Method for generating passenger trip OD of whole society based on railway OD statistics

Abstract

The application discloses a kind of whole society passenger travel OD generation method based on railway OD statistics.How to effectively deduce whole society travel OD according to railway OD statistical data has important significance to the scientific decision of traffic planning decision maker.The present application first divides traffic cell according to railway site distribution and city administrative planning;Railway, car, long-distance bus, aviation accessible virtual traffic network construction;According to the field characteristics of railway system ticket data, the railway travel volume between each traffic cell is counted, and the railway travel OD is obtained;Railway travel proportion matrix generation;Based on the traffic mode proportion between each traffic cell, the whole society passenger travel OD data is generated.The present application is low in cost, easy to operate, fast to obtain, strong in implementability and high in precision, and can fully meet the precision requirement of resident intercity travel characteristic research in traffic planning work.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of traffic planning, and particularly relates to a full-society passenger trip OD generation method based on railway OD statistics. BACKGROUND

[0002] The four-stage model is the most widely used model in the field of traffic planning at home and abroad, and the four stages refer to traffic generation, traffic distribution, traffic mode sharing and traffic assignment. The calculation of the traffic volume between the origin and destination of resident trips (OD trips) has always been a popular topic in the research of traffic planning. In the urban agglomeration traffic system, the intercity full-society resident trip OD survey is an important basis for intelligent decision-making, and its characteristics reflect the spatial distribution of resident trip demand.

[0003] In the traditional traffic planning decision-making, the OD matrix obtained through actual survey is needed as the original data source of traffic mode division, traffic assignment and other methods. The traditional OD matrix survey methods mainly include roadside inquiry method, household and floating population questionnaire survey, closed curve motor vehicle traffic volume survey method, and the cross-section motor vehicle traffic volume of the verification line is used to calibrate the accuracy of the OD matrix. However, these traditional survey methods consume a large amount of manpower and material resources, and the sampling range of the community is limited. After the survey, the data processing amount is large and the time is long. At the same time, some residents may not answer the questionnaire truthfully due to the fear of privacy leakage. The above reasons all lead to great defects in the authenticity, accuracy and feasibility of the full-society passenger OD survey.

[0004] Compared with the urban internal trip, the intercity passenger traffic mode is less, mainly including four traffic modes of railway, car, long-distance bus and aviation. In the railway system, all the railway trip ticket information (tripper ID, origin, destination, time, train type, etc.) is recorded in detail, and the railway OD trip data can be easily obtained according to the ticket record information.

[0005] Therefore, how to effectively deduce the full-society trip OD from the railway OD statistical data is of great significance for traffic planning decision-makers to make scientific decisions. It is necessary to statistically analyze the railway OD trip data according to the characteristics of the railway ticket sales data, and to design a full-society trip OD data generation technical method based on the same. SUMMARY

[0006] In order to make up for the shortcomings of the prior art, the present application provides a full-society passenger trip OD generation method based on railway OD statistics, which solves the problem that the intercity trip OD data collection is difficult at present, and overcomes the defects that the competition and cooperation relationship between various transportation modes is not considered.

[0007] In order to achieve the above object, the technical scheme adopted by the present application is:

[0008] The all-society passenger trip OD generation method based on railway OD statistics, characterized in that it comprises the following steps:

[0009] Step one: traffic cell division

[0010] According to the distribution of railway stations and urban administrative planning, traffic cells are divided;

[0011] Step two: construction of virtual traffic network reachable by railway, private car, long-distance bus and aviation

[0012] Based on the open-source map data platform and the aviation open-source data platform, the direct conditions between each traffic cell by the four traffic modes are obtained, and the virtual traffic network reachable by railway, private car, long-distance bus and aviation is constructed;

[0013] Step three: railway OD data extraction

[0014] According to the field characteristics of railway system ticket data, the railway trip volume between each traffic cell is counted to obtain the railway trip OD;

[0015] Step four: generation of railway trip proportion matrix

[0016] The Logit model is used to construct a trip mode division model, and the model parameters are calibrated through a trip mode willingness survey, and the railway trip proportion matrix between each traffic cell is output;

[0017] Step five: all-society passenger trip OD generation

[0018] Based on the traffic mode proportion between each traffic cell, the all-society passenger trip OD data is generated.

[0019] Further, the traffic cell division in step one includes dividing the research scope into traffic cells in units of administrative regions at the prefecture level and administrative division level.

[0020] Further, the construction of virtual traffic network reachable by railway, private car, long-distance bus and aviation in step two includes:

[0021] Step 2.1: construction of virtual traffic network reachable by railway

[0022] Firstly, the railway station information in each traffic cell is obtained based on the open-source map platform, and then the railway schedule information between each traffic cell is crawled based on the railway ticket information website; finally, the railway reachable traffic network between each cell is obtained according to the railway schedule information;

[0023] Step 2.2: construction of virtual traffic network reachable by private car

[0024] Obtain the reachable traffic network between each traffic zone and the attributes of all highways and national and provincial highways in the network based on the open source map platform;

[0025] Step 2.3: Construction of long-distance bus reachable virtual traffic network

[0026] First, obtain the long-distance passenger station information in each traffic zone based on the open source map platform, and then obtain the long-distance bus schedule information between each traffic zone based on the long-distance bus ticket information website; finally, obtain the long-distance bus reachable traffic network between each zone based on the long-distance bus schedule information.

[0027] Step 2.4: Construction of civil aviation reachable virtual traffic network

[0028] First, obtain the airport information in each traffic zone based on the open source map platform; then, obtain the civil aviation flight schedule information between each traffic zone based on the civil aviation ticket information website; finally, obtain the civil aviation reachable traffic network between each zone based on the civil aviation flight schedule information.

[0029] Further, the process of railway OD data extraction in step three is as follows:

[0030] Step 3.1: Invalid data processing

[0031] Invalid data includes missing data in key fields and duplicate data; for missing data in key fields, directly delete, for duplicate data, only keep one data, and delete the rest;

[0032] Step 3.2: Railway OD data extraction

[0033] Screen the railway station ticketing data of each traffic zone in a specific time period, match the departure station and arrival station in the ticketing record data with the railway stations in the traffic zone; for traffic zones with railway ticketing data, the traffic zone corresponding to the starting station in each ticketing data is the starting point of the passenger trip, i.e. O point, and the traffic zone corresponding to the terminal is the terminal of the passenger trip, i.e. D point; count the number of passengers from the starting point to the terminal for the traffic zone, and fill it into the railway OD table to obtain the railway travel OD matrix F of each traffic zone in the time period rail .

[0034] Further, the process of generating the railway travel proportion matrix in step four is as follows:

[0035] Step 4.1: Selection of traffic mode selection factors

[0036] This method mainly considers the influence of four factors such as speed, economy, safety and comfort of traffic mode on traffic mode selection;

[0037] Step 4.2: Quantification of selection factors

[0038] Step 4.3: Traffic mode utility function establishment

[0039] The above various traffic mode economic and technical attributes are unified to measure the time value, and the production method is used to determine the specific calculation formula:

[0040] TA = GDP / (P x T)

[0041] Where TA represents the unit time value of passengers; GDP represents the total domestic production of the region; P represents the number of employed population in the region; T represents the average labor time of the region;

[0042] According to the above economic and technical indicators, and combined with the time value of passengers, the traffic mode utility function is constructed as:

[0043]

[0044] Where r represents the utility value of the traffic mode, which is in the form of generalized cost, a and β represent the utility coefficients of economic and time attributes respectively, which can be determined by travel mode willingness survey, TA represents the unit time value of passengers, K represents the safety coefficient of the transportation tool, J represents the total travel cost of passengers, represents the comfort cost of the transportation tool, T represents the total travel time of passengers, Z represents the average delay time of the transportation tool, and F represents the additional time such as ticket purchase and waiting;

[0045] Step 4.4: Construction of traffic mode division Logit model

[0046] On the basis of constructing the utility function of traffic mode, the selection probability of passengers for various traffic modes is calculated through the Logit model of multi-objective decision, and the probability calculation model is:

[0047]

[0048] Where is the proportion of the i-th zone to the j-th zone using the m-th traffic mode, is the generalized cost (traffic impedance) of the i-th zone to the j-th zone using the m-th traffic mode;

[0049] Step 4.5: Generation of railway travel proportion matrix

[0050] Through the implementation of step 4.4, the railway travel proportion matrix A of each traffic zone is calculated rail .

[0051] Further, the process of generating the whole society passenger travel OD in step five is:

[0052] According to the railway travel OD matrix F obtained by implementing steps three and fourrail and the railway travel mode proportion matrix A rail the whole society passenger travel OD matrix F all F can be expressed as all F = F rail / A rail .

[0053] The beneficial effects of the present application are:

[0054] The present application solves the problems of great difficulty, high cost and low precision in the existing whole society passenger OD survey, fully utilizes the characteristics of large sample size and full information of railway ticket data, obtains railway travel OD by statistical analysis of ticket data, constructs travel mode division model by using Logit model, calibrates model parameters by travel mode willingness survey, and outputs railway travel proportion matrix between each traffic zone, so as to calculate the whole society passenger OD data. Compared with the traditional method, it has the advantages of low cost, convenient operation, rapid acquisition, strong implementability and high precision, and can fully meet the precision requirements of resident intercity travel characteristics research in traffic planning work. BRIEF DESCRIPTION OF DRAWINGS

[0055] Figure 1 The present application is a whole society passenger travel OD generation technical flowchart. DETAILED DESCRIPTION

[0056] The present application will be described in detail below in combination with specific embodiments.

[0057] Based on the division of traffic zones, the present application statistically analyzes intercity railway travel OD based on railway ticket data; after obtaining the intercity railway travel OD, the present application divides the intercity travel mode, and finally generates whole society passenger travel OD data, so as to obtain intercity travel passenger flow characteristics at low cost, quickly, widely, and diversely.

[0058] As shown in the figure, the present application includes the following steps: Figure 1

[0059] Step one: traffic zone division

[0060] According to the distribution of railway stations and urban administrative planning, the traffic zone is divided; the content of traffic zone division includes dividing the research range into traffic zones as a unit of administrative region at the level of prefecture-level city and administrative division level;

[0061] Step two: construction of virtual traffic network reachable by railway, car, long-distance bus and aviation

[0062] Based on the open source map data platform and aviation open source data platform, the direct conditions of the four traffic modes between each traffic zone are obtained, and the virtual traffic network reachable by railway, car, long-distance bus and aviation is constructed, including:​

[0063] Step 2.1: Railway accessible virtual transportation network construction

[0064] Firstly, the railway station information in each traffic zone is obtained based on the open source map platform, including station name, station location coordinates, station level, etc. Then, the railway schedule information between each traffic zone is crawled based on the railway ticket information website, including the name of the running class, vehicle type, class frequency, ticket price, class travel time, etc. Finally, the railway accessible transportation network between each zone is obtained according to the railway schedule information, and the railway accessibility between traffic zones is represented by constructing an adjacency matrix K1. The form of the accessibility matrix K1 is as follows:

[0065]

[0066] wherein, represents the railway accessibility from traffic zone i to traffic zone j, which is a 0-1 variable, represents that traffic zone i and traffic zone j can be directly accessible by railway, represents that traffic zone i and traffic zone j cannot be directly accessible by railway.

[0067] Step 2.2: Car accessible virtual transportation network construction

[0068] Based on the open source map platform, the accessible transportation network between each traffic zone and the properties of all highways and national and provincial highways in the network are obtained, including highway name, highway level, design speed, number of lanes, mileage. The highway accessibility between each traffic zone is represented by constructing an adjacency matrix K2. The form of the accessibility matrix K2 is as follows:

[0069]

[0070] wherein, represents the highway accessibility from traffic zone i to traffic zone j, which is a 0-1 variable, represents that traffic zone i and traffic zone j can be directly accessible by highway, represents that traffic zone i and traffic zone j cannot be directly accessible by highway.

[0071] Step 2.3: Long-distance bus accessible virtual transportation network construction

[0072] Firstly, the long-distance bus station information in each traffic zone is obtained based on the open source map platform, including station name, station location coordinates, station level, etc. Then, the long-distance bus schedule information between each traffic zone is crawled based on the long-distance bus ticket information website, including the running schedule name, vehicle type, schedule frequency, ticket price, and schedule travel time. Finally, the long-distance bus accessible traffic network between each zone is obtained according to the long-distance bus schedule information. The long-distance bus accessibility between each traffic zone is represented by constructing an adjacency matrix K3, and the accessibility matrix K3 is in the form of:

[0073]

[0074] wherein, represents the long-distance bus accessibility from traffic zone i to traffic zone j, which is a 0-1 variable, represents that traffic zone i and traffic zone j can be directly connected by long-distance bus, represents that traffic zone i and traffic zone j cannot be directly connected by long-distance bus.

[0075] Step 2.4: Construction of virtual civil aviation accessible traffic network

[0076] Firstly, the airport information in each traffic zone is obtained based on the open source map platform, including airport name, airport location coordinates, airport level, etc. Then, the civil aviation flight schedule information between each traffic zone is crawled based on the civil aviation ticket information website, including flight name, flight aircraft type, airline, flight frequency, ticket price, and flight travel time. Finally, the civil aviation accessible traffic network between each zone is obtained according to the civil aviation flight schedule information. The civil aviation accessibility between each traffic zone is represented by constructing an adjacency matrix K4, and the accessibility matrix K4 is in the form of:

[0077]

[0078] wherein, represents the civil aviation accessibility from traffic zone i to traffic zone j, which is a 0-1 variable,

[0079] represents that traffic zone i and traffic zone j can be directly connected by civil aviation, represents that traffic zone i and traffic zone j cannot be directly connected by civil aviation.

[0080] Step three: Railway OD data extraction

[0081] According to the field characteristics of railway system ticket data, the railway trip OD between each traffic zone is counted, and the railway OD data is extracted.

[0082] Step 3.1: Invalid data processing

[0083] Invalid data includes two categories: key field missing data and duplicate data. For key field missing data, directly delete it. For duplicate data, only keep one data and delete the rest. Key fields of railway ticket data include train number, seat type, departure station, destination station, departure time, arrival time, etc. The meaning of each key field is shown in Table 1.

[0084] Table 1: Meaning of key fields of railway ticket data

[0085]

[0086]

[0087] Step 3.2: Railway OD data extraction

[0088] First, according to the departure time, filter all railway station ticket data within a specific time period (day / week / month, etc.). Match the departure station and arrival station in the ticket record data with the railway stations in the traffic zone. For each ticket data, the traffic zone corresponding to the departure station is the origin, i.e., O point, and the traffic zone corresponding to the destination station is the destination, i.e., D point. According to the departure station and destination station, organize the ticket data of each traffic zone point pair and count the number of railway passengers between traffic zone point pairs f i,j , and fill it into the corresponding railway OD table to obtain the railway travel OD matrix F rail of each traffic zone in the time period. rail The form of the railway travel OD matrix F

[0089]

[0090] Step four: Generation of railway travel proportion matrix

[0091] The Logit model is used to construct the travel mode division model, and the travel mode willingness survey is conducted to calibrate the model parameters, and the railway travel proportion matrix between traffic zones is output. The process of generating the railway travel proportion matrix is as follows:

[0092] Step 4.1: Selection of traffic mode selection factors

[0093] This method mainly considers the influence of four factors, i.e., speed, economy, safety, and comfort, on traffic mode selection.

[0094] Step 4.2: Quantification of selection factors

[0095] Before the start of the selection factor quantification, first of all, according to the accessible virtual traffic network, the traffic mode accessibility between each traffic cell point pair is judged; for example, the accessibility of traffic mode m between traffic cell i and traffic cell j which indicates that the passenger cannot travel from traffic cell i to traffic cell j by traffic mode m, at this time, the selection factor quantification is not needed for this OD point pair; for example, the accessibility of traffic mode m between traffic cell i and traffic cell j which indicates that the passenger can travel from traffic cell i to traffic cell j by traffic mode m, at this time, the selection factor quantification is needed for this OD point pair.

[0096] For rapidity, the passenger is generally concerned about the journey time when making intercity traffic mode selection, so the journey time between each traffic cell is mainly used to quantify rapidity. For the journey time of public transportation such as railway, aviation and long-distance bus, it can be obtained from the traffic mode travel network extracted in step two. For the journey time of private car, the distance of the shortest path between the origin and destination can be obtained from the private car highway traffic network, and then the distance is divided by the speed to obtain the journey time.

[0097] For economy, the composition of the fee required by the passenger to complete the trip needs to be determined according to the type of traffic mode selected by the passenger. The composition of the trip fee of public transportation (railway, aviation, long-distance bus) is mainly the ticket price of each traffic mode, which can be obtained from the traffic mode travel network extracted in step two. The trip fee of the passenger who chooses to travel by private car needs to be specifically calculated by distance, unit mileage fuel consumption, unit oil price and other factors.

[0098] For safety, the expression of the safety of the transportation tool is relatively difficult in the construction of the intercity travel traffic mode selection model of the passenger. The safety is the prerequisite for the passenger to select various transportation tools. The safety can be estimated and represented by the frequency of passenger casualties of the traffic mode, i.e. the ratio of the annual average number of accidents to the annual average passenger turnover. According to the accident statistics of each traffic mode in the past ten years, the safety coefficients of various passenger transportation modes are obtained: about 0.99 for railway, about 0.99 for aviation, about 0.85 for private car, and about 0.95 for long-distance bus.

[0099] For comfort, the perception of the passenger is mainly reflected in the size of the personal space during the ride, the vibration of the transportation tool during operation, the travel time and other aspects. Under normal circumstances, the comfort level of the transportation tool is positively correlated with its fee or ticket price. Therefore, in order to simplify the calculation, the comfort factor can be represented by the trip fee in the modeling, and the comfort fee of the traffic mode is 5%-10% of the trip fee of the mode.

[0100] Step 4.3: Establishment of traffic mode utility function

[0101] In order to convert the economic and technical attributes of the various modes of transportation into a unified metric, this paper introduces the concept of time value and uses the production method to determine it. The specific calculation formula is: TA = GDP / (P × T), where TA represents the unit time value of passengers; GDP represents the region's gross domestic product; P represents the region's employed population; and T represents the region's average working hours per person.

[0102] Based on the aforementioned economic and technical indicators, and considering the time value of passengers, a transportation mode utility function can be constructed. This function allows for a clear and intuitive comparison of the utility values ​​of different transportation modes for different passengers. Through analysis, the utility functions for various transportation modes are expressed in the following forms:

[0103]

[0104] Where r represents the utility value of the mode of transportation, in the form of generalized cost, α and β represent the utility coefficients of economic and time attributes, respectively, which can be determined through a survey of travel intentions, TA represents the value per unit time of the passenger, K represents the safety coefficient of the transportation vehicle, J represents the total cost of the passenger's trip, represents the comfort cost of the transportation vehicle, T represents the total travel time of the passenger, Z represents the average delay time of the transportation vehicle, and F represents the additional time for purchasing tickets, waiting for the transportation vehicle, etc.

[0105] Step 4.4: Construction of the Logit Model for Transportation Mode Segmentation

[0106] Based on the utility function of transportation modes, the probability of passengers choosing various transportation modes is calculated using a multi-objective decision-making Logit model. The probability calculation model is as follows:

[0107]

[0108] in Let m be the proportion of the m-th mode of transportation used when traveling from the i-th community to the j-th community. For the i-th cell

[0109] The generalized cost (traffic impedance) of using the m-th mode of transportation to reach the j-th cell; For the accessibility of the i-th cell to the j-th cell using the m-th mode of transportation;

[0110] Step 4.5: Generating the Railway Travel Proportion Matrix

[0111] By implementing step 4.4, the railway travel mode ratio matrix A among the various traffic zones is calculated. rail Railway travel mode ratio matrix A rail The format is:

[0112]

[0113] Step five: whole society passenger trip OD generation

[0114] Based on the traffic mode proportion between each traffic zone, the whole society passenger trip OD data is generated, and the process of whole society passenger trip OD generation is as follows:

[0115] According to the railway trip OD matrix F obtained by implementing step three and step four rail and the railway trip mode proportion matrix A rail , the whole society passenger trip OD matrix F all can be expressed as F all = F rail / A rail .

[0116] The effectiveness and feasibility of the method are verified below by taking Chengdu metropolitan circle (Chengde Meibei) as an example.

[0117] The whole society passenger trip OD generation method of Chengdu metropolitan circle based on railway OD statistics is implemented according to the following steps:

[0118] According to the method in step one, Chengdu metropolitan circle (Chengde Meibei) is divided into four traffic zones, as shown in Table 2.

[0119] Table 2 Traffic zone division table

[0120] Number Traffic cell 1 Chengdu 2 Deyang 3 Meishan 4 Ziyang

[0121] According to the method in step two, the accessibility network of each traffic mode is constructed, and the results are as follows:

[0122] Railway accessibility matrix:

[0123]

[0124] Car accessibility matrix:

[0125]

[0126] Long-distance bus accessibility matrix:

[0127]

[0128] Civil aviation accessibility matrix:

[0129]

[0130] According to the method in step three, the railway OD data is extracted, and in this embodiment, the OD data within one day is extracted, and the results of Chengdu metropolitan circle railway OD matrix are as follows:

[0131]

[0132] According to the method for generating the railway travel proportion matrix in step four, the railway travel proportion matrix of the Chengdu metropolitan circle (Chengde Meibei) is as follows:

[0133]

[0134] According to the method for generating the whole society passenger travel OD matrix in step five, the whole society passenger travel OD matrix of the Chengdu metropolitan circle (Chengde Meibei) is as follows:

[0135]

[0136] The content of the present application is not limited to the examples listed, any equivalent transformation of the technical solutions of the present application taken by a person of ordinary skill in the art by reading the specification of the present application is covered by the claims of the present application.

Claims

1. A method for generating passenger trip ODs of the whole society based on railway OD statistics, characterized in that: Comprising the following steps: Step one: traffic cell division According to the distribution of railway stations and urban administrative planning, traffic cell division is carried out; Step two: construction of virtual traffic network accessible by railway, car, long-distance bus and aviation Based on the open source map data platform and the aviation open source data platform, the direct conditions of the four traffic modes between each traffic cell are obtained, and the virtual traffic network accessible by railway, car, long-distance bus and aviation is constructed; Step three: extraction of railway OD data According to the field characteristics of railway system ticket data, the railway trip volume between each traffic cell is counted to obtain the railway trip OD; Step four: generation of railway trip proportion matrix A trip mode division model is constructed by using Logit model, and the model parameters are calibrated by conducting trip mode willingness survey, and the railway trip proportion matrix between each traffic cell is output; Step five: generation of passenger trip OD of the whole society Based on the traffic mode proportion between each traffic cell, passenger trip OD data of the whole society is generated; The process of generating the railway trip proportion matrix in step four is as follows: Step 4.1: selection of traffic mode selection factors The influence of the four factors of traffic mode rapidity, economy, safety and comfort on traffic mode selection is considered; Step 4.2: quantification of selection factors Step 4.3: establishment of traffic mode utility function The economic and technical attributes of the above four traffic modes are measured by time value, and are determined by production method, and the specific calculation formula is: TA=GDP / (P×T) wherein TA represents the value per unit of time of the passenger; GDP GDP of the region; P Employment of the region; T Labor hours per capita of the region; According to the economic and technical indicators, and combining with the time value of passengers, the traffic mode utility function is constructed as follows: wherein r represents the utility value of the traffic mode, in the form of generalized cost, α、β respectively represent the utility coefficients of economic attribute and time attribute, determined by travel mode willingness survey, TA represents the unit time value of the passenger, K represents the safety coefficient of the traffic tool, J represents the total cost of the passenger trip, S represents the comfort cost of the traffic tool, T represents the total time consumption of the passenger trip, Z represents the average delay time of the traffic tool, F represents the additional time for ticket purchase and waiting of the traffic tool; Step 4.4: construction of traffic mode division Logit model On the basis of constructing the utility function of traffic mode, the selection probability of passengers for various traffic modes is calculated by the Logit model of multi-objective decision, and the probability calculation model is as follows: wherein is the proportion of the i-th cell to the j-th cell using the m-th traffic mode, is the generalized cost of the i-th cell to the j-th cell using the m-th traffic mode; Step 4.5: generation of railway trip proportion matrix By implementing step 4.4, the proportion matrix of railway travel mode between each traffic zone is calculated A rail . 2.The all-society passenger trip OD generation method based on railway OD statistics according to claim 1, characterized in that: The content of traffic cell division in step one includes dividing the research scope into traffic cells in the unit of administrative regions at the level of prefecture-level city and administrative division.

3. The all-society passenger trip OD generation method based on railway OD statistics according to claim 2, characterized in that: The construction of virtual traffic network accessible by railway, car, long-distance bus and civil aviation in step two includes: Step 2.1: construction of virtual traffic network accessible by railway Firstly, the railway station information in each traffic cell is obtained based on the open source map platform, and then the railway schedule information between each traffic cell is obtained based on the railway ticket information website; finally, the railway accessible traffic network between each cell is obtained according to the railway schedule information; Step 2.2: construction of virtual traffic network accessible by car Based on the open source map platform, the accessible traffic network between each traffic cell and the attributes of all expressways and national and provincial highways in the network are obtained; Step 2.3: construction of virtual traffic network accessible by long-distance bus Firstly, the long-distance passenger station information in each traffic cell is obtained based on the open source map platform, and then the long-distance bus schedule information between each traffic cell is obtained based on the long-distance bus ticket information website; finally, the long-distance bus accessible traffic network between each cell is obtained according to the long-distance bus schedule information; Step 2.4: construction of virtual traffic network accessible by civil aviation Firstly, the airport information in each traffic zone is obtained based on the open source map platform; secondly, the civil aviation flight information between each traffic zone is obtained based on the civil aviation ticket information website; finally, the civil aviation accessible traffic network between each zone is obtained according to the civil aviation flight information.

4. The all-society passenger trip OD generation method based on railway OD statistics according to claim 3, characterized in that: The process of railway OD data extraction in the step three is as follows: Step 3.1: invalid data processing Invalid data includes: key field missing data, repeated data; for key field missing data, directly delete, for repeated data, only keep one data, and delete the rest data; Step 3.2: railway OD data extraction The railway station ticketing data of each traffic cell in a specific time period is screened, and the departure station and arrival station in the ticketing record data are matched with the railway stations in the traffic cell; for the traffic cell with railway ticketing data, the traffic cell corresponding to the starting point station in each ticketing data is the starting point of the passenger trip corresponding to the train of the ticketing data, that is, the O point, and the traffic cell corresponding to the terminal point is the terminal point of the passenger trip corresponding to the train of the ticketing data, that is, the D point; the passenger number from the starting point to the terminal point of the traffic cell is filled into the railway OD table, and the railway trip OD matrix of each traffic cell in the specific time period is obtained F rail .

5. The railway-based OD statistics based society-wide passenger trip OD generation method according to claim 4, characterized in that: The process of generating the whole society passenger trip OD in the step five is as follows: Railway trip OD matrix according to implementation steps three and four F rail Railway trip mode share matrix A rail Social passenger trip OD matrix F all is represented as F all = F rail / A rail .

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