CN111243318B - Method and system for detecting position of bus midway station - Google Patents
Method and system for detecting position of bus midway station Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a system for detecting the position of a bus midway station, wherein the method comprises the following steps: according to the line information of the bus line to be detected, mapping the station position in the bus line to be detected into a vehicle track grid; obtaining the blocking probability corresponding to each specific grid contained in the vehicle track grid, and screening out the station with the correct position in the bus line to be detected based on the blocking probability; wherein the block probability is used to indicate the proportion of the bus that stays on a particular grid. The embodiment of the invention screens the confirmed correct original bus stop position through the blocking probability, efficiently and cheaply detects the bus midway stop with the wrong position, and recommends the possibly correct stop position so as to take measures to correct the stop with the wrong position and ensure the accuracy of the network data.
Description
Technical Field
The invention relates to the field of public transport, in particular to a method and a system for detecting the position of a bus midway station.
Background
The public traffic network data is basic data of intelligent traffic information service, is necessary data of application systems such as map service, real-time public traffic inquiry service, urban public traffic network optimization and the like, and has self-evident requirements on the accuracy of the data. On the other hand, with the acceleration of the urbanization process and the development of public transport service, the temporary adjustment and optimization of the public transport network are continuously carried out. In order to ensure the accuracy of the network data in time, especially the accuracy of the positions of the public transit intermediate stations, an efficient, cheap and automatic method is needed to detect whether the positions of the existing stations are wrong, so that the stations with wrong positions can be corrected by taking measures, and the accuracy of the network data is ensured.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a method and a system for detecting a bus stop position in the middle of a bus, which overcome the above problems or at least partially solve the above problems.
According to a first aspect of the embodiments of the present invention, a method for detecting a position of a bus stop in transit is provided, where the method includes: according to the line information of the bus line to be detected, mapping the station position in the bus line to be detected into a vehicle track grid; obtaining the blocking probability corresponding to each specific grid contained in the vehicle track grid, and screening out the station with the correct position in the bus line to be detected based on the blocking probability; wherein the block probability is used to indicate the proportion of the bus that stays on a particular grid.
According to a second aspect of the embodiments of the present invention, there is provided a system for detecting a position of a bus stop, the system including: the mapping module is used for mapping the stop position in the bus line to be detected into the vehicle track grid according to the line information of the bus line to be detected; the detection module is used for acquiring the blocking probability corresponding to each specific grid contained in the vehicle track grids and screening out the station with the correct position in the bus line to be detected based on the blocking probability; wherein the block probability is used to indicate the proportion of the bus that stays on a particular grid.
According to a third aspect of the embodiments of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the method for detecting a bus stop position according to any one of the various possible implementations of the first aspect.
According to a fourth aspect of embodiments of the present invention, there is provided a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for detecting a location of a transit stop as provided in any one of the various possible implementations of the first aspect.
According to the method and the system for detecting the bus midway stop position, provided by the embodiment of the invention, the position of the original bus midway stop which is confirmed to be correct is screened out through the blocking probability, the bus midway stop with the wrong position is efficiently and cheaply detected, and the position of the stop which is possibly correct is recommended, so that measures can be taken for correcting the stop with the wrong position, and the accuracy of the network data is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from these without inventive effort.
Fig. 1 is a schematic flow chart of a method for detecting a position of a bus stop in a transit station according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a bus stop location detection system according to an embodiment of the present invention;
fig. 3 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
With the development of cloud computing and big data technology, computing and storage resources become cheaper and cheaper, so that a machine learning algorithm can process large-scale data at a higher speed and at a lower cost, and further, the value of the data is fully mined and exerted. Meanwhile, the defect that the conventional technology for automatically detecting the wrong position of the bus stop cannot perform self-learning and iteration by taking a rule as a judgment standard can be avoided.
Based on the above, the embodiment of the invention provides a method for detecting a public transportation midway station with a wrong position based on machine learning, which can efficiently and cheaply detect the public transportation midway station with the wrong position, thereby providing a basis for timely correcting the station with the wrong position.
Referring to fig. 1, the method for detecting the position of the bus midway point provided by the embodiment of the invention comprises the following steps:
As an optional embodiment, the line information includes: route name, route direction and at least one site information; the line direction is used for indicating uplink or downlink; the station information includes a station name, a station serial number, a distance between the station and a previous station, and a station position.
Specifically, the line information L can be specifically expressed by: l ═ LineName, Direction, (state 1, state 2,. and state) }, wherein LineName is line name, Direction is line Direction (uplink or downlink), state represents information of a certain bus Station to be detected, state ═ state name, state order, Distance, position state (L, lat) }, state name is Station name, state order Station serial number, Distance is Distance between the Station and the last Station, and position state is Station position.
As an optional embodiment, the mapping of the station position in the bus route to be detected to the vehicle trajectory grid includes: mapping the station position in the bus line to be detected to a vehicle track grid of a space index with a preset distance as side length; accordingly, the station information further includes an index number of the station in the vehicle trajectory grid.
Specifically, the above-mentioned bus station position is mapped into a grid of a spatial index with s (e.g. s is 10m) as a side length, and the expression of the line information may be: state ═ statename, stateorder, Distance, positionstate (lng, lat), Grid }, where Grid is the index number of the space Grid.
As an optional embodiment, after mapping the station position in the bus route to be detected to the vehicle track grid, the method further includes: the method comprises the steps of obtaining driving track data of each bus of a bus route to be detected on the bus route to be detected, and mapping the track data to a bus track grid.
Specifically, the method comprises the steps of obtaining running track data of each bus of a bus line to be detected and the bus between the first station and the last station of the line within a certain time, and cleaning abnormal GPS points in the running track. The GPS points in the trajectory are mapped into a spatially indexed grid with s as a side length.
As an optional embodiment, after mapping the trajectory data into the vehicle trajectory grid, the method further includes: calculating the average speed of each bus passing through each specific grid and the proportion of the speed of each specific grid exceeding a set threshold; setting a first label for a specific grid comprising intersections or traffic lights; a second tag is added to the particular grid where user query behavior exists.
Specifically, the average speed of each bus passing through a specific grid is calculated. Calculating the proportion highSpeedRate of the specific grid exceeding the specified threshold speed, wherein the proportion is the proportion of the number of the vehicles exceeding the threshold to the whole speed; assuming, for example, that there are 100 speeds passing through the grid, of which 70 speeds exceed the threshold, the highSpeedRate of the grid is 70/100-0.7. And calculating whether all the grids contain intersections and traffic lights, and if the contained label is set to be 1 (namely the first label), the non-contained label is set to be 0. Acquiring query position information of a line queried by a user within a certain time, mapping the position information to the grid, setting a label to be 1 (namely a second label) when a user query behavior exists in the grid, and setting the label to be 0 when no user query behavior exists.
102, obtaining a blocking probability corresponding to each specific grid contained in a vehicle track grid, and screening out a station with a correct position in a bus line to be detected based on the blocking probability; wherein the block probability is used to indicate the proportion of the bus that stays on a particular grid.
As an optional embodiment, screening out a station with a correct position in the bus line to be detected based on the blocking probability includes: and if the specific grid with the retardation probability of 1 is judged to be the same as the specific grid corresponding to the position of the original station in the bus line to be detected, the original station is the station with the correct position.
Specifically, the blocking probability is first calculated. For example, a block probability StopRate of a bus for a particular grid is calculated, the block probability describing the proportion of the bus that stops on the grid. Assuming 100 vehicles passing through the grid, of which 70 have a velocity value of 0, then the stop rate for this particular grid is 70/100-0.7. And screening grid numbers with the blocking probability of 1 in the vehicle track grids, and comparing the grid numbers with grid numbers corresponding to the positions of the original line stations. If the grid number of the original line station is the same as the grid number with the blocking probability of 1 in the vehicle track, the station is the station with unchanged position, namely the station with the correct position of the original line station is detected. Wherein the remaining grids are to-be-detected grids.
Based on the content of the above embodiment, as an optional embodiment, after screening out the stop with the correct position in the bus line to be detected based on the blocking probability, the method further includes: inputting the characteristic data corresponding to the grid to be detected to a grid classification model for other grids to be detected except for the specific grid corresponding to the station with the correct position in the vehicle track grid, and acquiring a classification result output by the grid classification model; the classification result comprises the correct site position, the wrong site position or the recommended site position; the grid classification model is obtained after training based on historical characteristic data of the grid to be detected and corresponding labeling labels.
Specifically, the method for deducing the grid to be detected by using a machine learning method comprises the following steps:
1. constructing a feature set, comprising: line name (LineName), line Direction (Direction), average station distance (AvgStationDistance) of the line in the Direction, blocking probability (StopRate) of a grid to be detected, blocking probability (PrevStopRate) of the 1 st grid in front of the grid to be detected, blocking probability (NextStopRate) of the 1 st grid behind the grid to be detected, proportion (highSpeedRate) of the grid to be detected exceeding threshold speed, proportion (PrevhhighSpeedRate) of the 1 st grid behind the grid to be detected exceeding threshold speed, whether intersection exists in the grid to be detected, traffic light (Isross), whether user query data (IsQuery) exists in the grid to be detected, whether station (Otherion) of other lines exists in the grid to be detected, distance (PrevionDistance) of the grid to be detected from the 1 st correct station position in front of the grid, distance (IsQuery) of the station to be detected from the 1 st correct station position behind the grid, distance (NextStationDistance) of the grid to be detected, The distance (PrevDistance) between the grid to be detected and the 1 st grid to be detected in front of the grid to be detected, and the distance (Nextdistance) between the grid to be detected and the 1 st grid to be detected behind the grid to be detected.
2. Constructing a training set and labeling: acquiring data in a history period of time, and labeling grids of a training set, wherein the labeled labels are as follows: a correct site location, an incorrect site location, or a recommended site location.
3. Training a model: the embodiment of the present invention selects a supervised classification algorithm (e.g. SVM, decision tree) in machine learning to train, but the scope of the embodiment of the present invention is not limited thereto.
The embodiment of the invention screens out the positions of the original bus stops which are confirmed to be correct through the blocking probability, classifies the grids to be detected by introducing a machine learning method, efficiently and cheaply detects the bus midway stops with wrong positions, and recommends the positions of the stops which are possibly correct.
Based on the content of the above embodiment, the embodiment of the invention provides a system for detecting the position of a bus midway point, which is used for executing the method for detecting the position of the bus midway point in the above method embodiment. Referring to fig. 2, the system includes: the mapping module 201 is used for mapping the stop position in the bus line to be detected into the vehicle track grid according to the line information of the bus line to be detected; the detection module 202 is configured to obtain a blocking probability corresponding to each specific grid included in the vehicle trajectory grid, and screen out a station with a correct position in the bus route to be detected based on the blocking probability; wherein the block probability is used to indicate the proportion of the bus that stays on a particular grid.
An embodiment of the present invention provides an electronic device, as shown in fig. 3, the electronic device includes: a processor (processor)501, a communication Interface (Communications Interface)502, a memory (memory)503, and a communication bus 504, wherein the processor 501, the communication Interface 502, and the memory 503 are configured to communicate with each other via the communication bus 504. The processor 501 may call a computer program running on the memory 503 and on the processor 501 to execute the method for detecting the bus stop position provided by the foregoing embodiments, for example, the method includes: according to the line information of the bus line to be detected, mapping the station position in the bus line to be detected into a vehicle track grid; obtaining the blocking probability corresponding to each specific grid contained in the vehicle track grid, and screening out the station with the correct position in the bus line to be detected based on the blocking probability; wherein the block probability is used to indicate the proportion of the bus that stays on a particular grid.
In addition, the logic instructions in the memory 503 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
An embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to, when executed by a processor, perform the method for detecting a bus stop position provided in each of the above embodiments, for example, the method includes: according to the line information of the bus line to be detected, mapping the station position in the bus line to be detected into a vehicle track grid; obtaining the blocking probability corresponding to each specific grid contained in the vehicle track grid, and screening out the station with the correct position in the bus line to be detected based on the blocking probability; wherein the block probability is used to indicate the proportion of the bus that stays on a particular grid.
The above-described embodiments of the electronic device and the like are merely illustrative, and units illustrated as separate components may or may not be physically separate, and components displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute the various embodiments or some parts of the methods of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (5)
1. A method for detecting the position of a bus midway station is characterized by comprising the following steps:
according to the line information of the bus line to be detected, mapping the stop position in the bus line to be detected into a vehicle track grid;
obtaining the blocking probability corresponding to each specific grid contained in the vehicle track grid, and screening out the station with the correct position in the bus line to be detected based on the blocking probability; wherein the block probability is used to indicate the proportion of buses staying on the particular grid;
the line information includes: route name, route direction and at least one site information; wherein, the line direction is used for indicating uplink or downlink; the station information comprises a station name, a station serial number, a distance between the station and the previous station and the station position;
the mapping of the station position in the bus line to be detected to the vehicle track grid comprises:
mapping the station position in the bus line to be detected to a vehicle track grid of a space index with a preset distance as a side length;
correspondingly, the station information further comprises an index number of the station in the vehicle track grid;
after the station position in the bus route to be detected is mapped into the vehicle track grid, the method further comprises the following steps:
acquiring the running track data of each bus of the bus route to be detected on the bus route to be detected, and mapping the track data to the bus track grid;
after mapping the trajectory data into the vehicle trajectory grid, further comprising:
calculating the average speed of each bus passing through each specific grid and the proportion of each specific grid exceeding a set threshold speed; setting a first label for the specific grid containing intersections or traffic lights; adding a second label to the specific grid with user query behavior;
screening out the station with the correct position in the bus line to be detected based on the blocking probability, wherein the screening out the station with the correct position in the bus line to be detected comprises the following steps:
and if the specific grid with the retardation probability of 1 is judged to be the same as the specific grid corresponding to the position of the original station in the bus line to be detected, the original station is the station with the correct position.
2. The method according to claim 1, wherein after screening out the stop with the correct position in the bus line to be detected based on the blocking probability, the method further comprises:
for other grids to be detected, except for the specific grid corresponding to the station with the correct position, included in the vehicle track grid, inputting the characteristic data corresponding to the grids to be detected into a grid classification model, and acquiring a classification result output by the grid classification model; the classification result comprises a correct site position, an incorrect site position or a recommended site position; the grid classification model is obtained after training based on the historical characteristic data of the grid to be detected and the corresponding label.
3. A public transit stop position detection system is characterized by comprising:
the mapping module is used for mapping the stop position in the bus line to be detected into a vehicle track grid according to the line information of the bus line to be detected;
wherein the line information includes: route name, route direction and at least one site information; wherein, the line direction is used for indicating uplink or downlink; the station information comprises a station name, a station serial number, a distance between the station and the previous station and the station position;
the mapping of the station position in the bus line to be detected to the vehicle track grid comprises:
mapping the station position in the bus line to be detected to a vehicle track grid of a space index with a preset distance as a side length;
correspondingly, the station information further comprises an index number of the station in the vehicle track grid;
after the station position in the bus route to be detected is mapped into the vehicle track grid, the method further comprises the following steps:
acquiring the running track data of each bus of the bus route to be detected on the bus route to be detected, and mapping the track data to the bus track grid;
after mapping the trajectory data into the vehicle trajectory grid, further comprising:
calculating the average speed of each bus passing through each specific grid and the proportion of the speed of each specific grid exceeding a set threshold; setting a first label for the specific grid containing intersections or traffic lights; adding a second label to the specific grid with user query behavior;
the detection module is used for acquiring the blocking probability corresponding to each specific grid contained in the vehicle track grids and screening out the station with the correct position in the bus line to be detected based on the blocking probability; wherein the block probability is used to indicate the proportion of buses staying on the particular grid;
screening out the station with the correct position in the bus line to be detected based on the blocking probability, wherein the screening out the station with the correct position in the bus line to be detected comprises the following steps:
and if the specific grid with the retardation probability of 1 is judged to be the same as the specific grid corresponding to the position of the original station in the bus line to be detected, the original station is the station with the correct position.
4. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method for detecting a transit stop position according to claim 1 or 2 are implemented when the processor executes the program.
5. A non-transitory computer-readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for detecting a transit stop position according to claim 1 or 2.
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| CN105371857B (en) * | 2015-10-14 | 2018-05-22 | 山东大学 | A kind of device and method based on bus GNSS space-time trajectory data construction road network topologies |
| CN107403550B (en) * | 2016-05-20 | 2020-12-01 | 阿里巴巴（中国）有限公司 | Public transport road network data acquisition method and device and terminal equipment |
| CN105976604B (en) * | 2016-06-21 | 2018-07-20 | 东南大学 | A kind of public bus network matching process based on GIS and public transport GPS data |
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Denomination of invention: Method and system of bus stop location detection Effective date of registration: 20210330 Granted publication date: 20201211 Pledgee: Guanggu Branch of Wuhan Rural Commercial Bank Co.,Ltd. Pledgor: WUHAN YUANGUANG TECHNOLOGY Co.,Ltd. Registration number: Y2021420000016 |
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Denomination of invention: Location detection method and system of bus midway station Effective date of registration: 20220329 Granted publication date: 20201211 Pledgee: Guanggu Branch of Wuhan Rural Commercial Bank Co.,Ltd. Pledgor: WUHAN YUANGUANG TECHNOLOGY Co.,Ltd. Registration number: Y2022420000087 |
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Date of cancellation: 20230327 Granted publication date: 20201211 Pledgee: Guanggu Branch of Wuhan Rural Commercial Bank Co.,Ltd. Pledgor: WUHAN YUANGUANG TECHNOLOGY Co.,Ltd. Registration number: Y2022420000087 |