WO2024125253A1 - 公交车到达时间不确定性可视化方法、***、设备及介质 - Google Patents

公交车到达时间不确定性可视化方法、***、设备及介质 Download PDF

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WO2024125253A1
WO2024125253A1 PCT/CN2023/133440 CN2023133440W WO2024125253A1 WO 2024125253 A1 WO2024125253 A1 WO 2024125253A1 CN 2023133440 W CN2023133440 W CN 2023133440W WO 2024125253 A1 WO2024125253 A1 WO 2024125253A1
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arrival time
uncertainty
station
bus
queried
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PCT/CN2023/133440
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English (en)
French (fr)
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邵灵丹
王洋
曾伟
叶可江
须成忠
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中国科学院深圳先进技术研究院
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Publication of WO2024125253A1 publication Critical patent/WO2024125253A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/904—Browsing; Visualisation therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation
    • G06T11/20—Drawing from basic elements, e.g. lines or circles
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the invention belongs to the field of traffic technology and relates to a method, system, equipment and medium for visualizing uncertainty of bus arrival time.
  • buses are a common mode of travel for people.
  • the operation status of buses is unstable.
  • the operation of buses should follow the planned schedule, that is, the arrival time of buses is stable.
  • unstable bus operation may increase bus arrival delays and passengers' waiting time, and reduce bus operation efficiency and service level.
  • unforeseen situations (such as traffic accidents, etc.) are widespread, resulting in uncertain changes in bus arrival time, that is, bus arrival time uncertainty, which leads to unstable bus operation. Therefore, transportation planners and service providers engaged in public bus operations need a visual analysis system to analyze bus data and develop solutions to reduce bus arrival time uncertainty.
  • the above-mentioned method of improving bus operation by predicting bus agglomeration through deep learning models does not take into account human initiative, and these data-driven methods do not take into account the impact of real bus driving behavior and weather factors, and cannot support complex analysis tasks and meet the needs of field experts for bus exploration.
  • the Knotted-line visualization method can only analyze one or several routes and the analysis dimension is relatively single, and cannot systematically consider changes in space, time, and more levels.
  • the purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a bus arrival time uncertainty visualization method, system, device and medium.
  • the present invention provides a method for visualizing the uncertainty of bus arrival times, comprising: acquiring bus operation data, and obtaining the variance of the actual arrival time of each set arrival time at each station based on the bus operation data, as the arrival time uncertainty of each set arrival time at each station; obtaining an uncertainty contour density map of each set arrival time through a kernel density estimation algorithm based on the arrival time uncertainty of each set arrival time at each station; mapping each area in the uncertainty contour density map of each set arrival time to a one-dimensional space on the time coordinate axis in chronological order to obtain an uncertainty nested tracking map; and visualizing the uncertainty contour density map and the uncertainty nested tracking map.
  • the process before obtaining the variance of the actual arrival time of each set arrival time at each station based on the bus operation data, the process also includes deleting duplicate values, deleting abnormal values, and filling missing values in the bus operation data.
  • the method of obtaining an uncertainty contour density map of each set arrival time based on the arrival time uncertainty of each set arrival time at each station through a kernel density estimation algorithm includes: obtaining the longitude and latitude of each station; and obtaining triplet data of each set arrival time of each station based on the longitude and latitude of each station and the arrival time uncertainty of each set arrival time of each station; the triplet data includes longitude, latitude and arrival time uncertainty; setting the map size, threshold and bandwidth of the kernel density estimation algorithm, traversing each set arrival time, inputting the triplet data of each set arrival time of each station into the set kernel density estimation algorithm, and obtaining drawing data of each set arrival time of each station; based on the drawing data of each set arrival time of each station, using color depth to represent the size of the arrival time uncertainty and drawing a contour density map, to obtain an uncertainty contour density map of each set arrival time.
  • mapping each area in the uncertainty contour density map of each set arrival time to a one-dimensional space on the time coordinate axis in chronological order to obtain an uncertainty nested tracking map includes: obtaining the center point coordinates of each area in the uncertainty contour density map of each set arrival time in chronological order to obtain each coordinate to be converted for each set arrival time; and mapping each coordinate to be converted for each set arrival time to the one-dimensional space on the time coordinate axis through the Hilbert curve algorithm to obtain each one-dimensional conversion coordinate for each set arrival time; obtaining and determining the arrival time uncertainty level to which each one-dimensional conversion coordinate of each set arrival time belongs based on the arrival time uncertainty of each one-dimensional conversion coordinate of each set arrival time; and connecting the one-dimensional conversion coordinates of the same arrival time uncertainty level in each one-dimensional conversion coordinate of each set arrival time, and filling in the corresponding color of each arrival time uncertainty level to obtain an uncertainty nested tracking map.
  • it also includes: obtaining request information for the bus route to be queried; and obtaining the station sequence of the bus route to be queried and the set arrival time of each station based on the request information for the bus route to be queried; according to the station sequence of the bus route to be queried and the set arrival time of each station, as well as the arrival time uncertainty of each set arrival time of each station, using the station sequence as the vertical coordinate, the time as the horizontal coordinate, the arrival time uncertainty as the coordinate point on the graph, and using the depth of color to represent the size of the arrival time uncertainty, drawing the itinerary view of the bus route to be queried, and visualizing the itinerary view of the bus route to be queried.
  • it also includes: obtaining request information for the site to be queried; and based on the request information for the site to be queried, obtaining the site ID of the site to be queried, the number of bus routes passing the site, the ID of each bus route passing the site, the site sequence of each bus route passing the site, and the uncertainty of arrival time of each set arrival time; based on the site ID of the site to be queried, the number of bus routes passing the site, the ID of each bus route passing the site, the site sequence of each bus route passing the site, and the uncertainty of arrival time of each set arrival time, obtaining the site view of the site to be queried, and visualizing the site view of the site to be queried.
  • it also includes: obtaining attribute information of each bus route in a preset area; wherein the attribute information includes one or more of the following attributes: route ID, route type, departure frequency and arrival interval; according to the attribute information of each bus route in the preset area, taking the bus routes as rows and the attributes in the attribute information as columns, obtaining a bus route list view, and visualizing the bus route list view; obtaining attribute modification information of the bus route to be modified, and modifying the attribute information of the bus route to be modified according to the attribute modification information of the bus route to be modified, and modifying the bus route list view according to the modified attribute information of the bus route to be modified.
  • a bus arrival time uncertainty visualization system comprising: a data acquisition module for acquiring bus operation data, and obtaining the variance of the actual arrival time of each set arrival time at each station based on the bus operation data, as the arrival time uncertainty of each set arrival time at each station; a contour density map module for obtaining an uncertainty contour density map of each set arrival time through a kernel density estimation algorithm based on the arrival time uncertainty of each set arrival time at each station; a nested tracking map module for mapping each area in the uncertainty contour density map of each set arrival time to a one-dimensional space on the time coordinate axis in chronological order, to obtain an uncertainty nested tracking map; and a visualization module for visualizing the uncertainty contour density map and the uncertainty nested tracking map.
  • a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned bus arrival time uncertainty visualization method when executing the computer program.
  • a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned bus arrival time uncertainty visualization method are implemented.
  • the present invention has the following beneficial effects:
  • the bus arrival time uncertainty visualization method of the present invention first calculates the variance of the actual arrival time of each set arrival time at each station based on the bus operation data, and then uses this as the arrival time uncertainty of each set arrival time at each station. Based on the actual operation data, various factors in the bus operation process are fully considered to ensure the analyzability of the arrival time uncertainty. At the same time, based on the arrival time uncertainty data of each set arrival time at each station, the uncertainty contour density map and uncertainty nested tracking map of each set arrival time are obtained for visualization.
  • the distribution of arrival time uncertainty in the spatial area is intuitively displayed through the uncertainty contour density map, and the evolution of arrival time uncertainty over time is intuitively displayed through the uncertainty nested tracking map, so that users can perform uncertainty analysis from a time-space perspective, help better identify hot spots with high uncertainty, and observe the uncertainty evolution process in the time dimension, better help users identify areas with high uncertainty, provide reference for bus scheduling, facilitate bus cluster inspection and reasoning, and effectively avoid the phenomenon of bus agglomeration in certain areas.
  • FIG1 is a flow chart of a method for visualizing uncertainty of bus arrival time according to an embodiment of the present invention.
  • FIG. 2 is an uncertainty contour density diagram in a specific application scenario of an embodiment of the present invention.
  • FIG. 3 is an uncertainty nested tracking diagram in a specific application scenario of an embodiment of the present invention.
  • FIG. 4 is a structural block diagram of a bus arrival time uncertainty visualization system according to an embodiment of the present invention.
  • a method for visualizing the uncertainty of bus arrival times is provided, so that users can better understand the trends and details of the uncertainty of bus arrival times.
  • the method analyzes from a spatiotemporal perspective to help users better identify hot spots with high uncertainty of bus arrival times, and can observe the evolution of bus arrival time uncertainty in the time dimension.
  • the bus arrival time uncertainty visualization method includes the following steps:
  • S1 Obtain bus operation data, and obtain the variance of the actual arrival time of each set arrival time at each station based on the bus operation data as the arrival time uncertainty of each set arrival time at each station.
  • the bus arrival time uncertainty visualization method of the present invention first calculates the variance of the actual arrival time of each set arrival time at each station based on the bus operation data, and then uses this as the arrival time uncertainty of each set arrival time at each station. Based on the actual operation data, various factors in the bus operation process are fully considered to ensure the analyzability of the arrival time uncertainty. At the same time, based on the arrival time uncertainty data of each set arrival time at each station, the uncertainty contour density map and uncertainty nested tracking map of each set arrival time are obtained for visualization.
  • the distribution of arrival time uncertainty in the spatial area is intuitively displayed through the uncertainty contour density map, and the evolution of arrival time uncertainty over time is intuitively displayed through the uncertainty nested tracking map, so that users can perform uncertainty analysis from a spatiotemporal perspective, help better identify hot spots with high uncertainty, and observe the uncertainty evolution process in the time dimension, better help users identify areas with high uncertainty, provide reference for bus scheduling, facilitate bus cluster inspection and reasoning, and effectively avoid the phenomenon of bus agglomeration in certain areas.
  • bus operation data generally includes static data describing bus route information and dynamic data of bus driving data.
  • Static data can be station-route data, including bus route ID, driving direction, station ID, station name, station stop order, longitude and latitude, etc.
  • Dynamic data is the real-time data recorded at each station where the bus line passes, which can be bus ID, bus route ID, station ID, longitude, latitude, boarding location, getting off location, departure time, arrival time and stay time, etc.
  • the set arrival time of a station is the time when each station should arrive at the station under the scheduling plan.
  • the actual arrival time of the set arrival time of a station is the time when a bus that should arrive at the current station at the current set arrival time under the scheduling plan actually arrives at the station.
  • a certain number of data samples are collected for the actual arrival time of each set arrival time of each station, and then calculations are performed based on these data samples to obtain the variance of the actual arrival time of each set arrival time of each station.
  • the visualized uncertainty contour density map may be a visualization of the uncertainty contour density map of each set arrival time, or may be a visualization of the uncertainty contour density map of the set arrival time to be queried based on the query request of the set arrival time to be queried.
  • the user may select a specific time period, and select the time period of interest by dragging or clicking the time slider, and when the time changes, the uncertainty contour density map corresponding to the selected time will be displayed.
  • the process before obtaining the variance of the actual arrival time of each set arrival time at each station based on the bus operation data, the process further includes deleting duplicate values, deleting abnormal values, and filling missing values in the bus operation data.
  • the deletion of duplicate values that is, the two close arrival times of the same bus ID on the same route and the same station, one of the values needs to be deleted.
  • the deletion of outliers that is, the unreasonable departure time or the arrival time recorded once within an hour is deleted.
  • the completion of missing values that is, the missing station information in the itinerary, fills in the value in the order recorded in the static route table, and for attributes such as arrival time, the arrival time is calculated according to the distance ratio of adjacent stations, that is, the Haversing formula is used. Specifically, the geographical location of a station is mapped to the road network, the shortest path to the adjacent station is found, and the distance of the shortest path is measured as an approximate value. This function can be extended to fill in two or more consecutive missing values, as well as missing values at the start station and the end station, as long as there is at least one recorded value of the bus trip.
  • the arrival time of stations with the same bus route ID within a certain period of time is first connected to form several routes and the several routes are aligned.
  • the probability of the bus arrival time will be approximated to a normal distribution. Therefore, the arrival time uncertainty is regarded as the degree of deviation from the set arrival time, and then based on the aligned data, the variance of the actual arrival time of each set arrival time at each station is calculated as the arrival time uncertainty of each set arrival time at each station.
  • the method of obtaining an uncertainty contour density map of each set arrival time according to the arrival time uncertainty of each set arrival time of each station through a kernel density estimation algorithm includes: obtaining the longitude and latitude of each station; and obtaining triplet data of each set arrival time of each station according to the longitude and latitude of each station and the arrival time uncertainty of each set arrival time of each station; the triplet data includes longitude, latitude and arrival time uncertainty; setting the map size, threshold and bandwidth of the kernel density estimation algorithm, traversing each set arrival time, inputting the triplet data of each set arrival time of each station into the set kernel density estimation algorithm, and obtaining drawing data of each set arrival time of each station; according to the drawing data of each set arrival time of each station, using color depth to represent the size of arrival time uncertainty and drawing a contour density map, and obtaining an uncertainty contour density map of each set arrival time.
  • kernel density estimation is used to estimate the spatial uncertainty distribution.
  • the triple data of each set arrival time of each station is used as input data, and the kernel density estimation algorithm based on KDE for obtaining the contour density map is called to obtain a number of MultiPolygon geometric objects in GeoJSON format.
  • the MultiPolygon geometric objects in GeoJSON format d3.geoPath is called to draw the contour density map to obtain the uncertainty contour density map.
  • the input of the kernel density estimation algorithm also includes the specified map size, threshold and bandwidth. If not specified, the default value is used.
  • the input of the kernel density estimation algorithm is the coordinate point of the screen, the longitude and latitude coordinates need to be converted into screen coordinate points.
  • each MultiPolygon geometry object in GeoJSON format represents an area in the contour density map, and all values in each area are greater than or equal to the corresponding threshold of each area.
  • the uncertainty contour density map that is, the arrival time uncertainty of the current set arrival time of all stations in each area of the uncertainty contour density map is greater than or equal to the threshold of the area.
  • the depth of color is used to represent the size of the arrival time uncertainty, and each area is filled with the corresponding color.
  • the greater the arrival time uncertainty the darker the color. See Figure 2 for an example of an uncertainty contour density map in a specific application scenario.
  • mapping each area in the uncertainty contour density map of each set arrival time to a one-dimensional space on the time coordinate axis in chronological order to obtain an uncertainty nested tracking map includes: obtaining the center point coordinates of each area in the uncertainty contour density map of each set arrival time in chronological order to obtain each coordinate to be converted for each set arrival time; and mapping each coordinate to be converted for each set arrival time to the one-dimensional space on the time coordinate axis through the Hilbert curve algorithm to obtain each one-dimensional conversion coordinate for each set arrival time; obtaining and determining the arrival time uncertainty level to which each one-dimensional conversion coordinate of each set arrival time belongs based on the arrival time uncertainty of each one-dimensional conversion coordinate of each set arrival time; and connecting the one-dimensional conversion coordinates of the same arrival time uncertainty level in each one-dimensional conversion coordinate of each set arrival time, and filling in the corresponding color of each arrival time uncertainty level to obtain an uncertainty nested tracking map.
  • an uncertainty nested tracking graph is used to achieve this purpose. See Figure 3, an example of an uncertainty nested tracking graph in a specific application scenario. Specifically, the coordinates of all uncertainty nested tracking graphs are mapped to a one-dimensional space on the vertical axis with time as the horizontal coordinate, so as to achieve the tracking of the evolution of uncertainty over time, and then the time representation of uncertainty can be intuitively explored.
  • the two components of uncertainty evolution namely the uncertainty values at multiple levels in the region and the dynamic changes within and between regions, including spatial movement and changes in regional size
  • the nested hierarchical properties help to present multi-level uncertainty.
  • the topological structure of the nested tracking graph can easily capture the dynamic evolution of uncertainty over time.
  • the arrival time uncertainty level can be set in advance, and the arrival time uncertainty level is essentially a range division of the arrival time uncertainty, and the arrival time uncertainty within a certain range is used as an arrival time uncertainty level.
  • the bus arrival time uncertainty visualization method further includes: obtaining request information for a bus route to be queried; and obtaining the station sequence of the bus route to be queried and the set arrival time of each station based on the request information for the bus route to be queried; and drawing a trip view of the bus route to be queried based on the station sequence of the bus route to be queried and the set arrival time of each station, as well as the arrival time uncertainty of each set arrival time of each station, with the station sequence as the vertical coordinate, the time as the horizontal coordinate, the arrival time uncertainty as the coordinate point on the graph, and the color depth representing the size of the arrival time uncertainty, and visualizing the trip view of the bus route to be queried.
  • the trip view is set to provide detailed information about the bus route selected by the user, including the stops and time of each trip, thereby facilitating the exploration of arrival time uncertainty from the route perspective.
  • the bus arrival time uncertainty visualization method further includes: obtaining request information for the station to be queried; and according to the request information for the station to be queried, obtaining the station ID of the station to be queried, the number of bus routes passing the station, the ID of each bus route passing the station, the station sequence of each bus route passing the station, and the arrival time uncertainty of each set arrival time; according to the station ID of the station to be queried, the number of bus routes passing the station, the ID of each bus route passing the station, the station sequence of each bus route passing the station, and the arrival time uncertainty of each set arrival time, obtaining the station view of the station to be queried, and visualizing the station view of the station to be queried.
  • a station view is set to list the basic information of the selected station.
  • the information of the station will be displayed in the station view, including the current station ID, how many bus lines pass through the station, the ID of each bus route passing the station, the station sequence of each bus route passing the station, and the arrival time uncertainty of each set arrival time at the station.
  • the bus arrival time uncertainty visualization method further includes: obtaining attribute information of each bus route in a preset area; wherein the attribute information includes one or more of the following attributes: route ID, route type, departure frequency and arrival interval; according to the attribute information of each bus route in the preset area, taking the bus routes as rows and the attributes in the attribute information as columns, obtaining a bus route list view, and visualizing the bus route list view; obtaining attribute modification information of the bus route to be modified, and modifying the attribute information of the bus route to be modified according to the attribute modification information of the bus route to be modified, and modifying the bus route list view according to the modified attribute information of the bus route to be modified.
  • a bus route list view is set to facilitate users to check the basic information of all routes in the data set.
  • the bus route list view can be constructed in the form of a table structure.
  • a bus arrival time uncertainty visualization system which can be used to implement the above-mentioned bus arrival time uncertainty visualization method.
  • the bus arrival time uncertainty visualization system includes a data acquisition module, a contour density map module, a nested tracking map module and a visualization module.
  • the data acquisition module is used to obtain the bus operation data, and obtain the variance of the actual arrival time of each set arrival time at each station according to the bus operation data, as the arrival time uncertainty of each set arrival time at each station;
  • the contour density map module is used to obtain the uncertainty contour density map of each set arrival time according to the arrival time uncertainty of each set arrival time at each station through the kernel density estimation algorithm;
  • the nested tracking map module is used to map each area in the uncertainty contour density map of each set arrival time to the one-dimensional space on the time coordinate axis in chronological order, and obtain the uncertainty nested tracking map;
  • the visualization module is used to visualize the uncertainty contour density map and the uncertainty nested tracking map.
  • the process before obtaining the variance of the actual arrival time of each set arrival time at each station based on the bus operation data, the process further includes deleting duplicate values, deleting abnormal values, and filling missing values in the bus operation data.
  • the method of obtaining an uncertainty contour density map of each set arrival time according to the arrival time uncertainty of each set arrival time of each station through a kernel density estimation algorithm includes: obtaining the longitude and latitude of each station; and obtaining triplet data of each set arrival time of each station according to the longitude and latitude of each station and the arrival time uncertainty of each set arrival time of each station; the triplet data includes longitude, latitude and arrival time uncertainty; setting the map size, threshold and bandwidth of the kernel density estimation algorithm, traversing each set arrival time, inputting the triplet data of each set arrival time of each station into the set kernel density estimation algorithm, and obtaining drawing data of each set arrival time of each station; according to the drawing data of each set arrival time of each station, using color depth to represent the size of arrival time uncertainty and drawing a contour density map, and obtaining an uncertainty contour density map of each set arrival time.
  • mapping each area in the uncertainty contour density map of each set arrival time to a one-dimensional space on the time coordinate axis in chronological order to obtain an uncertainty nested tracking map includes: obtaining the center point coordinates of each area in the uncertainty contour density map of each set arrival time in chronological order to obtain each coordinate to be converted for each set arrival time; and mapping each coordinate to be converted for each set arrival time to the one-dimensional space on the time coordinate axis through the Hilbert curve algorithm to obtain each one-dimensional conversion coordinate for each set arrival time; obtaining and determining the arrival time uncertainty level to which each one-dimensional conversion coordinate of each set arrival time belongs based on the arrival time uncertainty of each one-dimensional conversion coordinate of each set arrival time; and connecting the one-dimensional conversion coordinates of the same arrival time uncertainty level in each one-dimensional conversion coordinate of each set arrival time, and filling in the corresponding color of each arrival time uncertainty level to obtain an uncertainty nested tracking map.
  • the bus arrival time uncertainty visualization system further includes a trip view module, which is used to obtain request information for the bus route to be queried; and based on the request information for the bus route to be queried, obtain the station sequence of the bus route to be queried and the set arrival time of each station; based on the station sequence of the bus route to be queried and the set arrival time of each station, as well as the arrival time uncertainty of each set arrival time of each station, the station sequence is used as the vertical coordinate, the time is used as the horizontal coordinate, the arrival time uncertainty is used as the coordinate point on the graph, and the color depth represents the size of the arrival time uncertainty, draw the trip view of the bus route to be queried, and visualize the trip view of the bus route to be queried.
  • a trip view module which is used to obtain request information for the bus route to be queried; and based on the request information for the bus route to be queried, obtain the station sequence of the bus route to be queried and the set arrival time of each station; based on the station sequence of the bus route to be queried
  • the bus arrival time uncertainty visualization system also includes a station view module, which is used to obtain request information for the station to be queried; and based on the request information for the station to be queried, obtain the station ID of the station to be queried, the number of bus routes passing the station, the ID of each bus route passing the station, the station sequence of each bus route passing the station, and the arrival time uncertainty of each set arrival time; based on the station ID of the station to be queried, the number of bus routes passing the station, the ID of each bus route passing the station, the station sequence of each bus route passing the station, and the arrival time uncertainty of each set arrival time, obtain the station view of the station to be queried, and visualize the station view of the station to be queried.
  • a station view module which is used to obtain request information for the station to be queried; and based on the request information for the station to be queried, obtain the station ID of the station to be queried, the number of bus routes passing the station, the ID of each bus route passing the station, the station sequence of each bus route passing
  • the bus arrival time uncertainty visualization system further includes a bus route list view module, which is used to obtain attribute information of each bus route in a preset area; wherein the attribute information includes one or more of the following attributes: route id, route type, departure frequency and arrival interval; according to the attribute information of each bus route in the preset area, a bus route list view is obtained with bus routes as rows and attributes in the attribute information as columns, and the bus route list view is visualized; the attribute modification information of the bus route to be modified is obtained, and the attribute information of the bus route to be modified is modified according to the attribute modification information of the bus route to be modified, and the bus route list view is modified according to the modified attribute information of the bus route to be modified.
  • a bus route list view module which is used to obtain attribute information of each bus route in a preset area; wherein the attribute information includes one or more of the following attributes: route id, route type, departure frequency and arrival interval; according to the attribute information of each bus route in the preset area, a bus route list view is obtained with bus routes
  • each functional module in each embodiment of the present invention may be integrated into one processor, or may exist physically separately, or two or more modules may be integrated into one module.
  • the above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.
  • a computer device comprising a processor and a memory, the memory being used to store a computer program, the computer program comprising program instructions, and the processor being used to execute the program instructions stored in the computer storage medium.
  • the processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a bus arrival time uncertainty visualization method.
  • CPU central processing unit
  • DSP digital signal processors
  • ASIC application-specific integrated circuits
  • FPGA field-programmable gate arrays
  • the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data.
  • a computer-readable storage medium can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device.
  • the computer-readable storage medium provides a storage space, which stores the operating system of the terminal.
  • one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes).
  • the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory.
  • the processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the bus arrival time uncertainty visualization method in the above embodiment.
  • embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
  • These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

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Abstract

一种公交车到达时间不确定性可视化方法、***、设备及介质，所述方法包括：获取公交车运行数据，根据公交车运行数据得到各站点各设定到达时间的实际到达时间的方差，作为各站点各设定到达时间的到达时间不确定性（S1）；根据各站点各设定到达时间的到达时间不确定性，通过核密度估计算法，得到各设定到达时间的不确定性轮廓密度图（S2）；按照时间顺序，将各设定到达时间的不确定性轮廓密度图内的各区域，映射到时间坐标轴上的一维空间中，得到不确定性嵌套跟踪图（S3）；可视化不确定性轮廓密度图和不确定性嵌套跟踪图（S4）。上述方法充分考虑公交车运行过程中的各种因素，确保该到达时间不确定性的可分析性，使得用户可以从时空角度进行不确定性分析。

Description

公交车到达时间不确定性可视化方法、***、设备及介质 技术领域
本发明属于交通技术领域，涉及一种公交车到达时间不确定性可视化方法、***、设备及介质。
 背景技术
在公共交通***中，公交车是人们常见的出行模式。然而，由于公交网络密集，与其他类型车辆的交通环境混合，且不同公交车的驾驶行为也不一样，还有多变的天气情况等因素，导致公交车的运行状态不稳定。理想情况下，公交车的运行应遵循计划的时间表，即公交车的到达时间是稳定的。但是，不稳定的公交运行可能会增加公交到达延误和乘客的等候时间，降低公交运营效率和服务水平。然而，不可预见的情况（例如交通事故等）广泛存在，导致公交车到达时间变化不确定，即公交车到达时间不确定性，这种不确定性导致了公交车运行的不稳定。因此，从事公共车运营的交通规划者和服务提供者需要一个可视分析***来分析公交车数据，并制定解决方案来减小公交车到达时间不确定性。
近年来，交通研究人员已经开发了综合全面的模型，并利用了深度学习技术，以改善公交车的运行。在此基础上，有学者提出了一个名为SD-seq2seq的供求seq2seq模型，其利用智能卡数据来预测公交车聚集现象。此外，还有学者对交通分析背景下的相关可视化技术和可视化分析***进行了概述，介绍了交通数据可视化中常见的数据流。基于此，有学者提出了一种新的交通***不确定性可视化模型，其根据格式塔心理学、色彩心理学和几何图形隐喻，提出包括小提琴图、多层环和风险指标在内的Knotted-line可视化方法。
但是，上述通过深度学习模型预测公交车集聚现象来改善公交车运行的方式，没有考虑人的主动性，并且这些由数据驱动的方法没有考虑到现实公交车驾驶行为及天气等因素的影响，无法支持复杂的分析任务，不能满足领域专家对公交车探索的需求。同时，Knotted-line可视化方法只能分析一条或几条路线且分析维度较单一，不能***地考虑空间、时间以及更多层次的变化。
 技术问题
本发明的目的在于克服上述现有技术的缺点，提供一种公交车到达时间不确定性可视化方法、***、设备及介质。
 技术解决方案
为达到上述目的，本发明采用以下技术方案予以实现：
本发明第一方面，提供一种公交车到达时间不确定性可视化方法，包括：获取公交车运行数据，并根据公交车运行数据得到各站点各设定到达时间的实际到达时间的方差，作为各站点各设定到达时间的到达时间不确定性；根据各站点各设定到达时间的到达时间不确定性，通过核密度估计算法，得到各设定到达时间的不确定性轮廓密度图；按照时间顺序，将各设定到达时间的不确定性轮廓密度图内的各区域，映射到时间坐标轴上的一维空间中，得到不确定性嵌套跟踪图；可视化不确定性轮廓密度图和不确定性嵌套跟踪图。
可选的，所述根据公交车运行数据得到各站点各设定到达时间的实际到达时间的方差前，还包括对公交车运行数据进行删除重复值、删除异常值和补全缺失值处理。
可选的，所述根据各站点各设定到达时间的到达时间不确定性，通过核密度估计算法，得到各设定到达时间的不确定性轮廓密度图包括：获取各站点的经度和纬度；并根据各站点的经度和纬度以及各站点各设定到达时间的到达时间不确定性，得到各站点的各设定到达时间的三元组数据；三元组数据包括经度、纬度以及到达时间不确定性；设定核密度估计算法的地图尺寸、阈值和带宽，遍历各设定到达时间，将各站点的各设定到达时间的三元组数据输入设定后的核密度估计算法，得到各站点的各设定到达时间的绘图数据；根据各站点的各设定到达时间的绘图数据，采用颜色深浅代表到达时间不确定性的大小并绘制轮廓密度图，得到各设定到达时间的不确定性轮廓密度图。
可选的，所述按照时间顺序，将各设定到达时间的不确定性轮廓密度图内的各区域，映射到时间坐标轴上的一维空间中，得到不确定性嵌套跟踪图包括：按照时间顺序，获取各设定到达时间的不确定性轮廓密度图内的各区域的中心点坐标，得到各设定到达时间的各待转换坐标；并将各设定到达时间的各待转换坐标，通过希尔伯特曲线算法映射到时间坐标轴上的一维空间中，得到各设定到达时间的各一维转换坐标；获取并根据各设定到达时间的各一维转换坐标的到达时间不确定性，确定各设定到达时间的各一维转换坐标所属的到达时间不确定性层次；以及将各设定到达时间的各一维转换坐标中相同到达时间不确定性层次的一维转换坐标连接，并填充各到达时间不确定性层次的对应颜色，得到不确定性嵌套跟踪图。
可选的，还包括：获取待查询公交路线请求信息；并根据待查询公交路线请求信息，获取待查询公交路线的站点顺序及各站点的设定到达时间；根据待查询公交路线的站点顺序及各站点的设定到达时间，以及各站点各设定到达时间的到达时间不确定性，以站点顺序为纵坐标，以时间为横坐标，以到达时间不确定性为图上坐标点，并以颜色深浅代表到达时间不确定性的大小，绘制待查询公交路线的行程视图，并可视化待查询公交路线的行程视图。
可选的，还包括：获取待查询站点请求信息；并根据待查询站点请求信息，获取待查询站点的站点id、过站点公交路线数量、各过站点公交路线id、各过站点公交路线的站点顺序以及各设定到达时间的到达时间不确定性；根据待查询站点的站点id、过站点公交路线数量、各过站点公交路线id、各过站点公交路线的站点顺序以及各设定到达时间的到达时间不确定性，得到待查询站点的站点视图，并可视化待查询站点的站点视图。
可选的，还包括：获取预设区域内各公交路线的属性信息；其中，属性信息包括下述属性中的一个或或几个：路线id、路线类型、发车频率和到达间隔；根据预设区域内各公交路线的属性信息，以公交路线为行，以属性信息中的属性为列，得到公交路线列表视图，并可视化公交路线列表视图；获取待修改公交路线的属性修改信息，并根据待修改公交路线的属性修改信息修改待修改公交路线的属性信息，并根据修改后的待修改公交路线的属性信息，修改公交路线列表视图。
本发明第二方面，提供一种公交车到达时间不确定性可视化***，包括：数据获取模块，用于获取公交车运行数据，并根据公交车运行数据得到各站点各设定到达时间的实际到达时间的方差，作为各站点各设定到达时间的到达时间不确定性；轮廓密度图模块，用于根据各站点各设定到达时间的到达时间不确定性，通过核密度估计算法，得到各设定到达时间的不确定性轮廓密度图；嵌套跟踪图模块，用于按照时间顺序，将各设定到达时间的不确定性轮廓密度图内的各区域，映射到时间坐标轴上的一维空间中，得到不确定性嵌套跟踪图；可视化模块，用于可视化不确定性轮廓密度图和不确定性嵌套跟踪图。
本发明第三方面，提供一种计算机设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述公交车到达时间不确定性可视化方法的步骤。
本发明第四方面，提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现上述公交车到达时间不确定性可视化方法的步骤。
与现有技术相比，本发明具有以下有益效果：
本发明公交车到达时间不确定性可视化方法，首先基于公交车运行数据，计算各站点各设定到达时间的实际到达时间的方差，进而以此作为各站点各设定到达时间的到达时间不确定性，以实际的运行数据为基础，充分考虑公交车运行过程中的各种因素，确保该到达时间不确定性的可分析性。同时，基于该各站点各设定到达时间的到达时间不确定性数据，得到各设定到达时间的不确定性轮廓密度图和不确定性嵌套跟踪图进行可视化，通过不确定性轮廓密度图直观显示到达时间不确定性在空间区域上的分布，通过不确定性嵌套跟踪图直观显示到达时间不确定性随时间的演变，使得用户可以从时空角度进行不确定性分析，帮助更好的识别不确定性高的热点区域，并观察在时间维度的不确定性演变过程，更好的帮助用户识别不确定性高的区域，可以为公交车调度提供参考，便于实现公交车集群检查和推理，也可有效避免某些区域的公交车集聚现象。
 附图说明
图1为本发明实施例的公交车到达时间不确定性可视化方法流程图。
图2为本发明实施例的某具体应用场景下的不确定性轮廓密度图。
图3为本发明实施例的某具体应用场景下的不确定性嵌套跟踪图。
图4为本发明实施例的公交车到达时间不确定性可视化***结构框图。
 本发明的实施方式
为了使本技术领域的人员更好地理解本发明方案，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分的实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本发明保护的范围。
需要说明的是，本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本发明的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、***、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
实施例
下面结合附图对本发明做进一步详细描述：
参见图1，本发明一实施例中，提供一种公交车到达时间不确定性可视化方法，使得用户可以更好地理解公交车到达时间不确定性的趋势和细节，该方法从时空角度分析，帮助用户更好的识别公交车到达时间不确定性高的热点区域，并可观察公交车到达时间不确定性在时间维度的演变过程。
具体的，该公交车到达时间不确定性可视化方法包括以下步骤：
S1：获取公交车运行数据，并根据公交车运行数据得到各站点各设定到达时间的实际到达时间的方差，作为各站点各设定到达时间的到达时间不确定性。
S2：根据各站点各设定到达时间的到达时间不确定性，通过核密度估计算法，得到各设定到达时间的不确定性轮廓密度图。
S3：按照时间顺序，将各设定到达时间的不确定性轮廓密度图内的各区域，映射到时间坐标轴上的一维空间中，得到不确定性嵌套跟踪图。
S4：可视化不确定性轮廓密度图和不确定性嵌套跟踪图。
综上，本发明公交车到达时间不确定性可视化方法，首先基于公交车运行数据，计算各站点各设定到达时间的实际到达时间的方差，进而以此作为各站点各设定到达时间的到达时间不确定性，以实际的运行数据为基础，充分考虑公交车运行过程中的各种因素，确保该到达时间不确定性的可分析性。同时，基于该各站点各设定到达时间的到达时间不确定性数据，得到各设定到达时间的不确定性轮廓密度图和不确定性嵌套跟踪图进行可视化，通过不确定性轮廓密度图直观显示到达时间不确定性在空间区域上的分布，通过不确定性嵌套跟踪图直观显示到达时间不确定性随时间的演变，使得用户可以从时空角度进行不确定性分析，帮助更好的识别不确定性高的热点区域，并观察在时间维度的不确定性演变过程，更好的帮助用户识别不确定性高的区域，可以为公交车调度提供参考，便于实现公交车集群检查和推理，也可有效避免某些区域的公交车集聚现象。
其中，公交车运行数据一般包括描述公交线路信息的静态数据以及公交车行驶数据的动态数据。静态数据可以是站点-路线数据，包括公交路线id、行驶方向、站点id、站点名称、站点停靠顺序、经度以及纬度等信息，动态数据就是公交线路经过的每个站点记录的实时数据，可以是公交车id、公交路线id、站点id、经度、纬度、上车位置、下车位置、出发时间、到达时间以及逗留时间等。
其中，站点的设定到达时间为各站点在调度计划下应当到达该站点的时间。站点的设定到达时间的实际到达时间为，在调度计划下应当在当前设定到达时间到达当前站点的公交车，在实际情况下到达该站点的时间。本实施方式中，对于各站点各设定到达时间的实际到达时间均采集一定数量的数据样本，进而基于这些数据样本进行计算，获取各站点各设定到达时间的实际到达时间的方差。
其中，可视化不确定性轮廓密度图，可以是可视化各设定到达时间的不确定性轮廓密度图，也可以是基于待查询设定到达时间的查询请求，可视化待查询设定到达时间的不确定性轮廓密度图。可选的，用户可以选择一个特定的时间段，通过拖动或单击时间滑块来选择感兴趣的时间段，当时间发生变化时，将显示选定时间对应的不确定性轮廓密度图。
在一种可能的实施方式中，所述根据公交车运行数据得到各站点各设定到达时间的实际到达时间的方差前，还包括对公交车运行数据进行删除重复值、删除异常值和补全缺失值处理。
具体的，在原始的公交车运行数据中存在大量的低质量数据，为了后续到达时间不确定性的有效分析，需要先进行数据预处理工作。
所述删除重复值，即同一公交车标识在同一路线和同一站点的两次近距离到达时间，需要删除其中一个值。所述删除异常值，即不合理的出发时间或一小时内一次记录的到达时间进行删除。所述补全缺失值，即行程中缺失的站点信息，按照静态路程表中记录的顺序填写该值，而对于到达时间等属性，根据相邻站点的距离比来计算到达时间，即利用了哈弗辛公式，具体的，将一个站点的地理位置映射到道路网络上，找到与相邻站点的最短路径，并测量最短路径的距离作为一个近似值。这个函数可以扩展到填写两个或两个以上连续的缺失值，以及在开始站点和结束站点的缺失值，只要至少有一个记录的公交行程的值。
在一种可能的实施方式中，在根据公交车运行数据得到各站点各设定到达时间的实际到达时间的方差时，首先将在一定时间内具有相同公交路线id的站点的到达时间连接起来，形成若干条路线并对若干条路线进行对齐，根据中心极限定理，公交车到达时间的概率将近似于一个正态分布。因此，将到达时间不确定性视为偏离设定到达时间的程度，进而基于对齐的数据，计算各站点各设定到达时间的实际到达时间的方差，作为各站点各设定到达时间的到达时间不确定性。
在一种可能的实施方式中，所述根据各站点各设定到达时间的到达时间不确定性，通过核密度估计算法，得到各设定到达时间的不确定性轮廓密度图包括：获取各站点的经度和纬度；并根据各站点的经度和纬度以及各站点各设定到达时间的到达时间不确定性，得到各站点的各设定到达时间的三元组数据；三元组数据包括经度、纬度以及到达时间不确定性；设定核密度估计算法的地图尺寸、阈值和带宽，遍历各设定到达时间，将各站点的各设定到达时间的三元组数据输入设定后的核密度估计算法，得到各站点的各设定到达时间的绘图数据；根据各站点的各设定到达时间的绘图数据，采用颜色深浅代表到达时间不确定性的大小并绘制轮廓密度图，得到各设定到达时间的不确定性轮廓密度图。
具体的，在得到各站点各设定到达时间的到达时间不确定性后，需要对到达时间不确定性设计合适的可视化方法，以便于实现到达时间不确定性的后续分析。
本实施方式中，采用核密度估计（KDE）来估计空间上的不确定性分布。具体的，以各站点的各设定到达时间的三元组数据作为输入数据，通过调用基于KDE的用于得到轮廓密度图的核密度估计算法，得到若干GeoJSON格式的MultiPolygon几何对象输出，进而根据若干GeoJSON格式的MultiPolygon几何对象，调用d3.geoPath来绘制轮廓密度图，得到不确定性轮廓密度图。
其中，核密度估计算法的输入还包括指定的地图尺寸、阈值和带宽，如果没有指定，则为默认值。同时，由于核密度估计算法的输入是屏幕的坐标点，还需要将经度和纬度坐标转换为屏幕坐标点。具体的，每个GeoJSON格式的MultiPolygon几何对象都分别代表轮廓密度图中的一个区域，在各区域中所有的值都大于或等于各区域相应的阈值。对应于不确定性轮廓密度图，即不确定性轮廓密度图的各区域中的所有站点的当前设定到达时间的到达时间不确定性都大于或等于该区域的阈值。同时，采用颜色深浅代表到达时间不确定性的大小，为各区域填充对应的颜色，本实施方式中，到达时间不确定性越大则颜色越深。参见图2，某具体应用场景下的不确定性轮廓密度图示例。
在一种可能的实施方式中，所述按照时间顺序，将各设定到达时间的不确定性轮廓密度图内的各区域，映射到时间坐标轴上的一维空间中，得到不确定性嵌套跟踪图包括：按照时间顺序，获取各设定到达时间的不确定性轮廓密度图内的各区域的中心点坐标，得到各设定到达时间的各待转换坐标；并将各设定到达时间的各待转换坐标，通过希尔伯特曲线算法映射到时间坐标轴上的一维空间中，得到各设定到达时间的各一维转换坐标；获取并根据各设定到达时间的各一维转换坐标的到达时间不确定性，确定各设定到达时间的各一维转换坐标所属的到达时间不确定性层次；以及将各设定到达时间的各一维转换坐标中相同到达时间不确定性层次的一维转换坐标连接，并填充各到达时间不确定性层次的对应颜色，得到不确定性嵌套跟踪图。
具体的，除了在特定时间间隔内的到达时间不确定性的空间表示外，还需要呈现不确定性沿时间维度的演变。本实施方式中，采用不确定性嵌套跟踪图实现这一目的，参见图3，某具体应用场景下的不确定性嵌套跟踪图示例。具体的，将所有不确定性嵌套跟踪图的坐标映射到以时间为横坐标的垂直坐标轴上的一维空间中，实现跟踪不确定性随时间的演化，进而可以直观地探索不确定性的时间表示。其中，不确定性演化的两个组成部分，即区域内多个层次的不确定性值和区域内和区域之间的动态变化，包括空间运动和区域大小的变化等，而通过利用嵌套跟踪图就可以有效实现这两个目标。一方面，嵌套的层次结构属性有助于呈现多层次的不确定性。另一方面，嵌套跟踪图的拓扑结构可以很容易地捕捉到不确定性随时间的动态演化。
其中，获取各设定到达时间的各一维转换坐标的到达时间不确定性时，首先确定当前一维转换坐标对应的待转换坐标，然后确定当前待转换坐标对应的不确定性轮廓密度图内的区域，然后将当前区域对应的到达时间不确定性作为当前一维转换坐标的到达时间不确定性。其中，到达时间不确定性层次可以预先设置，到达时间不确定性层次本质是对到达时间不确定性的范围划分，将一定范围内的到达时间不确定性作为一个到达时间不确定性层次。
在一种可能的实施方式中，所述的公交车到达时间不确定性可视化方法，还包括：获取待查询公交路线请求信息；并根据待查询公交路线请求信息，获取待查询公交路线的站点顺序及各站点的设定到达时间；根据待查询公交路线的站点顺序及各站点的设定到达时间，以及各站点各设定到达时间的到达时间不确定性，以站点顺序为纵坐标，以时间为横坐标，以到达时间不确定性为图上坐标点，并以颜色深浅代表到达时间不确定性的大小，绘制待查询公交路线的行程视图，并可视化待查询公交路线的行程视图。
具体的，设置行程视图提供用户选择的公交路线的详细信息，包括每次行程的站点及时间，进而便于从路线角度进行到达时间不确定性的探索。
在一种可能的实施方式中，所述公交车到达时间不确定性可视化方法，还包括：获取待查询站点请求信息；并根据待查询站点请求信息，获取待查询站点的站点id、过站点公交路线数量、各过站点公交路线id、各过站点公交路线的站点顺序以及各设定到达时间的到达时间不确定性；根据待查询站点的站点id、过站点公交路线数量、各过站点公交路线id、各过站点公交路线的站点顺序以及各设定到达时间的到达时间不确定性，得到待查询站点的站点视图，并可视化待查询站点的站点视图。
具体的，设置站点视图用于列出所选站点的基本信息，当用户在选择一个站时，该站点的信息将在站点视图显示，包括当前站点id，以及有几条公交线路经过该站点、各过站点公交路线id，各过站点公交路线的站点顺序以及该站点各设定到达时间的到达时间不确定性。
在一种可能的实施方式中，所述公交车到达时间不确定性可视化方法，还包括：获取预设区域内各公交路线的属性信息；其中，属性信息包括下述属性中的一个或或几个：路线id、路线类型、发车频率和到达间隔；根据预设区域内各公交路线的属性信息，以公交路线为行，以属性信息中的属性为列，得到公交路线列表视图，并可视化公交路线列表视图；获取待修改公交路线的属性修改信息，并根据待修改公交路线的属性修改信息修改待修改公交路线的属性信息，并根据修改后的待修改公交路线的属性信息，修改公交路线列表视图。
具体的，设置公交路线列表视图方便用户检查数据集中所有路线的基本信息。可选的，公交路线列表视图可以通过表结构形式构成。
下述为本发明的装置实施例，可以用于执行本发明方法实施例。对于装置实施例中未披露的细节，请参照本发明方法实施例。
参见图4，本发明再一实施例中，提供一种公交车到达时间不确定性可视化***，能够用于实现上述的公交车到达时间不确定性可视化方法，具体的，该公交车到达时间不确定性可视化***包括数据获取模块、轮廓密度图模块、嵌套跟踪图模块以及可视化模块。
其中，数据获取模块用于获取公交车运行数据，并根据公交车运行数据得到各站点各设定到达时间的实际到达时间的方差，作为各站点各设定到达时间的到达时间不确定性；轮廓密度图模块用于根据各站点各设定到达时间的到达时间不确定性，通过核密度估计算法，得到各设定到达时间的不确定性轮廓密度图；嵌套跟踪图模块用于按照时间顺序，将各设定到达时间的不确定性轮廓密度图内的各区域，映射到时间坐标轴上的一维空间中，得到不确定性嵌套跟踪图；可视化模块用于可视化不确定性轮廓密度图和不确定性嵌套跟踪图。
在一种可能的实施方式中，所述根据公交车运行数据得到各站点各设定到达时间的实际到达时间的方差前，还包括对公交车运行数据进行删除重复值、删除异常值和补全缺失值处理。
在一种可能的实施方式中，所述根据各站点各设定到达时间的到达时间不确定性，通过核密度估计算法，得到各设定到达时间的不确定性轮廓密度图包括：获取各站点的经度和纬度；并根据各站点的经度和纬度以及各站点各设定到达时间的到达时间不确定性，得到各站点的各设定到达时间的三元组数据；三元组数据包括经度、纬度以及到达时间不确定性；设定核密度估计算法的地图尺寸、阈值和带宽，遍历各设定到达时间，将各站点的各设定到达时间的三元组数据输入设定后的核密度估计算法，得到各站点的各设定到达时间的绘图数据；根据各站点的各设定到达时间的绘图数据，采用颜色深浅代表到达时间不确定性的大小并绘制轮廓密度图，得到各设定到达时间的不确定性轮廓密度图。
在一种可能的实施方式中，所述按照时间顺序，将各设定到达时间的不确定性轮廓密度图内的各区域，映射到时间坐标轴上的一维空间中，得到不确定性嵌套跟踪图包括：按照时间顺序，获取各设定到达时间的不确定性轮廓密度图内的各区域的中心点坐标，得到各设定到达时间的各待转换坐标；并将各设定到达时间的各待转换坐标，通过希尔伯特曲线算法映射到时间坐标轴上的一维空间中，得到各设定到达时间的各一维转换坐标；获取并根据各设定到达时间的各一维转换坐标的到达时间不确定性，确定各设定到达时间的各一维转换坐标所属的到达时间不确定性层次；以及将各设定到达时间的各一维转换坐标中相同到达时间不确定性层次的一维转换坐标连接，并填充各到达时间不确定性层次的对应颜色，得到不确定性嵌套跟踪图。
在一种可能的实施方式中，所述公交车到达时间不确定性可视化***，还包括行程视图模块，行程视图模块用于获取待查询公交路线请求信息；并根据待查询公交路线请求信息，获取待查询公交路线的站点顺序及各站点的设定到达时间；根据待查询公交路线的站点顺序及各站点的设定到达时间，以及各站点各设定到达时间的到达时间不确定性，以站点顺序为纵坐标，以时间为横坐标，以到达时间不确定性为图上坐标点，并以颜色深浅代表到达时间不确定性的大小，绘制待查询公交路线的行程视图，并可视化待查询公交路线的行程视图。
在一种可能的实施方式中，所述公交车到达时间不确定性可视化***，还包括站点视图模块，站点视图模块用于获取待查询站点请求信息；并根据待查询站点请求信息，获取待查询站点的站点id、过站点公交路线数量、各过站点公交路线id、各过站点公交路线的站点顺序以及各设定到达时间的到达时间不确定性；根据待查询站点的站点id、过站点公交路线数量、各过站点公交路线id、各过站点公交路线的站点顺序以及各设定到达时间的到达时间不确定性，得到待查询站点的站点视图，并可视化待查询站点的站点视图。
在一种可能的实施方式中，所述公交车到达时间不确定性可视化***，还包括公交路线列表视图模块，公交路线列表视图模块用于获取预设区域内各公交路线的属性信息；其中，属性信息包括下述属性中的一个或或几个：路线id、路线类型、发车频率和到达间隔；根据预设区域内各公交路线的属性信息，以公交路线为行，以属性信息中的属性为列，得到公交路线列表视图，并可视化公交路线列表视图；获取待修改公交路线的属性修改信息，并根据待修改公交路线的属性修改信息修改待修改公交路线的属性信息，并根据修改后的待修改公交路线的属性信息，修改公交路线列表视图。
前述的公交车到达时间不确定性可视化方法的实施例涉及的各步骤的所有相关内容均可以援引到本发明施例中的公交车到达时间不确定性可视化***所对应的功能模块的功能描述，在此不再赘述。
本发明实施例中对模块的划分是示意性的，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，另外，在本发明各个实施例中的各功能模块可以集成在一个处理器中，也可以是单独物理存在，也可以两个或两个以上模块集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。
本发明再一个实施例中，提供了一种计算机设备，该计算机设备包括处理器以及存储器，所述存储器用于存储计算机程序，所述计算机程序包括程序指令，所述处理器用于执行所述计算机存储介质存储的程序指令。处理器可能是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable GateArray，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等，其是终端的计算核心以及控制核心，其适于实现一条或一条以上指令，具体适于加载并执行计算机存储介质内一条或一条以上指令从而实现相应方法流程或相应功能；本发明实施例所述的处理器可以用于公交车到达时间不确定性可视化方法的操作。
本发明再一个实施例中，本发明还提供了一种存储介质，具体为计算机可读存储介质（Memory），所述计算机可读存储介质是计算机设备中的记忆设备，用于存放程序和数据。可以理解的是，此处的计算机可读存储介质既可以包括计算机设备中的内置存储介质，当然也可以包括计算机设备所支持的扩展存储介质。计算机可读存储介质提供存储空间，该存储空间存储了终端的操作***。并且，在该存储空间中还存放了适于被处理器加载并执行的一条或一条以上的指令，这些指令可以是一个或一个以上的计算机程序（包括程序代码）。需要说明的是，此处的计算机可读存储介质可以是高速RAM存储器，也可以是非不稳定的存储器(non-volatile memory)，例如至少一个磁盘存储器。可由处理器加载并执行计算机可读存储介质中存放的一条或一条以上指令，以实现上述实施例中有关公交车到达时间不确定性可视化方法的相应步骤。
本领域内的技术人员应明白，本发明的实施例可提供为方法、***、或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质（包括但不限于磁盘存储器、CD-ROM、光学存储器等）上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备（***）、和计算机程序产品的流程图和／或方框图来描述的。应理解可由计算机程序指令实现流程图和／或方框图中的每一流程和／或方框、以及流程图和／或方框图中的流程和／或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和／或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和／或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和／或方框图一个方框或多个方框中指定的功能的步骤。
最后应当说明的是：以上实施例仅用以说明本发明的技术方案而非对其限制，尽管参照上述实施例对本发明进行了详细的说明，所属领域的普通技术人员应当理解：依然可以对本发明的具体实施方式进行修改或者等同替换，而未脱离本发明精神和范围的任何修改或者等同替换，其均应涵盖在本发明的权利要求保护范围之内。

Claims (10)

  1. 一种公交车到达时间不确定性可视化方法，其特征在于，包括：
    获取公交车运行数据，并根据公交车运行数据得到各站点各设定到达时间的实际到达时间的方差，作为各站点各设定到达时间的到达时间不确定性；
    根据各站点各设定到达时间的到达时间不确定性，通过核密度估计算法，得到各设定到达时间的不确定性轮廓密度图；
    按照时间顺序，将各设定到达时间的不确定性轮廓密度图内的各区域，映射到时间坐标轴上的一维空间中，得到不确定性嵌套跟踪图；
    可视化不确定性轮廓密度图和不确定性嵌套跟踪图。
  2. 根据权利要求1所述的公交车到达时间不确定性可视化方法，其特征在于，所述根据公交车运行数据得到各站点各设定到达时间的实际到达时间的方差前，还包括对公交车运行数据进行删除重复值、删除异常值和补全缺失值处理。
  3. 根据权利要求1所述的公交车到达时间不确定性可视化方法，其特征在于，所述根据各站点各设定到达时间的到达时间不确定性，通过核密度估计算法，得到各设定到达时间的不确定性轮廓密度图包括：
    获取各站点的经度和纬度；并根据各站点的经度和纬度以及各站点各设定到达时间的到达时间不确定性，得到各站点的各设定到达时间的三元组数据；三元组数据包括经度、纬度以及到达时间不确定性；
    设定核密度估计算法的地图尺寸、阈值和带宽，遍历各设定到达时间，将各站点的各设定到达时间的三元组数据输入设定后的核密度估计算法，得到各站点的各设定到达时间的绘图数据；
    根据各站点的各设定到达时间的绘图数据，采用颜色深浅代表到达时间不确定性的大小并绘制轮廓密度图，得到各设定到达时间的不确定性轮廓密度图。
  4. 根据权利要求1所述的公交车到达时间不确定性可视化方法，其特征在于，所述按照时间顺序，将各设定到达时间的不确定性轮廓密度图内的各区域，映射到时间坐标轴上的一维空间中，得到不确定性嵌套跟踪图包括：
    按照时间顺序，获取各设定到达时间的不确定性轮廓密度图内的各区域的中心点坐标，得到各设定到达时间的各待转换坐标；并将各设定到达时间的各待转换坐标，通过希尔伯特曲线算法映射到时间坐标轴上的一维空间中，得到各设定到达时间的各一维转换坐标；
    获取并根据各设定到达时间的各一维转换坐标的到达时间不确定性，确定各设定到达时间的各一维转换坐标所属的到达时间不确定性层次；以及将各设定到达时间的各一维转换坐标中相同到达时间不确定性层次的一维转换坐标连接，并填充各到达时间不确定性层次的对应颜色，得到不确定性嵌套跟踪图。
  5. 根据权利要求1所述的公交车到达时间不确定性可视化方法，其特征在于，还包括：
    获取待查询公交路线请求信息；并根据待查询公交路线请求信息，获取待查询公交路线的站点顺序及各站点的设定到达时间；
    根据待查询公交路线的站点顺序及各站点的设定到达时间，以及各站点各设定到达时间的到达时间不确定性，以站点顺序为纵坐标，以时间为横坐标，以到达时间不确定性为图上坐标点，并以颜色深浅代表到达时间不确定性的大小，绘制待查询公交路线的行程视图，并可视化待查询公交路线的行程视图。
  6. 根据权利要求1所述的公交车到达时间不确定性可视化方法，其特征在于，还包括：
    获取待查询站点请求信息；并根据待查询站点请求信息，获取待查询站点的站点id、过站点公交路线数量、各过站点公交路线id、各过站点公交路线的站点顺序以及各设定到达时间的到达时间不确定性；
    根据待查询站点的站点id、过站点公交路线数量、各过站点公交路线id、各过站点公交路线的站点顺序以及各设定到达时间的到达时间不确定性，得到待查询站点的站点视图，并可视化待查询站点的站点视图。
  7. 根据权利要求1所述的公交车到达时间不确定性可视化方法，其特征在于，还包括：
    获取预设区域内各公交路线的属性信息；其中，属性信息包括下述属性中的一个或或几个：路线id、路线类型、发车频率和到达间隔；
    根据预设区域内各公交路线的属性信息，以公交路线为行，以属性信息中的属性为列，得到公交路线列表视图，并可视化公交路线列表视图；
    获取待修改公交路线的属性修改信息，并根据待修改公交路线的属性修改信息修改待修改公交路线的属性信息，并根据修改后的待修改公交路线的属性信息，修改公交路线列表视图。
  8. 一种公交车到达时间不确定性可视化***，其特征在于，包括：
    数据获取模块，用于获取公交车运行数据，并根据公交车运行数据得到各站点各设定到达时间的实际到达时间的方差，作为各站点各设定到达时间的到达时间不确定性；
    轮廓密度图模块，用于根据各站点各设定到达时间的到达时间不确定性，通过核密度估计算法，得到各设定到达时间的不确定性轮廓密度图；
    嵌套跟踪图模块，用于按照时间顺序，将各设定到达时间的不确定性轮廓密度图内的各区域，映射到时间坐标轴上的一维空间中，得到不确定性嵌套跟踪图；
    可视化模块，用于可视化不确定性轮廓密度图和不确定性嵌套跟踪图。
  9. 一种计算机设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现如权利要求1至7任一项所述公交车到达时间不确定性可视化方法的步骤。
  10. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现如权利要求1至7任一项所述公交车到达时间不确定性可视化方法的步骤。
PCT/CN2023/133440 2022-12-12 2023-11-22 公交车到达时间不确定性可视化方法、***、设备及介质 WO2024125253A1 (zh)

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