CN112418562A - Urban rail transit network passenger trip scheme estimation method - Google Patents
Urban rail transit network passenger trip scheme estimation method Download PDFInfo
- Publication number
- CN112418562A CN112418562A CN202011470356.3A CN202011470356A CN112418562A CN 112418562 A CN112418562 A CN 112418562A CN 202011470356 A CN202011470356 A CN 202011470356A CN 112418562 A CN112418562 A CN 112418562A
- Authority
- CN
- China
- Prior art keywords
- travel
- passenger
- time
- passengers
- selection
- 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.)
- Pending
Links
Images
Classifications
-
- 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"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- 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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Databases & Information Systems (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Educational Administration (AREA)
- Traffic Control Systems (AREA)
- Train Traffic Observation, Control, And Security (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
A method for estimating a passenger trip scheme of an urban rail transit network is an estimation method for carrying out multi-dimensional matching on the passenger trip scheme based on a travel time threshold of an urban rail riding scheme. The method comprises the following steps: collecting the inbound and outbound card swiping data of an AFC system of urban rail transit, the train operation data and path information data of an ATS system and the running time data obtained by investigation; calculating an O-D riding scheme travel time threshold of the passenger going out; firstly, preliminarily screening possible travel schemes of passengers on the basis of 2 estimated dimensions at the arrival time and the departure time; continue to add 3 matching putative dimensions: the method comprises the steps of taking bus shifts, transferring times and walking characteristics, further travel selection matching estimation is carried out, and finally travel selection of urban rail transit passengers is determined. The invention can provide theoretical basis for the calculation of the urban rail transit passenger flow space-time distribution in the networked operation stage, optimize the network operation coordination decision and improve the management level of the system.
Description
Technical Field
The invention belongs to the technical field of intelligent transportation, and relates to a method for estimating a passenger trip scheme.
Background
In recent years, urban rail transit has been rapidly developed in China due to the characteristics of large traffic volume, rapidness, punctuality and safety, and rail transit in large cities such as Beijing, Shanghai, Guangzhou and the like has successively entered a new stage of networked operation management. The passenger flow is the basis of urban rail transit networked operation management, and the scientific prediction and analysis of the distribution condition of the passenger flow on the network are the prerequisites and the basis for solving a series of important problems such as network operation coordination, train operation diagram compilation, reasonable fare clearing and clearing, operation risk control, emergency handling of emergencies and the like. And accurate estimation of the travel scheme (including the travel path and the riding scheme) of the passenger is the key of the calculation and analysis of the network passenger flow space-time distribution.
Under the 'one-ticket transfer' mode of the existing networked operation, an Automatic Fare Collection (AFC) system can only record the information of passengers entering and leaving a station, and cannot acquire the number of passengers taking a bus and transfer information, so that the difficulty in directly determining the selection of the passengers during traveling is the main difficulty in calculation and analysis of network passenger flow space-time distribution by an operation management department. At the present stage, the distribution model selected based on the multi-path travel probability is widely adopted by the domestic urban rail transit system to calculate the network passenger flow distribution situation, and on the basis, a comprehensive clear distribution flow model of distribution type and push type is further evolved, so that the original network distribution model is revised. The method provides a solution for determining a reasonable passenger trip scheme in a period of time, but with the continuous enlargement of the scale of the urban rail transit network, the increasing complexity of the network structure and the diversification of passenger trip behaviors, the deviation between the calculation result of the model and the actual trip of the passenger occurs occasionally.
The existing method mainly focuses on centralized probability distribution, and research for calculating individual passenger travel schemes in a non-centralized and accurate mode is still insufficient, so that the accuracy of a passenger flow space-time distribution calculation result of an urban rail transit network is influenced, and the requirement for refining passenger flow analysis in a new networked operation stage cannot be met. Therefore, it is necessary to develop a new method for estimating a passenger travel plan.
Disclosure of Invention
With the improvement of the technological level in the traffic field and the rapid development of the related technology, a large amount of data resources which can be used for passenger flow analysis are accumulated in the urban rail transit operation management process: the AFC system records the information of passengers entering and leaving the station including the time of swiping the card, can truly reflect the actual travel time of the passengers between O-D and is an important basis for estimating the traveling scheme of the passengers; the automatic train monitoring (ATS) system stores the running information of all trains on the network, the path information calculated according to the road network structure, the traveling time and the riding time information obtained according to field investigation, and the like, and provides new possibility for estimating the trip scheme of passengers.
The invention fully utilizes various data resources generated in the networked operation process of urban rail transit, considers each main influence factor of actual trip selection of passengers on the basis of analyzing the correlation between the trip selection and the trip time, and provides the multidimensional matching and estimation method of the passenger trip scheme based on the trip time threshold of the O-D riding scheme.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a passenger travel scheme multidimensional matching estimation method based on a travel time threshold of an urban rail riding scheme is disclosed, wherein:
(a) acquiring inbound (Origin, abbreviated as O) and outbound (Destination, abbreviated as D) card swiping data of an AFC system of the urban rail transit, train operation data of an ATS system, path information data and investigated traveling time data.
(b) And calculating the O-D riding scheme travel time threshold of the passenger going out. The O-D ride plan travel time threshold for passenger travel can be expressed as: for a feasible riding scheme on a certain travel path between O-D of the urban rail network, calculating the maximum value and the minimum value of the arrival time and the maximum value and the minimum value of the departure time of a passenger who can select the scheme to finish traveling, wherein a set formed by the two groups of numerical values is called an O-D riding scheme travel time threshold of passenger traveling (as shown in FIG. 1). And (b) taking the data resources generated in the operation process as the input data in the step (a) to analyze the traveling process of the urban rail transit passengers. Because the travel time of the transfer path between the O-D systems comprises transfer waiting time and transfer traveling time, which is different from the travel time of the transfer path, the O-D riding scheme travel time threshold under two conditions needs to be calculated respectively.
(c) And constructing a passenger travel selection multi-dimensional matching estimation model based on the travel time threshold of the riding scheme (as shown in figure 2). Firstly, on the basis of 2 estimated dimensions of the arrival time and the departure time, a possible trip scheme of passengers is preliminarily screened. Then continue to add 3 matching putative dimensions: the method comprises the steps of taking bus shifts, transferring times and walking characteristics, further travel selection matching estimation is carried out, and finally travel selection of urban rail transit passengers is determined.
The system realized based on the invention provides accurate estimated passenger travel selection for an external system through a data interface.
The method as described above, wherein:
the step (b) specifically comprises:
(b1) screening an O-D travel path: and for the specified O-D pair, acquiring all paths between the O-Ds according to the path information data.
(b2) Searching for a feasible riding scheme: and for each travel route between O-D, searching all feasible riding schemes on the route by combining train operation data under the limitation of the maximum riding times.
(b3) And calculating the maximum value and the minimum value of the corresponding entering and exiting moments for each riding scheme on each path by combining the traveling process of passengers based on the train operation data and the traveling time data to obtain the O-D riding scheme travel time threshold. The travel time threshold calculation flow without transfer paths is shown in fig. 3, and the travel time threshold calculation flow with transfer paths is shown in fig. 4.
The step (c) specifically comprises:
(c1) screening possible passenger traveling schemes based on the O-D riding scheme travel time threshold. For any passenger going out between O-D, the arrival time and the departure time of the passenger provided by AFC data are respectively compared with the maximum value and the minimum value provided by the travel time threshold of all O-D riding schemes one by one, and if the arrival time and the departure time are both in the corresponding value range, the corresponding departure scheme is listed in a set of possible departure choices of the passenger. In the screening process, the arrival time and the departure time which represent the actual travel time of the O-D are 2 dimensions for estimating the passenger travel selection.
(c2) The matching estimation based on the riding times aims at matching the actual traveling situation for the passenger, namely, whether the actual riding time corresponding to the traveling selection does not exceed the maximum riding time is judged. Firstly, combining train operation data and a train boarding scheme in possible trip selection of passengers, and determining the actual bus shift of the passengers who select the bus scheme at a boarding station; the bus-taking shift data records the number of passengers waiting for each train at each station platform in different time periods, and provides the maximum bus-taking shift information of each station. The actual travel times are compared with the maximum travel times, and if the actual travel times of the passengers at each station do not exceed the maximum travel times, the travel selection is considered to be in accordance with the actual travel condition and can be used as the possible travel selection of the passengers.
(c3) The matching inference based on transfer times aims to match less transfer times travel selections for passengers. The number of times of transfer of the passengers on the travel path can be determined according to the number of the passing transfer stations. And for each possible travel selection of the passenger, extracting the path transfer station information on the travel path of the passenger, determining the transfer times corresponding to the travel path in each possible travel selection of the passenger, and acquiring the minimum transfer times. For each possible travel selection, its transfer is compared to the minimum transfer. If the transfer times corresponding to the travel selection are equal to the minimum transfer times, the passenger is more inclined to select the travel route corresponding to the travel selection. In this case, the travel selection meets the requirement of transfer times, and can be used as a possible travel selection of the passenger to participate in subsequent matching estimation. If only one possible trip selection is available for the passengers obtained in the process, the trip selection is the final trip selection of the passengers, and the trip selection inversion estimation process of the passengers is completed; otherwise, based on all possible travel selections at present, continuing to perform matching estimation based on the travel characteristics.
(c4) The matching estimation based on the traveling characteristics aims to match travel selection meeting the requirement of traveling speed consistency for the passengers, namely the travel route is selected to be at the same level with the traveling speed of the passengers in the riding scheme in the whole travel process. In order to describe the traveling speed of the passengers, the traveling speed level of the passengers in all the passengers is characterized by using percentiles of the actual traveling time of the passengers between the fast traveling time and the slow traveling time. For a certain possible occurrence of a passenger, the arrival time, departure time and arrival time of the passenger can be determined in combination with AFC data. For the starting station O, the sum of the entering traveling time and the entering waiting time can be obtained according to the departure time and the entering time of the train; for the transfer station T station, the sum of the transfer traveling time and the transfer waiting time can be obtained according to the departure time and the arrival time of the train. Therefore, if the travel selection meets the consistency of the traveling speed, and the traveling speed levels of the passengers in different traveling processes are not changed, on the premise that the traveling time is calculated according to the traveling speed levels of the passengers, the waiting time of the passengers at the platform can be obtained in a separated mode, and the waiting time value is greater than or equal to zero, so that the reasonability of the travel selection can be judged.
(c5) And finally determining the travel scheme of the urban rail transit passengers through the step-by-step matching estimation process.
Due to the adoption of the technical scheme, the invention has the beneficial effects that: through actual inspection of test cases, the passenger travel scheme multi-dimensional matching estimation method and model based on the travel time threshold of the riding scheme, which are researched and proposed, can make unique matching estimation on more than 80% of actual travel passengers, and can obtain travel selection proportions and dynamic change conditions of the passengers on different paths among O-D in different periods of the whole day on the basis. The invention can provide theoretical basis for the calculation of urban rail transit passenger flow space-time distribution in the networked operation stage, optimize network operation coordination decision and improve the modernization management level of the urban rail transit system.
Drawings
Fig. 1 is a schematic diagram of travel time threshold content analysis of a riding scheme.
FIG. 2 is a diagram of the multi-dimensional matching estimation model architecture of the present invention.
FIG. 3 is a flow chart of travel time threshold calculation for a transfer-free path.
FIG. 4 is a flow chart of travel time threshold calculation with transfer paths.
Fig. 5 is a system configuration diagram of the embodiment of the present invention.
FIG. 6 is an exemplary road network diagram of an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples. The method is used for researching the matching estimation of the passenger trip scheme based on the O-D actual travel time of the urban rail transit passenger extracted from AFC data. The method is characterized in that a large amount of AFC data collected and stored is an important basis for implementation, and meanwhile, another important premise of the method is construction and maintenance of an urban rail transit network.
As shown in fig. 5, the system is composed of a road network data management module, an AFC data storage module, an operation plan management module, a network path set generation module, and a passenger flow matching estimation module. The software modules are independent and can be installed on one machine or a plurality of machines according to actual conditions. In addition, the system can also be used as a subsystem of an urban rail transit operation management assistant decision support system to be integrated into an urban rail transit operation management platform.
The functions of the above modules and the data relationship between the modules and the passenger flow matching estimation module will be described in detail with reference to fig. 5. The implementation process and the result of the method of the present invention are described with the emphasis of FIG. 2.
1. Road network data management module
The module provides management, maintenance and updating of various basic data required by the passenger flow matching estimation module by establishing a basic database of the urban rail transit network, and simultaneously can realize digital urban rail transit network and unified and standardized management of various basic information of the network. The module adopts a graphical and visual mode to uniformly manage various basic data information, and the basic data comprises: line infrastructure information, station infrastructure information, operation plan infrastructure information, passenger flow information, and site infrastructure configuration information such as inter-station facility equipment.
2. AFC data storage module
The module and an urban rail transit ACC (automatic clear Clearing center, ACC for short) are provided with a data interface, AFC data provided by the ACC are obtained, and the original AFC data are cleaned, sorted and stored and are used for station entering and leaving data of data required by the passenger flow matching module. The data structure of the AFC data is:
TABLE 1 AFC data Structure for incoming card swiping
| Numbering | Name of field | Type of field | Length of field | Description of the |
|
| 1 | CARD_ID | Int | 8 | |
|
| 2 | | Char | 4 | Ticket card type | |
| 3 | | Int | 4 | Number of |
|
| 4 | TIME_ENTRANCE | Int | 8 | Time of arrival |
TABLE 2 AFC data Structure for outbound card swiping
| Numbering | Name of field | Type of field | Length of field | Description of the |
|
| 1 | CARD_ID | Int | 8 | |
|
| 2 | | Char | 4 | |
|
| 5 | | Int | 4 | |
|
| 6 | TIME_EXIT | Int | 8 | Time of departure |
3. Operation plan management module
The module and the urban rail transit operation plan system are provided with a data interface for passenger flow matching and estimating the urban rail transit operation plan data required by the module, and the module comprises: road network full-day driving plan data, connection scheme data of first/last trains among lines and the like.
4. Network path set generation module
The module is an effective path set generation method based on a K short circuit algorithm and provided based on the actual background of a rail transit clearing management center (ACC) of each city in China. And calculating K gradually-shortened paths between the OD of the road network by using the comprehensive travel impedance in a time unit, and setting an absolute threshold and a relative threshold to judge the rationality of the K paths so as to generate an effective path set for the path information data required by the passenger flow matching and estimating module.
5. Passenger flow matching estimation module
The module carries out matching estimation on passenger trip schemes by utilizing massive AFC data provided by an AFC data storage module and running time data stored in a database based on urban rail transit road network basic data provided by a road network data management module and train operation plan data provided by an operation plan management module, and feeds back results to a passenger flow matching estimation module.
The implementation process can be specifically illustrated by the following calculation examples:
(a) example application data
The basic road network of the example takes 2016 Beijing urban rail transit as an example (as shown in FIG. 6), and the road network comprises 19 rail transit lines. Three pairs of O-D are selected for example application, and the estimation effects of the travel selection of passengers with different O-D are reflected: O-D (O-D): wangfu well (line 1) -Dawang road (line 1). O-D (2): tiantong yuan north (line 5) -Zhichun road (line 10). And O-D (c): the specific travel information of wuberberberberch (line 1) -west kingdom (line 2) is shown in table 3. AFC data, train operation data, travel time data for each time period, and ride shift data used during the example application are shown in tables 4-7.
TABLE 3 respective O-D Path information data
Note: O-D only has 1 travel path without transfer; 2 trip paths of O-D & lt 2 & gt are all transfer paths with the same transfer times; and all the 3 travel paths of the third O-D are transferred but the transfer times are different. The three pairs of O-D travel paths represent various possible situations and can be taken as typical O-D.
TABLE 4 AFC data for each O-D section
TABLE 5 train operating data for each O-D section
TABLE 6 respective O-D part running time data
TABLE 7 bus shift data for each O-D part
(b) Example application of algorithms
Because the number of passengers staying at the platform by Beijing urban rail transit passengers is generally not more than 4, the maximum number of passengers staying which influences the travel time threshold of the O-D riding scheme is set to be 4. And calculating the O-D riding scheme travel time thresholds of all riding schemes on the traveling path, and matching the final traveling schemes of the passengers one by one according to the provided multidimensional estimation method. Taking actual data of a certain working day of Beijing urban rail transit as an example, the matching estimation results of the actual trip schemes of the three pairs of typical O-D passengers are shown in tables 8 and 9.
TABLE 8 typical O-D passenger trip scenario match inference results (partial)
TABLE 9 statistics of passenger travel route selection at different O-D intervals
Note: the time interval is judged according to the original card swiping time when the passenger enters the station: time period 1-operation start-07: 00: 00; period 2: 07:00: 00-09: 00: 00; period 3: 09:00: 00-17: 00: 00; period 4: 17:00: 00-19: 00: 00; period 5: 19:00: 00-operation end.
The embodiments described above are presented to enable those skilled in the art to make and use the invention. It will be readily apparent to those skilled in the art that various modifications to these embodiments may be made, and the generic principles described herein may be applied to other embodiments without the use of the inventive faculty. Therefore, the present invention is not limited to the embodiments described herein, and those skilled in the art should make improvements and modifications within the scope of the present invention based on the disclosure of the present invention.
Claims (11)
1. A method for estimating a passenger trip scheme of an urban rail transit network is characterized by comprising the following steps: the method is an estimation method for carrying out multi-dimensional matching on a passenger travel scheme based on a travel time threshold of an urban rail riding scheme.
2. The urban rail transit network passenger travel plan estimation method according to claim 1, comprising the steps of:
(a) collecting the inbound and outbound card swiping data of an AFC system of urban rail transit, the train operation data and path information data of an ATS system and the running time data obtained by investigation;
(b) calculating an O-D riding scheme travel time threshold of the passenger going out;
the O-D riding scheme travel time threshold of the passenger trip is as follows: for a feasible riding scheme on a certain travel path between O-D of the urban rail network, calculating the maximum value and the minimum value of the arrival time and the maximum value and the minimum value of the departure time of a passenger who can select the scheme to finish traveling, wherein the set formed by the two groups of numerical values is called the O-D riding scheme travel time threshold of the passenger traveling; analyzing the traveling process of the urban rail transit passengers by taking the data resources generated in the operation process as the input data in the step (a);
(c) and constructing a passenger travel selection multidimensional matching estimation model based on the travel time threshold of the riding scheme, and determining the travel selection of the urban rail transit passengers.
3. The method for estimating the passenger trip plan of the urban rail transit network according to claim 2, wherein in the step (b), the travel time of the O-D riding plan in two cases needs to be calculated respectively because the travel time of the transfer route between the O-D riding plans includes transfer waiting time and transfer traveling time, which is different from the travel time of the transfer route.
4. The urban rail transit network passenger travel scheme estimation method according to claim 2, wherein in step (c), possible travel schemes of passengers are preliminarily screened on the basis of 2 estimation dimensions of the arrival time and the departure time.
5. The urban rail transit network passenger travel scheme estimation method of claim 4, wherein 3 matching estimated dimensions are continuously added: the method comprises the steps of taking bus shifts, transferring times and walking characteristics, further travel selection matching estimation is carried out, and finally travel selection of urban rail transit passengers is determined.
6. The urban rail transit network passenger travel plan estimation method according to claim 2, wherein step (b) comprises:
(b1) screening an O-D travel path: for the appointed O-D pair, acquiring all paths between the O-D according to the path information data;
(b2) searching for a feasible riding scheme: for each travel route between O-D, searching all feasible riding schemes on the route by combining train operation data under the limitation of maximum riding times;
(b3) and calculating the maximum value and the minimum value of the corresponding entering and exiting moments for each riding scheme on each path by combining the traveling process of passengers based on the train operation data and the traveling time data to obtain the O-D riding scheme travel time threshold.
7. The urban rail transit network passenger travel plan estimation method according to claim 2, wherein step (c) comprises:
(c1) screening possible passenger traveling schemes based on the O-D riding scheme travel time threshold;
(c2) the method comprises the following steps of estimating the matching of passenger travel times, namely judging whether the actual travel times corresponding to travel selection do not exceed the maximum travel times or not;
(c3) presume, aim at for passenger matching the travel choice that the number of times of transfer is less based on matching of the number of times of transfer;
(c4) the method comprises the following steps of matching and presuming on the basis of walking characteristics, aiming at matching travel selection meeting the requirement of consistency of walking speed for passengers, namely selecting the travel path to be at the same level with the walking speed of the passengers in a riding scheme in the whole travel process;
(c5) and finally determining the travel scheme of the urban rail transit passengers through the step-by-step matching estimation process.
8. The urban rail transit network passenger travel scheme estimation method according to claim 7, wherein in step (c1), for any passenger traveling between O-D, the arrival time and the departure time of the passenger provided by AFC data are respectively compared one by one with the maximum value and the minimum value provided by all O-D riding scheme travel time thresholds, and if the arrival time and the departure time are both within the corresponding value ranges, the corresponding travel scheme is listed in a set of possible travel choices of the passenger; in the screening process, the arrival time and the departure time which represent the actual travel time of the O-D are 2 dimensions for estimating the passenger travel selection.
9. The urban rail transit network passenger travel scheme estimation method according to claim 7, wherein in step (c2), the actual number of passengers at the boarding station for which the passenger selected the riding scheme is selected can be determined by combining train operation data with the boarding train scheme in the possible passenger travel selection; the bus taking shift data records the number of passengers waiting for each train at each station platform in different time periods, and provides the maximum bus taking shift information of each station; the actual travel times are compared with the maximum travel times, and if the actual travel times of the passengers at each station do not exceed the maximum travel times, the travel selection is considered to be in accordance with the actual travel condition and can be used as the possible travel selection of the passengers.
10. The method for estimating a passenger travel plan according to claim 7, wherein in the step (c3), for each possible travel selection of the passenger, the information of the route transfer station on the travel route of the passenger is extracted, the number of transfers corresponding to the travel route in each possible travel selection of the passenger is determined, and the minimum number of transfers is obtained from the information; comparing the transfer times with the minimum transfer times for each possible travel selection; if the transfer times corresponding to the travel selection are equal to the minimum transfer times, the passenger is more inclined to select a travel path corresponding to the travel selection; in this case, the travel selection meets the requirement of transfer times, and can be used as a possible travel selection of the passenger to participate in subsequent matching estimation; if only one possible trip selection is available for the passengers obtained in the process, the trip selection is the final trip selection of the passengers, and the trip selection inversion estimation process of the passengers is completed; otherwise, based on all possible travel selections at present, continuing to perform matching estimation based on the travel characteristics.
11. The method for estimating passenger travel plans in an urban rail transit network according to claim 7, wherein in step (c4), in order to describe the traveling speeds of passengers, the traveling speed levels of all passengers are characterized by percentiles of actual traveling times of the passengers between fast traveling times and slow traveling times, and for a certain possible occurrence selection of the passengers, the arrival time, departure time and arrival time of the passengers can be determined by combining AFC data; for the starting station O, the sum of the entering traveling time and the entering waiting time can be obtained according to the departure time and the entering time of the train; for the transfer station T station, the sum of transfer traveling time and transfer waiting time can be obtained according to the departure time and arrival time of the train; if the travel selection conforms to the consistency of the traveling speeds, and the traveling speed levels of the passengers in different traveling processes are not changed, on the premise that the traveling time is calculated according to the traveling speed levels of the passengers, the waiting time of the passengers at the platform can be obtained in a separated mode, and the waiting time value is larger than or equal to zero, so that the reasonability of the travel selection can be judged.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011470356.3A CN112418562A (en) | 2020-12-15 | 2020-12-15 | Urban rail transit network passenger trip scheme estimation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011470356.3A CN112418562A (en) | 2020-12-15 | 2020-12-15 | Urban rail transit network passenger trip scheme estimation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112418562A true CN112418562A (en) | 2021-02-26 |
Family
ID=74776627
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011470356.3A Pending CN112418562A (en) | 2020-12-15 | 2020-12-15 | Urban rail transit network passenger trip scheme estimation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112418562A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113095986A (en) * | 2021-04-09 | 2021-07-09 | 上海大学 | Public transport full load rate calculation method, system and computer readable storage medium |
| CN113536493A (en) * | 2021-06-23 | 2021-10-22 | 南京理工大学 | Effective path generation method based on clustering reverse thrust and section passenger flow estimation method |
| CN113657672A (en) * | 2021-08-18 | 2021-11-16 | 北京航空航天大学 | Dynamic planning-based comprehensive hub last bus connection method |
| CN115689089A (en) * | 2022-10-25 | 2023-02-03 | 深圳市城市交通规划设计研究中心股份有限公司 | Urban rail transit passenger riding probability calculation method, equipment and storage medium |
| CN115829147A (en) * | 2022-12-14 | 2023-03-21 | 广州地铁设计研究院股份有限公司 | Passenger flow OD correction method based on machine simulation under rapid and slow vehicle schedule change and application |
| CN117290618A (en) * | 2023-11-27 | 2023-12-26 | 江西鹭鹭行科技有限公司 | Space-time accompanying crowd searching method and system for urban rail transit |
| CN117649034A (en) * | 2023-12-08 | 2024-03-05 | 北京城建设计发展集团股份有限公司 | Multi-stage road network passenger travel simulation method and system under operation interruption event |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102354406A (en) * | 2011-06-07 | 2012-02-15 | 同济大学 | Real time statistical system and method for rail traffic network passenger flow based on RFID technology |
| US20170262803A1 (en) * | 2016-03-14 | 2017-09-14 | United Parcel Service Of America, Inc. | Determining estimated pick-up/delivery windows using clustering |
| US20180053401A1 (en) * | 2016-08-22 | 2018-02-22 | Rapidsos, Inc. | Predictive analytics for emergency detection and response management |
| CN109997163A (en) * | 2016-08-15 | 2019-07-09 | 谷歌有限责任公司 | For detecting the navigation to physics place and suggesting the system and method for alternative acts |
| CN110309962A (en) * | 2019-06-21 | 2019-10-08 | 清华大学 | Railway stroke route method and device for planning based on time extended model |
| CN111401614A (en) * | 2020-03-09 | 2020-07-10 | 西南交通大学 | Dynamic passenger flow distribution method and system for urban rail transit |
-
2020
- 2020-12-15 CN CN202011470356.3A patent/CN112418562A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102354406A (en) * | 2011-06-07 | 2012-02-15 | 同济大学 | Real time statistical system and method for rail traffic network passenger flow based on RFID technology |
| US20170262803A1 (en) * | 2016-03-14 | 2017-09-14 | United Parcel Service Of America, Inc. | Determining estimated pick-up/delivery windows using clustering |
| CN109074532A (en) * | 2016-03-14 | 2018-12-21 | 统包裹服务美国有限公司 | Determined using cluster it is estimated collect/deliver window |
| CN109997163A (en) * | 2016-08-15 | 2019-07-09 | 谷歌有限责任公司 | For detecting the navigation to physics place and suggesting the system and method for alternative acts |
| US20200219175A1 (en) * | 2016-08-15 | 2020-07-09 | Google Llc | Systems and methods for detection of navigation to physical venue and suggestion of alternative actions |
| US20180053401A1 (en) * | 2016-08-22 | 2018-02-22 | Rapidsos, Inc. | Predictive analytics for emergency detection and response management |
| CN110309962A (en) * | 2019-06-21 | 2019-10-08 | 清华大学 | Railway stroke route method and device for planning based on time extended model |
| CN111401614A (en) * | 2020-03-09 | 2020-07-10 | 西南交通大学 | Dynamic passenger flow distribution method and system for urban rail transit |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113095986A (en) * | 2021-04-09 | 2021-07-09 | 上海大学 | Public transport full load rate calculation method, system and computer readable storage medium |
| CN113536493A (en) * | 2021-06-23 | 2021-10-22 | 南京理工大学 | Effective path generation method based on clustering reverse thrust and section passenger flow estimation method |
| CN113657672A (en) * | 2021-08-18 | 2021-11-16 | 北京航空航天大学 | Dynamic planning-based comprehensive hub last bus connection method |
| CN115689089A (en) * | 2022-10-25 | 2023-02-03 | 深圳市城市交通规划设计研究中心股份有限公司 | Urban rail transit passenger riding probability calculation method, equipment and storage medium |
| CN115829147A (en) * | 2022-12-14 | 2023-03-21 | 广州地铁设计研究院股份有限公司 | Passenger flow OD correction method based on machine simulation under rapid and slow vehicle schedule change and application |
| CN117290618A (en) * | 2023-11-27 | 2023-12-26 | 江西鹭鹭行科技有限公司 | Space-time accompanying crowd searching method and system for urban rail transit |
| CN117290618B (en) * | 2023-11-27 | 2024-03-01 | 江西鹭鹭行科技有限公司 | Space-time accompanying crowd searching method and system for urban rail transit |
| CN117649034A (en) * | 2023-12-08 | 2024-03-05 | 北京城建设计发展集团股份有限公司 | Multi-stage road network passenger travel simulation method and system under operation interruption event |
| CN117649034B (en) * | 2023-12-08 | 2024-04-30 | 北京城建设计发展集团股份有限公司 | Multi-stage road network passenger travel simulation method and system under operation interruption event |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112418562A (en) | Urban rail transit network passenger trip scheme estimation method | |
| EP4030365A1 (en) | Multi-mode multi-service rail transit analog simulation method and system | |
| CN105788260B (en) | A kind of bus passenger OD projectional techniques based on intelligent public transportation system data | |
| CN111191816B (en) | System for identifying travel time chain of urban rail transit passengers | |
| CN110298516B (en) | Method and device for splitting overlong bus line based on passenger flow OD data, mobile terminal equipment and server | |
| CN111598427B (en) | Urban rail transit station importance evaluation method | |
| CN105279572A (en) | City track traffic passenger flow density index calculating and releasing system | |
| CN106898142B (en) | A kind of path forms time reliability degree calculation method considering section correlation | |
| CN109903555B (en) | Bus passenger getting-off data prediction method and system based on big data | |
| CN115018148A (en) | Urban rail transit network passenger flow distribution prediction method and system based on digital twin model | |
| CN115860594A (en) | Simulation system and method applied to intelligent bus scheduling | |
| Liu et al. | Data analytics approach for train timetable performance measures using automatic train supervision data | |
| CN106327867A (en) | Bus punctuality prediction method based on GPS data | |
| CN114118766A (en) | Passenger flow OD algorithm based on bus passenger travel multiple matching | |
| CN114707709A (en) | Safety early warning method and system for comprehensive passenger transport hub of railway | |
| CN116933958A (en) | Subway transfer station transfer passenger flow estimation method based on card swiping data | |
| CN115860404A (en) | Evaluation method, device, equipment, medium and product of transportation hub layout | |
| CN115600807A (en) | Method for evaluating railway passenger arrival check efficiency of public transportation operation | |
| CN115115233A (en) | Method for determining transfer service level between public transport and subway | |
| CN111275482B (en) | Machine learning-based real-time dynamic track traffic sorting method | |
| CN115206104A (en) | Urban resident traffic trip mode identification method based on mobile phone signaling data | |
| CN114996544A (en) | Travel path extraction method based on mobile phone signaling data and Mix-Markov model | |
| CN113536493A (en) | Effective path generation method based on clustering reverse thrust and section passenger flow estimation method | |
| CN113780682A (en) | Rail transit system service fault prediction method based on fault marker | |
| Bohan et al. | Short-term traffic flow prediction based on bus floating car |
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 | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20210226 |