Regional public transport reachability measurement method and device based on multi-source data fusion and storage medium

Abstract

The invention relates to the technical field of traffic planning and control, and particularly discloses a regional public traffic reachability measurement method and device based on multi-source data fusion and a storage medium, and the method comprises the steps: firstly obtaining the station data of a subway and a bus, then dividing the travel modes of residents according to whether the subway and the bus are transferred, and finally, carrying out the division of the travel modes of the residents; and establishing a transfer connection chain between different traffic modes based on the transfer travel mode. And finally, index extraction is performed through the transfer link chain and the single trip chain, and the public transport station accessibility of the single trip and the transfer trip can be measured according to the model, so that the measurement of the regional public transport accessibility is completed by aggregating the station accessibility. According to the method, the travel modes are classified based on the multi-source data, a single travel and transfer link chain is established by considering continuous transfer services among different vehicles, and the station reachability of the single travel and transfer travel modes can be measured at the same time.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic planning and control, and in particular to a method, device and storage medium for measuring regional public transportation accessibility based on multi-source data fusion. Background Art

[0002] With the acceleration of urbanization and the continuous growth of population, urban traffic problems have become increasingly prominent, especially the efficiency and service quality of public transportation systems have become the focus of attention of urban managers and residents. As the basic unit of the public transportation network, the accessibility of bus stops is directly related to the travel convenience of passengers and the overall efficiency of the public transportation system. However, traditional bus stop accessibility measurement methods have many limitations and are difficult to meet the needs of modern urban traffic management.

[0003] First, traditional methods often rely on a single data source. Traditional methods mainly rely on static, single data sources to evaluate the accessibility of public transportation. These data usually only include basic information such as the geographical location of bus routes, station settings, and operating hours. However, this information often cannot fully reflect the complexity of the public transportation system, especially the importance of transfers. Transfers are an indispensable part of the public transportation system, which is related to whether passengers can smoothly switch between different routes or different modes of transportation to reach their destination.

[0004] Secondly, a single data source often cannot provide complete transfer station information. This causes transfer convenience to be underestimated or ignored in accessibility measurement, thus affecting the accuracy of the measurement results. A single data source may also not accurately reflect changes in passengers' travel needs. If passengers' travel needs shift from one area to another, but a single data source fails to capture this change, the measurement results may not match the actual demand.

[0005] In addition, with the rapid development of information technology, new technologies such as big data, cloud computing, and the Internet of Things have provided unprecedented opportunities for urban traffic management. The acquisition and processing of multi-source data have become more convenient and efficient, providing new ideas and methods for measuring the accessibility of bus stops.

[0006] In summary, it is urgent to develop a regional public transportation accessibility measurement method based on multi-source data fusion, so that it has important practical significance and application value. Therefore, the regional bus station accessibility measurement method based on multi-source data fusion of the present invention came into being. This method takes into account the transfer trips in the station accessibility measurement, rather than just measuring a single trip in the traditional sense. By making full use of modern information technology means and integrating multiple data sources, a comprehensive and accurate measurement of bus station accessibility is achieved. This not only helps to improve the overall efficiency and service quality of the public transportation system, but also helps to provide a scientific basis for urban transportation planning, bus network optimization and bus service improvement. Summary of the invention

[0007] In order to solve the problems existing in the prior art, the present invention provides a regional public transportation accessibility measurement method, device and storage medium based on multi-source data fusion, which makes up for the lack of accuracy caused by the traditional method of only considering a single trip when calculating accessibility and ignoring the transfer combination effect of multiple transportation modes, ensuring that the accessibility evaluation can more comprehensively reflect the diverse transportation strategies that individuals may adopt in actual travel, and solves the problems mentioned in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: a method for measuring regional public transportation accessibility based on multi-source data fusion, comprising the following steps:

[0009] S1. Obtain bus station data and subway station data, pre-process the data, extract effective information from them, and establish a subway and bus database based on the survey results;

[0010] S2. The travel modes of passengers are divided into two categories: the first category is a single bus or subway travel mode, and the second category is transfer travel, which includes pure bus or subway transfer travel and bus-subway mixed travel;

[0011] S3. Establish a transfer connection chain for transfer travel modes;

[0012] S4. Extract the indicators required by the model from the transfer connection chain of single trips and transfer trips, and use different models for each travel mode to calculate the station accessibility of single trips and transfer trips;

[0013] S5. Aggregate the site accessibility obtained in step S4 in the traffic zone to obtain regional public transportation accessibility.

[0014] Preferably, in step S1, the valid information includes: line number, station number, station name, station distance, station longitude and latitude, and the number of points of interest near each station obtained through ArcGIS; including points of interest related to government agencies, shopping malls, work units, scientific and educational culture and medical units.

[0015] Preferably, in step S3, a transfer connection chain is established for the transfer travel mode, which specifically includes the following:

[0016] S31, let a line be X, when the line station x1 reaches the line station x2, x2 is regarded as the first transfer station;

[0017] S32, introduce the concept of diffusion circle, regard the station in the line as the diffusion center, and establish a diffusion circle with a diffusion range of 1500 meters with this station as the center. Based on this diffusion circle as the service range of the station, collect the location information of the central station and other stations that may exist in the surrounding area;

[0018] S33, searching for other line stations within the service range of its diffusion circle with x2 as the center;

[0019] S34. Repeat the operations of step S32-step S33 to find the next station until the maximum number of transfers is 4 or there is no station to search, and obtain the link between the station and other stations, and the stations include bus stations and subway stations. Based on this link, a transfer connection chain for continuous transfers between public transportation stations is established.

[0020] Preferably, in step S4, extracting the indicators required for the model from the transfer connection chain between a single trip and a transfer trip specifically includes: extracting two indicators required for the single trip model from a single trip: station distance and number of points of interest, wherein the station distance is used as E kj The input variable is the number of interest points as C j The input variables of transfer travel are as follows: the indicators required by the transfer travel model are extracted from the transfer connection chain of transfer travel: the number of opportunities within the station service range, the station distance, the transfer mode penalty coefficient α, the transfer number penalty coefficient β, and the different station transfer penalty coefficient γ.

[0021] Preferably, the index extraction steps required for the transfer travel model specifically include:

[0022] 1) Based on the bus and subway station data in step S1, the number of points of interest within the station service range is obtained according to the line station number to represent the number of opportunities within the station service range;

[0023] 2) Calculate the Euclidean distance between transfer stations, i.e. station distance, through the latitude and longitude data of different stations. The specific calculation formula is as follows:

[0024] d12 =6368.16×arccos(sinX+cosX)

[0025] in,

[0026] In the formula, long1, lat1, Long2, and lat2 are the longitude and latitude data of the stations before and after the transfer respectively;

[0027] 3) Count the transfer modes of each transfer link, and adjust the transfer mode penalty coefficient α according to the transfer mode. Specifically, when the transfer mode is bus-bus, the transfer mode penalty coefficient α is 1.8; when the transfer mode is subway-subway, the transfer mode penalty coefficient α is 1.2; when the transfer mode is bus-subway, the transfer mode penalty coefficient α is 1.5;

[0028] 4) Count the number of transfers for each transfer chain, and adjust the transfer penalty coefficient β according to the number of transfers, specifically: when there is one transfer, the transfer penalty coefficient β is 1.2; when there are two transfers, the transfer penalty coefficient β is 1.5; when there are three transfers, the transfer penalty coefficient β is 2.0; when there are four transfers, the transfer penalty coefficient β is 3.0;

[0029] 5) Based on the walking speed and station distance parameters, the transfer penalty coefficient γ for the same or different stations is uniformly set to 1.85.

[0030] Preferably, in step S4, different models are used for respective travel modes to calculate the station accessibility of single trip and transfer trip, specifically including:

[0031] The single travel model is an algorithm based on the gravity model. The calculation formula for the station accessibility of a single travel mode is as follows:

[0032]

[0033] Among them, A k is the public transportation accessibility of the kth station; E kj is the travel cost from station k to station j; C j is the number of opportunities within the service range of station j; m represents the total number of all stations on the travel route of station k;

[0034] The transfer travel model is a weighted algorithm based on a single travel model. The calculation formula for station accessibility of the transfer travel mode is as follows:

[0035]

[0036] Among them, α is the penalty coefficient for transfer mode; β is the penalty coefficient for number of transfers; γ is the penalty coefficient for transfers at different stations.

[0037] Preferably, according to the positional relationship of the cells to which the stations belong, the station accessibility obtained in step S4 is aggregated in the traffic cell to obtain the regional public transportation accessibility.

[0038] On the other hand, to achieve the above-mentioned purpose, the present invention also provides the following technical solution: an electronic device, the electronic device comprising: a processor; and a memory for storing one or more programs;

[0039] When the one or more programs are executed by a processor, the processor executes the method for measuring regional public transportation accessibility based on multi-source data fusion.

[0040] On the other hand, to achieve the above-mentioned purpose, the present invention also provides the following technical solution: a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for measuring regional public transportation accessibility based on multi-source data fusion.

[0041] The beneficial effects of the present invention are as follows: The method for measuring the accessibility of regional bus stops based on multi-source data fusion proposed by the present invention: First, the present invention uses a multi-dimensional data source, which is different from the previous single data source, and can integrate data from different channels. These data together provide more comprehensive information, which helps to more accurately evaluate the accessibility of the public transportation system. Secondly, in terms of the way of measuring accessibility, the present invention simultaneously measures based on single trips and transfer trips, which overcomes the traditional measurement method that ignores the transfer problem and causes the measurement results to fail to accurately reflect the actual travel experience of passengers, thereby reducing the accuracy of the accessibility measurement. By considering transfer trips, the actual travel process of passengers from the starting point to the end point can be more accurately evaluated, thereby obtaining more accurate accessibility results. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a flow chart of a method for measuring regional public transportation accessibility based on multi-source data fusion according to the present invention;

[0043] Figure 2 This is a schematic diagram of the structure of an electronic device in an embodiment of the present invention;

[0044] In the figure, 210 is a processor; 220 is a storage. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] See also Figure 1 The present invention provides a technical solution: a method for measuring regional public transportation accessibility based on multi-source data fusion, characterized in that it includes the following steps:

[0047] S1. Obtain bus stop data and subway station data, pre-process the data, extract effective information from it, and establish a subway and bus database based on the survey results.

[0048] The valid information includes: line number, station number, station name, station distance, station longitude and latitude, and the number of points of interest near each station obtained through ArcGIS; including points of interest related to government agencies, shopping malls, work units, scientific and educational culture and medical units.

[0049] The data used in the implementation of the present invention are the bus stop data and subway station data in Hangzhou in November 2023, where the original data of the bus and subway include line number, station number, station name, station distance, station longitude and latitude, and then the data is preprocessed. An example of the data structure is shown in Table 1.

[0050] Table 1 Site data structure

[0051]

[0052] ArcGIS was further used to establish a buffer zone with a radius of 500 meters around the site, so as to count the number of service opportunities near each site, including the number of points of interest related to government agencies, shopping malls, work units, science, education, culture and medical units. An example of the data structure is shown in Table 2.

[0053] Table 2 Site point of interest data structure

[0054]

[0055] S2. Passengers’ travel modes are divided into two categories: the first category is a single bus or subway travel mode, and the second category is transfer travel, which includes pure bus or subway transfer travel and bus-subway mixed travel.

[0056] When analyzing the travel modes of residents, the travel modes are divided into single travel and transfer travel modes, considering whether transfer is possible during travel and the connectivity between different transportation modes. The specific analysis process includes:

[0057] ① Residents have diverse travel needs, including long and short distances, different time periods, etc. A single bus or subway travel mode is suitable for simple travel needs, while bus transfers and mixed travel can better meet complex and changing travel needs. By considering whether transfers are possible in travel modes, residents' travel needs and preferences can be more accurately reflected;

[0058] ②With the rapid development of modern transportation systems, the connections and coverage of transportation routes have been continuously optimized, providing convenient travel for residents.

[0059] S3. Establish a transfer connection chain for transfer travel modes. Search for other stations that match the service capabilities of each station, and connect stations on different lines. In this way, continuous connection travel between different means of transportation can be achieved, and a transfer connection chain can be established accordingly.

[0060] Establish a transfer connection chain for transfer travel modes, including the following:

[0061] S31, let a line be X (including bus and subway lines), when the line station x1 reaches the line station x2, x2 is regarded as the first transfer station;

[0062] S32, introduce the concept of diffusion circle, regard the station in the line as the diffusion center, and establish a diffusion circle with a diffusion range of 1500 meters with this station as the center. Based on this diffusion circle as the service range of the station, collect the location information of the central station and other stations that may exist in the surrounding area;

[0063] S33, searching for other line stations within the service range of its diffusion circle with x2 as the center;

[0064] S34. Repeat the operations of step S32-step S33 to find the next station until the maximum number of transfers is 4 or there is no station to search, and obtain the link between the station and other stations, and the stations include bus stations and subway stations. Based on this link, a transfer connection chain for continuous transfers between public transportation stations is established.

[0065] Here are some examples:

[0066] ① Introduce the concept of diffusion circle, regard the station in the line as the diffusion center, and establish a diffusion circle with a diffusion range of 1,500 meters with this station as the center. Based on this diffusion circle as the service range of the station, collect the location information of the central station and other stations that may exist in the surrounding area;

[0067] ② Take a station x1 in a certain line X (including bus and subway lines) as the starting station. Station x1 can reach other stations x2, x3 and so on on the line. Point x2 can be taken as the first transfer station of the travel chain;

[0068] ③ Take x2 as the first transfer station, and search for other stations y1, y2, z1, z1, etc. near x2 except for this line according to the diffusion circle established in ①, and select one of the stations y1 as the second transfer station;

[0069] ④ Take y1 as the second transfer station, and continue to search for other stations q1, q2, p1, p2, etc. near y2 except for this line. Select one of the stations q1 as the third transfer station to repeat the above operation to find the next station until there is no station to be searched or the maximum number of transfers set is 4, then stop the search operation. At the same time, in order to prevent the lines from being mixed, it is stipulated that each line can only be used once;

[0070] ⑤ Connecting all the searched sites together will form a travel chain about the x1 site.

[0071] ⑥At the same time, when x2 is used as the first transfer station, due to the uncertainty of the station, the second transfer station can be y2, z1, z1, etc., and the third and fourth transfer stations (if they exist) can also be other stations. Based on this theory, multiple travel chains about x1 can be established;

[0072] ⑦ At the same time, a station x1 in line X can reach not only x2, but also x3, x4, x5, etc., which can be used as the second transfer station for x1, so multiple travel chains for x1 can also be established;

[0073] ⑧Based on the above, all travel chains about x1 can be established;

[0074] ⑨ Similarly, a travel chain about all stations can be established in this way, and if the stations include bus stations and subway stations, a transfer connection chain for pure bus or subway transfer travel and bus-subway mixed travel can be established.

[0075] S4. Extract the indicators required by the model from the transfer connection chain of single trips and transfer trips, and use different models for each travel mode to calculate the station accessibility of single trips and transfer trips.

[0076] Furthermore, the indicators required for the model are extracted from the transfer connection chain between a single trip and a transfer trip, including: extracting two indicators required for the single trip model from a single trip: station distance and number of points of interest, where the station distance is used as E kjThe input variable is the number of interest points as C j The input variables of transfer travel are as follows: the indicators required by the transfer travel model are extracted from the transfer connection chain of transfer travel: the number of opportunities within the station service range, the station distance, the transfer mode penalty coefficient α, the transfer number penalty coefficient β, and the different station transfer penalty coefficient γ.

[0077] Furthermore, the calculation of station accessibility for transfer travel requires extracting the indicators required by the model from the transfer connection chain. The steps for extracting the indicators required by the transfer travel model specifically include:

[0078] 1) Based on the bus and subway station data in step S1, the number of points of interest within the station service range is obtained according to the line station number to represent the number of opportunities within the station service range;

[0079] 2) Calculate the Euclidean distance between transfer stations, i.e. station distance, through the latitude and longitude data of different stations. The specific calculation formula is as follows:

[0080] d 12 =6368.16×arccos(sinX+cosX)

[0081] in,

[0082] In the formula, long1, lat1, Long2, and lat2 are the longitude and latitude data of the stations before and after the transfer respectively;

[0083] 3) Count the transfer modes of each transfer chain, and adjust the transfer mode penalty coefficient α according to the transfer mode. Specifically, when the transfer mode is bus-bus, the transfer mode penalty coefficient α is 1.8; when the transfer mode is subway-subway, the transfer mode penalty coefficient α is 1.2; when the transfer mode is bus-subway, the transfer mode penalty coefficient α is 1.5; as shown in Table 3.

[0084] Table 3 Adjustment method of transfer mode penalty coefficient

[0085] Transfer method Regular bus - Regular bus Rail Transit—Rail Transit Conventional bus-rail transit Value 1.8 1.2 1.5

[0086] 4) Count the number of transfers for each transfer chain, and adjust the transfer penalty coefficient β according to the number of transfers, specifically: when there is one transfer, the transfer penalty coefficient β is 1.2; when there are two transfers, the transfer penalty coefficient β is 1.5; when there are three transfers, the transfer penalty coefficient β is 2.0; when there are four transfers, the transfer penalty coefficient β is 3.0; as shown in Table 4.

[0087] Table 4 Adjustment method of transfer number penalty coefficient

[0088] Number of transfers Transfer 1 time Transfer 2 times 3 transfers 4 transfers Value 1.2 1.5 2.0 3.0

[0089] 5) Based on the walking speed and station distance parameters, the transfer penalty coefficient γ for the same or different stations is uniformly set to 1.85.

[0090] Furthermore, different models are used to calculate the station accessibility of single trips and transfer trips according to the respective travel modes, including:

[0091] The single travel model is an algorithm based on the gravity model. The calculation formula for the station accessibility of a single travel mode is as follows:

[0092]

[0093] Among them, A k is the public transportation accessibility of the kth station; E kj is the travel cost from station k to station j; C j is the number of opportunities within the service range of station j; m is the total number of all stations on the travel route of station k.

[0094] The transfer travel model is a weighted algorithm based on a single travel model. The calculation formula for station accessibility of the transfer travel mode is as follows:

[0095]

[0096] Among them, α is the penalty coefficient for transfer mode; β is the penalty coefficient for number of transfers; γ is the penalty coefficient for transfers at different stations.

[0097] S5. Aggregate the site accessibility obtained in step S4 in the traffic zone to obtain regional public transportation accessibility.

[0098] According to the positional relationship of the cells to which the stations belong, the station accessibility obtained in step S4 is aggregated in the traffic cell to obtain the regional public transportation accessibility.

[0099] Regional public transportation accessibility is obtained by superimposing the accessibility of all public transportation stations in the transportation community. First, the transportation communities where all public transportation stations are located are counted, and then the accessibility of the same type of points of interest in each community is accumulated to obtain the accessibility of regional public transportation. The data structure example is shown in Table 5.

[0100] Table 5 Regional public transportation accessibility

[0101]

[0102]

[0103] There is an inseparable and close connection between regional public transportation accessibility and the accessibility of nearby bus stops. The convenience of nearby bus stops is directly related to the efficiency and comfort of residents using public transportation, which in turn has a profound impact on the convenience of the overall transportation community to other important areas of the city. The two together reflect the degree of perfection of the urban transportation network, the rationality of the planning and layout, and the convenience and efficiency of residents' travel, and are important aspects that cannot be ignored in the development of urban transportation.

[0104] Based on the same inventive concept as the above method embodiment, the present application embodiment also provides an electronic device, such as Figure 2 As shown, the device includes: a processor 210; and a memory 220, for storing one or more programs;

[0105] When the one or more programs are executed by the processor 210, the processor executes the method for measuring regional public transportation accessibility based on multi-source data fusion.

[0106] The method for measuring regional public transportation accessibility based on multi-source data fusion specifically includes the following:

[0107] S1. Obtain bus station data and subway station data, pre-process the data, extract effective information from them, and establish a subway and bus database based on the survey results;

[0108] S2. The travel modes of passengers are divided into two categories: the first category is a single bus or subway travel mode, and the second category is transfer travel, which includes pure bus or subway transfer travel and bus-subway mixed travel;

[0109] S3. Establish a transfer connection chain for transfer travel modes;

[0110] S4. Extract the indicators required by the model from the transfer connection chain of single trips and transfer trips, and use different models for each travel mode to calculate the station accessibility of single trips and transfer trips;

[0111] S5. Aggregate the site accessibility obtained in step S4 in the traffic zone to obtain regional public transportation accessibility.

[0112] Based on the same inventive concept as the above method embodiment, the embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by the processor 210, the method for measuring regional public transportation accessibility based on multi-source data fusion is implemented.

[0113] The method for measuring regional public transportation accessibility based on multi-source data fusion specifically includes the following:

[0114] S1. Obtain bus station data and subway station data, pre-process the data, extract effective information from them, and establish a subway and bus database based on the survey results;

[0115] S2. The travel modes of passengers are divided into two categories: the first category is a single bus or subway travel mode, and the second category is transfer travel, which includes pure bus or subway transfer travel and bus-subway mixed travel;

[0116] S3. Establish a transfer connection chain for transfer travel modes;

[0117] S4. Extract the indicators required by the model from the transfer connection chain of single trips and transfer trips, and use different models for each travel mode to calculate the station accessibility of single trips and transfer trips;

[0118] S5. Aggregate the site accessibility obtained in step S4 in the traffic zone to obtain regional public transportation accessibility.

[0119] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0120] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention is essentially or partly contributed to the prior art or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk. It should be noted that, in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements inherent to such a process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0121] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0122] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0123] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0124] The "first\second" mentioned in the embodiments is only to distinguish similar objects, and does not represent a specific order for the objects. It is understandable that the "first\second" can be interchanged with the specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0125] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for measuring regional public transportation accessibility based on multi-source data fusion, characterized in that: The steps include: S1. Obtain bus station data and subway station data, pre-process the data, extract effective information from them, and establish a subway and bus database based on the survey results; S2. The travel modes of passengers are divided into two categories: the first category is a single bus or subway travel mode, and the second category is transfer travel, which includes pure bus or subway transfer travel and bus-subway mixed travel; S3. Establish a transfer connection chain for transfer travel modes; S4. Extract the indicators required by the model from the transfer connection chain of single trips and transfer trips, and use different models for each travel mode to calculate the station accessibility of single trips and transfer trips; S5. Aggregate the site accessibility obtained in step S4 in the traffic community to obtain regional public transportation accessibility.

2. The method for measuring regional public transportation accessibility based on multi-source data fusion according to claim 1 is characterized by: In step S1, the valid information includes: line number, station number, station name, station distance, station longitude and latitude, and the number of points of interest near each station obtained through ArcGIS; including points of interest related to government agencies, shopping malls, work units, scientific and educational culture and medical units.

3. The method for measuring regional public transportation accessibility based on multi-source data fusion according to claim 1 is characterized by: In step S3, a transfer connection chain is established for the transfer travel mode, which specifically includes the following: S31, let a line be X, when the line station x1 reaches the line station x2, x2 is regarded as the first transfer station; S32, introduce the concept of diffusion circle, regard the station in the line as the diffusion center, and establish a diffusion circle with a diffusion range of 1500 meters with this station as the center. Based on this diffusion circle as the service range of the station, collect the location information of the central station and other stations that may exist in the surrounding area; S33, searching for other line stations within the service range of its diffusion circle with x2 as the center; S34. Repeat the operations of step S32-step S33 to find the next station until the maximum number of transfers is 4 or there is no station to search, and obtain the link between the station and other stations, and the stations include bus stations and subway stations. Based on this link, a transfer connection chain for continuous transfers between public transportation stations is established.

4. The method for measuring regional public transportation accessibility based on multi-source data fusion according to claim 1 is characterized by: In step S4, the indicators required for the model are extracted from the transfer connection chain between the single trip and the transfer trip, specifically including: extracting two indicators required for the single trip model from the single trip: station distance and the number of points of interest, where the station distance is used as The input variable is the number of interest points as C j The input variables of transfer travel are as follows: the indicators required by the transfer travel model are extracted from the transfer connection chain of transfer travel: the number of opportunities within the station service range, the station distance, the transfer mode penalty coefficient α, the transfer number penalty coefficient β, and the different station transfer penalty coefficient γ.

5. The method for measuring regional public transportation accessibility based on multi-source data fusion according to claim 4 is characterized by: The steps for extracting indicators required for the transfer travel model include: 1) Based on the bus and subway station data in step S1, the number of points of interest within the station service range is obtained according to the line station number to represent the number of opportunities within the station service range; 2) Calculate the Euclidean distance between transfer stations, i.e. station distance, through the latitude and longitude data of different stations. The specific calculation formula is as follows: d 12 =6368.16×arccos(sinX+cosX) in, In the formula, long1, lat1, Long2, and lat2 are the longitude and latitude data of the stations before and after the transfer respectively; 3) Count the transfer modes of each transfer link, and adjust the transfer mode penalty coefficient α according to the transfer mode. Specifically, when the transfer mode is bus-bus, the transfer mode penalty coefficient α is 1.8; when the transfer mode is subway-subway, the transfer mode penalty coefficient α is 1.2; when the transfer mode is bus-subway, the transfer mode penalty coefficient α is 1.5; 4) Count the number of transfers for each transfer chain, and adjust the transfer penalty coefficient β according to the number of transfers, specifically: when there is one transfer, the transfer penalty coefficient β is 1.2; when there are two transfers, the transfer penalty coefficient β is 1.5; when there are three transfers, the transfer penalty coefficient β is 2.0; when there are four transfers, the transfer penalty coefficient β is 3.0; 5) Based on the walking speed and station distance parameters, the transfer penalty coefficient γ for the same or different stations is uniformly set to 1.

85.

6. The method for measuring regional public transportation accessibility based on multi-source data fusion according to claim 1 is characterized by: In step S4, different models are used for the respective travel modes to calculate the station accessibility of single trips and transfer trips, including: The single travel model is an algorithm based on the gravity model. The calculation formula for the station accessibility of a single travel mode is as follows: Among them, A k is the public transportation accessibility of the kth station; is the travel cost from station k to station j; C j is the number of opportunities within the service range of station j; m represents the total number of all stations on the travel route of station k; The transfer travel model is a weighted algorithm based on a single travel model. The calculation formula for station accessibility of the transfer travel mode is as follows: Among them, α is the penalty coefficient for transfer mode; β is the penalty coefficient for number of transfers; γ is the penalty coefficient for transfers at different stations.

7. The method for measuring regional public transportation accessibility based on multi-source data fusion according to claim 1 is characterized by: According to the positional relationship of the cells to which the stations belong, the station accessibility obtained in step S4 is aggregated in the traffic cell to obtain the regional public transportation accessibility.

8. An electronic device, characterized in that: The electronic device comprises: a processor (210); and a memory (220) for storing one or more programs; When the one or more programs are executed by the processor (210), the processor executes the regional public transportation accessibility measurement method based on multi-source data fusion as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by the processor (210), the method for measuring regional public transportation accessibility based on multi-source data fusion as described in any one of claims 1 to 7 is implemented.

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