CN116233823B - Identification method of cross-city commute ring, electronic equipment and storage medium - Google Patents

Identification method of cross-city commute ring, electronic equipment and storage medium Download PDF

Info

Publication number
CN116233823B
CN116233823B CN202310517259.2A CN202310517259A CN116233823B CN 116233823 B CN116233823 B CN 116233823B CN 202310517259 A CN202310517259 A CN 202310517259A CN 116233823 B CN116233823 B CN 116233823B
Authority
CN
China
Prior art keywords
commute
traffic
mobile phone
data
time
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202310517259.2A
Other languages
Chinese (zh)
Other versions
CN116233823A (en
Inventor
段谦
张凯
丘建栋
屈新明
罗钧韶
辛甜甜
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Urban Transport Planning Center Co Ltd
Original Assignee
Shenzhen Urban Transport Planning Center Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Urban Transport Planning Center Co Ltd filed Critical Shenzhen Urban Transport Planning Center Co Ltd
Priority to CN202310517259.2A priority Critical patent/CN116233823B/en
Publication of CN116233823A publication Critical patent/CN116233823A/en
Application granted granted Critical
Publication of CN116233823B publication Critical patent/CN116233823B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W8/00—Network data management
    • H04W8/18—Processing of user or subscriber data, e.g. subscribed services, user preferences or user profiles; Transfer of user or subscriber data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21—Design, administration or maintenance of databases
    • G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00—Information sensed or collected by the things
    • G16Y20/10—Information sensed or collected by the things relating to the environment, e.g. temperature; relating to location
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y20/00—Information sensed or collected by the things
    • G16Y20/40—Information sensed or collected by the things relating to personal data, e.g. biometric data, records or preferences
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/10—Detection; Monitoring
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/20—Analytics; Diagnosis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/60—Positioning; Navigation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Computing Systems (AREA)
  • General Physics & Mathematics (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • General Health & Medical Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Tourism & Hospitality (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Development Economics (AREA)
  • Signal Processing (AREA)
  • Quality & Reliability (AREA)
  • Probability & Statistics with Applications (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Primary Health Care (AREA)
  • Educational Administration (AREA)
  • Operations Research (AREA)
  • Fuzzy Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Computational Linguistics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Environmental & Geological Engineering (AREA)
  • Toxicology (AREA)
  • Game Theory and Decision Science (AREA)
  • Biomedical Technology (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

A method for identifying a cross-city commute circle, electronic equipment and a storage medium belong to the technical field of travel distribution prediction methods. In order to solve the problem of accurately identifying the range of the commuter ring. The invention collects the mobile phone signaling data and traffic network data of the urban ring core city and the surrounding city range; the collected mobile phone signaling data are subjected to data cleaning, then travel chain data of mobile phone users are identified, residence and work place data of the mobile phone users are identified according to the travel chain data of the mobile phone users, and commute OD data of the mobile phone users are obtained; calculating a centripetal commute index based on the commute OD data of the mobile phone user obtained in the step S2, and identifying a preliminary commute range; calculating traffic accessibility according to the obtained traffic network data, and identifying a traffic circle range; and obtaining a final cross-city commuting range according to the obtained preliminary commuting range, the obtained traffic circle range and the geographic lingering area. The invention has good practical effects of high accuracy and high implementation efficiency.

Description

Identification method of cross-city commute ring, electronic equipment and storage medium
Technical Field
The invention belongs to the technical field of travel distribution prediction methods, and particularly relates to a method for identifying a cross-city commute circle, electronic equipment and a storage medium.
Background
Currently, the economic development space structure of China is deeply changed, and urban groups and urban rings are becoming main forms of urban development patterns. As inter-urban connections between urban ring center cities and surrounding cities characterized by cross-urban commute become more compact, the degree of integration is continually increasing. The research of cross-city commute has important significance for space planning, traffic corridor planning and railway network planning. The identification of the commuting circle is particularly important, and the integrated development degree and trend of the core city and the surrounding cities in the urban circle can be mastered through the identification and prediction of the range of the commuting circle in the urban circle, so that the method has an important reference effect on planning layout of traffic facility supply and guiding space industry planning.
Secondly, with the continuous development of communication technology and big data technology, the mobile phone user quantity is continuously increased, the coverage rate is higher and higher, and a new thought is provided for obtaining the travel activity rule of personnel. Moreover, the mobile phone data has the advantages of large sample size, high precision, strong real-time dynamic property, low acquisition cost and the like, and is widely applied to the fields of population post spatial distribution, staff commute research, personnel activities, trip OD analysis and the like.
The patent publication No. CN 112948769A, the invention name is "a metropolitan area determining method and system based on big commute data", disclose a metropolitan area determining method and system based on big commute data, relate to city and regional space area quantitative analysis technical field, including calculating the network connectivity index of any two administrative areas according to the commute OD data, then confirm first order abdominal region and second order abdominal region according to network connectivity index, second order network theory and judgement rule. However, the technology only calculates the network connectivity index between two administrative areas through the commute connection OD, and as a division method of the urban area and the land, ignores the influence of the actual traffic network accessibility, which is also a factor which is explicitly emphasized to be taken into consideration in urban area and land space planning, and does not explicitly explain the source and the credibility of the commute big data.
Disclosure of Invention
The invention aims to solve the problem of accurately identifying the range of a commuting circle and provides an identification method of a cross-city commuting circle, electronic equipment and a storage medium.
In order to achieve the above purpose, the present invention is realized by the following technical scheme:
a method for identifying a cross-city commute ring comprises the following steps:
s1, acquiring mobile phone signaling data and traffic network data of urban ring core cities and surrounding urban ranges;
s2, carrying out data cleaning on the mobile phone signaling data acquired in the step S1, then identifying mobile phone user travel chain data, and identifying residence and work place data of the mobile phone user according to the mobile phone user travel chain data to obtain commute OD data of the mobile phone user;
s3, calculating a centripetal commute index based on the commute OD data of the mobile phone user obtained in the step S2, and identifying a preliminary commute range;
s4, calculating traffic accessibility according to the traffic network data obtained in the step S1, and identifying a traffic circle range;
s5, obtaining a final cross-city commute circle range according to the preliminary commute circle range obtained in the step S3, the traffic circle range obtained in the step S4 and the geographic lingering area.
Further, the specific implementation method of the step S2 includes the following steps:
s2.1, cleaning invalid data, drift data and ping-pong switching data in the mobile phone signaling data acquired in the step S1 to obtain cleaned mobile phone signaling data;
s2.2, identifying the record points with the residence time exceeding 30min within the radius of 500m as mobile phone user activity residence points for the cleaned mobile phone signaling data obtained in the step S2.1, and extracting residence points arranged in time sequence of the same mobile phone user to obtain mobile phone user travel chain data;
s2.3, identifying the residence and the workplace of the mobile phone user according to the travel chain data of the mobile phone user obtained in the step S2.2, identifying the resident population according to the residence days, and identifying the residence and the workplace according to the residence times, the distance radius and the residence time of the time period to obtain a residence and workplace data set of the resident population;
s2.4, according to the residence place and the working place data set of the resident population, the residence place to working place association data of the resident population are counted, and the residence place to working place association data of the resident population are counted to a mobile phone base station to obtain the commuting OD data of the mobile phone user, wherein the commuting OD data of the mobile phone user is one-way residence place to working place association data.
Further, the specific implementation method of step S2.3 includes the following steps:
s2.3.1, the threshold value of the resident population is set as: the total residence time of mobile phone users in a month is more than 18 days in the research range;
s2.3.2, the threshold value of the resident area of the resident population is set as: the household time period of the mobile phone user in one month is [ 21:00-7:00+1d ], and after the base stations with missing positions are removed, the base station with the largest occurrence number is counted to be used as a resident residence of a resident population;
s2.3.3, the threshold value of the resident population workplace is set as: and the base station with the largest occurrence number in the working day working period of the mobile phone user within one month is used as a candidate working place, the residence time exceeds 3 hours within the radius of the candidate working place within 500m, the working days exceed 60% of the total number of working days, and the mobile phone user is determined to be a working person, and the candidate working place is a resident population working place.
Further, the specific implementation method of the step S3 includes the following steps:
s3.1, determining the range of a core area of the urban ring: selecting a core city of the urban ring or a central urban area of the core city of the urban ring as a core area of the urban ring, and taking urban ring areas and potential contact areas outside the core area of the urban ring as peripheral areas;
s3.2, determining a basic space unit for index calculation: selecting a district county or village and town street or a self-divided space unit as a basic space unit according to the size of the scale of the research range, and then mapping the commute OD data of the mobile phone user obtained in the step S2 into the basic space unit;
s3.3, calculating centripetal commute indexes: the centripetal commute index comprises the number of centripetal commute people and the centripetal commute rate, and calculates a basic space unitThe commute number of the corresponding peripheral area flowing into the core area is centripetal commute number +.>Basic space unitIs>Is calculated as:
in the method, in the process of the invention,is a basic space unit->Is the total commute number of people;
s3.4, determining a preliminary commute range: the combination range of the basic space units meeting the following two index thresholds is selected as the preliminary commute range, and the judgment formula is as follows:
(number of people on duty)>Threshold->) OR (centripetal commute>>Threshold->)
Wherein, the liquid crystal display device comprises a liquid crystal display device,threshold for centripetal commute number, < >>Is a centripetal commute threshold.
Further, the specific implementation method of the step S4 includes the following steps:
s4.1, determining a traffic accessibility calculation center point: selecting an urban administrative center point as a traffic accessibility computing center point, or using an urban core area specified by national space planning as a traffic accessibility computing center area and using the boundary of the traffic accessibility computing center area as a traffic accessibility computing starting point;
s4.2, calculating the traffic accessibility according to the traffic network data acquired in the step S1, wherein the specific implementation method comprises the following steps:
s4.2.1, constructing a comprehensive traffic network model: in GIS or traffic planning platform software, constructing a comprehensive traffic network model containing a road network and a track network in the urban area;
the attribute fields of the road network comprise a road section serial number, a road section length, a road section direction, a road section name, a construction year, a road section grade, a road section traffic capacity, a road section free flow speed, a motor vehicle lane number, a road section charging condition and a road section free flow running time;
the attribute field of the track network comprises a line serial number, a line name, a line length, a line mode name, a construction year and a departure interval time;
s4.2.2, generating a basic space cell network link: generating a network connecting rod from a basic space unit centroid to a road network and a track network node;
s4.2.3, performing repeated iterative allocation of road traffic demands on a road network based on supply and demand balance and traffic allocation theory in a traffic demand model, and obtaining road travel reachable time between the centroids of basic space units;
the calculation formula of the road travel impedance GC is as follows, travel time cost and travel expense cost are calculated through the road network attribute, travel expense is converted into time cost according to the travel time value of residents, comprehensive travel cost is obtained by adding,
wherein:the unit is minutes for the comprehensive cost; />The walking time comprises the time outside the vehicle such as vehicle searching, parking and the like, and the unit is minutes; />The travel time in the vehicle is in minutes; />The fuel oil and gas costs are given by the unit; />Charging fees for road traffic in units of units; />The unit is the parking cost based on traffic location; />Time value, in yuan/min;
s4.2.4 according to the track length, the operation speed, the departure interval, the connection time from the two ends of the track to the centroid of the basic space unit through the road network and the connecting rod, and the calculation time in the track, the calculation formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,calculating the connection time from walking to the track station according to the road network and the link attribute, wherein the unit is minutes; />Calculating according to half of the track departure interval for the initial waiting time, wherein the unit is minutes;the running time in the vehicle after getting on is calculated according to the track running schedule, and the unit is minutes; />Calculating the transfer waiting time according to half of the track departure interval to be transferred, wherein the unit is minutes; />For transfer walking time, calculating according to a transfer path and walking speed, wherein the unit is minutes;
s4.2.5, determining comprehensive traffic accessibility: in the same basic space unit reachability calculation, the road reachability time and the track reachability time reaching the calculation center are selected to be smaller, and the traffic reachability of the basic space unit is calculated according to the following formula:
；
；
；
wherein, the liquid crystal display device comprises a liquid crystal display device,basic space unit for peripheral area->Reachability of (c) a; />Basic space unit for peripheral area->Road arrival time of (2); />Basic space unit for peripheral area->Track reach time of (2); setting the basic space unit set at the boundary of the computing center as H, and boundary basic space unit +.>If the calculation center is a single center point, H is the space unit where the center point is located, and if the calculation center is a center region, H comprises all basic space units at the boundary of the region; />Basic space unit for peripheral area->To boundary basic space element->Road travel time of (2);basic space unit for peripheral area->To boundary basic space element->Is a track travel time of (a);
s4.3, determining a traffic circle range: based on the traffic reachability data obtained in step S4.2, a basic space unit satisfying the peripheral area is selectedReachability-><Threshold->Is referred to as the traffic circle range.
Further, the specific implementation method of the step S5 includes the following steps:
s5.1, selecting a combination range of basic space units comprising a centripetal commute index threshold value and a traffic circle threshold value as a preliminary commute circle range according to the preliminary commute range obtained in the step S3 and the traffic circle range obtained in the step S4, wherein a calculation formula is as follows:
((number of centripetal commutes)>Threshold->) OR (centripetal commute>>Threshold->) AND (reachability)<Threshold->)；
S5.2, considering the geographical lingering area based on the preliminary commute circle range obtained in the step S5.1, and obtaining a final commute circle range.
The electronic equipment comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the identification method of the cross-city commute ring when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of identifying a cross-city commute circle.
The beneficial effects of the invention are as follows:
according to the identification method of the urban-crossing commuting ring, provided by the invention, the current urban-crossing commuting space distribution characteristic can be obtained based on the population occupancy characteristic and the commuting OD identified by the mobile phone signaling big data, and the range of the commuting ring can be accurately identified by combining with the accessibility of a traffic network. The method can be expanded, and based on the fine granularity data and the planning data, a cross-city commute prediction model can be established, and the identification of the planning commute ring can be completed according to the principle method.
The identification method of the cross-city commute ring can grasp the integrated development degree and trend of the core city and the surrounding cities in the city ring, has good practical effects of high accuracy and high implementation efficiency, and has important significance for supporting space planning and communication corridor layout planning of the city ring.
Drawings
FIG. 1 is a flow chart of a method of identifying a cross-city commute ring according to the present invention;
FIG. 2 is a schematic diagram of a town street distribution with a Shenzhen centripetal commute number of more than 1000 people in a method for identifying a cross-city commute ring according to the present invention;
FIG. 3 is a schematic diagram showing the distribution of village and town streets with a Shenzhen centripetal commute rate of more than 0.5% in the identifying method of the cross-city commute ring;
FIG. 4 is a schematic diagram of a road network model of a method for identifying a cross-city commuter circle according to the present invention;
FIG. 5 is a schematic diagram of a rail network model of a method for identifying a cross-city commuter circle according to the present invention;
FIG. 6 is a schematic diagram of a basic spatial unit network link of a method of identifying a cross-city commuter circle according to the present invention;
FIG. 7 is a schematic diagram showing accessibility to Shenzhen metropolitan core area in a method for identifying a cross-city commute ring according to the present invention;
FIG. 8 is a schematic diagram of a Shenzhen cross-city commuter circle in the method for identifying a cross-city commuter circle.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings and detailed description. It should be understood that the embodiments described herein are for purposes of illustration only and are not intended to limit the invention, i.e., the embodiments described are merely some, but not all, of the embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein can be arranged and designed in a wide variety of different configurations, and the present invention can have other embodiments as well.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
For further understanding of the invention, the following detailed description is to be taken in conjunction with fig. 1-8.
Detailed description of the preferred embodiments
A method for identifying a cross-city commute ring comprises the following steps:
s1, acquiring mobile phone signaling data and traffic network data of urban ring core cities and surrounding urban ranges;
s2, carrying out data cleaning on the mobile phone signaling data acquired in the step S1, then identifying mobile phone user travel chain data, and identifying residence and work place data of the mobile phone user according to the mobile phone user travel chain data to obtain commute OD data of the mobile phone user;
further, the specific implementation method of the step S2 includes the following steps:
s2.1, cleaning invalid data, drift data and ping-pong switching data in the mobile phone signaling data acquired in the step S1 to obtain cleaned mobile phone signaling data;
s2.2, identifying the record points with the residence time exceeding 30min within the radius of 500m as mobile phone user activity residence points for the cleaned mobile phone signaling data obtained in the step S2.1, and extracting residence points arranged in time sequence of the same mobile phone user to obtain mobile phone user travel chain data;
s2.3, identifying the residence and the workplace of the mobile phone user according to the travel chain data of the mobile phone user obtained in the step S2.2, identifying the resident population according to the residence days, and identifying the residence and the workplace according to the residence times, the distance radius and the residence time of the time period to obtain a residence and workplace data set of the resident population;
further, the specific implementation method of step S2.3 includes the following steps:
s2.3.1, the threshold value of the resident population is set as: the total residence time of mobile phone users in a month is more than 18 days in the research range;
s2.3.2, the threshold value of the resident area of the resident population is set as: the household time period of the mobile phone user in one month is [ 21:00-7:00+1d ], and after the base stations with missing positions are removed, the base station with the largest occurrence number is counted to be used as a resident residence of a resident population;
s2.3.3, the threshold value of the resident population workplace is set as: the base station with the largest occurrence number in the working day working period of the mobile phone user within one month is used as a candidate working place, the stay time exceeds 3 hours within the range of the radius of the candidate working place of 500m, the working days exceed 60% of the total number of working days, and the mobile phone user is determined to be a working person, and the candidate working place is a working place of a resident population;
s2.4, according to the residence place and the working place data set of the resident population, counting the related data from the residence place to the working place of the resident population, and counting the related data from the residence place to the working place of the resident population to a mobile phone base station to obtain the commuting OD data of the mobile phone user, wherein the commuting OD data of the mobile phone user is the related data from the residence place to the working place in a unidirectional way;
table 1 is a commute OD data sample for a cell phone user:
TABLE 1 Commuting OD data sample for Mobile phone user
S3, calculating a centripetal commute index based on the commute OD data of the mobile phone user obtained in the step S2, and identifying a preliminary commute range;
further, the specific implementation method of the step S3 includes the following steps:
s3.1, determining the range of a core area of the urban ring: selecting a core city of the urban ring or a central urban area of the core city of the urban ring as a core area of the urban ring, and taking urban ring areas and potential contact areas outside the core area of the urban ring as peripheral areas;
s3.2, determining a basic space unit for index calculation: selecting a district county or village and town street or a self-divided space unit as a basic space unit according to the size of the scale of the research range, and then mapping the commute OD data of the mobile phone user obtained in the step S2 into the basic space unit; basic space unit commute OD samples are shown in table 2, for example:
TABLE 2 basic space cell commute OD sample
S3.3, calculating centripetal commute indexes: the centripetal commute index comprises the number of centripetal commute people and the centripetal commute rate, and calculates a basic space unitThe commute number of the corresponding peripheral area flowing into the core area is centripetal commute number +.>Basic space unitIs>Is calculated as:
in the method, in the process of the invention,is a basic space unit->Is the total commute number of people;
s3.4, determining a preliminary commute range: the combination range of the basic space units meeting the following two index thresholds is selected as the preliminary commute range, and the judgment formula is as follows:
(number of people on duty)>Threshold->) OR (centripetal commute>>Threshold->)
Wherein, the liquid crystal display device comprises a liquid crystal display device,for the threshold of the number of heart commutes,/>threshold for centripetal commute rate
The threshold value can be selected and tested according to actual data conditions and urban ring development stage to achieve reasonable commute ring range selection, taking Shenzhen urban ring commute ring identification as an example,selected to be 1000->Selected to be 0.5%, the resulting schematic diagrams are shown in fig. 2 and 3.
S4, calculating traffic accessibility according to the traffic network data obtained in the step S1, and identifying a traffic circle range;
further, the specific implementation method of the step S4 includes the following steps:
s4.1, determining a traffic accessibility calculation center point: selecting an urban administrative center point as a traffic accessibility computing center point, or using an urban core area specified by national space planning as a traffic accessibility computing center area and using the boundary of the traffic accessibility computing center area as a traffic accessibility computing starting point;
s4.2, calculating the traffic accessibility according to the traffic network data acquired in the step S1, wherein the specific implementation method comprises the following steps:
s4.2.1, constructing a comprehensive traffic network model: in GIS or traffic planning platform software, constructing a comprehensive traffic network model containing a road network and a track network in the urban area;
the attribute fields of the road network comprise a road section serial number, a road section length, a road section direction, a road section name, a construction year, a road section grade, a road section traffic capacity, a road section free flow speed, a motor vehicle lane number, a road section charging condition and a road section free flow running time;
the attribute field of the track network comprises a line serial number, a line name, a line length, a line mode name, a construction year and a departure interval time;
the constructed comprehensive traffic network model is shown in fig. 4 and 5;
s4.2.2, generating a basic space cell network link: generating a network connecting rod from a basic space unit centroid to a road network and a track network node; as shown in fig. 6;
s4.2.3, performing repeated iterative allocation of road traffic demands on a road network based on supply and demand balance and traffic allocation theory in a traffic demand model, and obtaining road travel reachable time between the centroids of basic space units;
the calculation formula of the road travel impedance GC is as follows, travel time cost and travel expense cost are calculated through the road network attribute, travel expense is converted into time cost according to the travel time value of residents, comprehensive travel cost is obtained by adding,
wherein:the unit is minutes for the comprehensive cost; />The walking time comprises the time outside the vehicle such as vehicle searching, parking and the like, and the unit is minutes; />The travel time in the vehicle is in minutes; />The fuel oil and gas costs are given by the unit; />Charging fees for road traffic in units of units; />The unit is the parking cost based on traffic location; />Time value, in yuan/min;
carrying out iterative allocation of road traffic demands to obtain road reachable time: because the travel time attribute on the current road network is the travel time in the free flow (without congestion and delay) state at present, the travel time is different from the actual situation, and the travel time when the traveler runs on the road is required to be simulated. At this time, the supply and demand balance and the traffic distribution theory in the traffic demand model need to be used, and multiple iterative distribution of travel demands on a road network (the traffic capacity of a road is taken as a supply) needs to be performed. In each iteration, simulating the traveler to select in different paths according to the difference of travel impedance GC (travel time and comprehensive reaction of cost), wherein the selection of the traveler can influence the running and congestion conditions of a road, the travel time and cost can be changed, the travel time attribute of the road can be updated, the probability of the traveler selecting each path can be changed in the next iteration, after a certain number of iterations, the selection of the traveler can be in a steady state, at the moment, the distribution and presentation system of traffic demands on the whole road network is optimal, and the method can be used for acquiring the road travel available time between the centroids of basic space units (the travel time among the basic space units below represents the centroids);
s4.2.4 according to the track length, the operation speed, the departure interval, the connection time from the two ends of the track to the centroid of the basic space unit through the road network and the connecting rod, and the calculation time in the track, the calculation formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,calculating the connection time from walking to the track station according to the road network and the link attribute, wherein the unit is minutes; />Calculating according to half of the track departure interval for the initial waiting time, wherein the unit is minutes;the running time in the vehicle after getting on is calculated according to the track running schedule, and the unit is minutes; />Calculating the transfer waiting time according to half of the track departure interval to be transferred, wherein the unit is minutes; />For transfer walking time, calculating according to a transfer path and walking speed, wherein the unit is minutes;
s4.2.5, determining comprehensive traffic accessibility: in the same basic space unit reachability calculation, the road reachability time and the track reachability time reaching the calculation center are selected to be smaller, and the traffic reachability of the basic space unit is calculated according to the following formula:
；
；
；
wherein, the liquid crystal display device comprises a liquid crystal display device,basic space unit for peripheral area->Reachability of (c) a; />Basic space unit for peripheral area->Road arrival time of (2); />Basic space unit for peripheral area->Track reach time of (2); setting the basic space unit set at the boundary of the computing center as H, and boundary basic space unit +.>If the calculation center is a single center point, H is the space unit where the center point is located, and if the calculation center is a center region, H comprises all basic space units at the boundary of the region; />Basic space unit for peripheral area->To boundary basic space element->Road travel time of (2); />Basic space unit for peripheral area->To boundary basic space element->Is a track travel time of (a);
s4.3, determining a traffic circle range: based on the traffic reachability data obtained in step S4.2, a basic space unit satisfying the peripheral area is selectedReachability-><Threshold->Is taken as a traffic circle range;
the 1 hour traffic accessibility range suggested by the planning and programming of the national and local space of the urban ring is used as the suggestion of the traffic ring, so the traffic accessibility threshold valueSet to 1 hour. The reachability calculation center is a metropolitan core area designated in Shenzhen 2035 homeland space planning, and when the reachability of other basic space units to the metropolitan core area is calculated, the shortest travel time of the basic space unit to any basic space unit at the boundary of the core area is taken as the reachability of the basic space unit, as shown in fig. 7;
s5, obtaining a final cross-city commute circle range according to the preliminary commute circle range obtained in the step S3, the traffic circle range obtained in the step S4 and the geographic lingering area.
Further, the specific implementation method of the step S5 includes the following steps:
s5.1, selecting a combination range of basic space units comprising a centripetal commute index threshold value and a traffic circle threshold value as a preliminary commute circle range according to the preliminary commute range obtained in the step S3 and the traffic circle range obtained in the step S4, wherein a calculation formula is as follows:
((number of centripetal commutes)>Threshold->) OR (centripetal commute>>Threshold->) AND (reachability)<Threshold->)；
S5.2, considering the geographical lingering area based on the preliminary commute circle range obtained in the step S5.1, and obtaining a final commute circle range.
Further, taking Dongguan Qingxi town as an example, the village and town are in a 1-hour traffic circle, but both centripetal commute indexes do not meet the condition, but considering that the village and town are directly adjacent to Shenzhen and are also adjacent to other street towns of the identified commute circle, the village and town are included in the range of the Shenzhen commute circle. FIG. 8 shows the finally identified Shenzhen commute circle range.
Furthermore, the identification method of the cross-city commute ring based on the mobile phone signaling big data provided by the embodiment can grasp the integrated development degree and trend of the core city and the surrounding cities in the city ring, has good practical effects of high accuracy and high implementation efficiency, and has important significance for supporting space planning and communication corridor layout planning of the city ring.
Detailed description of the preferred embodiments
The electronic equipment comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the identification method of the cross-city commute ring when executing the computer program.
The computer device of the present invention may be a device including a processor and a memory, such as a single chip microcomputer including a central processing unit. And the processor is used for executing the computer program stored in the memory to realize the steps of the identification method of the cross-city commute ring.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. 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 (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Detailed description of the preferred embodiments
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of identifying a cross-city commute circle.
The computer readable storage medium of the present invention may be any form of storage medium that is readable by a processor of a computer device, including but not limited to non-volatile memory, ferroelectric memory, etc., on which a computer program is stored, which when read and executed by the processor of the computer device, can implement the steps of a method for identifying a cross-city commute circle as described above.
The computer program comprises computer program code which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
The technical key points and the points to be protected of the invention are as follows:
and (3) identifying the commuting OD data through mobile phone signaling big data, setting a centripetal commuting index to identify a commuting range, and simultaneously combining with traffic circle range identification and consideration of a continuous area to comprehensively judge and obtain the identification of the commuting circle centering on a core city in the urban circle by multiple factors.
It is noted that relational terms such as "first" and "second", and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Although the present application has been described hereinabove with reference to specific embodiments, various modifications thereof may be made and equivalents may be substituted for elements thereof without departing from the scope of the application. In particular, the features of the embodiments disclosed herein may be combined with each other in any manner so long as there is no structural conflict, and the exhaustive description of these combinations is not given in this specification solely for the sake of brevity and resource saving. Therefore, it is intended that the present application not be limited to the particular embodiments disclosed, but that the present application include all embodiments falling within the scope of the appended claims.

Claims (7)

1. The identification method of the cross-city commute ring is characterized by comprising the following steps of:
s1, acquiring mobile phone signaling data and traffic network data of urban ring core cities and surrounding urban ranges;
s2, carrying out data cleaning on the mobile phone signaling data acquired in the step S1, then identifying mobile phone user travel chain data, and identifying residence and work place data of the mobile phone user according to the mobile phone user travel chain data to obtain commute OD data of the mobile phone user;
s3, calculating a centripetal commute index based on the commute OD data of the mobile phone user obtained in the step S2, and identifying a preliminary commute range;
s4, calculating traffic accessibility according to the traffic network data obtained in the step S1, and identifying a traffic circle range;
the specific implementation method of the step S4 comprises the following steps:
s4.1, determining a traffic accessibility calculation center point: selecting an urban administrative center point as a traffic accessibility computing center point, or using an urban core area specified by national space planning as a traffic accessibility computing center area and using the boundary of the traffic accessibility computing center area as a traffic accessibility computing starting point;
s4.2, calculating the traffic accessibility according to the traffic network data acquired in the step S1, wherein the specific implementation method comprises the following steps:
s4.2.1, constructing a comprehensive traffic network model: in GIS or traffic planning platform software, constructing a comprehensive traffic network model containing a road network and a track network in the urban area;
the attribute fields of the road network comprise a road section serial number, a road section length, a road section direction, a road section name, a construction year, a road section grade, a road section traffic capacity, a road section free flow speed, a motor vehicle lane number, a road section charging condition and a road section free flow running time;
the attribute field of the track network comprises a line serial number, a line name, a line length, a line mode name, a construction year and a departure interval time;
s4.2.2, generating a basic space cell network link: generating a network connecting rod from a basic space unit centroid to a road network and a track network node;
s4.2.3, performing repeated iterative allocation of road traffic demands on a road network based on supply and demand balance and traffic allocation theory in a traffic demand model, and obtaining road travel reachable time between the centroids of basic space units;
the calculation formula of the road trip impedance GC is as follows, first, the trip Time cost and trip cost are calculated through the road network attribute, then the trip cost is converted into the Time cost according to the Time value of resident trip, the comprehensive trip cost is obtained by adding, gc=time Walk +Time Travel +(Fare Petro +Fare Toll +Fare Parking )/VOT
Wherein: GC is the integrated cost in minutes; time Walk The walking time comprises the time outside the vehicle such as vehicle searching, parking and the like, and the unit is minutes; time Travel The travel time in the vehicle is in minutes; fire Petro The fuel oil and gas costs are given by the unit; fire Toll Charging fees for road traffic in units of units; fire Parking The unit is the parking cost based on traffic location; VOT is time value in yuan/minute;
s4.2.4 according to the track length, the operation speed, the departure interval, the connection time from the two ends of the track to the centroid of the basic space unit through the road network and the connecting rod, and the calculation time in the track, the calculation formula is as follows:
Time＝WalkT+IWaitT+IVTT+XWaitT+XferT
the walk time is the connection time from walking to the track station, and is calculated according to the road network and the link attribute, wherein the unit is minutes; IWAITT is the initial waiting time, and is calculated according to half of the track departure interval, wherein the unit is minutes; IVTT is the in-vehicle travel time after getting on, calculated according to the track operation schedule, and the unit is minutes; XWaitT is transfer waiting time, and is calculated according to half of the track departure interval to be transferred, wherein the unit is minutes; xferT is transfer walking time, and is calculated according to a transfer path and walking speed, wherein the unit is minutes;
s4.2.5, determining comprehensive traffic accessibility: in the same basic space unit reachability calculation, the road reachability time and the track reachability time reaching the calculation center are selected to be smaller, and the traffic reachability of the basic space unit is calculated according to the following formula:
T i ＝Min(DT i ，GT i )
DT i ＝Min h∈H (DT ih )
GT i ＝Min h∈H (DT ih )
wherein T is i Reachability of basic spatial element i being a peripheral region; DT (DT) i Road reach time for basic space unit i of the peripheral area; GT (GT) i Track reach time for basic space unit i of the peripheral region; setting a basic space unit set at the boundary of a computing center as H, wherein the boundary basic space unit H epsilon H, if the computing center is a single center point, H is a space unit where the center point is located, and if the computing center is a central area, H comprises all basic space units at the boundary of the area; DT (DT) ih Road travel time from the basic space unit i of the peripheral area to the boundary basic space unit h; GT (GT) ih Track travel time from basic space unit i to boundary basic space unit h of the peripheral area;
s4.3, determining a traffic circle range: based on the traffic reachability data obtained in step S4.2, reachability T of basic space unit i satisfying the peripheral area is selected i <Threshold S t Is taken as a traffic circle range;
s5, obtaining a final cross-city commute circle range according to the preliminary commute circle range obtained in the step S3, the traffic circle range obtained in the step S4 and the geographic lingering area.
2. The method for identifying a cross-city commute ring according to claim 1, wherein the specific implementation method of step S2 comprises the following steps:
s2.1, cleaning invalid data, drift data and ping-pong switching data in the mobile phone signaling data acquired in the step S1 to obtain cleaned mobile phone signaling data;
s2.2, identifying the record points with the residence time exceeding 30min within the radius of 500m as mobile phone user activity residence points for the cleaned mobile phone signaling data obtained in the step S2.1, and extracting residence points arranged in time sequence of the same mobile phone user to obtain mobile phone user travel chain data;
s2.3, identifying the residence and the workplace of the mobile phone user according to the travel chain data of the mobile phone user obtained in the step S2.2, identifying the resident population according to the residence days, and identifying the residence and the workplace according to the residence times, the distance radius and the residence time of the time period to obtain a residence and workplace data set of the resident population;
s2.4, according to the residence place and the working place data set of the resident population, the residence place to working place association data of the resident population are counted, and the residence place to working place association data of the resident population are counted to a mobile phone base station to obtain the commuting OD data of the mobile phone user, wherein the commuting OD data of the mobile phone user is one-way residence place to working place association data.
3. The method for identifying a cross-city commute ring according to claim 2, wherein the specific implementation method of step S2.3 comprises the following steps:
s2.3.1, the threshold value of the resident population is set as: the total residence time of mobile phone users in a month is more than 18 days in the research range;
s2.3.2, the threshold value of the resident area of the resident population is set as: the mobile phone user's home period within one month is 21: 00-7: 00+1d, eliminating the base stations with missing positions, and counting the base stations with the largest occurrence number as resident areas of resident population;
s2.3.3, the threshold value of the resident population workplace is set as: the working day working period of the mobile phone user within one month is 9:00-17: the base station with the largest occurrence number in 00 is used as a candidate workplace, the residence time exceeds 3 hours within the radius of 500m of the candidate workplace, the working days exceed 60% of the total number of working days, and the mobile phone user is determined to be a staff and the candidate workplace is a resident population workplace.
4. A method for identifying a cross-city commute ring as claimed in claim 3, wherein the specific implementation method of step S3 comprises the steps of:
s3.1, determining the range of a core area of the urban ring: selecting a core city of the urban ring or a central urban area of the core city of the urban ring as a core area of the urban ring, and taking urban ring areas and potential contact areas outside the core area of the urban ring as peripheral areas;
s3.2, determining a basic space unit for index calculation: selecting a district county or village and town street or a self-divided space unit as a basic space unit according to the size of the scale of the research range, and then mapping the commute OD data of the mobile phone user obtained in the step S2 into the basic space unit;
s3.3, calculating centripetal commute indexes: the centripetal commute index comprises centripetal commute number and centripetal commute rate, and the commute number of the peripheral area corresponding to the basic space unit i flowing into the core area is calculated as centripetal commute number C i Centripetal commute rate RC of basic space unit i i Is calculated as:
RC i ＝C i /T i
wherein T is i The total commute number for base space unit i;
s3.4, determining a preliminary commute range: the combination range of the basic space units meeting the following two index thresholds is selected as the preliminary commute range, and the judgment formula is as follows:
(centripetal commute number C) i >Threshold S C ) OR (centripetal duty rate RC) i >Threshold S RC )
Wherein S is C Threshold for centripetal commute number S RC Is a centripetal commute threshold.
5. The method for identifying a cross-city commute ring of claim 4, wherein the specific implementation method of step S5 comprises the steps of:
s5.1, selecting a combination range of basic space units comprising a centripetal commute index threshold value and a traffic circle threshold value as a preliminary commute circle range according to the preliminary commute range obtained in the step S3 and the traffic circle range obtained in the step S4, wherein a calculation formula is as follows:
((centripetal commute number C) i >Threshold S C ) OR (centripetal duty rate RC) i >Threshold S RC ) AND (reachability T) i <Threshold S t )；
S5.2, considering the geographical lingering area based on the preliminary commute circle range obtained in the step S5.1, and obtaining a final commute circle range.
6. An electronic device comprising a memory and a processor, the memory storing a computer program, the processor implementing the steps of a method of identifying a cross-city commute circle as claimed in any one of claims 1 to 5 when the computer program is executed.
7. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a method of identifying a cross-city commute ring as claimed in any of claims 1-5.
CN202310517259.2A 2023-05-10 2023-05-10 Identification method of cross-city commute ring, electronic equipment and storage medium Active CN116233823B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310517259.2A CN116233823B (en) 2023-05-10 2023-05-10 Identification method of cross-city commute ring, electronic equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310517259.2A CN116233823B (en) 2023-05-10 2023-05-10 Identification method of cross-city commute ring, electronic equipment and storage medium

Publications (2)

Publication Number Publication Date
CN116233823A CN116233823A (en) 2023-06-06
CN116233823B true CN116233823B (en) 2023-08-01

Family

ID=86575422

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310517259.2A Active CN116233823B (en) 2023-05-10 2023-05-10 Identification method of cross-city commute ring, electronic equipment and storage medium

Country Status (1)

Country Link
CN (1) CN116233823B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117332376B (en) * 2023-12-01 2024-02-27 北京航空航天大学 Method and system for identifying commuter and mode based on mobile phone signaling data

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107886723A (en) * 2017-11-13 2018-04-06 深圳大学 A kind of traffic trip survey data processing method
CN112215666A (en) * 2020-11-03 2021-01-12 广州市交通规划研究院 Characteristic identification method for different trip activities based on mobile phone positioning data
WO2021243516A1 (en) * 2020-06-01 2021-12-09 深圳先进技术研究院 Urban public transport passenger travel trajectory estimation method and system, terminal, and storage medium
CN115099542A (en) * 2022-08-26 2022-09-23 深圳市城市交通规划设计研究中心股份有限公司 Cross-city commuting trip generation and distribution prediction method, electronic device and storage medium

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105513351A (en) * 2015-12-17 2016-04-20 北京亚信蓝涛科技有限公司 Traffic travel characteristic data extraction method based on big data
CN112948769B (en) * 2021-03-10 2023-01-31 中国城市规划设计研究院 City circle range determining method and system based on commuting big data

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107886723A (en) * 2017-11-13 2018-04-06 深圳大学 A kind of traffic trip survey data processing method
WO2021243516A1 (en) * 2020-06-01 2021-12-09 深圳先进技术研究院 Urban public transport passenger travel trajectory estimation method and system, terminal, and storage medium
CN112215666A (en) * 2020-11-03 2021-01-12 广州市交通规划研究院 Characteristic identification method for different trip activities based on mobile phone positioning data
CN115099542A (en) * 2022-08-26 2022-09-23 深圳市城市交通规划设计研究中心股份有限公司 Cross-city commuting trip generation and distribution prediction method, electronic device and storage medium

Also Published As

Publication number Publication date
CN116233823A (en) 2023-06-06

Similar Documents

Publication Publication Date Title
Xia et al. Exploring human mobility patterns in urban scenarios: A trajectory data perspective
Rekha et al. Accessibility analysis of health care facility using geospatial techniques
Poonawala et al. Singapore in motion: Insights on public transport service level through farecard and mobile data analytics
CN112133090A (en) Multi-mode traffic distribution model construction method based on mobile phone signaling data
CN111653099B (en) Bus passenger flow OD obtaining method based on mobile phone signaling data
CN107194525A (en) A kind of down town appraisal procedure based on mobile phone signaling
CN104167092A (en) Method and device for determining taxi pick-up and drop-off hot spot region center
CN105677804A (en) Determination of authority stations and building method and device of authority station database
CN110972073B (en) Living circle defining method based on mobile phone big data and walking accessibility
CN116233823B (en) Identification method of cross-city commute ring, electronic equipment and storage medium
CN102332210A (en) Method for extracting real-time urban road traffic flow data based on mobile phone positioning data
Zheng et al. Exploring both home-based and work-based jobs-housing balance by distance decay effect
Gong et al. Exploring spatiotemporal characteristics of intra-urban trips using metro smartcard records
CN108495254B (en) Traffic cell population characteristic estimation method based on signaling data
CN102333274A (en) Cell phone signal data based-method for processing commuting information and apparatus thereof
CN107403550A (en) Transit network collecting method, device and terminal device
CN110160538A (en) A kind of map-matching method based on mobile phone signaling data
CN115100012A (en) Method for calculating walking accessibility of rail transit station
Zhu et al. Ridership Prediction of Urban Rail Transit Stations Based on AFC and POI Data
CN109447882B (en) Population exchange amount estimation method based on signaling data
Qin et al. Travel trajectories analysis based on call detail record data
Huo et al. Exploring the influencing factors on demand-responsive customized bus ridership using multi-source data
Lwin et al. Identification of various transport modes and rail transit behaviors from mobile CDR data: A case of Yangon City
CN113313307A (en) Tour route mining method based on signaling big data
Cheng et al. Characterising travel behaviour patterns of transport hub station area users using mobile phone data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant