Visualization Method, Device, Equipment and Medium for Shuttle Route Planning

Abstract

The present invention discloses a visualization method, device, equipment and medium for shuttle bus route planning, which relates to the field of fintech. The visualization method for shuttle bus route planning includes the following steps: clustering the collected ride data to obtain optional stops, generating a shuttle bus route comparison view based on the multi-dimensional spatio-temporal information of the optional stops, then outputting a shuttle bus route comparison result based on the shuttle bus route comparison view, determining a candidate shuttle bus route based on the comparison result and screening conditions, next determining multi-dimensional consideration indicators corresponding to the candidate shuttle bus route according to the multi-dimensional spatio-temporal information of each stop in the candidate shuttle bus route, and generating a visualization analysis chart of the candidate shuttle bus route based on the multi-dimensional consideration indicators. The present invention improves the planning efficiency of shuttle bus routes, thereby reducing the commuting time of employees taking the shuttle bus and indirectly improving the work efficiency of employees.

Description

Technical Field

[0001] The present invention relates to the technical field of data visualization, and in particular to a visualization method, device, equipment and medium for shuttle bus route planning. Background Art

[0002] With the expansion of the city and the division of urban functional areas, the problem of separation between the workplace and residence of corporate employees is becoming increasingly serious. In addition, many companies are located near the core areas of the city. Although transportation is convenient, the cost of living is also high. Many employees cannot afford the high rent of living near subway stations or bus stations. Even if they live near nearby subway stations or bus stations, the establishment of these public transportation is planned and designed based on the transportation needs of the entire city. The operation speed is slow and time-consuming. Therefore, the separation of workplace and residence has led to the continuous increase in the average commuting time and travel distance of urban residents. Against this background, corporate commuter buses came into being. Corporate commuter buses are one of the customized commuter buses. Corporate commuter buses are service vehicles with fixed routes and scheduled travel arranged by companies to facilitate employees to go to and from get off work.

[0003] At present, the route design of existing corporate commuter buses mainly relies on manual demand analysis, collecting employees' home addresses and then manually setting routes. A small number of automated methods mainly focus on improving existing shuttle routes, obtaining employee data at certain stations, and then optimizing existing routes on this basis, or setting up stations according to employees' home addresses. Ideally, shuttle bus settings not only rely on location information, but are also closely related to employees' travel time and regular travel patterns. Existing shuttle bus route planning methods cannot tap into deep user needs. For example, employees in some departments often need to work overtime and cannot catch the bus. Summary of the invention

[0004] The main purpose of the present invention is to provide a visualization method, device, equipment and medium for shuttle bus route planning, aiming to solve the technical problem that the existing shuttle bus route planning has unreasonable stops and travel times, resulting in long commuting time for employees taking the shuttle bus, which indirectly affects the work efficiency of employees.

[0005] To achieve the above object, the present invention provides a visualization method for shuttle bus route planning, the visualization method for shuttle bus route planning comprising the steps of:

[0006] Clustering the collected riding data to obtain optional sites, and generating a shuttle bus route comparison view based on the multi-dimensional spatiotemporal information of the optional sites;

[0007] Output the shuttle route comparison result based on the shuttle route comparison view, and determine the candidate shuttle routes based on the comparison result and the filtering conditions;

[0008] Determine the multi-dimensional consideration indicators corresponding to the candidate shuttle routes according to the multi-dimensional spatio-temporal information of each stop in the candidate shuttle routes, and generate a visual analysis chart of the candidate shuttle routes based on the multi-dimensional consideration indicators.

[0009] Optionally, the step of clustering the collected ride data to obtain optional stops and generating a shuttle route comparison view based on the multi-dimensional spatio-temporal information of the optional stops includes;

[0010] Obtain the ride data of the starting boarding position corresponding to the shuttle route, perform angle clustering and distance clustering on the ride data in sequence, determine the optional stops according to the clustering results, and obtain the clustering area data and clustering coefficients;

[0011] Obtain the location data of the optional stops, the reference distances from the optional stops to adjacent stops, and obtain the reference travel times for the optional stops to reach adjacent stops at the target time point, as well as the index data for the optional stops to reach alternative stops through a third-party platform;

[0012] Correspondingly save the clustering area data, the clustering coefficients, the location data of the optional stops, the reference travel times, the reference distances, and the index data as the multi-dimensional spatio-temporal information;

[0013] Display one or more of the multi-dimensional spatio-temporal information to generate the shuttle route comparison view.

[0014] Optionally, the shuttle route comparison view includes a clustering view, a projection view, and a route adjustment view. The step of displaying one or more of the multi-dimensional spatio-temporal information to generate the shuttle route comparison view includes:

[0015] Construct the clustering view according to the clustering area data and the clustering coefficients;

[0016] Determine the driving direction of the shuttle route based on the clustering view, and generate the projection view according to the location data of the optional stops corresponding to the driving direction;

[0017] Represent the reference travel times, the reference distances, and the index data of each optional stop determined by the projection view in the driving direction to generate the route adjustment view.

[0018] Optionally, the step of determining the driving direction of the shuttle route based on the clustering view and generating the projection view according to the location data of the optional stops corresponding to the driving direction includes:

[0019] Obtain a target time point, and determine optional stations corresponding to the driving direction based on the target time point;

[0020] Represent the position data of the optional stations corresponding to the driving direction in a pre-generated map to generate the perspective view.

[0021] Optionally, the step of representing the reference travel time, the reference distance, and the index data of each optional station in the driving direction in the perspective view to generate the route adjustment view includes:

[0022] Use a bar distribution diagram to group and display the reference travel time, the reference distance, and the index data of each optional station within the group, where each optional station within the group is determined based on the clustering result of the riding data;

[0023] Select an optional station as a candidate station in each group, and connect the candidate stations to obtain the route adjustment view.

[0024] Optionally, the visualization analysis diagram of the candidate shuttle bus route includes a timetable view corresponding to the candidate shuttle bus route, and the multi-dimensional consideration indicators include but are not limited to the arrival time and the driving distance corresponding to the candidate shuttle bus route reaching each candidate station;

[0025] The step of generating the visualization analysis diagram of the candidate shuttle bus route based on the multi-dimensional consideration indicators includes:

[0026] Set the arrival time as the abscissa of the timetable and the driving distance as the ordinate of the timetable;

[0027] Determine the coordinate values of each candidate station according to the arrival time and the driving distance;

[0028] Represent each candidate station in the coordinates according to the coordinate values respectively, and connect the coordinate points representing the candidate stations to obtain the timetable view.

[0029] Optionally, the visualization analysis diagram of the candidate shuttle bus route includes a radar view of the candidate shuttle bus route, and the step of generating the visualization analysis diagram of the candidate shuttle bus route based on the multi-dimensional consideration indicators includes:

[0030] Obtain the multi-dimensional consideration indicators based on the shuttle bus route comparison view, where the multi-dimensional consideration indicators include but are not limited to the number of passengers, the average walking time and distance, the route driving time and distance corresponding to the candidate shuttle bus route;

[0031] Based on the figure formed by the number of passengers, average walking time and distance, route driving time and distance corresponding to the candidate shuttle route, a radar view of the candidate shuttle route is obtained.

[0032] In addition, to achieve the above object, the present invention further provides a visualization device for shuttle route planning, and the visualization device for shuttle route planning includes:

[0033] A first generation module, configured to cluster the collected ride data to obtain optional stops, and generate a comparison view of shuttle routes based on the multi-dimensional spatio-temporal information of the optional stops;

[0034] A screening module, configured to output a comparison result of shuttle routes based on the comparison view of shuttle routes, and determine candidate shuttle routes based on the comparison result and screening conditions;

[0035] A second generation module, configured to determine multi-dimensional consideration indicators corresponding to the candidate shuttle route according to the multi-dimensional spatio-temporal information of each stop in the candidate shuttle route, and generate a visualization analysis chart of the candidate shuttle route based on the multi-dimensional consideration indicators.

[0036] In addition, to achieve the above object, the present invention further provides a visualization device for shuttle route planning, and the visualization device for shuttle route planning includes a memory, a processor, and a visualization program for shuttle route planning stored on the memory and executable on the processor. When the visualization program for shuttle route planning is executed by the processor, the steps of the visualization method for shuttle route planning are implemented.

[0037] In addition, to achieve the above object, the present invention further provides a readable storage medium, on which a visualization program for shuttle route planning is stored. When the visualization program for shuttle route planning is executed by a processor, the steps of the visualization method for shuttle route planning as described above are implemented.

[0038] The present invention clusters the collected ride data to obtain optional stops, generates a comparison view of shuttle routes based on the multi-dimensional spatio-temporal information of the optional stops, then outputs a comparison result of shuttle routes based on the comparison view of shuttle routes, determines candidate shuttle routes based on the comparison result and screening conditions, and then determines multi-dimensional consideration indicators corresponding to the candidate shuttle route according to the multi-dimensional spatio-temporal information of each stop in the candidate shuttle route, and generates a visualization analysis chart of the candidate shuttle route based on the multi-dimensional consideration indicators. It realizes the effective mining of deep user needs such as the travel time and travel rules of employees taking the shuttle through the content displayed in the visual view, thereby reasonably setting the shuttle route and stops, improving the planning efficiency of the shuttle route, reducing the commuting time of employees taking the shuttle, and indirectly improving the work efficiency of employees. Brief Description of the Drawings

[0039] Figure 1 is a schematic flowchart of the first embodiment of the visualization method for shuttle bus route planning according to the present invention;

[0040] Figure 2 is a schematic diagram of a clustering view in the embodiment of the visualization method for shuttle bus route planning according to the present invention;

[0041] Figure 3 is a schematic diagram of a route adjustment view in the embodiment of the visualization method for shuttle bus route planning according to the present invention;

[0042] Figure 4 is a schematic diagram of a statistical view in the embodiment of the visualization method for shuttle bus route planning according to the present invention;

[0043] Figure 5 is a schematic diagram of a timetable view and a radar view in the embodiment of the visualization method for shuttle bus route planning according to the present invention;

[0044] Figure 6 is a functional schematic diagram module diagram of the preferred embodiment of the visualization device for shuttle bus route planning according to the present invention;

[0045] Figure 7 is a schematic structural diagram of the hardware operating environment involved in the embodiment solution of the visualization method for shuttle bus route planning according to the present invention.

[0046] The implementation, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed Embodiment

[0047] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] The present invention provides a visualization method for shuttle bus route planning. Refer to Figure 1 , Figure 1 , which is a schematic flowchart of the first embodiment of the visualization method for shuttle bus route planning according to the present invention.

[0049] The embodiments of the present invention provide embodiments of the visualization method for shuttle bus route planning. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than here.

[0050] The visualization method for shuttle bus route planning includes:

[0051] Step S100, clustering the collected riding data to obtain optional stops, and generating a shuttle bus route comparison view based on the multi-dimensional spatio-temporal information of the optional stops;

[0052] In this embodiment, the ride data at least includes: the rider, the start time point and end time point of the ride, the location and identifier of the drop-off point, and the ride distance. The ride data for planning the shuttle bus route can be obtained from the taxi reimbursement data of employees in various departments of the company, or from historical shuttle bus data. Specifically, the rider can be identified by name or employee number, etc. Based on the rider, their department can be determined, and then the ride data corresponding to their department can be statistically analyzed. Through the start time point and end time point of the ride, the ride time corresponding to this ride record can be calculated. If there are multiple ride data with the same drop-off point, the average time from the company to this drop-off point can be calculated. The drop-off location generally refers to the actual longitude and latitude of the drop-off point, that is, the location coordinates, which can accurately mark the drop-off point. The drop-off point identifier refers to its name, which is used to identify the drop-off location. The ride distance can be calculated from the drop-off location and the company location.

[0053] Furthermore, the amount of collected ride data is very large. Valuable optional stops are obtained through clustering, and then a comparison view of the shuttle bus route is generated based on the multi-dimensional spatio-temporal information of these optional stops.

[0054] Specifically, step S100 includes:

[0055] Step S110, obtain the ride data of the starting boarding location corresponding to the shuttle bus route, perform angle clustering and distance clustering on the ride data in sequence, determine the optional stops according to the clustering results, and obtain the clustering area data and clustering coefficients;

[0056] Step S120, obtain the location data of the optional stops through a third-party platform, the reference distance for the optional stops to reach adjacent stops, obtain the reference time-consuming for the optional stops to reach adjacent stops at the target time point, and the index data for the optional stops to reach alternative stops;

[0057] Step S130, correspondingly save the clustering area data, the clustering coefficients, the location data of the optional stops, the reference time-consuming, the reference distance, and the index data as the multi-dimensional spatio-temporal information;

[0058] Step S140, display one or more of the multi-dimensional spatio-temporal information to generate the comparison view of the shuttle bus route.

[0059] In this embodiment, obtain the ride data of the starting boarding location corresponding to the shuttle bus route. The starting boarding location is the originating station corresponding to the shuttle bus route, which generally refers to the company. For the sake of convenience of description, the starting boarding location corresponding to the shuttle bus route is defaulted to the company.

[0060] After obtaining the ride data, the amount of ride data collected is huge. The existing technologies mainly rely on manual demand analysis or improvement of existing shuttle bus routes, and cannot effectively dig out deep user needs, resulting in long commuting times for employees taking the shuttle bus, indirectly affecting the work efficiency of employees. Therefore, this application adopts a visualization method to determine candidate stations based on the ride data, and compare and analyze the parameters of the candidate stations, so as to guide the planning of shuttle bus routes, improve the planning efficiency of shuttle bus routes, reduce the commuting time of employees taking the shuttle bus, and indirectly improve the work efficiency of employees. The visualization views include: clustering view, projection view, shuttle bus route comparison view, timetable view, and radar view. The clustering view is used to display the correspondence between the planned number of shuttle bus routes and the directions of the shuttle bus routes. Among them, the clustering number is the number of directions in which the shuttle buses are planned to run; the projection view is used to display the positions of each alighting point in the ride data on the electronic map; the route adjustment view can display the index parameters of the optional stations, and the stations can be screened according to the index parameters; the timetable view is used to display all the station names and arrival times of a shuttle bus route for users to view; the radar view displays the index data corresponding to the shuttle bus route and can be used for comparison between routes.

[0061] According to the positions of each alighting point in the ride data, with the location of the company as the origin, the angle of each alighting point relative to the company can be calculated, and then angle clustering can be performed. The K-Means clustering method can be used to obtain the clustering region data corresponding to different clustering coefficients. Among them, the clustering coefficients include the clustering number and the silhouette coefficient of the clustering, and the clustering region data includes the number of alighting stations corresponding to each clustering direction and the angle distribution. Further, select a clustering direction, which includes many alighting stations, and continue to perform distance clustering on all the alighting points in this clustering direction to obtain the optional stations corresponding to different clusters. All the alighting points in the same cluster share an optional station, and the passengers corresponding to these alighting points are default to get off at the same optional station.

[0062] Further, through a third-party platform, such as Baidu Map, etc., obtain the location data of the optional stations, the reference distance from the optional stations to adjacent stations, and obtain the reference time taken for the optional stations to reach adjacent stations at the target time point, as well as the index data of the optional stations to reach alternative stations. Corresponding save the clustering region data, clustering coefficients, location data of the optional stations, reference time taken, reference distance, and index data as multi-dimensional spatio-temporal information; display one or more of the multi-dimensional spatio-temporal information to generate a shuttle bus route comparison view.

[0063] Specifically, step S140 includes:

[0064] Step S141, constructing the clustering view according to the clustering region data and the clustering coefficients;

[0065] In this embodiment, the clustering coefficient includes the number of clusters and the silhouette coefficient of the clusters. The cluster region data includes the number of downstream stations corresponding to each clustering direction and the angular distribution. As Figure 2 shown, the clustering view is introduced. The clustering view consists of two parts. The abscissa of the upper graph is the number of clusters, and the ordinate is the silhouette coefficient of the clusters. The larger the silhouette coefficient, the more appropriate the number of clusters. Therefore, a maximum value is selected from the silhouette coefficients of the clusters, and the corresponding number of clusters is 9, that is, it is recommended to operate shuttle buses in 9 directions. It should be noted that in this graph, the number of clusters corresponding to the maximum value of the silhouette coefficient is 2, indicating that it is recommended to operate shuttle buses in 2 directions. However, it is unreasonable for a company to operate shuttle buses in 2 directions, so 2 is not selected. The following graph shows the distribution of data for each cluster region represented by a box-and-whisker plot. When the number of clusters is determined to be 9, shuttle buses in 9 directions are operated. In this graph, the ordinate represents the identifier of each clustering direction and the number of downstream stations it includes; the abscissa is the angle. Taking the location of the company as the origin, the box-and-whisker plot represents the angular distribution of downstream stations in different clustering directions. The minimum angle and the maximum angle corresponding to the downstream stations included in this cluster region are also marked in the graph. Among them, the box-and-whisker plot is a statistical graph used to display the dispersion of a set of data.

[0066] Step S142, determine the driving direction of the shuttle bus route based on the clustering view, and generate the projection view according to the position data of the optional stations corresponding to the driving direction;

[0067] Specifically, step S142 includes:

[0068] Step a, obtain the target time point, and determine the optional stations corresponding to the driving direction based on the target time point;

[0069] Step b, represent the position data of the optional stations corresponding to the driving direction in a pre-generated map to generate the projection view.

[0070] In this embodiment, location data corresponding to each optional stop in the ride data is determined according to the ride data, and the driving direction of the shuttle route is determined, so as to screen out the optional stops corresponding to the driving direction. Then, the location data of the optional stops corresponding to the driving direction is represented on the electronic map to generate a projection view. Specifically, first, the driving direction of the shuttle route is determined, and at the same time, the departure time of the shuttle is determined. Different departure times may result in different numbers of optional stops, the number of passengers, the distance, and the route driving time, etc. After determining the departure time of the shuttle, the optional stops corresponding to the driving direction are determined, and finally, the location data of the optional stops is projected onto the map. The projection view shows the locations corresponding to each drop-off point on the electronic map. If site planning is carried out for the clustering direction, that is, the driving direction of the shuttle route is determined, the optional stops corresponding to the driving direction are displayed in the projection view. At the same time, the recommended stops will be highlighted in the projection view, and the specific distribution of the valid drop-off points will also be displayed in the route adjustment view. There is a coordinate schematic diagram at the lower left corner of the projection view. The abscissa represents time, and the ordinate represents the number of passengers. Generally, a time with a relatively large number of people is selected as the departure time, such as 21:30 or 21:55. In addition, each small circle in the projection view represents a drop-off point. In the projection view, each clustering direction is divided by a solid line on the map according to the clustering area data.

[0071] Step S143: Represent the reference travel time, the reference distance, and the index data of each optional stop determined by the projection view in the driving direction to generate the route adjustment view.

[0072] Specifically, step S143 includes:

[0073] Step c: Use a bar distribution diagram to group and display the reference travel time, the reference distance, and the index data of each optional stop within the group, where each optional stop within the group is determined based on the clustering result of the ride data;

[0074] Step d: Select an optional stop as a candidate stop in each group, and connect the candidate stops to obtain the route adjustment view.

[0075] In this embodiment, based on the ride data, the reference travel time and reference distance from an optional stop to other alighting points, as well as the metric data, are determined. The metric data includes: the reach probability and distance cost from the optional stop to its alternative stop. Among them, the reach probability includes reachable within 200 meters, reachable within 400 meters, reachable within 600 meters, reachable within 800 meters, and reachable within 1000 meters. The reach probability and distance cost are used to calculate the distances between alighting points, and it is recommended to keep them within 1 km, that is, to reduce the walking distance after getting off the vehicle. Both the reference travel time and reference distance are calculated using the weighted average method. An optional stop corresponds to multiple passengers, and the weight of this optional stop is equal to the number of passengers corresponding to this optional stop divided by the total number of passengers in its cluster. The distances and travel times between various alighting stops or stops can be obtained from a third-party platform, such as Baidu Map, etc.

[0076] For example, as Figure 3 shown, R-Cluster 0 to R-Cluster 8 in the figure represent that all alighting points in the selected clustering direction are divided into 9 groups after clustering. It can be seen in the figure the number of optional stops included in each clustering group. For example, there are 4 rectangular boxes below R-Cluster 6, indicating 4 optional stops. Currently, the second optional stop is selected as the candidate stop. Suppose 5 passengers get off at the first optional stop, 7 passengers get off at the second optional stop (candidate stop), 3 passengers get off at the third optional stop, and 4 passengers get off at the fourth optional stop. Then the weight of the first optional stop is: 5 / (5 + 3 + 4) = 0.42, the weight of the third optional stop is: 3 / (5 + 3 + 4) = 0.25, and the weight of the fourth optional stop is: 4 / (5 + 3 + 4) = 0.33.

[0077] Furthermore, the adjustment of candidate stops is supported in the route adjustment view. For example, there are 4 rectangular boxes below R-Cluster 6, indicating 4 optional stops. The optional stops can be compared according to the corresponding metric data to determine a better optional stop as the candidate stop. For example, there are 2 optional stops in R-Cluster 2. Suppose when the first optional stop is selected as the candidate stop, the average distance is 820 meters, the average time is 9.68 minutes, and the 800-meter coverage rate is 93%; when the second optional stop is selected as the candidate stop, the average distance is 768 meters, the average time is 12.94 minutes, and the 800-meter coverage rate is 76%. Although the first optional stop has a longer distance, its average time is shorter, and the 800-meter coverage rate is larger. Maybe due to the existence of some overpasses and other factors, although the distance is short, the travel time is long. Therefore, after comprehensive comparison, the first optional stop is used as a candidate stop for clustering in this area.

[0078] Step S200: Output the shuttle route comparison result based on the shuttle route comparison view, and determine the candidate shuttle routes based on the comparison result and the screening criteria.

[0079] In this embodiment, the shuttle route comparison view includes a clustering view, a projection view, and a route adjustment view. The clustering view is used to display the correspondence between the number of clusters and the silhouette coefficient. The larger the silhouette coefficient, the more appropriate the number of clusters. Thus, the number of driving directions corresponding to the candidate shuttle routes can be determined through the clustering view. Further, the projection view is used to display the positions of each alighting point in the ride data on the electronic map, and supports displaying the positions of the alighting points of one or more driving directions separately. Each small circle in the projection map represents an alighting point. Through the projection map, the distribution of each alighting point can be intuitively viewed, providing a reference for the screening of candidate stops. Further, the route adjustment view can display the index parameters of the candidate stops. According to the index parameters, stop screening can be performed to determine a more suitable candidate shuttle route. Among them, the index parameters include the reference time and distance for the candidate stop to reach other alighting points, as well as the reach probability and distance cost from the candidate stop to its alternative stop. Through these index parameters, comparisons can be made from multiple dimensions, thereby planning a more reasonable shuttle route that better meets the needs of employees.

[0080] Step S300: Determine the multi-dimensional consideration indicators corresponding to the candidate shuttle routes according to the multi-dimensional spatio-temporal information of each stop in the candidate shuttle routes, and generate a visual analysis chart of the candidate shuttle routes based on the multi-dimensional consideration indicators.

[0081] In this embodiment, the multi-dimensional consideration indicators include, but are not limited to, the arrival time and driving distance corresponding to each candidate stop of the candidate shuttle route, the number of passengers, the average walking time and distance, the route driving time and distance of the candidate shuttle route. Based on these multi-dimensional consideration indicators, a visual analysis chart of the candidate shuttle routes is further generated. The visual analysis chart includes a timetable view and a radar view. The timetable view is used to display all the stop names and arrival times of a shuttle route for the user to view; the radar view displays index data such as the number of passengers, the average walking time and distance, and the route driving time and distance of the shuttle route, and route comparisons can be made.

[0082] It should be noted that the visual view also includes a statistical view. The statistical view can display the overall situation of the ride data. The statistical chart separately conducts statistics on the ride data of each department according to the departments to which the riders belong. The ride data at least includes: the rider, the start time point and end time point of the ride, the alighting point position and alighting point identifier, and the ride distance. The ride data can be obtained from the taxi reimbursement data of employees in each department of the company, or from historical shuttle data.

[0083] Visually encode each ride data to obtain the visualized ride data. Establish the mapping relationship between riders and departments in advance. According to the start time point and end time point of the ride, obtain the corresponding ride time of the visualized ride record. Taking the department as a unit, count the ride data of each department, and correspondingly obtain the total mileage, order quantity, and average mileage of the visualized department. Specifically, use a table form to represent the visualized ride data. The statistical chart also includes the start time point and end time point of the ride displayed in the form of a shaded graph. Specifically, as Figure 4 shown, the first table from top to bottom represents the total mileage, order quantity, and average mileage of the visualized department, and the second graph represents the start time point and end time point of the ride.

[0084] In this embodiment, the optional stations are obtained by clustering the collected ride data. Based on the multi-dimensional spatio-temporal information of the optional stations, a shuttle route comparison view is generated. Then, based on the shuttle route comparison view, the shuttle route comparison result is output. Based on the comparison result and the screening conditions, the candidate shuttle route is determined. Next, according to the multi-dimensional spatio-temporal information of each station in the candidate shuttle route, the multi-dimensional consideration indicators corresponding to the candidate shuttle route are determined. Based on the multi-dimensional consideration indicators, a visualized analysis graph of the candidate shuttle route is generated. It realizes the effective mining of deep user needs such as the travel time and travel patterns of employees taking the shuttle through the content displayed in the visual view, the comparison view, and the analysis view, so as to reasonably set the shuttle route and stations, thereby improving the planning efficiency of the shuttle route, reducing the corresponding commuting time of employees taking the shuttle, and indirectly improving the work efficiency of employees.

[0085] Further, a second embodiment of the visualization method for shuttle route planning of the present invention is proposed. The difference between the second embodiment of the visualization method for shuttle route planning and the first embodiment of the visualization method for shuttle route planning is that the visualized analysis graph of the candidate shuttle route includes the schedule view corresponding to the candidate shuttle route. The multi-dimensional consideration indicators include, but are not limited to, the arrival time and driving distance corresponding to the candidate shuttle route reaching each candidate station. The step S300 includes:

[0086] Step S310, set the arrival time as the abscissa of the schedule and the driving distance as the ordinate of the schedule;

[0087] Step S320, determine the coordinate values of each candidate station according to the arrival time and the driving distance;

[0088] Step S330, respectively represent each candidate station in the coordinate according to the coordinate values, and connect the coordinate points representing the candidate stations to obtain the schedule view.

[0089] In this embodiment, the visual analysis graph of the candidate shuttle route includes a timetable view corresponding to the candidate shuttle route. The timetable data of the candidate stops corresponding to the shuttle route is obtained in the route adjustment view. The timetable data includes at least the arrival time and driving distance of the candidate shuttle route at the candidate stop, and the identifier of the candidate stop. Then, it is displayed in the timetable view corresponding to the shuttle route planning according to the index data of the candidate stop.

[0090] Specifically, the abscissa of the timetable view is the arrival time, and the ordinate is the clustering direction, which is used to indicate which clustering direction the stop belongs to. And the driving distance from the company to each candidate stop is also given for each clustering direction. As Figure 5 shown, from left to right, the first graph is the timetable view. There are 9 clustering directions in the graph, that is, there are 9 candidate stops. The departure times of the 2 shuttles are 21:30 and 21:55 respectively. The arrival times of each candidate stop can be viewed from the graph.

[0091] Furthermore, the visual analysis graph of the candidate shuttle route includes a radar view of the candidate shuttle route. Specifically, step S300 includes:

[0092] Step S340, obtaining the multi-dimensional consideration indicators based on the shuttle route comparison view, where the multi-dimensional consideration indicators include but are not limited to the number of passengers corresponding to the candidate shuttle route, the average walking time and distance, the route driving time and distance;

[0093] Step S350, obtaining the radar view of the candidate shuttle route based on the graph formed by the number of passengers corresponding to the candidate shuttle route, the average walking time and distance, the route driving time and distance.

[0094] In this embodiment, the ride data determines the index data corresponding to the shuttle route, and a radar view is constructed according to the index data corresponding to the shuttle route. Specifically, the number of passengers corresponding to the candidate shuttle route, the average walking time and distance, the route driving time and distance, the reach probability, etc. are determined according to the ride data. As Figure 5 shown, from left to right, the second graph is the radar view. The index data corresponding to the shuttle route includes: driving distance, driving time, number of passengers, average walking distance, average walking time, and 800-meter reach probability. According to the index data corresponding to multiple shuttle routes displayed in the radar view, further intuitive comprehensive comparison is carried out.

[0095] In this embodiment, by determining the multi-dimensional consideration indexes corresponding to the candidate shuttle bus routes according to the multi-dimensional spatio-temporal information of each stop in the candidate shuttle bus routes, generating a visual analysis graph of the candidate shuttle bus routes based on the multi-dimensional consideration indexes, and reasonably setting the shuttle bus routes and stops according to the content displayed in the visual analysis graph, the planning efficiency of the shuttle bus routes is improved, thereby reducing the commuting time of the employees taking the shuttle bus and indirectly improving the work efficiency of the employees.

[0096] In addition, the present invention also provides a visualization device for shuttle bus route planning. Referring to Figure 6 , the visualization device for shuttle bus route planning includes:

[0097] A first generation module 10, configured to cluster the collected boarding data to obtain optional stops, and generate a shuttle bus route comparison view based on the multi-dimensional spatio-temporal information of the optional stops;

[0098] A screening module 20, configured to output a shuttle bus route comparison result based on the shuttle bus route comparison view, and determine a candidate shuttle bus route based on the comparison result and screening conditions;

[0099] A second generation module 30, configured to determine the multi-dimensional consideration indexes corresponding to the candidate shuttle bus route according to the multi-dimensional spatio-temporal information of each stop in the candidate shuttle bus route, and generate a visual analysis graph of the candidate shuttle bus route based on the multi-dimensional consideration indexes.

[0100] Further, the first generation module 10 is further configured to:

[0101] Obtain the boarding data of the starting boarding position corresponding to the shuttle bus route, perform angle clustering and distance clustering on the boarding data in sequence, determine the optional stops according to the clustering results, and obtain clustering area data and clustering coefficients;

[0102] Obtain the location data of the optional stops, the reference distance from the optional stops to adjacent stops, and obtain the reference time taken for the optional stops to reach adjacent stops at a target time point, as well as the index data of the optional stops to reach alternative stops through a third-party platform;

[0103] Correspondingly save the clustering area data, the clustering coefficients, the location data of the optional stops, the reference time taken, the reference distance, and the index data as the multi-dimensional spatio-temporal information;

[0104] Display one or more of the multi-dimensional spatio-temporal information to generate the shuttle bus route comparison view.

[0105] Further, the first generation module 10 is further configured to:

[0106] Construct the clustering view based on the clustering region data and the clustering coefficient;

[0107] Determine the driving direction of the shuttle route based on the clustering view, and generate the perspective view according to the position data of the optional stops corresponding to the driving direction;

[0108] Represent the reference travel time, the reference distance, and the index data of each optional stop in the driving direction determined by the perspective view, and generate the route adjustment view.

[0109] Further, the first generation module 10 is further configured to:

[0110] Obtain the target time point, and determine the optional stops corresponding to the driving direction based on the target time point;

[0111] Represent the position data of the optional stops corresponding to the driving direction in a pre-generated map, and generate the perspective view.

[0112] Further, the first generation module 10 is further configured to:

[0113] Use a bar distribution diagram to group and display the reference travel time, the reference distance, and the index data of each optional stop within the group, where each optional stop within the group is determined based on the clustering result of the ride data;

[0114] Select an optional stop as a candidate stop in each group, and connect the candidate stops to obtain the route adjustment view.

[0115] Further, the second generation module 30 is further configured to:

[0116] The step of generating the visual analysis diagram of the candidate shuttle route based on the multi-dimensional consideration index includes:

[0117] Set the arrival time as the abscissa of the timetable, and set the driving distance as the ordinate of the timetable;

[0118] Determine the coordinate values of each candidate stop according to the arrival time and the driving distance;

[0119] Represent each candidate stop in the coordinates according to the coordinate values respectively, and connect the coordinate points representing the candidate stops to obtain the timetable view.

[0120] Further, the second generation module 30 is further configured to:

[0121] Obtain the multi-dimensional consideration metrics based on the shuttle route comparison view, where the multi-dimensional consideration metrics include, but are not limited to, the number of passengers corresponding to the candidate shuttle route, the average walking time and distance, the route driving time and distance;

[0122] Based on the figure formed by the number of passengers corresponding to the candidate shuttle route, the average walking time and distance, and the route driving time and distance, obtain the radar view of the candidate shuttle route.

[0123] The specific implementation manner of the visualization device for shuttle route planning in the present invention is basically the same as that of the various embodiments of the visualization method for shuttle route planning, and will not be elaborated here.

[0124] In addition, the present invention also provides a visualization device for shuttle route planning. As Figure 7 shown, Figure 7 is a schematic structural diagram of the hardware operating environment involved in the embodiment solution of the present invention.

[0125] It should be noted that Figure 7 can be the schematic structural diagram of the hardware operating environment of the visualization device for shuttle route planning. The visualization device for shuttle route planning in the embodiment of the present invention can be a terminal device such as a PC or a portable computer.

[0126] As Figure 7 shown, the visualization device for shuttle route planning may include: a processor 1001, such as a CPU, a memory 1005, a user interface 1003, a network interface 1004, and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard). Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the foregoing processor 1001.

[0127] Those skilled in the art can understand that Figure 7 the structural diagram of the visualization device for shuttle route planning shown in

[0128] As Figure 7As shown, the memory 1005, which is a computer storage medium, may include an operating system, a network communication module, a user interface module, and a visualization program for shuttle route planning. Among them, the operating system is a program that manages and controls the hardware and software resources of the visualization device for shuttle route planning, and supports the operation of the visualization program for shuttle route planning and other software or programs.

[0129] In Figure 7 In the visualization device for shuttle route planning shown, the user interface 1003 is mainly used to connect to a terminal device and communicate with the terminal device for data, such as receiving an image to be recognized or an image to be trained sent by the terminal device; the network interface 1004 is mainly used to communicate with a background server for data; the processor 1001 can be used to call the visualization program for shuttle route planning stored in the memory 1005 and execute the steps of the visualization method for shuttle route planning as described above.

[0130] The specific implementation manner of the visualization device for shuttle route planning of the present invention is basically the same as each embodiment of the above visualization method for shuttle route planning, and will not be repeated here.

[0131] It should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of another identical element in the process, method, article or device including that element.

[0132] The serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0133] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0134] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A visualization method for shuttle bus route planning, characterized in that, the visualization method for shuttle bus route planning includes the following steps: Clustering the collected ride data to obtain optional stops, and generating a shuttle bus route comparison view based on the multi-dimensional spatio-temporal information of the optional stops; the shuttle bus route comparison view includes a clustering view, a projection view, and a route adjustment view, and the ride data at least includes: riders, start time point and end time point of the ride, location of the drop-off point and drop-off point identifier, ride distance; Outputting a shuttle bus route comparison result based on the shuttle bus route comparison view, and determining a candidate shuttle bus route based on the comparison result and screening conditions; Determining multi-dimensional consideration indicators corresponding to the candidate shuttle bus route according to the multi-dimensional spatio-temporal information of each stop in the candidate shuttle bus route, and generating a visualization analysis chart of the candidate shuttle bus route based on the multi-dimensional consideration indicators; The step of clustering the collected ride data to obtain optional stops and generating a shuttle bus route comparison view based on the multi-dimensional spatio-temporal information of the optional stops includes: Obtaining the ride data of the starting boarding location corresponding to the shuttle bus route, sequentially performing angle clustering and distance clustering on the ride data, determining the optional stops according to the clustering results, and obtaining clustering area data and clustering coefficients; Obtaining the location data of the optional stops through a third-party platform, the reference distance for the optional stops to reach adjacent stops, obtaining the reference travel time for the optional stops to reach adjacent stops at the target time point, and the index data for the optional stops to reach alternative stops; Correspondingly saving the clustering area data, the clustering coefficients, the location data of the optional stops, the reference travel time, the reference distance, and the index data as the multi-dimensional spatio-temporal information; Constructing the clustering view according to the clustering area data and the clustering coefficients; Determining the driving direction of the shuttle bus route based on the clustering view, and generating the projection view according to the location data of the optional stops corresponding to the driving direction; Representing the reference travel time, the reference distance, and the index data of each optional stop determined by the projection view in the driving direction, and generating the route adjustment view; The visualization analysis chart of the candidate shuttle bus route includes a timetable view corresponding to the candidate shuttle bus route, and the multi-dimensional consideration indicators include but are not limited to the arrival time and driving distance corresponding to the candidate shuttle bus route reaching each candidate stop; The step of generating the visualization analysis chart of the candidate shuttle bus route based on the multi-dimensional consideration indicators includes: Setting the arrival time as the abscissa of the timetable and the driving distance as the ordinate of the timetable; Determining the coordinate values of each candidate stop according to the arrival time and the driving distance; Respectively representing each candidate stop in the coordinates according to the coordinate values, and connecting the coordinate points representing the candidate stops to obtain the timetable view; And / or the visualization analysis chart of the candidate shuttle bus route includes a radar view of the candidate shuttle bus route, and the step of generating the visualization analysis chart of the candidate shuttle bus route based on the multi-dimensional consideration indicators includes: Obtain the multi-dimensional consideration metrics based on the shuttle bus route comparison view, where the multi-dimensional consideration metrics include, but are not limited to, the number of passengers corresponding to the candidate shuttle bus route, the average walking time and distance, the route driving time and distance; Obtain the radar view of the candidate shuttle bus route based on the figure formed by the number of passengers corresponding to the candidate shuttle bus route, the average walking time and distance, and the route driving time and distance.

2. The method according to claim 1, characterized in that, the step of generating the projection view based on determining the driving direction of the shuttle bus route according to the clustering view and the position data of the optional stations corresponding to the driving direction includes: Obtain the target time point, and determine the optional stations corresponding to the driving direction based on the target time point; Represent the position data of the optional stations corresponding to the driving direction in a pre-generated map to generate the projection view.

3. The method according to claim 1, characterized in that, the step of generating the route adjustment view by representing the reference time consumption, the reference distance, and the metric data of each optional station in the driving direction determined by the projection view includes: Use a bar distribution diagram to group and display the reference time consumption, the reference distance, and the metric data of each optional station within the group, where each optional station within the group is determined based on the clustering result of the riding data; Select an optional station as a candidate station in each group, and connect the candidate stations to obtain the route adjustment view.

4. A visualization device for shuttle bus route planning, characterized in that, the visualization device for shuttle bus route planning includes: A first generation module, configured to cluster the collected riding data to obtain optional stations, and generate a shuttle bus route comparison view based on the multi-dimensional spatio-temporal information of the optional stations; the riding data at least includes: the rider, the start time point and end time point of the ride, the location and identification of the disembarkation point, and the riding distance; the first generation module is further configured to obtain the riding data of the starting boarding position corresponding to the shuttle bus route, perform angle clustering and distance clustering on the riding data in sequence, determine the optional stations according to the clustering result, and obtain the clustering area data and clustering coefficient; obtain the position data of the optional stations, the reference distance between the optional stations and adjacent stations through a third-party platform, and obtain the reference time consumption of the optional stations reaching adjacent stations at the target time point, as well as the metric data of the optional stations reaching alternative stations; correspondingly save the clustering area data, the clustering coefficient, the position data of the optional stations, the reference time consumption, the reference distance, and the metric data as the multi-dimensional spatio-temporal information; construct a clustering view based on the clustering area data and the clustering coefficient; determine the driving direction of the shuttle bus route based on the clustering view, and generate a projection view according to the position data of the optional stations corresponding to the driving direction; represent the reference time consumption, the reference distance, and the metric data of each optional station in the driving direction determined by the projection view to generate a route adjustment view; A screening module, configured to output a shuttle route comparison result based on the shuttle route comparison view, and determine candidate shuttle routes based on the comparison result and screening conditions; A second generation module, configured to determine multi-dimensional consideration indicators corresponding to the candidate shuttle routes according to multi-dimensional spatio-temporal information of each stop in the candidate shuttle routes, generate a visual analysis graph of the candidate shuttle routes based on the multi-dimensional consideration indicators. The second generation module is further configured to set the arrival time as the abscissa of the timetable and the driving distance as the ordinate of the timetable; determine the coordinate values of each candidate stop according to the arrival time and the driving distance; represent each candidate stop in the coordinates according to the coordinate values respectively, and connect the coordinate points representing the candidate stops to obtain a timetable view; The second generation module is further configured to obtain the multi-dimensional consideration indicators based on the shuttle route comparison view, wherein the multi-dimensional consideration indicators include, but are not limited to, the number of passengers corresponding to the candidate shuttle routes, the average walking time and distance, the route driving time and distance; based on the figure formed by the number of passengers corresponding to the candidate shuttle routes, the average walking time and distance, the route driving time and distance, obtain the radar view of the candidate shuttle routes.

5. A visualization device for shuttle route planning, characterized in that the visualization device for shuttle route planning includes a memory, a processor, and a visualization program for shuttle route planning stored on the memory and executable on the processor. When the visualization program for shuttle route planning is executed by the processor, the steps of the visualization method for shuttle route planning described in any one of claims 1 to 3 are implemented.

6. A readable storage medium, characterized in that a visualization program for shuttle route planning is stored on the readable storage medium. When the visualization program for shuttle route planning is executed by a processor, the steps of the visualization method for shuttle route planning described in any one of claims 1 to 3 are implemented.

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