Multi-mode passenger travel volume calculation method based on multi-source data

Abstract

The invention provides a multi-mode passenger travel volume calculation method based on multi-source data. The method comprises the following steps: firstly, preprocessing by collecting and integrating transportation data of various traffic transportation modes and mobile internet data, and carrying out passenger interlink travel identification in combination with mobile phone signaling and APP track data; and then, performing statistics on various interlinkage modes, generalized interlinkage travel volume and narrow interlinkage travel volume, and performing scientific evaluation on the interlinkage modes, the generalized interlinkage travel volume and the narrow interlinkage travel volume. According to the method, the sum price and quantity linked travel demand can be more accurately counted, reasonable traffic service is provided, and the method has important reference value for urban traffic planning and management.

Description

Technical Field

[0001] The present invention belongs to the field of big data passenger travel statistics, and more particularly relates to a method for calculating multi-mode passenger travel volume based on multi-source data. Background Art

[0002] With the acceleration of globalization and the deepening of urbanization, the demand for passenger travel is growing, and the transportation system is facing increasing pressure. Especially in large cities and economically developed areas, problems such as traffic congestion, low travel efficiency, and inconvenient transfers are becoming increasingly prominent, seriously affecting people's travel experience and quality of life. Therefore, how to improve the convenience and efficiency of passenger travel has become an important issue that needs to be solved in the field of transportation.

[0003] In this context, intermodal transport has gradually gained attention. Intermodal transport refers to the way passengers use two or more modes of transportation (such as aviation, railways, roads, etc.) in one trip. By optimizing the connection and transfer between different modes of transportation, travel efficiency can be effectively improved, waiting time for passengers can be reduced, and the organic combination of multiple modes of transportation can be achieved, thereby meeting the growing travel needs.

[0004] However, there are still some shortcomings in passenger intermodal transport. First, although various modes of transportation have developed relatively independently, there is a lack of effective linkage mechanism, resulting in an insufficient connection between different modes of transportation. For example, at some transportation hubs, passengers often face long walking distances and waiting times when transferring between different modes of transportation, which affects the overall travel efficiency. Secondly, the lack of comprehensive intermodal travel data statistics and analysis has led to an insufficient understanding of passenger travel needs, making it difficult to provide a scientific basis for transportation planning and policy formulation.

[0005] In addition, when passengers use intermodal transport, they are often less satisfied with their travel. According to relevant surveys, many passengers are dissatisfied with the service quality, information transparency, time arrangement, etc. during the transfer process. These factors not only affect the travel experience of passengers, but also restrict the promotion and application of intermodal transport mode.

[0006] In order to solve these problems, local transportation management departments and related enterprises have begun to explore the development and evaluation mechanism of passenger intermodal transport. By collecting and analyzing passenger travel data, we can better identify travel patterns and needs, and then formulate targeted improvement measures. At the same time, the establishment of a scientific evaluation system will help various regions to make horizontal comparisons on the level of intermodal transport development, identify advantages and disadvantages, and promote the rational allocation of resources and the effective implementation of policies. Summary of the invention

[0007] This paper proposes a statistical and evaluation method based on passenger intermodal travel trajectories, aiming to improve the development level of passenger intermodal transportation in a data-driven way. This method not only covers the statistics of intermodal travel in a broad sense and a narrow sense, but also combines multi-dimensional indicators such as travel efficiency, satisfaction and diversity of transportation modes to comprehensively evaluate the development status of intermodal transportation in various places.

[0008] In order to achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0009] The method includes:

[0010] Data collection: collects transportation data of various modes of transportation, such as railways, aviation, and public transportation, including ticketing, station, and route information; collects mobile Internet data and APP tracks to analyze passengers' actual travel tracks;

[0011] Data preprocessing, integrating data from different modes of transportation; establishing a unified data model to clean and supplement missing information in the data to ensure data quality

[0012] Identify passenger inter-trips: Analyze data such as mobile phone signaling and APP trajectories to identify passengers who use two or more modes of transportation to make inter-trips. Combined with transportation data, further refine the specific routes and transfers of inter-trips.

[0013] Statistics of inter-trip travel volume: based on the identified inter-trip travel trajectories, statistics are collected on various inter-trip modes, such as rail + aviation, bus + high-speed rail. The inter-trip travel volume in a broad sense and the inter-trip travel volume in a narrow sense are distinguished, and their respective statistical indicators are calculated.

[0014] Passenger intermodal transport evaluation: After completing the statistics of intermodal travel volume, the next step is to scientifically evaluate the development level of passenger intermodal transport in various places.

[0015] In one embodiment, the data collection includes:

[0016] Transportation data collection

[0017] Railway data: including train information, line information, station information, and ticket information, obtained from the 12306 channel;

[0018] Aviation data: including flight information, route information, airport information, and ticket information, which can be obtained from the airline’s official website or third-party platforms;

[0019] Public transportation data: including route information, station information, and operating timetables, obtained from local transportation departments or public transportation companies;

[0020] Mobile Internet data collection includes:

[0021] Mobile phone signaling data: collects user location trajectory data through base station signals to analyze actual travel paths;

[0022] APP trajectory data: travel trajectory data collected through the user’s mobile phone APP.

[0023] In one embodiment, the data preprocessing includes:

[0024] Data cleaning: Check the completeness and accuracy of raw data, identify and correct erroneous data; handle missing values ​​and fill them using interpolation methods;

[0025] Data fusion: Establish a unified data model to integrate data from different sources; match the same entity in different data sources to eliminate duplicate information;

[0026] Data conversion: Unify data formats to facilitate subsequent analysis and calculation of travel time and number of transfers.

[0027] In one embodiment, the passenger connecting trip identification includes:

[0028] Trajectory data extraction: extract travel trajectories from mobile phone signaling and APP trajectories, record the departure time, arrival time, starting point and destination of each passenger; set the format of trajectory data, T i ={(t start ,p start ),(t end ,p end ),...}

[0029] Where T i represents the trajectory of the i-th passenger, t start and t end Respectively represent the start and end time, p start and p end Respectively represent the starting and ending positions;

[0030] Inter-trip travel mode identification: Based on the trajectory data, the travel mode is identified using the following rules:

[0031] If a passenger uses two or more modes of transportation within the same time period, it is considered to be a connecting trip;

[0032] Set a time window W to determine the time range of transfer; if the passenger is at t start and t end If different modes of transport are used between them, a transfer is considered to exist;

[0033] Details of transfer situation:

[0034] For each passenger’s trajectory, analyze the mode of transportation used; assume that the set of transportation modes is M = {m1,m2,...,m k}, the following formula is used to express the mode of transportation used by each passenger: U i ={m j |m j In T i Appears in}

[0035] Among them U i represents the set of transportation modes used by the i-th passenger;

[0036] Records of routes and transfers:

[0037] For each identified inter-trip travel mode, record its specific path and transfer information. Use a structure or database table to store the information;

[0038] Ri=(m j ,p j ,t j )|j=1,2,...,n}

[0039] Where Ri represents the travel record of the i-th passenger, mj represents the mode of transportation used, pj represents the corresponding station, and t j Indicates the arrival time;

[0040] Statistics on interline travel volume:

[0041] According to the identified inter-trip travel modes, the travel volume of each mode is counted; the travel volume of mode m is set to N m , expressed by the following formula:

[0042]

[0043] Where I(·) is the indicator function, when U i Returns 1 if pattern m is included, otherwise returns 0, and N is the total number of passengers.

[0044] In one solution, the statistics of connecting trip volume include:

[0045] Before counting the number of interline trips, ensure that the identification of interline trips of passengers has been completed, and the travel trajectory and transportation mode used by each passenger have been obtained; assuming that the travel record R of each passenger has been obtained. i , which contains the mode of transport used and the corresponding departure and arrival information;

[0046] (1) Statistics of generalized inter-trip travel volume:

[0047] Broadly speaking, intermodal travel volume refers to the total volume of passengers using two or more modes of transportation during their travels;

[0048] Traverse the travel records of all passengers R i ; For each passenger, check the set of transportation modes used by him / her U i ; if U i If it involves two or more modes of transportation, it will be recorded as a generalized interline trip; the number of all eligible passengers will be counted;

[0049]

[0050] Where N total represents the total amount of generalized inter-trip travel, |U i | represents the number of modes of transportation used by the i-th passenger, I(·) is an indicator function that returns 1 when the condition is met and 0 otherwise;

[0051] (2) Statistics on intermodal travel in a narrow sense; intermodal travel in a narrow sense refers to the situation of passengers transferring between different modes of transportation on the basis of intermodal travel in a broad sense;

[0052] Traverse the travel records of all passengers R i For each passenger’s trajectory, record its starting time t start , end time t end And the mode of transport used i ;

[0053] Within the set transfer time window W, if the time interval between two or more modes of transportation used by a passenger is less than W, it is recorded as a narrow-sense connecting trip;

[0054] Count all eligible passengers;

[0055]

[0056] Where N narrow represents the total amount of inter-trip travel in a narrow sense, m and m′ Respectively represent different modes of transportation. and Respectively indicate the end and start time of using different modes of transportation.

[0057] (3) Calculation of statistical indicators

[0058] The proportion of generalized interline travel:

[0059]

[0060] where p total represents the proportion of generalized connecting trips to the total trip volume, and N is the total number of passengers traveling;

[0061] The proportion of narrow interline travel:

[0062]

[0063] where p narrow It indicates the proportion of narrow connecting trips to the total trip volume.

[0064] In one embodiment, the passenger intermodal transport evaluation includes:

[0065] Construction of evaluation index system

[0066] The indicator system for evaluating passenger intermodal transport is constructed, including the following aspects:

[0067] Travel volume indicator: generalized inter-trip volume N total and the narrow sense connecting trip volume N narrow ;

[0068] Travel efficiency index: average transfer time T transfer and the average travel time T total ;

[0069] Travel satisfaction index: passenger satisfaction score S collected through questionnaire survey satisfaction ;Transport mode diversity indicator: the number of transport modes used M;

[0070] Evaluation model construction

[0071] Use weighted comprehensive evaluation method to establish a comprehensive evaluation model; set the weight of each indicator w j , then the comprehensive evaluation score E is expressed as:

[0072]

[0073] in:

[0074] E is the comprehensive evaluation score,

[0075] w j is the weight of the jth indicator, satisfying

[0076] Z j is the jth index value after standardization,

[0077] k is the total number of indicators;

[0078] Evaluation level classification

[0079] The evaluation level is divided according to the comprehensive evaluation score E.

[0080] Excellent: E ≥ 0.8

[0081] Good: 0.6≤E<0.8

[0082] Medium: 0.4≤E<0.6

[0083] Poor: E<0.4.

[0084] Beneficial effects of the present invention:

[0085] Through comprehensive evaluation of passenger intermodal transport, connection problems and transfer bottlenecks between different modes of transport can be identified, thereby optimizing the layout of transportation hubs and transfer processes. This will significantly reduce passengers' transfer time, improve overall travel efficiency, and enable passengers to complete intermodal travel more conveniently.

[0086] The present invention focuses on the travel experience of passengers, comprehensively considering factors such as travel efficiency, service quality and information transparency. Through the investigation and analysis of passenger satisfaction, targeted improvement suggestions can be provided to local transportation management departments to improve service quality, thereby enhancing the overall satisfaction of passengers.

[0087] The present invention uses modern information technology to collect and analyze passenger travel data to form a scientific evaluation system. This data-driven method can provide accurate travel demand analysis and trend forecasting for transportation management departments, support scientific decision-making, and promote the effective implementation of transportation policies.

[0088] The passenger intermodal transport statistics and evaluation method of the present invention can not only improve the travel efficiency and satisfaction of passengers, but also provide scientific decision-making support for transportation management departments, promote the coordinated development of various modes of transportation, promote the rational allocation of resources and the effective implementation of policies, and ultimately achieve sustainable social and economic development. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 is a flow chart of the method of the present invention;

[0090] Figure 2 This is a flow chart of passenger connecting travel identification according to the present invention. DETAILED DESCRIPTION

[0091] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Typical embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in the present invention. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0092] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used in the present invention in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Typical embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described in the present invention. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0093] like Figure 1 As shown, a method for calculating multi-modal passenger travel volume based on multi-source data includes:

[0094] S1. Data collection: collect transportation data of various modes of transportation, such as railways, aviation, and public transportation, including ticketing, stations, and route information; collect mobile Internet data and APP tracks to analyze passengers' actual travel tracks.

[0095] The data collection includes:

[0096] Transportation data collection

[0097] Railway data: including train information, line information, station information, ticket information, etc. It can be obtained from 12306 and other channels.

[0098] Aviation data: including flight information, route information, airport information, ticket information, etc. It can be obtained from the airline's official website or third-party platforms.

[0099] Public transportation data: including route information, station information, operation schedule, etc. It can be obtained from local transportation departments or public transportation companies.

[0100] Other data: such as taxi and shared bicycle data, can be obtained from relevant operators.

[0101] Mobile Internet Data Collection

[0102] Mobile phone signaling data: collects user location trajectory data through base station signals, which can be used to analyze actual travel paths. Need to cooperate with operators to obtain.

[0103] APP trajectory data: The travel trajectory data collected by the user's mobile phone APP can accurately analyze individual travel conditions. Cooperation with the APP developer is required.

[0104] Social media data: such as travel information posted by users on social media, which can help us understand the travel experiences of some users. This data needs to be obtained through API or crawlers.

[0105] S2. Data preprocessing: integrating data from different modes of transportation; establishing a unified data model, cleaning and supplementing missing information in the data, and ensuring data quality.

[0106] The data preprocessing includes:

[0107] Data cleaning: Check the completeness and accuracy of raw data, identify and correct erroneous data; handle missing values ​​and fill them using interpolation methods;

[0108] Interpolation formula in data cleaning:

[0109]

[0110] Data fusion: Establish a unified data model to integrate data from different sources; match the same entity in different data sources to eliminate duplicate information;

[0111] Similarity calculation in data fusion:

[0112]

[0113] Data conversion: Unify data formats to facilitate subsequent analysis and calculation of travel time and number of transfers.

[0114] Feature Engineering in Data Transformation:

[0115]

[0116] In summary, data collection involves the acquisition of transportation data and mobile Internet data, and pre-processing through steps such as data cleaning, fusion, and conversion, laying the foundation for subsequent inter-trip identification and statistical calculation.

[0117] S3. Identification of passenger inter-trip travel: analyzing data such as mobile phone signaling and APP trajectories to identify the trajectories of passengers using two or more modes of transportation for inter-trip travel. Combined with transportation data, the specific routes and transfer situations of inter-trip travel can be further refined.

[0118] like Figure 2 As shown, the passenger connecting trip identification includes:

[0119] (1) Extracting trajectory data: Extracting travel trajectories from mobile phone signaling and APP trajectories, recording the departure time, arrival time, starting point, and destination of each passenger; setting the format of trajectory data, T i ={(t start ,p start ),(t end ,p end ),...}

[0120] Where T irepresents the trajectory of the i-th passenger, t start and t end Respectively represent the start and end time, p start and p end Respectively represent the starting and ending positions;

[0121] (2) Identification of inter-trip travel modes: Based on the trajectory data, the travel mode is identified using the following rules:

[0122] If a passenger uses two or more modes of transportation within the same time period, it is considered to be a connecting trip;

[0123] Set a time window W to determine the time range of transfer; if the passenger is at t start and t end If different modes of transport are used between them, a transfer is considered to exist;

[0124] (3) Detailed description of transfer conditions:

[0125] For each passenger’s trajectory, analyze the mode of transportation used; assume that the set of transportation modes is M = {m1,m2,...,m k}, the following formula is used to express the mode of transportation used by each passenger: U i ={m j |m j In T i Appears in}

[0126] Among them U i represents the set of transportation modes used by the i-th passenger;

[0127] (4) Records of routes and transfers:

[0128] For each identified inter-trip travel mode, record its specific path and transfer information. Use a structure or database table to store the information;

[0129] Ri=(m j ,p j ,t j )|j=1,2,...,n}

[0130] Where Ri represents the travel record of the i-th passenger, m j Indicates the mode of transport used, p j represents the corresponding site, t j Indicates the arrival time;

[0131] (5) Statistics on inter-trip travel volume:

[0132] According to the identified inter-trip travel modes, the travel volume of each mode is counted; the travel volume of mode m is set to N m, expressed by the following formula:

[0133]

[0134] Where I(·) is the indicator function, when U i Returns 1 if pattern m is included, otherwise returns 0, and N is the total number of passengers.

[0135] S4. Statistics of inter-trip travel volume: based on the identified inter-trip travel trajectories, statistics are collected on various inter-trip modes, such as rail + aviation, bus + high-speed rail travel volume; the inter-trip travel volume in a broad sense and the inter-trip travel volume in a narrow sense are distinguished, and their respective statistical indicators are calculated.

[0136] The inter-trip travel statistics include:

[0137] Before counting the number of interline trips, ensure that the identification of interline trips of passengers has been completed, and the travel trajectory and transportation mode used by each passenger have been obtained; assuming that the travel record R of each passenger has been obtained. i , which contains the mode of transport used and the corresponding departure and arrival information;

[0138] (1) Statistics of generalized inter-trip travel volume:

[0139] Broadly speaking, intermodal travel volume refers to the total volume of passengers using two or more modes of transportation during their travels;

[0140] Traverse the travel records of all passengers R i ; For each passenger, check the set of transportation modes used by him / her U i ; if U i If it involves two or more modes of transportation, it will be recorded as a generalized interline trip; the number of all eligible passengers will be counted;

[0141]

[0142] Where N total represents the total amount of generalized inter-trip travel, |U i | represents the number of modes of transportation used by the i-th passenger, I(·) is an indicator function that returns 1 when the condition is met and 0 otherwise;

[0143] (2) Statistics on intermodal travel in a narrow sense; intermodal travel in a narrow sense refers to the situation of passengers transferring between different modes of transportation on the basis of intermodal travel in a broad sense;

[0144] Traverse the travel records of all passengers R i For each passenger’s trajectory, record its starting time t start , end time t end And the mode of transport used i ;

[0145] Within the set transfer time window W, if the time interval between two or more modes of transportation used by a passenger is less than W, it is recorded as a narrow-sense connecting trip;

[0146] Count all eligible passengers;

[0147]

[0148] Where N narrow represents the total amount of inter-trip travel in a narrow sense, m and m′ Respectively represent different modes of transportation. and Respectively indicate the end and start time of using different modes of transportation.

[0149] (3) Calculation of statistical indicators

[0150] The proportion of generalized interline travel:

[0151]

[0152] where p total It represents the proportion of broad-sense connecting trips to the total trip volume, N is the total number of passengers; the proportion of narrow-sense connecting trips is:

[0153]

[0154] where p narrow It indicates the proportion of narrow connecting trips to the total trip volume.

[0155] S5. Evaluation of intermodal passenger transport. After completing the statistics of intermodal travel volume, the development level of intermodal passenger transport in various regions will be scientifically evaluated.

[0156] The passenger intermodal transport evaluation includes:

[0157] (1) Construction of evaluation index system

[0158] The indicator system for evaluating passenger intermodal transport is constructed, including the following aspects:

[0159] Travel volume indicator: generalized inter-trip volume N total and the narrow sense connecting trip volume N narrow ;

[0160] Travel efficiency index: average transfer time T transfer and the average travel time T total ;

[0161] Travel satisfaction index: passenger satisfaction score S collected through questionnaire survey satisfaction;Transport mode diversity indicator: the number of transport modes used M;

[0162] (2) Evaluation model construction

[0163] Use weighted comprehensive evaluation method to establish a comprehensive evaluation model; set the weight of each indicator w j , then the comprehensive evaluation score E is expressed as:

[0164]

[0165] in:

[0166] E is the comprehensive evaluation score,

[0167] w j is the weight of the jth indicator, satisfying

[0168] Z j is the jth index value after standardization,

[0169] k is the total number of indicators;

[0170] (3) Evaluation level classification

[0171] The evaluation level is divided according to the comprehensive evaluation score E.

[0172] Excellent: E ≥ 0.8

[0173] Good: 0.6≤E<0.8

[0174] Medium: 0.4≤E<0.6

[0175] Poor: E<0.4.

[0176] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0177] It should be understood that the detailed description of the technical solutions of the present invention by means of the preferred embodiments is illustrative rather than restrictive. A person skilled in the art may modify the technical solutions described in the embodiments, or replace some of the technical features by equivalents, based on reading the specification of the present invention; and these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating multi-modal passenger travel volume based on multi-source data, characterized in that: The method includes: Data collection: collects transportation data of various modes of transportation, such as railways, aviation, and public transportation, including ticketing, station, and route information; collects mobile Internet data and APP tracks to analyze passengers' actual travel tracks; Data preprocessing, integrating data from different modes of transportation; establishing a unified data model to clean and supplement missing information in the data to ensure data quality; Identify passenger inter-trips: Analyze mobile phone signaling and APP trajectory data to identify the trajectory of passengers using two or more modes of transportation for inter-trips. Combined with transportation data, further refine the specific routes and transfer conditions of inter-trips. Statistics of inter-trip travel volume: based on the identified inter-trip travel trajectories, statistics are collected on various inter-trip modes, such as rail + aviation, bus + high-speed rail. The inter-trip travel volume in a broad sense and the inter-trip travel volume in a narrow sense are distinguished, and their respective statistical indicators are calculated. Passenger intermodal transport evaluation, after completing the statistics of intermodal travel volume, scientific evaluation of the development level of passenger intermodal transport in various places will be carried out.

2. The method for calculating multi-modal passenger travel volume based on multi-source data according to claim 1, characterized in that: The data collection includes: (1) Transportation data collection Railway data: including train information, line information, station information, and ticket information, obtained from the 12306 channel; Aviation data: including flight information, route information, airport information, and ticket information, which can be obtained from the airline’s official website or third-party platforms; Public transportation data: including route information, station information, and operating timetables, obtained from local transportation departments or public transportation companies; (2) Mobile Internet data collection includes: Mobile phone signaling data: collects user location trajectory data through base station signals to analyze actual travel paths; APP trajectory data: travel trajectory data collected through the user’s mobile phone APP.

3. The method for calculating multi-modal passenger travel volume based on multi-source data according to claim 1, characterized in that: The data preprocessing includes: Data cleaning: Check the completeness and accuracy of raw data, identify and correct erroneous data; handle missing values ​​and fill them using interpolation methods; Data fusion: Establish a unified data model to integrate data from different sources; match the same entity in different data sources to eliminate duplicate information; Data conversion: Unify data formats to facilitate subsequent analysis and calculation of travel time and number of transfers.

4. The method for calculating multi-modal passenger travel volume based on multi-source data according to claim 1, characterized in that: The passenger connecting trip identification includes: Trajectory data extraction: extract travel trajectories from mobile phone signaling and APP trajectories, record the departure time, arrival time, starting point and destination of each passenger; set the format of trajectory data, T i ={(t start ,p start ),(t end ,p end ),...} Where T i represents the trajectory of the i-th passenger, t start and t end Respectively represent the start and end time, p start and p end Respectively represent the starting and ending positions; Inter-trip travel mode identification: Based on the trajectory data, the travel mode is identified using the following rules: If a passenger uses two or more modes of transportation within the same time period, it is considered to be a connecting trip; Set a time window W to determine the time range of transfer; if the passenger is at t start and t end If different modes of transport are used between them, a transfer is considered to exist; Details of transfer situation: For each passenger’s trajectory, analyze the mode of transportation used; assume that the set of transportation modes is M = {m1,m2,...,m k }, the following formula is used to express the mode of transportation used by each passenger: U i ={m j |m j In T i Appears in}; among them U i represents the set of transportation modes used by the i-th passenger; Records of routes and transfers: For each identified inter-trip travel mode, record its specific path and transfer information. Use a structure or database table to store the information; Ri=(m j ,p j ,t j )|j=1,2,...,n} Where Ri represents the travel record of the i-th passenger, m j Indicates the mode of transport used, p j represents the corresponding site, t j Indicates the arrival time; Statistics on interline travel volume: According to the identified inter-trip travel modes, the travel volume of each mode is counted; the travel volume of mode m is set to N m , expressed by the following formula: Where I(·) is the indicator function, when U i Returns 1 if pattern m is included, otherwise returns 0, and N is the total number of passengers.

5. The method for calculating multi-modal passenger travel volume based on multi-source data according to claim 1, characterized in that: The inter-trip travel statistics include: Before counting the number of interline trips, ensure that the identification of interline trips of passengers has been completed, and the travel trajectory and transportation mode used by each passenger have been obtained; assuming that the travel record R of each passenger has been obtained. i , which contains the mode of transport used and the corresponding departure and arrival information; (1) Statistics of generalized inter-trip travel volume: Broadly speaking, intermodal travel volume refers to the total volume of passengers using two or more modes of transportation during their travels; Traverse the travel records of all passengers R i ; For each passenger, check the set of transportation modes used by him / her U i ; If U i If it involves two or more modes of transportation, it will be recorded as a generalized interline trip; the number of all eligible passengers will be counted; Where N total represents the total amount of generalized inter-trip travel, |U i | represents the number of modes of transportation used by the i-th passenger, I(·) is an indicator function that returns 1 when the condition is met and 0 otherwise; (2) Statistics on intermodal travel in a narrow sense; intermodal travel in a narrow sense refers to the situation of passengers transferring between different modes of transportation on the basis of intermodal travel in a broad sense; Traverse the travel records of all passengers R i For each passenger’s trajectory, record its starting time t start , end time t end And the mode of transport used i ; Within the set transfer time window W, if the time interval between two or more modes of transportation used by a passenger is less than W, it is recorded as a narrow-sense connecting trip; Count all eligible passengers; Where N narrow represents the total amount of inter-trip travel in a narrow sense, m and m′ Respectively represent different modes of transportation. and Respectively indicate the end and start time of using different modes of transportation. (3) Calculation of statistical indicators The proportion of generalized interline travel: where p total represents the proportion of generalized connecting trips to the total trip volume, and N is the total number of passengers traveling; The proportion of narrow interline travel: where p narrow It indicates the proportion of narrow connecting trips to the total trip volume.

6. The method for calculating multi-modal passenger travel volume based on multi-source data according to claim 1, characterized in that: The passenger intermodal transport evaluation includes: Construction of evaluation index system The indicator system for evaluating passenger intermodal transport is constructed, including the following aspects: Travel volume indicator: generalized inter-trip volume N total and the narrow sense connecting trip volume N narrow ; Travel efficiency index: average transfer time T transfer and the average travel time T total ; Travel satisfaction index: passenger satisfaction score S collected through questionnaire survey satisfaction ; Transport mode diversity indicator: the number of transport modes used M; Evaluation model construction Use weighted comprehensive evaluation method to establish a comprehensive evaluation model; set the weight of each indicator w j , then the comprehensive evaluation score E is expressed as: Where: E is the comprehensive evaluation score, w j is the weight of the jth indicator, satisfying Z j is the jth indicator value after standardization, k is the total number of indicators; Evaluation level classification The evaluation level is divided according to the comprehensive evaluation score E. Excellent: E ≥ 0.8; Good: 0.6≤E<0.8; Medium: 0.4≤E<0.6; Poor: E<0.4.

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