corresponding to the target passenger flow data are identified, and based on these factors, a public transport passenger flow assessment result for the target area is generated. This assessment result includes the public transport passenger flow for each community in each time period.

[0063] In this embodiment, target passenger flow data is obtained based on station passenger flow data and multiple community vector data. Community vector data is then used to evaluate bus passenger flow. Furthermore, based on target influencing factors that characterize factors affecting bus passenger flow, bus passenger flow evaluation results for the target area are generated, thereby improving the accuracy of bus passenger flow evaluation results.

[0064] Optionally, generating station passenger flow data based on the bus card data, the bus trajectory data, and the bus route data includes:

[0065] Data cleaning is performed on the bus card data, the bus trajectory data, and the bus route data;

[0066] Station name data is obtained based on the station topology map and the cleaned bus route data; the station topology map is obtained based on the cleaned bus trajectory data.

[0067] Based on the station name data and the cleaned bus card data, station passenger flow data is obtained.

[0068] In this embodiment, after obtaining the bus card data, bus trajectory data, and bus route data, data cleaning is performed on these data. Optionally, the data cleaning process includes handling missing values, detecting and handling outliers, recording duplicates, and standardizing the data format.

[0069] After cleaning the bus trajectory data, a station topology map is obtained.

[0070] Based on the station topology map and the cleaned bus route data, station name data is obtained. Specifically, the bus stops included in the station topology map are matched with the bus stops included in the bus route data to determine the accurate bus stop names, thus obtaining the station name data.

[0071] Furthermore, passenger flow data is obtained from the station name data and the cleaned bus card data. Specifically, the bus card data includes card swipe records and swipe locations. Passenger flow at each bus station can be determined based on the card swipe records and locations, and then combined with the station name data to obtain station passenger flow data representing the passenger flow at each station.

[0072] In this embodiment, by cleaning the bus card data, bus trajectory data, and bus route data, the station passenger flow is obtained based on the cleaned bus card data, bus trajectory data, and bus route data. In this way, by processing bus data from multiple dimensions, the station passenger flow data is obtained, ensuring the accuracy of the station passenger flow data.

[0073] Optionally, obtaining the target passenger flow data based on the station passenger flow data and multiple community vector data includes:

[0074] Perform data aggregation processing on the station passenger flow data and the multiple community vector data to obtain community passenger flow data;

[0075] Cluster analysis is performed on the community passenger flow data to obtain the target passenger flow data.

[0076] In this embodiment, after obtaining the station passenger flow data, data aggregation processing is performed on the station passenger flow data and multiple community vector data to obtain community passenger flow data. The community passenger flow data can reflect the passenger flow situation of each community.

[0077] Furthermore, cluster analysis can be performed on the community passenger flow data, i.e., an unsupervised classification model can be used to analyze the community passenger flow data to obtain the target passenger flow data.

[0078] For a better understanding of the technical solution, please refer to [link / reference]. Figure 2 ,like Figure 2As shown, firstly, data cleaning is performed on the bus card data, bus trajectory data, and bus route data. Based on the cleaned bus trajectory data, a station topology map is obtained, and then station name data is derived from the station topology map and the cleaned bus route data.

[0079] The station name data is matched with the cleaned bus card data to obtain station passenger flow data. Based on the station passenger flow data and the acquired community vector data, community passenger flow data is generated. Furthermore, cluster analysis is performed on the community passenger flow data to obtain target passenger flow data.

[0080] Optionally, determining the multiple target influencing factors corresponding to the target passenger flow data includes:

[0081] Principal component analysis and regression analysis are performed on the target passenger flow data and multiple preset benchmark influencing factors to determine the linear relationship between the target passenger flow data and each benchmark influencing factor;

[0082] The nonlinear relationship between the target passenger flow data and each benchmark influencing factor is determined using a machine learning model.

[0083] Based on the linear and nonlinear relationships, multiple target influencing factors are determined.

[0084] It should be noted that the influencing factor includes primary indicators, secondary indicators, and tertiary indicators.

[0085] In one optional implementation, the setting of the benchmark impact factor is shown in the table below:

[0086]

[0087] In this embodiment, the benchmark influence factors can first be tested for collinearity using the variance inflation factor (VIF) to exclude benchmark influence factors with collinearity interference. Then, a significance analysis is performed on the benchmark influence factors after excluding collinearity interference and the community passenger flow data to determine the linear relationship between the target passenger flow data and each benchmark influence factor.

[0088] Optionally, the benchmark influencing factors can be ranked linearly based on the linear relationship between the target passenger flow data and each benchmark influencing factor, wherein the benchmark influencing factors ranked higher have a greater influence on the target passenger flow data than the benchmark influencing factors ranked lower.

[0089] After determining the linear relationship between the target passenger flow data and each benchmark influencing factor, the nonlinear relationship between each influencing factor indicator and the community passenger flow data can be determined by using the Gradient Boosting Decision Tree (GBDT) model. Based on the nonlinear relationship, the impact of each benchmark influencing factor on the target passenger flow data is ranked.

[0090] As described above, on the one hand, the benchmark influence factors are ranked based on the linear relationship between the target passenger flow data and each benchmark influence factor; on the other hand, the benchmark influence factors are ranked based on the non-linear relationship between the target passenger flow data and each benchmark influence factor. Furthermore, the target influence factor can be determined based on the above two rankings. For specific technical solutions, please refer to subsequent embodiments.

[0091] In this embodiment, the benchmark influence factors are screened based on the linear relationship between the target passenger flow data and each benchmark influence factor, as well as the nonlinear relationship between the target passenger flow data and each benchmark influence factor, to obtain the target influence factors that conform to the actual situation of the target area.

[0092] The following details how to determine the target impact factor:

[0093] Optionally, the determination of multiple target influencing factors based on the linear relationship and the nonlinear relationship includes:

[0094] Based on the linear relationship between each benchmark influence factor and the target passenger flow data, a first score corresponding to each benchmark influence factor is determined;

[0095] Based on the nonlinear relationship between each benchmark influence factor and the target passenger flow data, a second score corresponding to each benchmark influence factor is determined;

[0096] Based on the first and second scores corresponding to each benchmark impact factor, the target impact factor among the plurality of benchmark impact factors is determined.

[0097] As described above, the benchmark influence factors can be ranked linearly based on the linear relationship between the target passenger flow data and each benchmark influence factor. In this embodiment, the first score corresponding to each benchmark influence factor can be determined based on the above ranking. It should be understood that the first score corresponding to the benchmark factor ranked higher is higher than the first score corresponding to the benchmark factor ranked lower.

[0098] As described above, the benchmark influence factors can be non-linearly ranked based on the non-linear relationship between the target passenger flow data and each benchmark influence factor. In this embodiment, the second score corresponding to each benchmark influence factor can be determined based on the above ranking. It should be understood that the second score corresponding to the benchmark factor ranked higher is higher than the second score corresponding to the benchmark factor ranked lower.

[0099] Furthermore, the sum of the first and second scores corresponding to each benchmark impact factor is calculated, and the benchmark impact factor with the higher sum of scores is determined as the target impact factor.

[0100] For example, calculate the score and value corresponding to each benchmark impact factor, and sort the benchmark impact factors in descending order of score and value, and then determine the top 10 benchmark impact factors as target impact factors.

[0101] Optionally, generating public transport passenger flow assessment results for the target area based on the multiple target influencing factors includes:

[0102] Based on the target influencing factors corresponding to each community, the public transport passenger flow assessment results for each community are determined.

[0103] Based on the public transport passenger flow assessment results for each community, the public transport passenger flow assessment results for the target area are generated.

[0104] It should be noted that each community corresponds to a different target impact factor, and each target impact factor corresponds to a different preset indicator.

[0105] In this embodiment, for each community, a target impact factor is determined, and based on the relationship between the target impact factor and the corresponding preset indicator, a public transport passenger flow assessment result for that community is generated.

[0106] For example, if the target impact factor is population density, and the population density represented by the target impact factor is greater than the population density represented by the preset indicator, then the public transport passenger flow assessment result for the community is determined to include excessive population density.

[0107] Furthermore, after obtaining the public transport passenger flow assessment results for each community, the public transport passenger flow assessment results for each community are aggregated to obtain the public transport passenger flow assessment results for the target area.

[0108] Optionally, after generating the public transport passenger flow assessment results for the target area based on the multiple target influencing factors, the method further includes:

[0109] Extract the bus passenger flow for each community in each time period from the bus passenger flow assessment results;

[0110] Based on the bus passenger flow of each community in each time period, calculate the average bus passenger flow of each community.

[0111] The time period in which the corresponding bus passenger flow is higher than the average bus passenger flow is determined as the target time period;

[0112] Based on the target time period and the communities associated with the target time period, an early warning message is sent.

[0113] It should be noted that the public transport passenger flow assessment results include the public transport passenger flow for each community in each time period.

[0114] In this embodiment, the bus passenger flow corresponding to each community in each time period is extracted from the bus passenger flow assessment results. For any community, the average bus passenger flow corresponding to that community in each time period is calculated.

[0115] The time period when the corresponding bus passenger flow is higher than the average bus passenger flow is determined as the target time period. The target time period can be understood as the time period when the bus passenger flow is overloaded.

[0116] Based on the target time period and the associated communities, early warning information is generated. In other words, the early warning information can characterize the time periods during which public transportation passenger overload occurs in each community. After receiving the early warning information, personnel can use it to manage traffic or optimize bus routes, thereby improving public transportation capacity.

[0117] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a public transport passenger flow assessment device provided in an embodiment of this application, as shown below. Figure 3 As shown, the bus passenger flow assessment device 300 includes:

[0118] The acquisition module 301 is used to acquire bus card data, bus trajectory data, and bus route data based on the bus operation status of the target area; the bus card data includes bus card swiping data of the target area, and the bus trajectory data includes bus vehicle data of the target area;

[0119] The first generation module 302 is used to generate station passenger flow data based on the bus card data, the bus trajectory data, and the bus route data; the station passenger flow data is used to characterize the passenger flow at each bus stop on the bus route.

[0120] The second generation module 303 is used to obtain target passenger flow data based on the station passenger flow data and multiple community vector data; the target area includes multiple communities, and each community corresponds one-to-one with community vector data;

[0121] The first determining module 304 is used to determine multiple target influencing factors corresponding to the target passenger flow data;

[0122] The third generation module 305 is used to generate a public transport passenger flow assessment result for the target area based on the multiple target influencing factors.

[0123] Optionally, the first generation module 302 is specifically used for:

[0124] Data cleaning is performed on the bus card data, the bus trajectory data, and the bus route data;

[0125] Station name data is obtained based on the station topology map and the cleaned bus route data; the station topology map is obtained based on the cleaned bus trajectory data.

[0126] Based on the station name data and the cleaned bus card data, station passenger flow data is obtained.

[0127] Optionally, the second generation module 303 is specifically used for:

[0128] Perform data aggregation processing on the station passenger flow data and the multiple community vector data to obtain community passenger flow data;

[0129] Cluster analysis is performed on the community passenger flow data to obtain the target passenger flow data.

[0130] Optionally, the first determining module 304 is specifically used for:

[0131] Principal component analysis and regression analysis are performed on the target passenger flow data and multiple preset benchmark influencing factors to determine the linear relationship between the target passenger flow data and each benchmark influencing factor;

[0132] The nonlinear relationship between the target passenger flow data and each benchmark influencing factor is determined using a machine learning model.

[0133] Based on the linear and nonlinear relationships, multiple target influencing factors are determined.

[0134] Optionally, the first determining module 304 is further specifically used for:

[0135] Based on the linear relationship between each benchmark influence factor and the target passenger flow data, a first score corresponding to each benchmark influence factor is determined;

[0136] Based on the nonlinear relationship between each benchmark influence factor and the target passenger flow data, a second score corresponding to each benchmark influence factor is determined;

[0137] Based on the first and second scores corresponding to each benchmark impact factor, the target impact factor among the plurality of benchmark impact factors is determined.

[0138] Optionally, the third generation module 305 is specifically used for:

[0139] Based on the target influencing factors corresponding to each community, the public transport passenger flow assessment results for each community are determined.

[0140] Based on the public transport passenger flow assessment results for each community, the public transport passenger flow assessment results for the target area are generated.

[0141] Optionally, the public transport passenger flow assessment device 300 further includes:

[0142] The extraction module is used to extract the bus passenger flow corresponding to each community in each time period from the bus passenger flow assessment results;

[0143] The calculation module is used to calculate the average bus passenger flow for each community based on the bus passenger flow for each time period.

[0144] The second determining module is used to determine the time period in which the corresponding bus passenger flow is higher than the average bus passenger flow as the target time period;

[0145] The sending module is used to send early warning information based on the target time period and the communities associated with the target time period.

[0146] The bus passenger flow assessment device is capable of realizing each process of the above-mentioned bus passenger flow assessment method, with one-to-one correspondence of technical features and achieving the same technical effect. To avoid repetition, it will not be described in detail here.

[0147] For details, see Figure 4 This application also provides an electronic device, including a bus 401, a transceiver 402, an antenna 403, a bus interface 404, a processor 405, and a memory 406.

[0148] The transceiver 402 is used to acquire bus card data, bus trajectory data, and bus route data based on the bus operation status of the target area; the bus card data includes bus card swiping data of the target area, and the bus trajectory data includes bus vehicle data of the target area.

[0149] The processor 405 is used to generate station passenger flow data based on the bus card data, the bus trajectory data, and the bus route data; the station passenger flow data is used to characterize the passenger flow at each bus stop on the bus route.

[0150] Based on the station passenger flow data and multiple community vector data, target passenger flow data is obtained; the target area includes multiple communities, and each community corresponds one-to-one with community vector data.

[0151] Multiple target influencing factors are determined corresponding to the target passenger flow data; the target influencing factors are used to characterize the factors affecting public transport passenger flow.

[0152] Based on the aforementioned multiple target influencing factors, a public transport passenger flow assessment result is generated for the target region.

[0153] exist Figure 4 In this context, a bus architecture (represented by bus 401) is used. Bus 401 can include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 405 and memory represented by memory 406. Bus 401 can also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 404 provides an interface between bus 401 and transceiver 402. Transceiver 402 can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 405 is transmitted over a wireless medium via antenna 403, which further receives data and transmits data to processor 405.

[0154] Processor 405 is responsible for managing bus 401 and general processing, and can also provide various functions, including timing, peripheral interface, voltage regulation, power management, and other control functions. Memory 406 can be used to store data used by processor 405 during operation.

[0155] Optionally, the processor 405 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD).

[0156] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the various processes of the above-described public transport passenger flow assessment method embodiments and achieves the same technical effects. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0157] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0158] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0159] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for assessing public transport passenger flow, characterized in that, The method includes: Based on the public transportation operation status of the target area, obtain public transportation card data, bus trajectory data, and bus route data; the public transportation card data includes public transportation card swiping data of the target area, and the bus trajectory data includes public transportation vehicle data of the target area. Based on the bus card data, the bus trajectory data, and the bus route data, station passenger flow data is generated; the station passenger flow data is used to characterize the passenger flow at each bus stop on the bus route. Based on the station passenger flow data and multiple community vector data, target passenger flow data is obtained; the target area includes multiple communities, and each community corresponds one-to-one with community vector data. Multiple target influencing factors are determined corresponding to the target passenger flow data; the target influencing factors are used to characterize the factors affecting public transport passenger flow. Based on the multiple target influencing factors, a public transport passenger flow assessment result for the target area is generated. The determination of multiple target influencing factors corresponding to the target passenger flow data includes: Principal component analysis and regression analysis are performed on the target passenger flow data and multiple preset benchmark influencing factors to determine the linear relationship between the target passenger flow data and each benchmark influencing factor; The nonlinear relationship between the target passenger flow data and each benchmark influencing factor is determined using a machine learning model. Based on the linear and nonlinear relationships, multiple target influencing factors are determined.

2. The method according to claim 1, characterized in that, The process of generating station passenger flow data based on the bus card data, the bus trajectory data, and the bus route data includes: Data cleaning is performed on the bus card data, the bus trajectory data, and the bus route data; Station name data is obtained based on the station topology map and the cleaned bus route data; the station topology map is obtained based on the cleaned bus trajectory data. Based on the station name data and the cleaned bus card data, station passenger flow data is obtained.

3. The method according to claim 1, characterized in that, The target passenger flow data is obtained based on the station passenger flow data and multiple community vector data, including: Perform data aggregation processing on the station passenger flow data and the multiple community vector data to obtain community passenger flow data; Cluster analysis is performed on the community passenger flow data to obtain the target passenger flow data.

4. The method according to claim 1, characterized in that, Based on the linear and nonlinear relationships, multiple target influencing factors are determined, including: Based on the linear relationship between each benchmark influence factor and the target passenger flow data, a first score corresponding to each benchmark influence factor is determined; Based on the nonlinear relationship between each benchmark influence factor and the target passenger flow data, a second score corresponding to each benchmark influence factor is determined; Based on the first and second scores corresponding to each benchmark impact factor, the target impact factor among the plurality of benchmark impact factors is determined.

5. The method according to claim 1, characterized in that, The process of generating public transport passenger flow assessment results for the target area based on the multiple target influencing factors includes: Based on the target influencing factors corresponding to each community, the public transport passenger flow assessment results for each community are determined. Based on the public transport passenger flow assessment results for each community, the public transport passenger flow assessment results for the target area are generated.

6. The method according to claim 1, characterized in that, After generating public transport passenger flow assessment results for the target area based on the multiple target influencing factors, the method further includes: Extract the bus passenger flow for each community in each time period from the bus passenger flow assessment results; Based on the bus passenger flow of each community in each time period, calculate the average bus passenger flow of each community. The time period in which the corresponding bus passenger flow is higher than the average bus passenger flow is determined as the target time period; Based on the target time period and the communities associated with the target time period, an early warning message is sent.

7. A public transport passenger flow assessment device, characterized in that, The device includes: The acquisition module is used to acquire bus card data, bus trajectory data, and bus route data based on the bus operation status of the target area; the bus card data includes bus card swiping data of the target area, and the bus trajectory data includes bus vehicle data of the target area; The first generation module is used to generate station passenger flow data based on the bus card data, the bus trajectory data, and the bus route data; the station passenger flow data is used to characterize the passenger flow at each bus stop on the bus route. The second generation module is used to obtain target passenger flow data based on the station passenger flow data and multiple community vector data; the target area includes multiple communities, and each community corresponds one-to-one with community vector data; The first determining module is used to determine multiple target influencing factors corresponding to the target passenger flow data; The third generation module is used to generate public transport passenger flow assessment results for the target area based on the multiple target influencing factors. The first determining module determines multiple target influencing factors corresponding to the target passenger flow data, including: Principal component analysis and regression analysis are performed on the target passenger flow data and multiple preset benchmark influencing factors to determine the linear relationship between the target passenger flow data and each benchmark influencing factor; The nonlinear relationship between the target passenger flow data and each benchmark influencing factor is determined using a machine learning model. Based on the linear and nonlinear relationships, multiple target influencing factors are determined.

8. An electronic device, characterized in that, include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the steps of the public transport passenger flow assessment method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the public transport passenger flow assessment method as described in any one of claims 1 to 6.

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