CN113901109B - Method for calculating total number of people and time distribution of passenger and truck travel modes on intercity highway - Google Patents
Method for calculating total number of people and time distribution of passenger and truck travel modes on intercity highway Download PDFInfo
- Publication number
- CN113901109B CN113901109B CN202111183336.2A CN202111183336A CN113901109B CN 113901109 B CN113901109 B CN 113901109B CN 202111183336 A CN202111183336 A CN 202111183336A CN 113901109 B CN113901109 B CN 113901109B
- Authority
- CN
- China
- Prior art keywords
- data
- traffic
- track
- trip
- passenger
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 22
- 230000011664 signaling Effects 0.000 claims abstract description 26
- 238000004364 calculation method Methods 0.000 claims abstract description 11
- 230000009471 action Effects 0.000 claims description 9
- 230000003993 interaction Effects 0.000 claims description 8
- 230000014759 maintenance of location Effects 0.000 claims 1
- 238000004458 analytical method Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000011835 investigation Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- 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
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- 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
- G06F16/2477—Temporal data 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
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Probability & Statistics with Applications (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Fuzzy Systems (AREA)
- Tourism & Hospitality (AREA)
- Remote Sensing (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Traffic Control Systems (AREA)
Abstract
A method for calculating the total number of people and time distribution in an intercity highway passenger and truck travel mode comprises the following steps of S1: acquiring mobile phone signaling data of a certain operator to carry out track calculation to obtain sample trip volume data corresponding to each track route in a set area; s2: carrying out sample expansion on the sample output quantity data to obtain full sample track output quantity data; s3: dividing the full-sample track traffic data, and extracting road traffic data; s4: obtaining a passenger-truck trip proportion according to traffic condition survey data provided by a traffic condition survey station, dividing passenger-truck trip amount according to the trip proportion on the highway trip amount data, and obtaining the total number of passengers and truck trip modes and time distribution; the invention calculates the passenger and truck traffic volume through the mobile phone signaling, more typically calculates the total number of people going out, and realizes the distinction between the passenger and truck traffic volumes in the highway traffic volume.
Description
Technical Field
The invention relates to the field of road OD (traffic volume) calculation, in particular to a method for calculating the total number of people and time distribution of passenger and truck travel modes on an intercity road.
Background
The road traffic volume between cities is an important index for reflecting the development of the inter-city social economy and the level of traffic transportation service, and accurate and reliable road traffic volume data are directly related to the scientific formulation of a regional development strategy and the decision management of traffic road network planning. With the continuous development of the 'double-circulation' economic pattern and the continuous upgrading and optimization of the industrial structure in China, the connection between the areas is tighter, and the requirements of the transportation structure, the structure of the traffic network, the travel mode and the like are not changed. In order to better support business requirements of road network planning, road evaluation, operation monitoring and early warning and the like under a 'dual-cycle' structure, the understanding of the times and time distribution characteristics of people who travel in the modes of passenger cars, trucks and the like between different cities is of great importance.
The existing method for calculating the OD (origin destination) traffic of the intercity highway mainly comprises the following steps: (1) Automatically surveying the traffic condition of the cross section road and inquiring vehicles by vehicles; (2) calculating GPS track data collected by vehicle-mounted equipment; (3) a calculation method of internet data such as mobile phone navigation; and (4) a calculation method based on mobile phone signaling.
For the method 1, since the traffic volume of the section can only reflect the traffic volume change of the road on the section, if the starting place and the arrival place of the vehicle are needed to be known, a secondary police car is required to be arranged and the OD of the vehicle is inquired one by one, and the road routes between two cities are various, so that the problems of large workload, insufficient data sample representativeness and the like exist. For the method 2 and the method 3, the track route of the vehicle can be clearly outlined, so that the departure place and the arrival place of each travel of the vehicle can be judged, but the method also has the problem of insufficient data representativeness, most vehicles provided with vehicle-mounted GPS equipment are special vehicles (such as a class bus, a tourist bus, a dangerous goods transport vehicle and the like), most users using mobile phones for navigation are also owners unfamiliar with the road conditions, the proportion of the number of the navigated travel vehicles is low, the total number of people in the travel is difficult to calculate due to the fact that the navigation is not closed midway, the travel is split due to the fact that the navigation is closed, and the travel characteristics of the whole society cannot be represented. With regard to method 4, it cannot be determined whether the user's travel mode is to select a passenger car or a truck.
Disclosure of Invention
The invention aims to provide a method for calculating the number and time distribution of passengers in highway trip modes of a passenger car and a truck in a city, aiming at the problem that the specific number and time distribution of trip numbers in different trip modes in intercity highway trip cannot be accurately judged, so that the number and time distribution of the passengers in different cities and based on two different trip modes of the passenger car and the truck and the time distribution characteristics of departure time can be calculated.
In order to achieve the purpose, the invention adopts the technical scheme that:
the embodiment of the invention provides a method for calculating the total number of people and time distribution in a passenger-truck travel mode on an intercity highway, which comprises the following steps:
s1: acquiring mobile phone signaling data of a certain operator to carry out track calculation to obtain sample trip volume data corresponding to each track route in a set area;
s2: carrying out sample expansion on the sample run-out quantity data to obtain full sample track run-out quantity data;
s3: dividing the full sample trajectory trip volume data based on different trip modes, and extracting road trip volume data from the full sample trajectory trip volume data; the highway traffic data comprises time information, a starting place and a destination;
s4: and obtaining the passenger-truck trip proportion according to the traffic condition survey data provided by the traffic condition survey station, and dividing the passenger-truck trip amount according to the trip proportion on the highway trip amount data to obtain the passenger-truck trip mode head count and time distribution.
Further, said S1 comprises, in combination,
s11: acquiring mobile phone signaling data from a mobile phone operator, wherein the mobile phone signaling data comprises base station positioning track data;
s12: slicing the map, dividing the map into grids with the size of 250 × 250m, and converting the positioning track data of the base station into grid position data through a weighted centroid model;
s13: setting space constraint and time constraint in the grid position data to identify a stop point and a passing point to obtain an action track; the space constraint is the setting of an allowable deviation range; the time constraint is the setting of the shortest dwell time; the passing points are track points except the stopping points.
S14: matching the action track to the belonged geographic position and extracting a track departure place, a destination and travel time to obtain travel OD data;
s15: and summarizing the travel OD data with the same departure place and destination in the S14 according to an hour and day time window to obtain sample track travel amount data.
Further, the weighted centroid model is that a node position calculation function is constructed according to the base station position and the number of times of interaction with nearby base stations within the statistical time, and the position of the node M is (x) m,t ,y m,t )，
Wherein (x) 1 ,y 1 )，(x 2 ,y 2 )，(x n ,y n ) Indicating the nearby base station location, (f) 1 ,f 2 ...f n ) Indicating the number of interactions with nearby base stations and t indicating the statistical time.
Further, the step S2 is to obtain call records of the current operator user and other operator users according to the mobile phone signaling, calculate a local market proportion of the current operator in the call records, and expand the sample traffic data to the full sample traffic data according to the local market proportion of the current operator.
Further, in S3, the step of extracting the road traffic includes,
s21: dividing travel modes into highways, waterways, tracks and aviation;
s22: extracting track routes of a fixed passageway and a travel path from the full-sample track traffic to obtain waterway traffic data and track traffic data;
s23: extracting a track route of long-distance position movement with simultaneous signal interruption and signal connection from the full sample travel data to obtain aviation travel data;
s24: and removing the waterway traffic data, the track traffic data and the aviation traffic in the S22 and the S23 from the full sample traffic data to obtain highway traffic data.
Further, in the step S4, road traffic volume data collected on the main road in the same time period is selected, poisson correlation between traffic condition survey data and road traffic volume data calculated by the mobile phone signaling is calculated, and accuracy of the road traffic volume data based on the mobile phone signaling is judged by analyzing correlation between the mobile phone signaling data and fluctuation trend of the traffic survey data.
Further, in S1, the mobile phone signaling includes a ticket data and a visitor location registration data, where the ticket data and the visitor registration data both include information of a phone call received, information of interaction with the base station, a mobile grid number, and time information.
Further, in S1, the data set is reduced by removing the tracks of less than 8 active networks in the call ticket data and the visitor location registration data.
The invention has the beneficial effects that:
the method determines that strong correlation exists between the mobile phone signaling data and the traffic condition investigation data by verifying the Poisson coefficient of the road traffic data and the traffic condition investigation data based on the mobile phone signaling; according to the method, the vehicle type proportion in the traffic condition survey data is combined, and the passenger car running quantity and the truck running quantity are distinguished.
Drawings
FIG. 1 is a schematic diagram of a method for calculating the total number and time distribution of passenger and truck travel modes on an intercity highway according to the present invention;
fig. 2 is a schematic diagram of an action track extracted from call ticket data.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further described with the specific embodiments.
In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "rear", "both ends", "one end", "the other end", and the like indicate orientations or positional relationships based on orientations or positional relationships shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply that the device or element referred to must have a specific orientation, be configured in a specific orientation, and operate, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless explicitly stated or limited otherwise, the terms "mounted," "disposed," "connected," and the like are to be construed broadly, such as "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
As shown in fig. 1, a method for calculating the total number of people and time distribution in a passenger-truck travel mode on an intercity highway comprises the following steps:
s1: acquiring mobile phone signaling data of a certain operator to carry out track calculation to obtain sample traveling volume data corresponding to each track route in a set area;
s2: carrying out sample expansion on the sample run-out quantity data to obtain full sample track run-out quantity data;
s3: dividing the full sample trajectory trip volume data based on different trip modes, and extracting road trip volume data from the full sample trajectory trip volume data; the highway traffic data includes time information, a start location, and a destination.
S4: and obtaining the passenger-truck trip proportion according to the traffic condition survey data provided by the traffic condition survey station, and dividing the passenger-truck trip amount according to the trip proportion on the highway trip amount data to obtain the passenger-truck trip mode head count and time distribution.
In order to further implement the technical scheme, the accuracy check is carried out by combining the traffic condition difference adjustment data, wherein the accuracy check comprises the steps of selecting road traffic volume data collected on main roads in cities in the same time period, and calculating the poisson correlation between traffic condition survey data and road traffic volume data calculated by mobile phone signaling. Through verification, the Changsha-Yueyang Poisson correlation coefficient is 0.803, the result shows that strong correlation exists between the mobile phone signaling data and the traffic condition investigation data, and the result calculated by the mobile phone signaling data can support inter-city OD data analysis.
In order to further implement the above technical solution, in S1, the trajectory estimation step includes,
s11: acquiring call ticket data guard visitor position registration data from a mobile phone operator; the call ticket data (CDR) and visitor location registration data (VLR) are full sample data of a certain operator, and comprise a user mobile phone answering call, a grid number of each activity and a timestamp; as shown in fig. 2, a trajectory graph is extracted from the ticket data, each trajectory contains a series of active points, each active point includes area position information Gi and time information Ti, a polygon is a position area of a parent love of a base station, and an arrow is an event time sequence;
s12: slicing the map, dividing the map into grids with the size of 250 × 250m, and converting the base station positioning track data in visitor registration data into grid position data through a weighted centroid model; taking the grid where the base station position in the call ticket data is as grid position data; the calculated grid position data has higher precision than the positioning track data of the base station, and the size of the data set can be greatly reduced.
S13: setting space constraint and time constraint according to the grid position to identify a stop point and a pass point to obtain an action track; spatial constraints allow the setting of deviation ranges, the size of which is usually related to signal accuracy, and the constraints of spatial positions have been satisfied since the trace points have been assigned to a grid of 250 × 250m size; the time constraint is the setting of the shortest staying time, if the shortest staying time is 2.5 hours, only if the track point stays in one grid for more than 2.5 hours, the track point is determined as a staying point; the difference in time between the first and last recording of a grid is typically taken as the dwell time;
s14: matching the action track to the belonged geographic position, and extracting a track departure place, a destination and a shape-appearing time; after the action track of the user is extracted, matching the grid position to the city to which the geographic position belongs: city i I =1,2,. N; starting in each action trackIntegrating the land, the arrival place and the departure time to obtain trip data trip (u, o) i ,d j T), where u represents a user id, o represents a departure place, d represents a destination, and t represents a departure time;
s15: and (3) collecting and summarizing the travel data with the same place of departure and destination according to an hour and day time window: m (o) i ,d j ,t r,h ) Represents the departure point o i To destination d i At a time period t r,h And (4) summarizing the summarized results into a three-dimensional matrix to obtain sample running amount data corresponding to each track route in the set area, wherein r represents the starting date, and h represents the starting hour.
In order to further implement the technical scheme, the weighted centroid model is used for constructing a node position function according to the positions of the base stations and the interaction times with the nearby base stations in the impact time. VRL data will transmit data packets during packet-switched transmission, and device e will preferentially select the closest base station B 1 And carrying out transmission. When the load of the nearest base station reaches the critical value, the mobile terminal will automatically select other nearby base stations (B) 2 ,B 3 ,...B n ). The position coordinates of each base station are respectively (x) 1 ,y 1 )，(x 2 ,y 2 )，(x n ,y n ). Counting the number of times of interaction between the mobile terminal and the nearby base station within the time period t (f) 1 ,f 2 ,...,f n ). According to the weighted centroid point model, when the time period t is obtained through calculation, the position of the node M is (x) m,t ,y m,t )，
In order to further implement the technical scheme, in S1, the track of less than 8 active networks in the call ticket data and the visitor position registration data is removed to reduce the data set
In order to further implement the technical scheme, the sample expansion mode is that call records of a current operator user and other operator users are determined according to the call ticket data, the local market proportion of the operator is obtained, and then the sample trajectory data is expanded to the full sample trajectory data.
Because the acquired CDR and VLR data come from a single operator, and the market share of the operators in different areas is different. If single mobile phone signaling data is directly used for inter-city highway trip analysis, analysis results can have deviation due to different market share, and similarly, if one market share is uniformly used nationwide, analysis results can also have deviation due to different regions, so that in order to obtain accurate trip times of the whole society, the existing data sets of all the regions need to be expanded to a full sample.
The call records between the current operator and all operators in the whole society can be obtained through the ticket data, and the call is randomly carried out and independent of the operation commodity card, so that the local market share of the operator can be judged through the call records of the users in a certain area. For a region
Counting the number of times that all users make calls in the attribution of the commodity brand b operated on the day as
The number of times that the called party is operator b is V b . Then
Wherein
Indicating brand b is attributed to
The market share of (c). The full sample data can be calculated according to the market share of the operator in each region.
In order to further implement the above technical solution, in S3, the step of extracting the road traffic includes,
s21: dividing travel modes into roads (R)) A waterway (W), a track (S) and an aviation (A), wherein the total travel times between cities in d days are N d Then N is d ＝R d +W d +S d +A d ；
S22: extracting track routes of a fixed passageway and a travel path from the full-sample track traffic to obtain waterway traffic data and track traffic data;
s23: extracting a track route of long-distance position movement with simultaneous signal interruption and signal connection from the full sample traffic data to obtain aviation traffic data;
s24: and removing the waterway traffic data, the track traffic data and the aviation traffic in the S22 and the S23 from the full sample traffic data to obtain highway traffic data.
The traffic condition survey is an important part of the Chinese traffic statistics work and is also an important means for acquiring the running state of a road network. By installing the acquisition sensors at fixed positions on the road at certain intervals and establishing a traffic condition survey station, the system can acquire the traffic flow and the vehicle type ratio of the section of the road in real time.
The foregoing shows and describes the general principles and features of the present invention, together with the advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are given by way of illustration of the principles of the present invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, and such changes and modifications are within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. A method for calculating the total number of people and time distribution in an intercity highway passenger and truck travel mode is characterized by comprising the following steps:
s1: acquiring mobile phone signaling data of a certain operator to carry out track calculation to obtain sample trip volume data corresponding to each track route in a set area;
said S1 is comprised of a group consisting of,
s11: acquiring mobile phone signaling data from a certain mobile phone operator, wherein the mobile phone signaling data comprises base station positioning track data;
s12: slicing the map, dividing the map into grids with the size of 250 × 250m, and converting the positioning track data of the base station into grid position data through a weighted centroid model;
in S12, the weighted centroid model is a node position calculation function constructed according to the positions of the nearby base stations and the interaction times with the nearby base stations within the statistical time; calculating the position of the node M as (x) by the node position calculation function m，t ，y m，t ) Wherein, in the step (A),
wherein (x) 1 ，y 1 )，(x 2 ，y 2 )，(x n ，y n ) Respectively, indicate the positions of nearby base stations, (f) 1 ，f 2 ...f n ) Representing the number of interactions with nearby base stations, and t representing the statistical time;
s13: setting space constraint and time constraint in grid position data to judge a stop point and a passing point to obtain an action track; the space constraint is set as an allowable deviation range, and the time constraint is set as the shortest retention time;
s14: matching the action track to the belonged geographic position, and extracting a track departure place, a destination and travel time;
s15: summarizing the trips with the same departure place and destination according to hour and day time windows to obtain sample trip amount data corresponding to each track route in a set area;
s2: carrying out sample expansion on the sample output data to obtain full sample track output data;
s3: dividing the full sample trajectory trip volume data based on different trip modes, and extracting road trip volume data from the full sample trajectory trip volume data; the highway traffic data comprises time information, a starting place and a destination;
s4: and obtaining the trip proportion of the passenger car and the freight car according to the traffic condition survey data provided by the traffic condition survey station, and dividing the trip amount of the passenger car and the freight car according to the trip proportion on the highway trip amount data to obtain the total number of the passenger car and the freight car trip modes and the time distribution.
2. The method for calculating the total number of people and the time distribution in the intercity highway passenger and truck travel mode according to claim 1, wherein the step S2 is that a call record of a current operator user and other operator users is obtained according to the mobile phone signaling, and the local market share of the current operator in the call record is calculated; and expanding the sample traffic data to full sample traffic data according to the ratio of the current operator in the local market.
3. The method for calculating the total number of people and time distribution in the manner of intercity highway passenger and truck travel according to claim 1, wherein the step S3 comprises the following steps:
s21: dividing travel modes into highways, waterways, tracks and aviation;
s22: extracting track routes of a fixed entrance and a fixed exit and a travel path from the full sample track traffic to obtain waterway traffic data and track traffic data;
s23: extracting a track route of long-distance position movement with simultaneous signal interruption and signal connection from the full sample traffic data to obtain aviation traffic data;
s24: and (4) removing the waterway traffic data, the track traffic data and the aviation traffic in the S22 and the S23 from the full sample traffic data to obtain highway traffic data.
4. The method for calculating the total number of people and time distribution in the manner of intercity highway passenger and truck travel according to claim 1, wherein in the step S4, highway traffic data collected on main roads in the same time period is selected, poisson correlation between traffic condition survey data and highway traffic data calculated based on mobile phone signaling is calculated, and the accuracy of the highway traffic data based on mobile phone signaling is judged by analyzing the correlation between the mobile phone signaling data and the fluctuation trend of the traffic survey data.
5. The method for calculating the total number of people in the intercity highway passenger and truck travel mode and the time distribution according to claim 1, wherein in S1, the mobile phone signaling comprises call ticket data and visitor position registration data, and the call ticket data and the visitor registration data comprise mobile phone answering information, interaction information with a base station, an active grid number and time information.
6. The method for calculating the population and time distribution in the manner of intercity highway passenger-truck travel according to claim 5, wherein in S1, the data set is reduced by eliminating tracks of less than 8 active networks in the ticket data and the visitor location registration data.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111183336.2A CN113901109B (en) | 2021-10-11 | 2021-10-11 | Method for calculating total number of people and time distribution of passenger and truck travel modes on intercity highway |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111183336.2A CN113901109B (en) | 2021-10-11 | 2021-10-11 | Method for calculating total number of people and time distribution of passenger and truck travel modes on intercity highway |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113901109A CN113901109A (en) | 2022-01-07 |
| CN113901109B true CN113901109B (en) | 2022-11-18 |
Family
ID=79191250
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111183336.2A Active CN113901109B (en) | 2021-10-11 | 2021-10-11 | Method for calculating total number of people and time distribution of passenger and truck travel modes on intercity highway |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113901109B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114501336B (en) * | 2022-02-16 | 2023-03-17 | 交通运输部科学研究院 | Road traffic volume measuring and calculating method and device, electronic equipment and storage medium |
| CN115376314B (en) * | 2022-08-09 | 2024-04-16 | 交通运输部规划研究院 | Method for measuring and calculating total travel amount of bus highway |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111145562A (en) * | 2018-11-06 | 2020-05-12 | 交通运输部规划研究院 | Intercity highway traffic statistical method and device and electronic equipment |
| CN112133090A (en) * | 2020-08-14 | 2020-12-25 | 南京瑞栖智能交通技术产业研究院有限公司 | Multi-mode traffic distribution model construction method based on mobile phone signaling data |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5852721B1 (en) * | 2014-10-08 | 2016-02-03 | 株式会社コロプラ | Route bus operation analysis evaluation system, analysis evaluation method, and program |
| US10206193B2 (en) * | 2016-08-31 | 2019-02-12 | At&T Intellectual Property, L.P. | Mobile device location based on detection of radiation sources |
| CN112150796B (en) * | 2020-07-20 | 2022-11-15 | 南京瑞栖智能交通技术产业研究院有限公司 | Customized bus stop layout method based on mobile phone signaling data |
-
2021
- 2021-10-11 CN CN202111183336.2A patent/CN113901109B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111145562A (en) * | 2018-11-06 | 2020-05-12 | 交通运输部规划研究院 | Intercity highway traffic statistical method and device and electronic equipment |
| CN112133090A (en) * | 2020-08-14 | 2020-12-25 | 南京瑞栖智能交通技术产业研究院有限公司 | Multi-mode traffic distribution model construction method based on mobile phone signaling data |
Non-Patent Citations (1)
| Title |
|---|
| 分布式异步测量传感器网络机器人控制;钟练宗等;《自动化与仪表》;20110515(第05期);第1-4页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113901109A (en) | 2022-01-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP1532600B1 (en) | Apparatus and method for providing traffic information | |
| Caceres et al. | Review of traffic data estimations extracted from cellular networks | |
| ES2234846T3 (en) | METHOD FOR THE IDENTIFICATION OF THE ROUTE OF A VEHICLE. | |
| Yim et al. | Investigation of vehicles as probes using global positioning system and cellular phone tracking: field operational test | |
| US9786161B2 (en) | Methods and systems for estimating road traffic | |
| JP3849590B2 (en) | Traffic information system | |
| Wang et al. | Estimating dynamic origin-destination data and travel demand using cell phone network data | |
| CN113901109B (en) | Method for calculating total number of people and time distribution of passenger and truck travel modes on intercity highway | |
| KR20140128063A (en) | Traffic prediction system | |
| Kaack et al. | Truck traffic monitoring with satellite images | |
| CN109754606B (en) | Method for predicting road congestion condition based on taxi positioning | |
| Thajchayapong et al. | Enhanced detection of road traffic congestion areas using cell dwell times | |
| Chen et al. | A new method for urban traffic state estimation based on vehicle tracking algorithm | |
| Magsino | Investigating the operational dynamics of different transportation modes in vehicular networks | |
| Lorkowski et al. | New ITS applications for metropolitan areas based on Floating Car Data | |
| Montero Mercadé et al. | A case study on cooperative car data for traffic state estimation in an urban network | |
| WO2002089089A1 (en) | Method of determination of travel times and travel time forecasts in a traffic network using the positioning of mobile telephones and system for the implementation thereof | |
| Mohammadi et al. | Effect of traffic information on travel time of medium-distance trips: A case study in Tehran | |
| WO2024090093A1 (en) | Traffic-volume estimation device, update device, traffic-volume estimation method, update method, and computer program | |
| Sumathi | Analyzing travel time reliability of a bus route in a limited data set scenario: A case study | |
| Ali et al. | Global positioning systems data for performance evaluation of hov and gp lanes on i-66 and i-395/i-95 | |
| Shollar | Arterial performance and evaluation using Bluetooth and GPS data | |
| Bacchus et al. | An opportunity assessment of wireless monitoring of network-wide road traffic conditions | |
| Flaskou | Developing Freight Performance Measures To Address Map-21 Objectives Using Truck GPS Data | |
| CN105788262B (en) | The method and system of estimating road traffic |
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 |