CN115587503A - Individual trip chain restoration method based on multi-mode simulation - Google Patents
Individual trip chain restoration method based on multi-mode simulation Download PDFInfo
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Abstract
The invention provides an individual trip chain restoration method based on multi-mode simulation, which comprises the following steps: s1, generating a trip chain data set to be restored; s2, inputting the trip chain data set to be restored into a multi-mode simulation model, and outputting an individual possible trip scheme set; s3, processing data which are possibly inconsistent with the card punching data in the individual trip scheme set, and deleting the trip scheme which is not in accordance with the actual situation; s4, accessing mobile phone signaling data, and extracting individual trip stay point and travel point set information; and S5, carrying out space-time matching on the individual possible travel scheme set and the space-time aggregation information of the mobile phone signaling residence point, and calculating the probability of a travel chain. The method solves the technical problems that the generation of large-scale individual trip chains cannot be met, the dependence on the service quality is excessive and the data accuracy is low in the prior art, greatly improves the accuracy of restoring the large-scale individual trip chains, and ensures that the restored trip chains have higher accuracy.
Description
Technical Field
The application relates to an individual trip chain reduction method, in particular to an individual trip chain reduction method based on multi-mode simulation, and belongs to the technical field of trip chain reduction.
Background
The trip chain refers to the displacement in space generated by an individual to complete one or more activities, and includes information of multiple dimensions such as trip time, trip mode, trip track, trip route and the like. By analyzing the individual trip chain, on one hand, the whole process of the resident trip can be restored, the trip track of the resident is tracked, the passing site is analyzed, and the trip chain is undoubtedly a very good analysis means for tracing infectious agents, joint seal personnel, sub-joint seal personnel and major epidemic sites; on the other hand, the individual trip chain is widely applied to trip prediction and traffic planning, for example, the trip characteristics and trip demands of residents in an area are known through analysis of the individual trip chain.
The early generation of the trip chain is mainly based on a traffic survey mode, individual trip characteristics and trip modes are predicted through sample data, and due to the limitation of conditions, no big data is used as support to carry out inspection and verification, so that the accuracy is difficult to guarantee.
In order to solve the above problems, research and development personnel propose:
1. CN114820264A proposes a method, apparatus, device, and storage medium for processing transfer data of public transportation means, which analyzes data of card swiping by a bus in a target area within a specified time range, and macroscopically draws a complete user travel link based on a user transfer behavior;
2. CN113256987A proposes a resident travel chain generation method and a co-passenger query method based on multi-source data fusion, which acquire an individual travel origin-destination point, a corresponding time and a travel location from mobile phone signaling data, acquire travel tracks and riding times in different travel modes through a path planning service API, and combine the origin-destination point of each individual travel to generate an individual travel chain.
The above prior art mainly has the following problems:
1. the method mainly uses traffic survey, can only be applied to the trip chain reduction of a single station, cannot meet the requirement of large-scale trip chain reduction, and is difficult to apply.
2. Restoring the pedestrian travel track to excessively depend on the service quality through the path planning service; and a travel mode is restored by calling a path planning service API service through the travel origin-destination OD and origin-destination time, and the travel mode is a planned path for restoring historical time, and the time requirement for planning service is higher. The current travel planning services such as a high-grade map, a Baidu map, an Tencent map and the like are real-time path planning, and under a travel chain epidemic prevention application scene, the problem of accuracy can exist for historical path data needing to be searched for multiple days.
If the positioning point of the pedestrian OD is remote or inaccurate, some situations of path planning failure exist, and the possible path is generally processed by adopting a deleting mode at the moment, so that the final result is incomplete.
The public transport route can be accurately expressed by the public transport timetable information, and for a scene that an individual actually takes a trip chain in a middle road section has a pause or a transfer, the accuracy of a pedestrian trip chain is not high due to the fact that the individual actually takes a vehicle and cannot be accurately restored, and secondary processing is needed in an application scene.
3. Limited or inaccurate data; most of the existing trip chain restoration methods are limited in data, most of the existing trip chain restoration methods analyze resident trip chains through mobile phone signaling, for the trip chain processing process of a large-scale city level, the accuracy of the mobile phone signaling completely depends on the accuracy of individual mobile phone signals and base stations, the mobile phone signaling has large difference in accuracy, the processing process is complicated, and the accuracy of restoration results needs to be improved.
In order to better understand the travel condition of residents and restore the real urban travel condition, particularly, epidemic situation prevention and control special environment can quickly and accurately restore the epidemic people and the places, and the urban residence is more conveniently assisted with the dispatching engineering, so that efficient and accurate restoration is necessarily carried out on the urban resident travel chain.
Disclosure of Invention
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. It should be understood that this summary is not an exhaustive overview of the invention. It is not intended to determine the key or important part of the present invention, nor is it intended to limit the scope of the present invention. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later.
In view of this, in order to solve the technical problems that the large-scale individual trip chain generation cannot be met, the service quality is excessively depended on, and the data accuracy is low in the prior art, the invention provides an individual trip chain restoration method based on multi-mode simulation.
According to the first scheme, the individual trip chain restoration method based on multi-mode simulation comprises the following steps:
s1, generating a trip chain data set to be restored;
s2, inputting the trip chain data set to be restored into a multimode simulation model, and outputting an individual possible trip scheme set;
s3, processing data with conflict between the individual possible travel scheme set and the site code card punching data, and deleting the travel scheme which does not conform to the actual situation;
s4, accessing mobile phone signaling data, and extracting individual trip stay point and travel point set information;
and S5, carrying out space-time matching on the individual possible travel scheme set and the space-time aggregation information of the mobile phone signaling residence point, calculating the probability of the travel chain, and completing individual travel chain restoration.
Preferably, S1 specifically includes the following steps:
s11, extracting individual data; acquiring resident registration data, social security personal information data, community grid population registration data, virus detection data, electronic sentinel scanning code data and place scanning code data of an individual;
s12, preprocessing the individual data, and extracting individual basic data, residence information and working place information to form individual basic data; preprocessing individual data: deleting the repeated individual data, and using the latest individual data for redundant individual data;
s13, forming individual trip card reading data according to the individual card reading and code scanning data; the trip card punching data comprises an individual ID, card punching time, a card punching place code ID, a place name, place longitude and latitude and individual health code information; clearing repeated card punching data, and sequencing and processing the data according to the card punching time sequence to form individual complete card punching data;
s14, obtaining the place information of the individual passing through the trip chain, namely trip chain link point information, based on the individual basic information data and the individual trip data, and generating a trip chain data set to be restored;
and S15, connecting the individual basic data and the individual card punching data by the trip chain data set to be restored to form wide table data containing all trip chain nodes of the individual.
Preferably, S2 specifically includes the following steps:
s21, grouping through individuals according to a trip chain data set to be restored, and processing according to time sequence, wherein one trip chain to be restored is formed between every two data of the individuals; sequentially calling a multi-mode simulation interface for the trip chain data to be restored formed between every two pieces of data;
s22, simulating an individual traveling track by a simulation engine, wherein the individual subway traveling scheme is simulated by combining track simulation and pedestrian simulation, the individual walking scheme is formed by independently simulating pedestrian simulation, the real-time online simulation and the pedestrian simulation are combined, and the individual car traveling scheme and the individual bus traveling scheme are simulated and formed by combining a pedestrian-vehicle mixed flow simulation technology;
s23, forming a scheme set of all possible trips based on the individual trip time sequence of all the trip schemes output by simulation; the individual possible travel scheme set comprises travel track, travel time, arrival time and travel chain key information of a travel mode.
Preferably, the trip plan not meeting the actual situation is that if the departure time is less than the time of card punching when the departure place arrives, the plan is judged not to meet the actual trip.
Preferably, S4 specifically includes the following steps:
s41, sequencing the mobile phone signaling according to a time sequence, screening mobile phone signaling record data, eliminating mobile phone numbers which are difficult to identify and abnormal data which cannot be positioned, removing repeated time point location data, and obtaining a signaling big data sample which conforms to a condition;
s42, extracting a mobile phone signaling residence point to generate mobile phone signaling residence point data;
s43, extracting the mobile phone signaling point set to form an individual mobile phone signaling-based travel point set.
Preferably, S5 specifically includes the following steps:
s51, calculating the spatial similarity of each travel track in the individual possible travel scheme set and the mobile phone signaling travel point and the residence point: assuming that the travel track and the corresponding track point sequence data of the mobile phone signaling travel point time are A and B, the length of the longest sequence of the two tracks is:
wherein, the first and the second end of the pipe are connected with each other,
a similarity threshold between the tracks is indicated,
to representThe distance between the point at the time t in the sequence A and the point at the time i in the sequence B, the corresponding time of the travel track sequence A is t =1,2
(ii) a Sequence B corresponds to a time instant i =1,2
(ii) a Calculating the length of the longest sequence according to the formula, and adjusting and calculating the result precision through a threshold value;
s52, calculating a similarity formula between each travel track in the individual possible travel scheme set and the mobile phone signaling time track, wherein the similarity formula comprises the following steps:
wherein the content of the first and second substances,
which indicates the length of the sequence a,
representing the length of the sequence B, and calculating and acquiring the similarity of the travel track and the mobile phone signaling track through the formula;
and S53, taking the trip scheme most similar to the mobile phone signaling as an individual trip travel, processing the individual trip travel into individual trip chain information, backfilling the individual trip chain information into a trip chain data set to be restored, and finishing individual trip chain restoration to form a complete trip chain.
And the second scheme is that the electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the steps of the individual trip chain restoration method based on the multi-mode simulation when executing the computer program.
And a third aspect is a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the individual trip chain restoration method based on multi-mode simulation according to the first aspect.
The invention has the following beneficial effects:
1) Supporting simulation reduction under large-scale data;
2) By fusing multi-source data, the travel information of individual residents can be effectively tracked, the historical travel information of pedestrians can be calculated in a short time, the travel chain information of the pedestrians is restored, epidemic situation prevention and control and intensive crowd are quickly positioned, and the manpower investigation cost is greatly reduced;
3) Based on a mobile phone signaling fusion mode, the accuracy of restoring the large-scale individual trip chain is greatly improved through a space-time matching mode, and the restored trip chain has higher precision.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart of an individual trip chain restoration method based on multi-mode simulation;
FIG. 2 is a schematic diagram of arrival times of places in the travel chain;
fig. 3 is a schematic diagram of trip chain information drawn based on personal card punching data;
FIG. 4 is a schematic flow chart of a process for obtaining a set of possible individual travel plans;
FIG. 5 is a diagram illustrating the results of a set of possible individual travel plans;
FIG. 6 is a schematic flow chart of a travel plan not conforming to the actual situation;
fig. 7 is a schematic diagram of signaling data of a mobile phone.
Detailed Description
In order to make the technical solutions and advantages in the embodiments of the present application more clearly understood, the following description of the exemplary embodiments of the present application with reference to the accompanying drawings is made in further detail, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and are not exhaustive of all the embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Example 1, the present embodiment is described with reference to fig. 1 to 7, and an individual trip chain restoration method based on multi-mode simulation includes the following steps:
s1, generating a trip chain data set to be restored, comprising the following steps:
s11, extracting individual data; acquiring resident registration data, social security personal information data, community grid population registration data, virus detection data, electronic sentinel scanning code data and place scanning code data of an individual;
the resident registration data and the community grid population registration data comprise individual identity card numbers, names, sexes and residence information;
the social security personal information data comprises individual work information, and individual basic residence and work place information is formed by mapping work place information through individual identity information and combining the work place information with residence place information;
the location code scanning data and the electronic sentinel scanning data comprise individual entrance and exit location information and time, and individual activity space-time information is determined;
the virus detection data marks the state of the individual health code and is used for tracing and restoring the travel track of the positive individual in epidemic prevention operation;
the location code scanned data and the electronic sentinel scanned data can determine the activity space-time information of the individual; the virus detection data can identify the individual health state, and can be applied to positive individual trip tracing operation in the epidemic prevention process.
In an actual scene data center, resident registration data, community grid population registration data and social security personal information data have the characteristics of large data volume, high redundancy, certain hysteresis of the data and the like, and the data needs to be cleaned before use. The three tables belong to static tables, and for individuals, the data has the characteristics of slow change and update, long timeliness and the like.
The site code scanning data, the electronic sentinel scanning data and the virus detection data belong to incremental data, and data volume is continuously added in production and life. In an actual scene, the detection data of the infectious viruses generally needs to be kept for n days, and the data of the electronic sentinel and the site code has higher repeatability and redundancy. After the location codes are scanned, partitioning is carried out according to individuals, sorting is carried out according to time according to the code scanning and card scanning data, the repeated data of the card scanning and code scanning are cleaned, and the data repetition is avoided; and finally, establishing a repetition time threshold (default is 3 minutes), optimizing the repetition time threshold subsequently according to the simulation precision, processing individual card punching data in the same place within the range of the repetition time threshold into repeated data, selecting the latest repeated data as effective data, clearing invalid data and reducing data redundancy.
S12, preprocessing the individual data, and extracting individual basic data, residence information and working place information to form individual basic data;
preprocessing individual data: deleting the repeated individual data, and using the latest individual data for redundant individual data;
the data corresponding to the individual residence place work place information comprises the following data: individual ID, identity card number, name, residence ID, residence name, residence latitude and longitude, work place ID, work place name and work ground latitude;
s13, forming individual trip card punching data according to the individual card punching and code scanning data; the trip card punching data comprises an individual ID, card punching time, a card punching place code ID, a place name, place longitude and latitude and individual health code information; clearing repeated card punching data, and sequencing the data according to the card punching time sequence to form individual complete card punching data; as shown in fig. 2, the ID, longitude and latitude and name information of each place are obtained, the arrival time of the place in the trip chain can be obtained through the data of punching the card, but the time information of leaving the place, the trip time information of the trip 1 and the trip 2, the trip mode, the trip track and other information cannot be obtained;
s14, obtaining the passing place information of the individuals in the trip chain, namely trip chain link point information, based on the individual basic information data and the individual trip data, and generating a trip chain data set to be restored; as shown in fig. 3, missing data needs to be restored based on trip chain information drawn by personal punch card data, in order to accurately restore an individual trip chain, accurate data description needs to be performed on a trip 1 and a trip 2 in the individual trip chain, and information of an individual ID, a trip ID, departure time, a departure place longitude and latitude, an arrival place, and a arrival place longitude and latitude is extracted to form a trip chain sub-trip data table 1.
Table 1 travel chain travel data table
As shown in table 1, a travel chain sub-travel data table is generated for describing each sub-travel information of an individual travel full chain, wherein an individual ID is used for uniquely identifying residents, a travel ID is used for identifying a sub-travel, the reduced data are serially connected to form the whole travel chain through the travel ID, the departure time/arrival time is the time of the whole travel, the information of an individual vehicle can be accurately reduced by combining with the city-level public transportation travel time, and the latitude and longitude of the departure place/arrival place are used for describing individual space information.
And S15, connecting the individual basic data and the individual card punching data by the trip chain data set to be restored to form wide table data containing all individual trip chain nodes, and providing data support for the trip chain restoration in the subsequent steps through trip node information.
And S2, inputting the trip chain data set to be restored into a multimode simulation model, and outputting a possible individual trip scheme set, wherein the multimode simulation is used for simulating the selected behaviors of individual trips in a transportation mode and a trip path and deducing an individual trip chain. The method can predict the traffic flow of each mode based on the deduction result, can support the running state evaluation of different types of traffic networks and the research and judgment analysis of demand transfer, and meanwhile, the individual trip chain information including the detailed vehicle getting-on and getting-off empty and running paths can support multi-model fusion simulation.
Based on basic data such as city-level wire network data, operation schedule data, detection data and the like, simulation platforms of various traffic modes such as pedestrian simulation, rail simulation, real-time online simulation, pedestrian-vehicle mixed flow simulation, intelligent agent distribution and the like are fused, and the simulation platforms not only comprise the simulation of cars, but also comprise conventional buses, taxis, rail traffic and the like; in the multi-mode simulation, all individual trips in an area can be accurately described, an interface for individual trip chain deduction is provided, and individual trip information including trip time, trip modes, trip tracks and the like can be generated by inputting departure and arrival place longitude and latitude information and time information by using an HTTP (hyper text transport protocol). The multimode emulation interface is as follows:
multi-mode simulation interface information:
the service request method comprises the following steps: get to
Url：http://10.3.3.107:8089/otp/routers/default/plan
Port: 8089
Service request parameter field:
a service return field:
as shown in the service request parameter field and the service return parameter field, the input parameters mainly comprise departure or arrival time, the latitude and longitude of a departure place, the latitude and longitude of an arrival place and the like, the information is input, the individual trip is simulated in the urban multi-mode simulation, the individual trip chain is restored, as shown in the service return field, the individual trip chain mainly comprises the departure time, the arrival time, the departure place, the arrival place and the like of the whole trip, and meanwhile, each trip scheme in the whole trip chain is output and comprises sub-trip information, sub-trip nodes, tracks and time information, and all schemes of the individual in the whole traveling process are restored.
The method specifically comprises the following steps:
s21, grouping through individuals according to a trip chain data set to be restored, and processing according to time sequence, wherein a trip chain to be restored is formed between every two data of the individuals; calling a multi-mode simulation interface for the trip chain data to be restored formed between every two pieces of data in sequence;
s22, simulating an individual traveling track by a simulation engine, wherein the individual subway traveling scheme is simulated by combining track simulation and pedestrian simulation, the individual walking scheme is formed by independently simulating pedestrian simulation, the real-time online simulation and the pedestrian simulation are combined, and the individual car traveling scheme and the individual bus traveling scheme are simulated and formed by combining a pedestrian-vehicle mixed flow simulation technology;
s23, forming a scheme set of all possible trips by all trip schemes output by simulation based on individual trip time sequences; the individual possible travel scheme set comprises travel track, travel time, arrival time and travel chain key information of a travel mode. As shown in fig. 4;
and S3, processing data which are inconsistent with the place code card punching data in the individual possible travel scheme set, and deleting the travel schemes which are not in accordance with the actual situation, wherein the travel schemes which are not in accordance with the actual situation are in the possible travel scheme set, and if the departure time is less than the card punching time when the departure place arrives, the scheme is judged to be not in accordance with the actual travel.
Filtering data which are inconsistent with the data of card punching after all travel possible scheme sets are obtained; as shown in fig. 5, an individual calls a multimode simulation interface to obtain three travel schemes when the time to punch a card in the place 1 is 10 00 and the time to punch a card in the place 2 is 10, but the departure time of the scheme 1 is 9; as shown in fig. 6, starting and ending point longitude and latitude and travel time are input, the whole trip is simulated through simulation, two schemes are obtained, wherein the scheme 1 is a car trip scheme, the scheme 2 is a subway trip scheme, transfer is performed midway, the whole trip process of an individual is restored through simulation, the stopping condition of the individual in the whole trip can be determined through the simulation time, the individual trip description is more accurate, meanwhile, the scheme 1 and the scheme 2 jointly form a scheme set of an individual trip chain, and a data base is made for judging according to mobile phone signaling.
S4, mobile phone signaling data are accessed, individual trip stay point and trip point set information are extracted, main contents of the mobile phone signaling information comprise mobile phone numbers, mobile phone signaling time and mobile phone signaling longitude and latitude information, real-name authentication is carried out, and individuals can be uniquely identified; and identifying the individual time-space information by using the mobile phone signaling recording time and longitude and latitude information, and restoring the individual travel track points through the time-space information.
The method comprises the following steps:
s41, sequencing the mobile phone signaling according to a time sequence, screening mobile phone signaling record data, eliminating mobile phone numbers which are difficult to identify and abnormal data which cannot be positioned, removing repeated time point location data, and obtaining a signaling big data sample which conforms to a condition;
s42, extracting a mobile phone signaling residence point to generate mobile phone signaling residence point data;
mobile phone signaling residence point: in one place, within the time threshold range, the distance between the continuous points of the mobile phone signaling is within the distance threshold; and extracting all the resident points of the individuals, and summarizing the resident points into resident point data based on the individuals, so that the integration of the next mobile phone signaling and the trip chain is accurately performed.
S43, extracting the mobile phone signaling point set to form an individual mobile phone signaling-based travel point set.
A stroke point: the mobile phone signaling point information identifies a point passed by the person in the traveling process, and the mobile phone signaling point is definitely present near the individual travel trajectory line, so that the mobile phone travel point is regarded as a point on the travel forming line; as shown in fig. 7;
s5, carrying out space-time matching on the individual possible trip scheme set and the space-time aggregation information of the mobile phone signaling residence point, calculating the trip chain probability, and completing individual trip chain restoration, wherein the method comprises the following steps:
s51, calculating the spatial similarity of each travel track in the individual possible travel scheme set and the mobile phone signaling travel point and the residence point: assuming that the track point sequence data corresponding to travel track and mobile phone signaling travel point time are A and B, the length of the longest sequence of the two tracks is:
wherein the content of the first and second substances,
a similarity threshold between the traces is indicated,
represents the distance between the point of time t in the A sequence and the point of time i in the B sequence, the travel track A corresponds to the time t =1,2
(ii) a Sequence B corresponds to a time instant i =1,2
(ii) a Calculating the length of the longest sequence according to the formula, and adjusting and calculating the result precision through a threshold value;
s52, calculating a similarity formula between each travel track in the individual possible travel scheme set and the mobile phone signaling time track, wherein the similarity formula comprises the following steps:
wherein the content of the first and second substances,
which indicates the length of the sequence a,
representing the length of the sequence B, and calculating and acquiring the similarity of the travel track and the mobile phone signaling track through the formula;
and S53, taking the trip scheme most similar to the mobile phone signaling as an individual trip travel, processing the individual trip travel into individual trip chain information, backfilling the individual trip chain information into a trip chain data set to be restored, and finishing individual trip chain restoration to form a complete trip chain.
In embodiment 2, the computer device of the present invention may be a device including a processor, a memory, and the like, for example, a single chip microcomputer including a central processing unit, and the like. And the processor is used for implementing the steps of the recommendation method for modifying the relationship-driven recommendation data based on the CREO software when executing the computer program stored in the memory.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Embodiment 3 computer-readable storage Medium embodiments
The computer readable storage medium of the present invention may be any form of storage medium that can be read by a processor of a computer device, including but not limited to non-volatile memory, ferroelectric memory, etc., and the computer readable storage medium has stored thereon a computer program that, when the computer program stored in the memory is read and executed by the processor of the computer device, can implement the above-mentioned steps of the CREO-based software that can modify the modeling method of the relationship-driven modeling data.
The computer program comprises computer program code which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, and software distribution medium, etc. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this description, will appreciate that other embodiments can be devised which do not depart from the scope of the invention as disclosed herein. Furthermore, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter. Accordingly, many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the appended claims. The present invention has been disclosed in an illustrative rather than a restrictive sense, and the scope of the present invention is defined by the appended claims.
Claims (8)
1. An individual trip chain restoration method based on multi-mode simulation is characterized by comprising the following steps:
s1, generating a trip chain data set to be restored;
s2, inputting the trip chain data set to be restored into a multimode simulation model, and outputting an individual possible trip scheme set;
s3, processing data with conflict between the individual possible travel scheme set and the site code card punching data, and deleting the travel scheme which does not conform to the actual situation;
s4, accessing mobile phone signaling data, and extracting individual trip stay point and trip point set information;
and S5, carrying out space-time matching on the individual possible travel scheme set and the space-time aggregation information of the mobile phone signaling residence point, calculating the probability of the travel chain, and completing individual travel chain restoration.
2. The individual trip chain restoration method based on multi-mode simulation according to claim 1, wherein S1 specifically comprises the following steps:
s11, extracting individual data; acquiring resident registration data, social security personal information data, community grid population registration data, virus detection data, electronic sentinel scanning code data and place scanning code data of an individual;
s12, preprocessing the individual data, extracting individual basic data, residence place information and working place information, and forming individual basic data;
s13, forming individual trip card reading data according to the individual card reading and code scanning data; the trip card punching data comprises an individual ID, card punching time, a card punching place code ID, a place name, place longitude and latitude and individual health code information; clearing repeated card punching data, and sequencing and processing the data according to the card punching time sequence to form individual complete card punching data;
s14, obtaining the passing place information of the individuals in the trip chain, namely trip chain link point information, based on the individual basic information data and the individual trip data, and generating a trip chain data set to be restored;
and S15, connecting the individual basic data and the individual card punching data by the trip chain data set to be restored to form wide table data containing all individual trip chain nodes.
3. The individual trip chain restoration method based on multi-mode simulation according to claim 2, wherein S2 specifically comprises the following steps:
s21, grouping through individuals according to a trip chain data set to be restored, and processing according to time sequence, wherein one trip chain to be restored is formed between every two data of the individuals; sequentially calling a multi-mode simulation interface for the trip chain data to be restored formed between every two pieces of data;
s22, simulating an individual traveling track by a simulation engine, wherein the individual subway traveling scheme is simulated by combining track simulation and pedestrian simulation, the individual walking scheme is formed by independently simulating pedestrian simulation, the real-time online simulation and the pedestrian simulation are combined, and the individual car traveling scheme and the individual bus traveling scheme are simulated and formed by combining a pedestrian-vehicle mixed flow simulation technology;
s23, forming a scheme set of all possible trips by all trip schemes output by simulation based on individual trip time sequences; the individual possible travel scheme set comprises travel track, travel time, arrival time and travel chain key information of a travel mode.
4. The individual trip chain restoration method based on multi-mode simulation of claim 3, wherein the trip plan not conforming to the actual situation is that if the departure time is less than the time of card punching when the departure place arrives, the plan is determined not to conform to the actual trip.
5. The individual trip chain restoration method based on multi-mode simulation according to claim 4, wherein S4 specifically comprises the following steps:
s41, sequencing the mobile phone signaling according to a time sequence, screening mobile phone signaling record data, eliminating mobile phone numbers which are difficult to identify and abnormal data which cannot be positioned, removing repeated time point location data, and obtaining a signaling big data sample which conforms to a condition;
s42, extracting a mobile phone signaling residence point to generate mobile phone signaling residence point data;
s43, extracting the mobile phone signaling point set to form an individual mobile phone signaling-based travel point set.
6. The individual trip chain restoration method based on multi-mode simulation according to claim 5, wherein S5 specifically comprises the following steps:
s51, calculating the spatial similarity of each travel track in the individual possible travel scheme set and the mobile phone signaling travel point and the residence point: assuming that the travel track and the corresponding track point sequence data of the mobile phone signaling travel point time are A and B, the length of the longest sequence of the two tracks is:
wherein the content of the first and second substances,
a similarity threshold between the traces is indicated,
represents the distance between the point of time t in the A sequence and the point of time i in the B sequence, the travel track A corresponds to the time t =1,2
(ii) a Sequence B corresponds to a time instant i =1,2
(ii) a Calculating the length of the longest sequence according to the formula, and adjusting and calculating the result precision through a threshold value;
s52, calculating a similarity formula between each travel track in the individual possible travel scheme set and the mobile phone signaling time track, wherein the similarity formula comprises the following steps:
wherein the content of the first and second substances,
which indicates the length of the sequence a and,
representing the length of the sequence B, and calculating and acquiring the similarity of the travel track and the mobile phone signaling track through the formula;
and S53, taking the trip scheme most similar to the mobile phone signaling as an individual trip travel, processing the individual trip travel into individual trip chain information, backfilling the individual trip chain information into a trip chain data set to be restored, and finishing individual trip chain restoration to form a complete trip chain.
7. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the individual trip chain restoration method based on multi-mode simulation according to any one of claims 1 to 6 when executing the computer program.
8. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the individual trip chain restoration method based on multi-modal simulation of any of claims 1-6.
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