A method for constructing full-volume trip OD based on integer solutions

Abstract

The present invention provides a method for constructing a total travel OD based on integer solutions: (1) obtaining the total personal information data, resident travel survey and mobile phone signaling data, and a list of sequence correspondences at a specified time; (2) converting the travel chain station sequence into a travel chain station type sequence; (3) classifying the population according to personal information; (4) initializing the personal travel chain station type sequence by probability sampling; (5) updating the sequence of traffic districts where the travel chain station is located; (6) calculating the probability distribution from the departure traffic district to the destination traffic district; (7) updating the traffic district number of the station type as a living station or other station index position. This method is based on a small sample feature survey and mobile phone signaling big data travel behavior analysis, and uses a stratified probability sampling method to mine and obtain personal life travel destinations and other destinations, thereby achieving the acquisition of a travel distribution feature library for the total population of the city.

Description

Technical Field

[0001] The invention relates to the technical field of traffic planning, and relates to a method for constructing a full travel OD based on integer solutions. Background Art

[0002] Most of the existing travel distribution acquisition methods are based on the theory of mining the travel activity characteristics of small sample data, mathematical optimization, and utility maximization, and simulate the travel activities of groups and individuals to obtain the travel distribution of the city. People's travel activities are closely related to their basic attributes. The spatial location of residence, work place, etc. determines the spatial distribution of travelers' activities. The existing travel distribution acquisition methods still have insufficient consideration of the spatial location attributes of travelers. With the development of science and technology, traffic model technology and traffic survey data acquisition methods are also constantly innovating, but its theoretical framework is still mainly based on small sample survey data to deduce the overall travel activities of the city, and the accuracy and efficiency of the model still have room for improvement.

[0003] The advent of the big data era has provided a variety of ways and data sources for obtaining the travel activity characteristics of the city as a whole and individual citizens. This has also provided conditions for giving travel entities more attributes. These conditions will help to explain travel activities and will inevitably improve the accuracy of traffic planning models in analyzing urban activities.

[0004] Based on the integer solution method of probability sampling travel activities, we can make full use of personal stability and urban stability to simulate the travel activities of urban residents; the introduction of individual attributes of travelers makes the travel destination more accurate; travel activity analysis is no longer a blind mathematical optimization matching but inherits the genes of the city, making travel behavior analysis move from traditional mathematical optimization methods and utility maximization to causal inference. Summary of the invention

[0005] Based on small sample characteristic survey and mobile phone signaling big data travel behavior analysis, this paper introduces more individual travel activity attributes into the generation of urban population travel chain, and uses stratified probability sampling method to mine and obtain personal life travel destinations and other destinations according to the stability of travelers' activities. It realizes the acquisition of travel distribution characteristics of the entire urban population.

[0006] To achieve the above purpose, the technical solution of the present invention is as follows: a method for constructing a full-volume trip OD based on integer solutions, the specific steps are as follows:

[0007] S1. Obtain personal information data of the entire population of the city within the research scope, residents' travel survey data, mobile phone signaling data, and a list of corresponding relationships between traffic areas, traffic centers, and traffic communities;

[0008] The traffic zone is STAZ = [0, 1, 2, 3, ..., i, ..., s], where s is the number of traffic zones; the traffic center zone is MTAZ = [0, 1, 2, 3, ..., m], where m is the number of traffic centers; the traffic area is LTAZ = [0, 1, 2, 3, ..., l], where l is the number of traffic areas; the corresponding relationship list of traffic areas, traffic centers, and traffic zones is as follows:

[0009] {STAZ i ,MTAZ j ,LTAZ k}

[0010] i, j, k are the list indexes of traffic area, traffic center area, and traffic area respectively;

[0011] S2. Traverse the residents' travel survey data and mobile phone signaling data to convert the departure / destination type and spatial location data in the personal travel data into the station type to form the station type sequence S of the personal travel chain and the traffic community sequence T where the station is located:

[0012] S={S1,S2,S3,...,S n}

[0013] T={T1,T2,T3,...,T n}

[0014] n is the length of the travel station type sequence;

[0015] S3. Divide the population into five categories based on age, occupation, and schooling status: working population, non-working active population, primary and secondary school students, college students, and inactive population, and update the classification category TYPE;

[0016] S4. Use the resident travel survey data as the travel chain seed library for each type of population, and select the probability distribution list of different types of travel station sequences according to individual population classification and residential traffic community The probability sampling method is used to initialize the station type sequence S of the personal travel chain of different classification populations and residential traffic areas;

[0017] S5. Obtain the STAZ index i of the traffic zone sequence where the residence (H) and work / school (W / S) station points are located in each travel chain station point type sequence, add the traffic zone sequence where the station point is located for the personal travel chain, the sequence length is the length of the travel chain station point type sequence, and update the traffic zone number of the traffic zone sequence index position of the travel chain station point to STAZ i ;

[0018] S6. Using the seed library generated in step S4 as input data, split all the trip chain travel point sequences S in the seed library into travel itineraries {Si , S i+1}, filter all destination points S i+1 The stop type is daily life stop (L) or other stop (0), and the stop S of different population types, residential transportation communities, and different departure points are divided into i Station type, destination station S i+1 Classify the station type and count the STAZ of different types of departure transportation areas o Until you reach the traffic area STAZ d The probability distribution matrix Type o The departure point type, Type d The destination station type;

[0019] S7, using a stratified probability sampling method, updating the traffic community number of the traffic community sequence where the station type is a daily life station (L) or other station (0) index position in the traffic community sequence where the travel chain station is located, and completing the update of the traffic community sequence where the personal travel chain station is located; step S7 is specifically:

[0020] (1) For the individual travel chain station sequence S travel itinerary {S i , S i+1} traverse, if S i+1 It is a daily life station (L) or other station (0), according to population classification TYPE, residential transportation community STAZ i 、Departure point type Type o To the destination point type d Get the generated in step S6 Combined with the sequence correspondence of traffic area, traffic center area and traffic small area obtained in step S1 {STAZ i ,MTAZ j ,LTAZ k}Calculate S i The departure transportation area STAZ o Probability distribution list of all destination traffic areas

[0021]

[0022] (2) Probability sampling method is used to select travel itineraries {S i , S i+1} i+1 Departure transportation zone LTAZ k ; Combined with the sequence correspondence of traffic area, traffic center area and traffic small area obtained in step S1 {STAZ i ,MTAZ j ,LTAZ k}Calculate Si The departure transportation area STAZ o To the destination transportation area LTAZ k All traffic in Central Area MTAZ d A list of probability distributions of ;

[0023]

[0024] (3) Probability sampling method is used to select travel itineraries {S i , S i+1} i+1 Location and arrival transportation center MTAZ k ; Combined with the sequence correspondence of traffic area, traffic center area and traffic small area obtained in step S1 {STAZ i ,MTAZ j ,LTAZ k}Calculate S i The departure transportation area STAZ o Transportation to the destination MTAZ j All traffic areas in STAZ d A list of probability distributions of ;

[0025]

[0026] (4) Probability sampling method is used to select travel itineraries {S i , S i+1} i+1 The transportation area where you are located is STAZ i ; Update the traffic zone number of the traffic zone sequence index position where the travel chain station is located to STAZ i ;

[0027] (5) After traversal, the sequence of traffic zones where the travel stops of the travel chain are updated.

[0028] Preferably, the personal information data includes personal number, year of birth, age, gender, living status, personnel generation code, family generation number, family number, residential transportation community, work transportation community, school transportation community, school type, whether living on campus, occupation type, transportation mode preference, marital status, spouse ID, number of children, and child ID list.

[0029] Preferably, the stops in the itinerary of personal travel data in the resident travel survey data and mobile phone signaling data are divided into four types of stops according to the destination, including residence (H), work / school (W / S), daily life stops (L) and other stops (O).

[0030] Preferably, the resident travel survey data obtained in step S3 is used as the travel chain seed library for students and inactive population, and the resident travel survey data obtained in step S33 and the mobile phone signaling data are mixed in a 1:1 ratio as the travel chain seed library for working population and non-working active population; according to the population type and the classification of the residential traffic area, the probability distribution list of the travel chain station type sequence S in the seed library is calculated

[0031] Based on small sample characteristic survey and mobile phone signaling big data travel behavior analysis, the present invention introduces more individual travel activity attributes in the generation of urban population travel chain, and uses stratified probability sampling method to mine and obtain personal life travel destinations and other destinations according to the stability of travelers' activities. It realizes the acquisition of travel distribution characteristics of the entire urban population. Compared with the existing technology, the present invention has the following advantages:

[0032] (1) It can fully utilize the attributes of travelers themselves and the spatiotemporal distribution attributes of urban travel activities to simulate the travel activities of urban residents;

[0033] (2) The introduction of travelers’ personal attributes makes travel destinations more accurate;

[0034] (3) The simulation method based on probability sampling makes various travel activities no longer blind mathematical optimization matching but inherits the genes of the city, making travel behavior modeling move from the mathematical optimization model and utility maximization of traditional traffic models to causal inference;

[0035] (4) The original travel chain seed library was established using residents' travel survey data and mobile phone signaling data, and the individual travel chain was initialized according to family and personal attribute characteristics; the explanation of steady-state life travel was realized based on the probability sampling model, and the purpose of urban accidental travel was inferred based on the probability sampling model using the characteristics of urban and group activities.

[0036] (5) The method relies on causal inference and probability sampling, and does not use optimization solutions, which greatly reduces the demand for computing power for the activity model. The essence of the traffic planning model is to use traffic model technology to explain the operation status of urban traffic. Travelers are the cells that make up the city, and the attributes of travelers are the genes of the city. This method uses the "genes" of urban travel activities at three levels: individual, group, and city as input, which significantly enhances the ability of traffic to interpret the distribution of urban population travel. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the embodiments, and do not constitute a limitation of the embodiments of the present invention.

[0038] Figure 1 A step diagram of the full trip 0D construction method based on integer solutions DETAILED DESCRIPTION

[0039] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0040] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0041] The present invention provides a method for constructing a full-volume OD based on an integer solution, and the specific steps are as follows:

[0042] Step 1: Obtain personal information data of the entire urban population within the research scope, residents’ travel survey data, mobile phone signaling data, and a list of correspondence between traffic major areas, traffic central areas, and traffic small areas.

[0043] Personal information data includes personal number, year of birth, age, gender, living status, generation code, family generation number, family number, residential transportation community, work transportation community, school transportation community, school type, whether living on campus, occupation type, transportation mode preference, marital status, spouse ID, number of children, and child ID list.

[0044] The resident travel survey and mobile phone signaling data are orderly travel data including personal information, departure / destination spatial location data, type, travel journey time, and destination stay time.

[0045] The traffic zone is STAZ = [0, 1, 2, 3, ..., i, ..., s], where s is the number of traffic zones.

[0046] The traffic center area is MTAZ = [0, 1, 2, 3, ..., m], where m is the number of traffic center areas.

[0047] The traffic zone is LTAZ=[0, 1, 2, 3, ..., l], where l is a large number of the traffic zone.

[0048] The corresponding relationship between the traffic area, traffic center area and traffic area is:

[0049] {STAZ i ,MTAZ j ,LTAZ k}

[0050] i, j, k are the list indexes of small traffic area, middle traffic area and large traffic area respectively.

[0051] Step 2: The stops in the itinerary of personal travel data in the resident travel survey data and mobile phone signaling data are divided into four types of stops according to the destination, including residence (H), work / school (W / S), daily life stops (L) and other stops (0). The departure / destination type and spatial location data in the personal travel data are converted into stop type by traversing the resident travel survey and mobile phone signaling data to form a stop type sequence S of the personal travel chain and a sequence T of the traffic area where the stop is located.

[0052] S={S1,S2,S3,...,S n}

[0053] T={T1,T2,T3,...,T n}

[0054] n is the length of the trip station type sequence.

[0055] Step 3: Divide the population into five categories based on age, occupation, and schooling status: working population, non-working active population, primary and secondary school students, college students, and inactive population, and update the classification category TYPE.

[0056] Step 4: For students and inactive population, use the resident travel survey data obtained in step 3 as the travel chain seed library. For working population and inactive population, use the resident travel survey data obtained in step 3 and mobile phone signaling data 1:1 mixed as the travel chain seed library. According to the population type and residential traffic community classification, calculate the probability distribution list of the travel chain station type sequence S in the seed library The probability distribution matrix of different types of travel stop sequences is selected according to individual population classification and residential traffic district. The probability sampling method is used to initialize the station type sequence S of personal travel chain of different classification populations and residential traffic communities.

[0057] Step 5. The personal information already contains the traffic zone where the residence (H) and the place of work / study (W / S) are located. Get the STAZ index i of the traffic zone sequence where the residence (H) and the place of work / study (W / S) are located in each travel chain station type sequence, add the traffic zone sequence where the station is located to the personal travel chain, the sequence length is the length of the travel chain station type sequence, and update the traffic zone number of the traffic zone sequence index position where the travel chain station is located to STAZ i .

[0058] Step 6: Using the seed library generated in step 4 as input data, split all the trip chain travel point sequences S in the seed library into travel itineraries {S i, S i+1}, filter all destination points S i+1 The stop type is daily life stop (L) or other stop (0), and the stop S of different population types, residential transportation communities, and different departure points are divided into i Station type, destination station S i+1 Classify the station type and count the STAZ of different types of departure transportation areas o Until you reach the traffic area STAZ d The probability distribution matrix Type o The departure point type, Type d The destination station type.

[0059] Step 7: Using the stratified probability sampling method, update the traffic community number of the traffic community sequence where the station type is daily life station (L) or other station (0) index position in the traffic community sequence where the travel chain station is located, and complete the update of the traffic community sequence where the personal travel chain station is located.

[0060] The specific method is as follows:

[0061] (1) For a personal travel chain station sequence S travel itinerary {S i , S i+1} traverse, if S i+1 It is a daily life station (L) or other station (0), according to population classification TYPE, residential transportation community STAZ i 、Departure point type Type o To the destination point type d Get the generated in step S6 Combined with the sequence correspondence of traffic area, traffic center area and traffic small area obtained in step 1 {STAZ i ,MTAZ j ,LTAZ k}Calculate S i The departure transportation area STAZ o Probability distribution list of all destination traffic areas

[0062]

[0063] The probability sampling method is used to select travel itineraries. i , S i+1} i+1 Departure transportation zone LTAZ k .

[0064] (2) Combined with the sequence correspondence of traffic area, traffic center area and traffic small area obtained in step 1 {STAZ i ,MTAZj ,LTAZ k}Calculate S i The departure transportation area STAZ o To the destination transportation area LTAZ k All traffic in Central Area MTAZ d A list of probability distributions of .

[0065]

[0066] The probability sampling method is used to select travel itineraries. i , S i+1} i+1 Location and arrival transportation center MTAZ k .

[0067] (3) Combined with the sequence correspondence of traffic area, traffic center area and traffic small area obtained in step 1 {STAZ i ,MTAZ j ,LTAZ k}Calculate S i The departure transportation area STAZ o Transportation to the destination MTAZ j All traffic areas in STAZ d List of probability distributions

[0068]

[0069] The probability sampling method is used to select travel itineraries. i , S i+1} i+1 The transportation area where you are located is STAZ i Update the traffic zone number of the traffic zone sequence index position where the travel chain station is located to STAZ i .

[0070] (4) After traversal, the sequence of traffic zones where the travel stops of the travel chain are updated.

[0071] The specific implementation methods of the technical solution of the present invention are further clearly and completely described below in conjunction with the embodiments and drawings. The specific implementation methods of the present invention are exemplary and are not intended to limit the present invention.

[0072] Figure 1 As shown, a full trip 0D construction method based on integer solutions includes the following steps.

[0073] Step 1: Obtain personal information data of the entire urban population within the research time and scope, residents' travel survey data, mobile phone signaling data, and a list of correspondence between traffic areas, traffic centers, and traffic communities.

[0074] Personal information data includes personal number, year of birth, age, gender, living status, generation code, family generation number, family number, residential transportation community, work transportation community, school transportation community, school type, whether living on campus, occupation type, transportation mode preference, marital status, spouse ID, number of children, and child ID list.

[0075] The resident travel survey and mobile phone signaling data are orderly travel data including personal information, departure / destination spatial location data, type, travel journey time, and destination stay time.

[0076] The traffic zone is STAZ = [0, 1, 2, 3, ..., i, ..., s], where s is the number of traffic zones.

[0077] The traffic center area is MTAZ = [0, 1, 2, 3, ..., j, ..., m], where m is the number of traffic center areas.

[0078] The traffic zone is LTAZ = [0, 1, 2, 3, ..., k, ... l] where l is the number of traffic zones.

[0079] The corresponding relationship between the traffic area, traffic center area and traffic area is:

[0080] {STAZ i ,MTAZ j ,LTAZ k}

[0081] i, j, k are the list indexes of small traffic area, middle traffic area and large traffic area respectively.

[0082] Step 2: The stops in the itinerary of personal travel data in the resident travel survey data and mobile phone signaling data are divided into four types of stops according to the destination, including residence (H), work / school (W / S), daily life stops (L) and other stops (0) to distinguish between normal travel and occasional travel. All the departure / destination types and spatial location data in the resident travel survey and personal travel data in the mobile phone signaling data are converted into stop type, forming a sequence S of stop types in the personal travel chain and a sequence T of traffic zones where the stop is located.

[0083] S={S1,S2,S3,…,S n}

[0084] T={T1,T2,T3,...,T n}

[0085] n is the length of the trip station type sequence.

[0086] The example result data of some travel chain station point sequences after conversion are shown in the following table:

[0087]

[0088] Step 3: Divide the population into five categories based on age, occupation, and schooling: working population, non-working active population, primary and secondary school students, college students, and non-active population, and update the classification category TYPE. Non-working active population refers to non-working adults under the age of 65, and non-active population refers to the population group over the age of 65.

[0089] The example result data of the stationary point sequence of the travel chain after conversion is shown in the following table

[0090]

[0091] Step 4: For students and inactive population, the resident travel survey data obtained in step 3 is used as the travel chain seed library. For working population and non-working active population, the resident travel survey data obtained in step 3 and mobile phone signaling data are mixed in a ratio of 1:1 as the travel chain seed library. According to the population type and residential traffic community classification, the probability distribution list of the travel chain station type sequence S in the seed library is calculated.

[0092] The following table lists the results of the trip stop probability example for a type of working population and a residential traffic community.

[0093] serial number Travel sequence S Proportion serial number Travel sequence S Proportion 1 HWH 18.77％ 7 HLWHL 3.62％ 2 HWOWH 11.37％ 8 HWH 3.27％ 3 HWHWH 10.50％ 9 HWHWHWH 2.53％ 4 HWOH 9.40％ 10 HWOWHWH 2.15％ 5 HLWHW 7.69％ 11 HWOHWH 1.56％ 6 HWLHW 4.39％ 12 HLW 0.78％

[0094] Probability distribution list of different types of travel stop sequences selected according to individual population classification and residential traffic area The probability sampling method is used to select travel sequences to initialize the individual travel chain stop type sequence S of different classifications of population and residential traffic areas.

[0095] Step 5. The personal information already contains the traffic community where the residence (H) and the place of work / study (W / S) are located. Get the STAZ index i of the traffic community sequence where the residence (H) and the place of work / study (W / S) are located in the station type sequence of the personal travel chain. Add the traffic community sequence where the station is located to the personal travel chain. The sequence length is the length of the station type sequence of the travel chain. Update the traffic community number at the index position of the traffic community sequence where the travel chain station is located to STAZ i .

[0096] The example results of updating the transportation community where the residence (H) and the place of work / study (W / S) of the personal travel chain station are shown in the following table:

[0097]

[0098] Step 6: Calculate the probability distribution matrix from the departure transportation area to the arrival transportation area for different population types, residential transportation areas, different departure place types, arrival place types, and the specific steps are as follows:

[0099] (1) Taking the seed library generated in step 4 as input data, split all the trip chain travel point sequences S in the seed library into travel itineraries {S i , S i+1}, filter all destination types S i+1 For trips with daily life stops (L) or other stops (0). For example, if the stop type sequence of the trip chain is HWOH and the traffic district sequence where the stops are located is 11-9-10-11, the stop type sequence is split into HW, WO, and OH, and the trip WO with the destination being the daily life stop (L) or other stops (0) is screened out, and the traffic district sequence 9-10 where the outbound stop sequence is located is obtained from the seed library, where 9 is the departure traffic district number and 10 is the arrival traffic district number.

[0100] (2) Different population types, residential transportation communities, and different departure point types S i , Destination Type S i+1 Classify and count the STAZ of different types of departure transportation areas o Until you reach the traffic area STAZ d The probability distribution matrix Type o For the departure type, Type d is the destination type. The number of rows and columns in the matrix is ​​the number of all traffic zones within the research scope.

[0101] Step 7: Using the stratified probability sampling method, update the traffic community number of the traffic community sequence where the station type is daily life station (L) or other station (0) index position in the traffic community sequence where the travel chain station is located, and complete the update of the traffic community sequence where the personal travel chain station is located.

[0102] The specific method is as follows:

[0103] (1) For a personal travel chain station sequence S travel itinerary {S i , S i+1} traverse, if S i+1 It is a daily life station (L) or other station (0), according to population classification TYPE, residential transportation community STAZ i 、Departure point type Type o To the destination point type d Get the generated in step S6 Combined with the sequence correspondence of traffic area, traffic center area and traffic small area obtained in step 1 {STAZ i ,MTAZj ,LTAZ k}Calculate S i The departure transportation area STAZ o Probability distribution list of all destination traffic areas

[0104]

[0105] TYPE is population classification, STAZ i For residential and traffic areas, S i is the departure point type, S i+1 For the arrival station type, STAZ o For departure transportation area, LTAZ d For the purpose of transportation area.

[0106] The probability sampling method is used to select travel itineraries. i , S i+1} i+1 Departure transportation zone LTAZ k . k is the traffic area index position.

[0107] (2) Combined with the sequence correspondence of traffic area, traffic center area and traffic small area obtained in step 1 {STAZ i ,MTAZ j ,LTAZ k}Calculate S i The departure transportation area STAZ o To the destination transportation area LTAZ k All traffic in Central Area MTAZ d A list of probability distributions of .

[0108]

[0109] TYPE is population classification, STAZ i For residential and traffic areas, S i is the departure point type, S i+1 For the arrival station type, STAZ o For departure transportation area, LTAZ k To reach the transportation area, MTAZ d For the purpose of transportation in the central area.

[0110] The probability sampling method is used to select travel itineraries. i , S i+1} i+1 Location and arrival transportation center MTAZ j . j is the index position of the traffic center area.

[0111] (3) Combined with the sequence correspondence of traffic area, traffic center area and traffic small area obtained in step 1 {STAZ i ,MTAZ j ,LTAZ k}Calculate S i The departure transportation area STAZ o Transportation to the destination MTAZ j All traffic areas in STAZ d List of probability distributions

[0112]

[0113] TYPE is population classification, STAZ i For residential and traffic areas, S i is the departure point type, S i+1 For the arrival station type, STAZ o For departure transportation area, LTAZ k To reach the transportation area, MTAZ j To reach the traffic center, STAZ d For the purpose of traffic area.

[0114] The probability sampling method is used to select travel itineraries. i , S i+1} i+1 The transportation area where you are located is STAZ i Update the traffic zone number of the traffic zone sequence index position where the travel chain station is located to STAZ i , i is the traffic cell index position.

[0115] (4) After traversal, the sequence of traffic zones where the travel stops of the travel chain are updated.

[0116] The sample data of the results of implementing the personal data travel chain is as follows

[0117]

[0118] Statement: The information obtained by this patent has been agreed by the user and complies with relevant laws and regulations.

[0119] The above description is only a preferred embodiment of the present invention. For example, there may be multiple combinations of different configurations and characteristic parameter settings. The embodiments of the present invention only show example parameters and are not intended to limit the present invention. For those skilled in the art, the present invention may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for constructing full-volume OD based on integer solutions, characterized in that: The specific steps are as follows: S1. Obtain personal information data of the entire population of the city within the research scope, residents' travel survey data, mobile phone signaling data, and a list of corresponding relationships between traffic areas, traffic centers, and traffic areas; The traffic zone is STAZ = [0, 1, 2, 3, ..., i, ..., s], where s is the number of traffic zones; the traffic center zone is MTAZ = [0, 1, 2, 3, ..., m], where m is the number of traffic centers; the traffic area is LTAZ = [0, 1, 2, 3, ..., l], where l is the number of traffic areas; the corresponding relationship list of traffic areas, traffic centers, and traffic zones is as follows: {STATUS i ,MTAZ j ,LTAZ k } i, j, k are the list indexes of traffic area, traffic center area, and traffic area respectively; S2. Traverse the residents' travel survey data and mobile phone signaling data to convert the departure / destination type and spatial location data in the personal travel data into the station type to form the station type sequence S of the personal travel chain and the traffic community sequence T where the station is located: <h2 style=";text-align:left;direction:ltr">S = {S1, S2, S3,…, S<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr">} T={T1,T2,T3,...,T n } n is the length of the travel station type sequence; S3. Divide the population into five categories based on age, occupation, and schooling status: working population, non-working active population, primary and secondary school students, college students, and inactive population, and update the classification category TYPE; S4. Use the resident travel survey data as the travel chain seed library for each type of population, and select the probability distribution list of different types of travel station sequences according to individual population classification and residential traffic community The probability sampling method is used to initialize the station type sequence S of the personal travel chain of different classification populations and residential traffic areas; S5. Obtain the STAZ index i of the traffic zone sequence where the residence (H) and work / school (W / S) station points are located in each travel chain station point type sequence, add the traffic zone sequence where the station point is located for the personal travel chain, the sequence length is the length of the travel chain station point type sequence, and update the traffic zone number of the traffic zone sequence index position of the travel chain station point to STAZ i ; S6. Using the seed library generated in step S4 as input data, split all the trip chain travel point sequences S in the seed library into travel itineraries {S i , S i+1 }, filter all destination points S i+1 The stop type is daily life stop (L) or other stop (0), and the stop S of different population types, residential transportation communities, and different departure points are divided into i Station type, destination station S i+1 Classify the station type and count the STAZ of different types of departure transportation areas o Until you reach the traffic area STAZ d The probability distribution matrix Type o The departure point type, Type d The destination station type; S7, using a stratified probability sampling method, updating the traffic community number of the traffic community sequence where the station type is a daily life station (L) or other station (0) index position in the traffic community sequence where the travel chain station is located, and completing the update of the traffic community sequence where the personal travel chain station is located; step S7 is specifically: (1) For a personal travel chain station sequence S travel itinerary {S i , S i+1 } to traverse, if S i+1 It is a daily life station (L) or other station (0), according to population classification TYPE, residential transportation community STAZ i 、Departure point type Type o To the destination point type d Get the generated in step S6 Combined with the sequence correspondence of traffic area, traffic center area and traffic small area obtained in step S1 {STAZ i ,MTAZ j ,LTAZ k }Calculate S i The departure transportation area STAZ o Probability distribution list of all destination traffic areas (2) Probability sampling method is used to select travel itineraries {S i , S i+1 } i+1 Departure transportation zone LTAZ k ; Combined with the sequence correspondence of traffic area, traffic center area and traffic small area obtained in step S1 {STAZ i ,MTAZ j ,LTAZ k }Calculate S i The departure transportation area STAZ o To the destination transportation area LTAZ k All traffic in Central Area MTAZ d A list of probability distributions of ; (3) Probability sampling method is used to select travel itineraries {S i , S i+1 } i+1 Location and arrival transportation center MTAZ k ; Combined with the sequence correspondence of traffic area, traffic center area and traffic small area obtained in step S1 {STAZ i ,MTAZ j ,LTAZ k }Calculate S i The departure transportation area STAZ o Transportation to the destination MTAZ j All traffic areas in STAZ d A list of probability distributions of ; (4) Probability sampling method is used to select travel itineraries {S i , S i+1 } i+1 The transportation area where you are located is STAZ i ; Update the traffic zone number of the traffic zone sequence index position where the travel chain station is located to STAZ i ; (5) After traversal, the sequence of traffic zones where the travel stops of the travel chain are updated.

2. A method for constructing full-volume OD based on integer solutions according to claim 1, characterized in that: Personal information data includes personal number, year of birth, age, gender, living status, generation code, family generation number, family number, residential transportation community, work transportation community, school transportation community, school type, whether living on campus, occupation type, transportation mode preference, marital status, spouse ID, number of children, and child ID list.

3. A method for constructing full-volume OD based on integer solutions according to any one of claims 1 to 2, characterized in that: The stops in the itinerary of personal travel data in the resident travel survey data and mobile phone signaling data are divided into four categories according to the destination, including residence (H), work / school (W / S), daily life stops (L) and other stops (0).

4. A method for constructing full-volume OD based on integer solutions according to claim 3, characterized in that: For students and inactive population, the resident travel survey data obtained in step S2 is used as the travel chain seed library. For working population and non-working active population, the resident travel survey data obtained in step S2 and mobile phone signaling data are mixed in a 1:1 ratio as the travel chain seed library; according to the population type and residential traffic community classification, the probability distribution list of the travel chain station type sequence S in the seed library is calculated.

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