A driving data management and control method and device for integrating a public transportation system

Abstract

This invention discloses a method and apparatus for managing driving data integrated with a public transportation system. The method includes: acquiring driving data for a target area, wherein the driving data is extracted from traffic data stored in the public transportation system corresponding to the target area; dividing the target area into multiple roads based on the driving data; for each road segment, performing a preset segmented traffic condition prediction process based on the driving data corresponding to that road segment to obtain a segmented traffic condition prediction result for that road segment; and feeding back at least one segmented traffic condition prediction result to the user corresponding to that prediction result to notify the user of the current traffic condition information for the road segment corresponding to that prediction result. Therefore, implementing this invention can achieve precise guidance and collection of traffic condition data, expand the application scenarios of traffic condition prediction results, and improve the accuracy of traffic condition prediction results.

Description

Technical Field

[0001] This invention relates to the field of traffic data processing technology, and in particular to a method and apparatus for managing and controlling driving data integrated with a public transportation system. Background Technology

[0002] Existing traffic prediction systems and methods all require the detection, collection, and acquisition of traffic data. This data is then analyzed using big data or artificial intelligence to obtain real-time traffic information and predictions. However, existing traffic data processing methods have the following drawbacks:

[0003] 1. The acquisition and statistics of various types of traffic data are not clear enough. Often, a portion of traffic data is arbitrarily selected for training the prediction model. In other words, the selection of traffic data is not precise enough and there is a deficiency of data uniformity. As a result, the training degree of the prediction model is not high and the prediction results obtained have great limitations.

[0004] 2. Due to the lack of a clear guideline for obtaining this traffic data, the data collection process is also difficult, and the collected data contains redundancy, duplication, and errors, which delays the analysis process.

[0005] It is evident that providing a corresponding solution to the technical problems existing in traffic prediction methods, such as unclear data collection, significant limitations in traffic prediction results, and low accuracy of traffic prediction results, is of paramount importance. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method and device for managing driving data in a public transportation system, which can realize the accurate guidance and collection of road condition data, expand the application scenarios of road condition prediction results and improve the accuracy of road condition prediction results.

[0007] To address the aforementioned technical problems, the first aspect of this invention discloses a method for managing and controlling driving data integrated into a public transportation system, the method comprising:

[0008] Acquire driving data corresponding to the target area to be managed. The driving data is extracted from the traffic data stored in the public transportation system corresponding to the target area. The driving data includes traffic information of all roads in the target area.

[0009] Based on the driving data, the target area is divided into multiple roads, each of which includes at least one driving road.

[0010] For each of the defined roads, based on the driving data corresponding to the defined road, a preset segmented traffic condition prediction process is performed on the defined road to obtain the segmented traffic condition prediction result corresponding to the defined road.

[0011] At least one of the segmented traffic condition prediction results is fed back to the user who is querying the segmented traffic condition prediction result, so as to notify the user of the current traffic condition information of the segmented road corresponding to the segmented traffic condition prediction result.

[0012] As an optional implementation, in the first aspect of the present invention, the step of performing road segmentation on the target area based on the driving data to obtain multiple segmented roads includes:

[0013] The system detects whether a traffic query request triggered by a user has been received. If no traffic query request triggered by a user has been received, the system analyzes the driving data to obtain the traffic density of each road in the target area.

[0014] For all the aforementioned roads, the target area is divided into multiple sub-roads according to the traffic flow density of each road; wherein each sub-road has a corresponding upper limit for traffic flow density;

[0015] For all the aforementioned roads, the target area is divided into multiple sub-roads according to the traffic flow density of each road, including:

[0016] For all the aforementioned driving roads, based on determining multiple upper limit traffic flow densities, and combining the traffic flow density of each of the aforementioned driving roads, the target area is divided into multiple dividing roads;

[0017] For any given road division, the lower the traffic density of each of the roads included in the road division, the greater the number of roads included in the road division.

[0018] As an optional implementation, in the first aspect of the present invention, when a user-triggered traffic query request is detected, the step of dividing the target area into multiple roads based on the driving data further includes:

[0019] The user's real-time location and the corresponding query road are determined based on the traffic query request.

[0020] From all the driving roads, determine the target query road that corresponds to the real-time positioning and the query road, and use it as a dividing road corresponding to the target area;

[0021] For the remaining driving roads in the target area other than the target query road, lanes are divided according to the traffic flow density of each remaining driving road to obtain lane division results corresponding to all the remaining driving roads;

[0022] All the lane division results and the target query road are determined as multiple divided roads in the target area.

[0023] As an optional implementation, in the first aspect of the present invention, for each of the divided roads, performing a preset segmented traffic condition prediction process on the divided road based on the driving data corresponding to the divided road to obtain the segmented traffic condition prediction result corresponding to the divided road includes:

[0024] For each of the defined roads, based on the driving data corresponding to the defined road, a segmentation operation is performed on the defined road to obtain multiple lane segments corresponding to the defined road.

[0025] For each of the aforementioned road segments, the segmental traffic condition prediction results for that road segment are determined by combining multiple preset congestion parameters with each lane segment in that road segment as a unit.

[0026] The segmented traffic prediction results for each of the divided roads include the segmental congestion value of each lane segment in the divided road and the classification interval to which the value belongs; as well as the overall congestion classification of the divided road.

[0027] As an optional implementation, in the first aspect of the present invention, for each of the divided roads, determining the segmented traffic condition prediction result of the divided road by taking each lane segment of the divided road as a unit and combining it with a preset number of congestion parameters includes:

[0028] For each lane segment in each of the aforementioned road sections, calculate the ratio of the segment traffic flow to the segment length to obtain the congestion value for that lane segment.

[0029] For each of the defined roads, each of the congestion values ​​corresponding to the defined road is compared with a preset first congestion standard to obtain the classification interval to which each of the congestion values ​​corresponding to the defined road belongs;

[0030] Calculate the proportion of the number of each type of classification interval in the road segmentation to the total number of intervals in all classification intervals, and obtain the classification proportion of each type of classification interval;

[0031] Based on the preset second congestion standard, the proportion of all the categories corresponding to the road division is analyzed to obtain the congestion category corresponding to the road division and the proportion of all the categories, which is used as the segmented traffic condition prediction result of the road division.

[0032] The congestion classification includes at least three categories: severe congestion, mild congestion, and smooth traffic.

[0033] As an optional implementation, in the first aspect of the present invention, before feeding back at least one of the segmented traffic prediction results to the user corresponding to the segmented traffic prediction result, the method further includes:

[0034] From all the segmented traffic prediction results, determine the target traffic prediction result that matches the query road;

[0035] Establish an interaction link with the user based on the traffic query request;

[0036] The step of feeding back at least one of the segmented traffic prediction results to the user who viewed the corresponding segmented traffic prediction result includes:

[0037] The target road condition prediction result is fed back to the user terminal corresponding to the querying user through the interaction link.

[0038] As an optional implementation, in the first aspect of the present invention, acquiring the driving data corresponding to the target area to be managed includes:

[0039] Determine the area identifier corresponding to the target area to be controlled;

[0040] Based on the area identifier, identify all public transportation systems that have established traffic associations with the target area, and identify the target interface for establishing data communication with each of the public transportation systems;

[0041] Data collection operations are performed on each of the public transportation systems through the target interface to obtain the regional record data for the target area recorded by each of the public transportation systems;

[0042] Filter the data from all the recorded data of the area that corresponds to all driving roads in the target area, and use the driving data of the target area as the driving data of the target area;

[0043] The data recorded in each area includes at least one of the following: parking meter data, parking lot data, road engineering data, traffic situation and forecast data, map data, POI data, and road network data.

[0044] A second aspect of the present invention discloses a vehicle data management and control device integrated with a public transportation system, the device comprising:

[0045] The acquisition module is used to acquire driving data corresponding to the target area to be managed. The driving data is extracted from the traffic data stored in the public transportation system corresponding to the target area. The driving data includes traffic information of all roads in the target area.

[0046] The segmentation module is used to perform road segmentation on the target area based on the driving data, resulting in multiple segmented roads, each of which includes at least one driving road;

[0047] The traffic prediction module is used to perform a preset segmented traffic prediction process on each of the divided roads based on the driving data corresponding to the divided road, and obtain the segmented traffic prediction result corresponding to the divided road.

[0048] The feedback module is used to send at least one of the segmented traffic condition prediction results back to the user who is consulting the corresponding segmented traffic condition prediction result, so as to notify the user of the current traffic condition information of the segmented road corresponding to the segmented traffic condition prediction result.

[0049] As an optional implementation, in a second aspect of the present invention, the method by which the segmentation module performs road segmentation on the target area based on the driving data to obtain multiple segmented roads specifically includes:

[0050] The system detects whether a traffic query request triggered by a user has been received. If no traffic query request triggered by a user has been received, the system analyzes the driving data to obtain the traffic density of each road in the target area.

[0051] For all the aforementioned roads, the target area is divided into multiple sub-roads according to the traffic flow density of each road; wherein each sub-road has a corresponding upper limit for traffic flow density;

[0052] For all the aforementioned roads, the target area is divided into multiple sub-roads according to the traffic flow density of each road, including:

[0053] For all the aforementioned driving roads, based on determining multiple upper limit traffic flow densities, and combining the traffic flow density of each of the aforementioned driving roads, the target area is divided into multiple dividing roads;

[0054] For any given road division, the lower the traffic density of each of the roads included in the road division, the greater the number of roads included in the road division.

[0055] As an optional implementation, in a second aspect of the present invention, the method by which the segmentation module performs road segmentation on the target area based on the driving data to obtain multiple segmented roads further includes:

[0056] When a traffic query request triggered by a user is detected, the user's real-time location and the query road corresponding to the traffic query request are determined based on the traffic query request.

[0057] From all the driving roads, determine the target query road that corresponds to the real-time positioning and the query road, and use it as a dividing road corresponding to the target area;

[0058] For the remaining driving roads in the target area other than the target query road, lanes are divided according to the traffic flow density of each remaining driving road to obtain lane division results corresponding to all the remaining driving roads;

[0059] All the lane division results and the target query road are determined as multiple divided roads in the target area.

[0060] As an optional implementation, in the second aspect of the present invention, the traffic prediction module performs a preset segmented traffic prediction process on each of the divided roads based on the driving data corresponding to that divided road, and obtains the segmented traffic prediction result corresponding to that divided road in the following specific ways:

[0061] For each of the defined roads, based on the driving data corresponding to the defined road, a segmentation operation is performed on the defined road to obtain multiple lane segments corresponding to the defined road.

[0062] For each of the aforementioned road segments, the segmental traffic condition prediction results for that road segment are determined by combining multiple preset congestion parameters with each lane segment in that road segment as a unit.

[0063] The segmented traffic prediction results for each of the divided roads include the segmental congestion value of each lane segment in the divided road and the classification interval to which the value belongs; as well as the overall congestion classification of the divided road.

[0064] As an optional implementation, in the second aspect of the present invention, the traffic prediction module determines the segmented traffic prediction result of each divided road by taking each lane segment of the divided road as a unit and combining multiple preset congestion parameters. Specifically, this includes:

[0065] For each lane segment in each of the aforementioned road sections, calculate the ratio of the segment traffic flow to the segment length to obtain the congestion value for that lane segment.

[0066] For each of the defined roads, each of the congestion values ​​corresponding to the defined road is compared with a preset first congestion standard to obtain the classification interval to which each of the congestion values ​​corresponding to the defined road belongs;

[0067] Calculate the proportion of the number of each type of classification interval in the road segmentation to the total number of intervals in all classification intervals, and obtain the classification proportion of each type of classification interval;

[0068] Based on the preset second congestion standard, the proportion of all the categories corresponding to the road division is analyzed to obtain the congestion category corresponding to the road division and the proportion of all the categories, which is used as the segmented traffic condition prediction result of the road division.

[0069] The congestion classification includes at least three categories: severe congestion, mild congestion, and smooth traffic.

[0070] As an optional implementation, in a second aspect of the invention, the apparatus further includes:

[0071] The determination module is used to determine, before the feedback module feeds back at least one of the segmented traffic prediction results to the user corresponding to the segmented traffic prediction result, a target traffic prediction result that matches the query road from all the segmented traffic prediction results.

[0072] The interaction module is used to establish an interaction link with the user based on the traffic query request;

[0073] The feedback module specifically includes the following methods for feeding back at least one segmented traffic condition prediction result to the user who views the corresponding segmented traffic condition prediction result:

[0074] The target road condition prediction result is fed back to the user terminal corresponding to the querying user through the interaction link.

[0075] As an optional implementation, in the second aspect of the present invention, the method by which the acquisition module acquires the driving data corresponding to the target area to be managed specifically includes:

[0076] Determine the area identifier corresponding to the target area to be controlled;

[0077] Based on the area identifier, identify all public transportation systems that have established traffic associations with the target area, and identify the target interface for establishing data communication with each of the public transportation systems;

[0078] Data collection operations are performed on each of the public transportation systems through the target interface to obtain the regional record data for the target area recorded by each of the public transportation systems;

[0079] Filter the data from all the recorded data of the area that corresponds to all driving roads in the target area, and use the driving data of the target area as the driving data of the target area;

[0080] The data recorded in each area includes at least one of the following: parking meter data, parking lot data, road engineering data, traffic situation and forecast data, map data, POI data, and road network data.

[0081] A third aspect of the present invention discloses another vehicle data management and control device for integrating a public transportation system, the device comprising:

[0082] Memory containing executable program code;

[0083] A processor coupled to the memory;

[0084] The processor calls the executable program code stored in the memory to execute the driving data management method for the integrated public transportation system disclosed in the first aspect of the present invention.

[0085] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the driving data management method for the integrated public transportation system disclosed in the first aspect of the present invention.

[0086] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0087] This invention provides a method for managing driving data integrated with a public transportation system. The method includes: acquiring driving data corresponding to a target area to be managed, wherein the driving data is extracted from traffic data stored in the public transportation system corresponding to the target area; the driving data includes traffic information for all roads in the target area; dividing the target area into multiple road segments based on the driving data, each segment including at least one road; for each road segment, performing a preset segmented traffic condition prediction process based on the driving data corresponding to that road segment to obtain a segmented traffic condition prediction result; and feeding back at least one segmented traffic condition prediction result to the user corresponding to that prediction result to notify the user of the current traffic condition information for the road segment corresponding to the prediction result. As can be seen, implementing this invention extracts driving data by integrating traffic data stored in public transportation systems, then divides this data into roads, and finally predicts traffic conditions based on the divided roads, ultimately obtaining the segmented traffic condition prediction results required by the user. This method of integrating public transportation systems improves the convenience of data acquisition. Furthermore, the more public transportation systems integrated, the more comprehensive and accurate the data, which to some extent helps improve the accuracy of prediction results obtained after subsequent data analysis operations. In addition, the prediction of driving data can be further subdivided from detailed road divisions to precise segmented traffic conditions within those divisions. This refined traffic condition information prediction meets the user's refined requirements for viewing traffic conditions while improving the precision and accuracy of traffic condition prediction. Attached Figure Description

[0088] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0089] Figure 1 This is a flowchart illustrating a method for managing and controlling driving data in a public transportation system, as disclosed in an embodiment of the present invention.

[0090] Figure 2 This is a flowchart illustrating another method for managing and controlling driving data in a public transportation system disclosed in an embodiment of the present invention;

[0091] Figure 3 This is a schematic diagram of the structure of a vehicle data management and control device that integrates a public transportation system, as disclosed in an embodiment of the present invention.

[0092] Figure 4This is a schematic diagram of another vehicle data management and control device for integrating a public transportation system disclosed in an embodiment of the present invention;

[0093] Figure 5 This is a schematic diagram of the structure of another vehicle data management and control device integrating a public transportation system disclosed in an embodiment of the present invention. Detailed Implementation

[0094] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0095] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0096] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0097] This invention discloses a method and apparatus for managing driving data integrated with public transportation systems. It extracts driving data by integrating traffic data stored in public transportation systems, then divides this data into roads, and finally predicts traffic conditions based on these road divisions. Ultimately, it obtains the segmented traffic condition prediction results required by the user. This method of integrating public transportation systems improves the convenience of data acquisition. Furthermore, the more public transportation systems integrated, the more comprehensive and accurate the data, which to some extent improves the accuracy of prediction results obtained after subsequent data analysis operations. In addition, the driving data prediction can be further subdivided from detailed road divisions to precise segmented traffic conditions within each road division. This refined traffic condition information prediction meets the user's requirements for detailed traffic condition viewing while improving the precision and accuracy of traffic condition prediction. Detailed explanations follow.

[0098] Example 1

[0099] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for managing and controlling vehicle traffic data integrated into a public transportation system, as disclosed in an embodiment of the present invention. Figure 1 The described method for managing and controlling driving data in an integrated public transportation system can be applied to driving data management and control devices within the same system; however, this invention does not limit its application. For example... Figure 1 As shown, the method for managing driving data in this integrated public transportation system may include the following operations:

[0100] 101. Obtain the driving data corresponding to the target area to be controlled. The driving data is extracted from the traffic data stored in the public transportation system corresponding to the target area.

[0101] In this embodiment of the invention, the driving data includes traffic information for all roads in the target area.

[0102] 102. Based on driving data, perform road division on the target area to obtain multiple road divisions.

[0103] In this embodiment of the invention, each divided road includes at least one driving road.

[0104] 103. For each road segment, based on the driving data corresponding to that road segment, perform a preset segmented traffic condition prediction process to obtain the segmented traffic condition prediction result for that road segment.

[0105] 104. Feed back at least one segment of traffic condition prediction results to the user who is viewing the corresponding segment of traffic condition prediction results, so as to notify the user of the current traffic condition information of the road segment corresponding to the traffic condition prediction results.

[0106] In this embodiment of the invention, before step 104 feeds back at least one segment traffic prediction result to the user corresponding to the segment traffic prediction result, the method further includes:

[0107] From all segmented traffic prediction results, identify the target traffic prediction result that matches the query road;

[0108] Establish an interactive link with the user based on the traffic query request;

[0109] At least one segment of traffic condition prediction results will be fed back to the user who viewed the corresponding segment of traffic condition prediction results, including:

[0110] The target road condition prediction results are fed back to the user terminal corresponding to the querying user through the interactive link.

[0111] As can be seen, in this embodiment of the invention, an information feedback transmission process is set up. After the road conditions of all roads in the target area are predicted and calculated in advance, and when a user's road condition query request is detected, the target road condition prediction result matching the query road can be quickly and accurately determined from all the pre-calculated segmented road condition prediction results. Thus, the user's road condition query request is responded to and fed back through the interactive link, which helps to improve the response speed and data call accuracy of responding to the user's road condition query information.

[0112] It is evident that implementation Figure 1 The described method for managing driving data by integrating public transportation systems extracts driving data from the traffic data stored in these systems, then divides this data into roads, and finally predicts traffic conditions based on these road divisions. This results in the segmented traffic condition predictions required by the user. This approach of integrating public transportation systems improves the convenience of data acquisition. Furthermore, the more public transportation systems integrated, the more comprehensive and accurate the data becomes, which to some extent improves the accuracy of predictions obtained after subsequent data analysis. In addition, the driving data prediction can be further refined from detailed road divisions to precise segmented traffic conditions within each road division. This refined traffic condition prediction meets the user's requirement for detailed traffic condition viewing while improving the precision and accuracy of traffic condition predictions.

[0113] In an optional embodiment, step 102 above, which involves dividing the target area into multiple roads based on driving data, specifically includes the following methods:

[0114] The system detects whether a traffic query request triggered by a user has been received. If no traffic query request triggered by a user has been received, the system analyzes the driving data to obtain the traffic density of each road in the target area.

[0115] For all driving roads, the target area is divided into multiple sub-roads according to the traffic flow density of each driving road; each sub-road has its corresponding upper limit traffic flow density;

[0116] For all driving roads, the target area is divided into multiple sub-roads based on the traffic density of each driving road, including:

[0117] For all driving roads, based on determining multiple upper limit traffic flow densities, and combining the traffic flow density of each driving road, the target area is divided into multiple sub-roads;

[0118] For any given road segment, the lower the traffic density of each road segment, the more roads the segment includes.

[0119] In this optional embodiment, the traffic density of each roadway is obtained by calculating the ratio of the number of vehicles on the roadway to the length of the roadway during a preset monitoring period; wherein, the preset monitoring period can be 1s, 5s or 10s, and the embodiments of the present invention are not limited thereto.

[0120] In this optional embodiment, when dividing the target area into multiple roads, the number of roads to be divided is preset, or a corresponding upper limit traffic density is set for each road.

[0121] For each road, the current traffic density varies, ranging from excessively high (above a set threshold), moderate, to excessively low (virtually no traffic). When determining road divisions based on traffic density, roads with excessively high traffic density can be separated into individual roads. This avoids interference from other roads and reduced data processing accuracy when merging these exceptional cases (excessively high traffic density) with other roads. For multiple consecutive roads with excessively low or moderate traffic density, they are merged according to the road division algorithm. This merging process reduces the number of roads to be analyzed, decreases data processing volume, and improves the efficiency of traffic condition analysis.

[0122] In this optional embodiment, when merging more than one roadway, the merged roadways are "adjacent" or "continuous" on the map.

[0123] As can be seen, in this optional embodiment, when dividing the target area into roads, the traffic flow density of a single road and the traffic flow density of adjacent / continuously adjacent roads are comprehensively considered. This ensures that when the traffic flow density of a single road is too high, that road can be identified as a separate road. This reduces the possibility of merging roads due to special cases (excessively high traffic flow density), which would lead to a decrease in the precision and accuracy of subsequent data analysis. For multiple roads with excessively high traffic flow density, they are intelligently merged into a single road. This merging of simple data (excessively high traffic flow density) helps reduce the workload of data processing and improves data processing efficiency to a certain extent.

[0124] In this optional embodiment, when a user-triggered traffic query request is detected, step 102 above, which involves dividing the target area into multiple roads based on driving data, further includes:

[0125] Determine the user's real-time location and the corresponding road based on the traffic query request;

[0126] From all driving roads, identify the target query road corresponding to the real-time location and query road, and use it as a dividing road corresponding to the target area;

[0127] For the remaining driving roads in the target area other than the target query road, lanes are divided according to the traffic flow density of each remaining driving road to obtain the lane division results corresponding to all remaining driving roads;

[0128] All lane division results and the target query road are identified as multiple lane division roads in the target area.

[0129] In this optional embodiment, it should be noted that the lane division of the remaining driving roads can refer to the steps described above for road division when no user-triggered traffic query request is received, and will not be repeated in this optional embodiment.

[0130] As can be seen, in this optional embodiment, when there is a user-triggered traffic query request, the user's request can be taken as the main focus, and the road queried by the user can be divided into a separate road. This means that when predicting traffic conditions for the road in the subsequent road division, the influence of the other road divisions has been eliminated. Thus, the traffic data of the separately determined road division can be directly called and fed back to the user terminal. In other words, this setting reduces the influence of redundant road division data while improving the accuracy and speed of responding to the user's traffic query request.

[0131] Example 2

[0132] Please see Figure 2 , Figure 2 This is a flowchart illustrating another method for managing and controlling vehicle traffic data in a public transportation system, as disclosed in an embodiment of the present invention. Figure 2 The described method for managing and controlling driving data in an integrated public transportation system can be applied to driving data management and control devices within the same system; however, this invention does not limit its application. For example... Figure 2 As shown, the method for managing driving data in this integrated public transportation system may include the following operations:

[0133] 201. Obtain the driving data corresponding to the target area to be controlled. The driving data is extracted from the traffic data stored in the public transportation system corresponding to the target area.

[0134] 202. Based on driving data, road division is performed on the target area to obtain multiple road divisions.

[0135] 203. For each road segment, based on the driving data corresponding to that road segment, perform a segmentation operation on that road segment to obtain multiple lane segments corresponding to that road segment.

[0136] 204. For each segmented road, taking each lane segment of the segmented road as a unit, and combining multiple preset congestion parameters, determine the segmented road condition prediction result for that segmented road.

[0137] In this embodiment of the invention, the segmented traffic condition prediction result for each segmented road includes the segmental congestion value of each lane segment in the segmented road and the classification interval to which the value belongs; and also includes the overall congestion classification corresponding to the segmented road.

[0138] 205. Feed back at least one segment of traffic condition prediction results to the user who is viewing the corresponding segment of traffic condition prediction results, so as to notify the user of the current traffic condition information of the road segment corresponding to the traffic condition prediction results.

[0139] For further descriptions of steps 201-202 and 205 in this embodiment of the invention, please refer to the other specific descriptions of steps 101-102 and 104 in Embodiment 1. These descriptions will not be repeated in this embodiment of the invention.

[0140] It is evident that implementation Figure 2 The described method for managing traffic data in a public transportation system, based on dividing the target area into multiple roads, further refines each road into multiple lane segments, thereby improving the granularity and precision of traffic condition prediction. The segmented traffic condition prediction results obtained by combining multiple congestion parameters further enhance the accuracy and detail of the segmented traffic condition prediction results.

[0141] In an optional embodiment, step 204, for each divided road, determines the segmented traffic condition prediction result of the divided road by taking each lane segment of the divided road as a unit and combining multiple preset congestion parameters, specifically includes the following methods:

[0142] For each lane segment in each divided road, calculate the ratio of the segment traffic flow to the segment length to obtain the congestion value for that lane segment.

[0143] For each road segment, the congestion value corresponding to that road segment is compared with a preset first congestion standard to obtain the classification interval to which each congestion value corresponding to that road segment belongs.

[0144] Calculate the percentage of each category interval in the road segmentation as a percentage of the total number of intervals in all categories, and obtain the category percentage of each category interval.

[0145] Based on the preset second congestion standard, the proportion of all categories corresponding to the road division is analyzed to obtain the congestion category corresponding to the road division and the proportion of all categories, which is used as the segmented road condition prediction result for the road division.

[0146] Congestion can be classified into at least three categories: severe congestion, light congestion, and smooth traffic.

[0147] In this optional embodiment, the pre-set classification intervals may include 5 intervals, that is, the first congestion standard may include 5 levels of congestion: smooth, basically smooth, light congestion, moderate congestion, and severe congestion, and their corresponding classification intervals are: [0, 2), [2, 4), [4, 6), [6, 8), [8, 10).

[0148] In this optional embodiment, for the second congestion criterion, it is necessary to calculate the proportion of the number of each category interval to the total number of intervals. Taking severe congestion as an example, the corresponding calculation formula can be:

[0149] (Length of severe congestion + length of moderate congestion) / total length >= a preset value of 1 (e.g., 0.5)

[0150] The length of severe / moderate congestion can be replaced by the number of lane segments in severe / moderate congestion, and the total length is the total length of the divided road. This optional embodiment is not limited.

[0151] Similarly, for the congestion category of "smooth traffic," the corresponding calculation formula can be:

[0152] (Length of unobstructed passage + Length of basically unobstructed passage) / Total length >= a preset value 2

[0153] The length of "unobstructed / basically unobstructed" can be replaced by the number of lane segments that are unobstructed / basically unobstructed; this optional embodiment is not limited.

[0154] The congestion category of mild congestion is the remaining value excluding the categories of severe congestion and smooth traffic. The specific calculation formula is analogous to the calculation formula for severe congestion and smooth traffic. This optional implementation will not be elaborated further.

[0155] As can be seen, in this optional embodiment, when calculating the segmented traffic condition prediction results for each road segment, each road segment is used as the first level of data analysis, and each lane segment within that road segment is used as the second level of data analysis. This hierarchical approach refines and intelligently calculates the congestion value of each lane segment, the classification interval of each congestion value, and the classification percentage of each classification interval, ultimately qualitatively determining the congestion classification of the road segment. This method of refining a single road segment into lane segments further improves the precision of lane prediction. Finally, the overall road segmentation obtained by integrating these refined multi-lane segments improves the data richness and prediction accuracy of the final segmented traffic condition prediction results.

[0156] In another optional embodiment, when the segmented traffic prediction results are fed back to the querying user in step 205, two feedback modes can be set, as follows:

[0157] The first method involves the traffic monitoring system assessing and predicting traffic congestion on clear roads every minute. If the probability of congestion is predicted to be 50% or higher for two consecutive times, the system will push the congestion prediction data to the app's "Traffic Prediction Data Receiving" interface. If the next traffic monitoring system prediction of a road congestion probability is also 50% or higher, it will not push the road congestion prediction data to the app's "Traffic Prediction Data Receiving" interface.

[0158] The second method involves the traffic information backend assessing and predicting congested roads every minute. When the probability of road congestion is predicted to be below 50% twice in a row, the traffic information backend pushes the traffic prediction data for smooth traffic to the "Traffic Prediction Data Receiving" interface of the APP. If the probability of congestion is also below 50% in the next prediction, the traffic information backend will not push the traffic prediction data to the "Traffic Prediction Data Receiving" interface of the APP.

[0159] In this optional embodiment, the method for predicting and calculating the congestion probability can be as follows:

[0160] 1) Predict a 100% probability of road congestion, with the highest probability set at 90%.

[0161] 2) Predict the probability of road congestion being less than 50%, with the minimum probability set at 60%.

[0162] Based on this, for the calculation process of determining the segmented traffic condition prediction results corresponding to steps 201-205 above, when the basic condition is met (the segmented traffic condition prediction results indicate that the congestion is classified as mild), the probability is set to 60%. After that, for each additional segment of road where the congestion reaches or exceeds the predicted level, the probability increases by 10%, until it reaches the maximum value (90%).

[0163] That is, in this optional embodiment, the segmented traffic prediction result obtained in step 204 includes the congestion classification of the overall road segment calculated in real time. Furthermore, the segmented traffic prediction result may also include the traffic prediction probability (probability of road congestion, probability of road smoothness) for the prediction time period (such as 5 minutes, 10 minutes, etc.); at the same time, a congestion situation push mode based on the traffic prediction probability is also set, which improves the intelligence and accuracy of information push.

[0164] In yet another optional embodiment, step 201, obtaining the driving data corresponding to the target area to be managed, specifically includes:

[0165] Determine the area identifier corresponding to the target area to be controlled;

[0166] Identify all public transportation systems that have established traffic associations with the target area based on the area identifier, and identify the target interface for establishing data communication with each public transportation system;

[0167] By performing data collection operations on each public transportation system through the target interface, regional record data for the target area recorded by each public transportation system is obtained;

[0168] Filter the data from all recorded data in all regions to find the data that corresponds to all driving roads in the target region, and use this data as the driving data for the target region.

[0169] The data recorded in each area includes at least one of the following: parking meter data, parking lot data, road engineering data, traffic situation and forecast data, map data, POI data, and road network data.

[0170] As can be seen, in this optional embodiment, when acquiring driving data of the target area, after determining the area identifier of the target area in advance, all public transportation systems that have established traffic associations with the target area are automatically identified. Then, the data communication interface is determined and data is collected and analyzed. This method of integrating multiple public transportation systems results in a rich variety of driving data types and volumes. Subsequently, the required data is analyzed and extracted from this rich data, which improves the accuracy of the acquired driving data. In addition, this rich and accurate driving data is beneficial to improving the accuracy of subsequent road condition predictions to a certain extent.

[0171] Example 3

[0172] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a vehicle data management and control device for an integrated public transportation system disclosed in an embodiment of the present invention. The device can be a vehicle data management and control terminal, equipment, system, or server for an integrated public transportation system. The server can be a local server, a remote server, or a cloud server (also known as a cloud-based server). When the server is not a cloud server, it can communicate with the cloud server; this embodiment of the present invention does not impose any limitations. Figure 3 As shown, the vehicle data management and control device for the integrated public transportation system may include an acquisition module 301, a segmentation module 302, a traffic condition prediction module 303, and a feedback module 304, wherein:

[0173] The acquisition module 301 is used to acquire driving data corresponding to the target area to be managed. The driving data is extracted from the traffic data stored in the public transportation system corresponding to the target area. The driving data includes traffic information of all driving roads in the target area.

[0174] The segmentation module 302 is used to perform road segmentation on the target area based on driving data, resulting in multiple segmented roads, each of which includes at least one driving road;

[0175] The traffic prediction module 303 is used to perform a preset segmented traffic prediction process on each segmented road based on the driving data corresponding to that segmented road, and obtain the segmented traffic prediction result corresponding to that segmented road.

[0176] The feedback module 304 is used to send at least one segment road condition prediction result back to the user who is looking for the corresponding segment road condition prediction result, so as to notify the user of the current road condition information of the road segment corresponding to the segment road condition prediction result.

[0177] It is evident that implementation Figure 3The described vehicle data management device, which integrates public transportation systems, extracts vehicle data from the traffic data stored in these systems. It then divides this data into road segments and uses these segments as units to predict road conditions, ultimately providing the user with the segmented road condition predictions. This integration of public transportation systems improves the convenience of data acquisition. Furthermore, the more public transportation systems integrated, the more comprehensive and accurate the data becomes, which to some extent improves the accuracy of predictions obtained after subsequent data analysis. In addition, the vehicle data prediction can be further refined from detailed road segmentation to precise segmented road conditions within each segment. This refined road condition information prediction meets the user's refined requirements for viewing road conditions while improving the precision and accuracy of road condition predictions.

[0178] In an optional embodiment, the segmentation module 302 performs road segmentation on the target area based on driving data, and the specific methods for obtaining multiple segmented roads include:

[0179] The system detects whether a traffic query request triggered by a user has been received. If no traffic query request triggered by a user has been received, the system analyzes the driving data to obtain the traffic density of each road in the target area.

[0180] For all driving roads, the target area is divided into multiple sub-roads according to the traffic flow density of each driving road; each sub-road has its corresponding upper limit traffic flow density;

[0181] The above-mentioned method of dividing the target area into multiple roads based on the traffic density of each road specifically includes:

[0182] For all driving roads, based on determining multiple upper limit traffic flow densities, and combining the traffic flow density of each driving road, the target area is divided into multiple sub-roads;

[0183] For any given road segment, the lower the traffic density of each road segment, the more roads the segment includes.

[0184] As can be seen, in this optional embodiment, when dividing the target area into roads, the traffic flow density of a single road and the traffic flow density of adjacent / continuously adjacent roads are comprehensively considered. This ensures that when the traffic flow density of a single road is too high, that road can be identified as a separate road. This reduces the possibility of merging roads due to special cases (excessively high traffic flow density), which would lead to a decrease in the precision and accuracy of subsequent data analysis. For multiple roads with excessively high traffic flow density, they are intelligently merged into a single road. This merging of simple data (excessively high traffic flow density) helps reduce the workload of data processing and improves data processing efficiency to a certain extent.

[0185] In this optional embodiment, the segmentation module 302 performs road segmentation on the target area based on driving data, and the method of obtaining multiple segmented roads further includes:

[0186] When a user-triggered traffic query request is detected, the user's real-time location and the corresponding query road are determined based on the traffic query request.

[0187] From all driving roads, identify the target query road corresponding to the real-time location and query road, and use it as a dividing road corresponding to the target area;

[0188] For the remaining driving roads in the target area other than the target query road, lanes are divided according to the traffic flow density of each remaining driving road to obtain the lane division results corresponding to all remaining driving roads;

[0189] All lane division results and the target query road are identified as multiple lane division roads in the target area.

[0190] As can be seen, in this optional embodiment, when there is a user-triggered traffic query request, the user's request can be taken as the main focus, and the road queried by the user can be divided into a separate road. This means that when predicting traffic conditions for the road in the subsequent road division, the influence of the other road divisions has been eliminated. Thus, the traffic data of the separately determined road division can be directly called and fed back to the user terminal. In other words, this setting reduces the influence of redundant road division data while improving the accuracy and speed of responding to the user's traffic query request.

[0191] In another optional embodiment, the traffic prediction module 303 performs a preset segmented traffic prediction process on each segmented road based on the driving data corresponding to that segmented road, and obtains the segmented traffic prediction result for that segmented road in the following specific ways:

[0192] For each road segment, based on the driving data corresponding to that road segment, a segmentation operation is performed on that road segment to obtain multiple lane segments corresponding to that road segment.

[0193] For each road segment, the segmental traffic condition prediction result is determined by combining multiple preset congestion parameters with each lane segment of the road segment as a unit.

[0194] The segmented traffic prediction results for each road segment include the segmental congestion value of each lane segment in the segmented road and the classification interval to which the value belongs; as well as the overall congestion classification of the segmented road.

[0195] As can be seen, in this optional embodiment, based on dividing the target area into multiple roads, each road can be further refined into multiple lane segments, taking each road as the analysis object, thereby improving the granularity and precision of road condition prediction; then, the segmented road condition prediction results obtained by combining multiple congestion parameters are beneficial to improving the accuracy and detail of the segmented road condition prediction results.

[0196] In another optional embodiment, the traffic prediction module 303 determines the segmented traffic prediction result for each divided road by taking each lane segment of the divided road as a unit and combining multiple preset congestion parameters. The specific method includes:

[0197] For each lane segment in each divided road, calculate the ratio of the segment traffic flow to the segment length to obtain the congestion value for that lane segment.

[0198] For each road segment, the congestion value corresponding to that road segment is compared with a preset first congestion standard to obtain the classification interval to which each congestion value corresponding to that road segment belongs.

[0199] Calculate the percentage of each category interval in the road segmentation as a percentage of the total number of intervals in all categories, and obtain the category percentage of each category interval.

[0200] Based on the preset second congestion standard, the proportion of all categories corresponding to the road division is analyzed to obtain the congestion category corresponding to the road division and the proportion of all categories, which is used as the segmented road condition prediction result for the road division.

[0201] Congestion can be classified into at least three categories: severe congestion, light congestion, and smooth traffic.

[0202] As can be seen, in this optional embodiment, when calculating the segmented traffic condition prediction results for each road segment, each road segment is used as the first level of data analysis, and each lane segment within that road segment is used as the second level of data analysis. This hierarchical approach refines and intelligently calculates the congestion value of each lane segment, the classification interval of each congestion value, and the classification percentage of each classification interval, ultimately qualitatively determining the congestion classification of the road segment. This method of refining a single road segment into lane segments further improves the precision of lane prediction. Finally, the overall road segmentation obtained by integrating these refined multi-lane segments improves the data richness and prediction accuracy of the final segmented traffic condition prediction results.

[0203] In another alternative embodiment, the device further includes:

[0204] The determination module 305 is used to determine the target traffic prediction result that matches the query road from all the traffic prediction results before the feedback module 304 feeds back at least one segment traffic prediction result to the querying user corresponding to the segment traffic prediction result.

[0205] Interaction module 306 is used to establish an interaction link with the user based on the traffic query request;

[0206] The feedback module 304 feeds back at least one segment of traffic condition prediction results to the user who viewed the corresponding segment of traffic condition prediction results in the following ways:

[0207] The target road condition prediction results are fed back to the user terminal corresponding to the querying user through the interactive link.

[0208] As can be seen, in this optional embodiment, an information feedback transmission process is set up. After the road conditions of all roads in the target area are predicted and calculated in advance, and when a user's road condition query request is detected, the target road condition prediction result matching the query road can be quickly and accurately determined from all the pre-calculated segmented road condition prediction results. Thus, the response and feedback are carried out through the interactive link of the user's road condition query request, which helps to improve the response speed and data call accuracy of the user's road condition query information.

[0209] In yet another optional embodiment, the acquisition module 301 acquires the driving data corresponding to the target area to be managed in the following specific ways:

[0210] Determine the area identifier corresponding to the target area to be controlled;

[0211] Identify all public transportation systems that have established traffic associations with the target area based on the area identifier, and identify the target interface for establishing data communication with each public transportation system;

[0212] By performing data collection operations on each public transportation system through the target interface, regional record data for the target area recorded by each public transportation system is obtained;

[0213] Filter the data from all recorded data in all regions to find the data that corresponds to all driving roads in the target region, and use this data as the driving data for the target region.

[0214] The data recorded in each area includes at least one of the following: parking meter data, parking lot data, road engineering data, traffic situation and forecast data, map data, POI data, and road network data.

[0215] As can be seen, in this optional embodiment, when acquiring driving data of the target area, after determining the area identifier of the target area in advance, all public transportation systems that have established traffic associations with the target area are automatically identified. Then, the data communication interface is determined and data is collected and analyzed. This method of integrating multiple public transportation systems results in a rich variety of driving data types and volumes. Subsequently, the required data is analyzed and extracted from this rich data, which improves the accuracy of the acquired driving data. In addition, this rich and accurate driving data is beneficial to improving the accuracy of subsequent road condition predictions to a certain extent.

[0216] Example 4

[0217] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of another vehicle data management and control device integrating a public transportation system disclosed in an embodiment of the present invention. Figure 5 As shown, the vehicle data management device for the integrated public transportation system may include:

[0218] Memory 401 storing executable program code;

[0219] Processor 402 coupled to memory 401;

[0220] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the driving data management method for the integrated public transportation system described in Embodiment 1 or Embodiment 2 of the present invention.

[0221] Example 5

[0222] This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the vehicle data management method for an integrated public transportation system described in Embodiment 1 or Embodiment 2 of this invention.

[0223] Example 6

[0224] This invention discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the vehicle data management method for an integrated public transportation system described in Embodiment 1 or Embodiment 2.

[0225] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0226] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0227] Finally, it should be noted that the driving data management method and apparatus for integrated public transportation systems disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for managing and controlling driving data integrated into a public transportation system, characterized in that, The method includes: Acquire driving data corresponding to the target area to be managed. The driving data is extracted from the traffic data stored in the public transportation system corresponding to the target area. The driving data includes traffic information of all roads in the target area. Based on the driving data, the target area is divided into multiple roads, each of which includes at least one driving road. For each of the defined roads, based on the driving data corresponding to the defined road, a preset segmented traffic condition prediction process is performed on the defined road to obtain the segmented traffic condition prediction result corresponding to the defined road. At least one of the segmented traffic condition prediction results is fed back to the user who is consulting the segmented traffic condition prediction result, so as to notify the user of the current traffic condition information of the segmented road corresponding to the segmented traffic condition prediction result; The process involves dividing the target area into multiple roads based on the driving data, including: Detect whether a traffic query request triggered by a user has been received. When a traffic query request triggered by a user is detected, determine the user's real-time location and the query road corresponding to the traffic query request based on the traffic query request. From all the driving roads, determine the target query road that corresponds to the real-time positioning and the query road, and use it as a dividing road corresponding to the target area; For the remaining driving roads in the target area other than the target query road, lanes are divided according to the traffic flow density of each remaining driving road to obtain lane division results corresponding to all the remaining driving roads; All the lane division results and the target query road are determined as multiple divided roads in the target area.

2. The method for managing and controlling driving data in an integrated public transportation system according to claim 1, characterized in that, The step of dividing the target area into multiple roads based on the driving data also includes: When it is detected that no traffic query request triggered by the user has been received, the driving data is analyzed to obtain the traffic density of each driving road in the target area; For all the aforementioned roads, the target area is divided into multiple sub-roads according to the traffic flow density of each road; wherein each sub-road has a corresponding upper limit for traffic flow density; For all the aforementioned roads, the target area is divided into multiple sub-roads according to the traffic flow density of each road, including: For all the aforementioned driving roads, based on determining multiple upper limit traffic flow densities, and combining the traffic flow density of each of the aforementioned driving roads, the target area is divided into multiple dividing roads; For any given road division, the lower the traffic density of each of the roads included in the road division, the greater the number of roads included in the road division.

3. The method for managing and controlling driving data in an integrated public transportation system according to claim 1 or 2, characterized in that, For each of the defined roads, based on the driving data corresponding to that road, a preset segmented traffic condition prediction process is performed on that road to obtain the segmented traffic condition prediction result for that road, including: For each of the defined roads, based on the driving data corresponding to the defined road, a segmentation operation is performed on the defined road to obtain multiple lane segments corresponding to the defined road. For each of the aforementioned road segments, the segmental traffic condition prediction results for each road segment are determined by combining multiple preset congestion parameters, with each lane segment in the road segment as a unit. The segmented traffic prediction results for each of the divided roads include the segmental congestion value of each lane segment in the divided road and the classification interval to which the value belongs; as well as the overall congestion classification of the divided road.

4. The method for managing and controlling driving data in an integrated public transportation system according to claim 3, characterized in that, For each of the defined roads, taking each lane segment within that defined road as a unit, and combining it with multiple preset congestion parameters, the segmental traffic condition prediction result for that defined road is determined, including: For each lane segment in each of the aforementioned road sections, calculate the ratio of the segment traffic flow to the segment length to obtain the congestion value for that lane segment. For each of the defined roads, each of the congestion values ​​corresponding to the defined road is compared with a preset first congestion standard to obtain the classification interval to which each of the congestion values ​​corresponding to the defined road belongs; Calculate the proportion of the number of each type of classification interval in the road segmentation to the total number of intervals in all classification intervals, and obtain the classification proportion of each type of classification interval; Based on the preset second congestion standard, the proportion of all the categories corresponding to the road division is analyzed to obtain the congestion category corresponding to the road division and the proportion of all the categories, which is used as the segmented traffic condition prediction result of the road division. The congestion classification includes at least three categories: severe congestion, mild congestion, and smooth traffic.

5. The method for managing and controlling driving data in an integrated public transportation system according to claim 2, characterized in that, Before feeding back at least one of the segmented traffic prediction results to the user corresponding to the segmented traffic prediction result, the method further includes: From all the segmented traffic prediction results, determine the target traffic prediction result that matches the query road; Establish an interaction link with the user based on the traffic query request; The step of feeding back at least one of the segmented traffic prediction results to the user who viewed the corresponding segmented traffic prediction result includes: The target road condition prediction result is fed back to the user terminal corresponding to the querying user through the interaction link.

6. The method for managing and controlling driving data in an integrated public transportation system according to claim 1, 2, 4, or 5, characterized in that, The acquisition of driving data corresponding to the target area to be managed includes: Determine the area identifier corresponding to the target area to be controlled; Based on the area identifier, identify all public transportation systems that have established traffic associations with the target area, and identify the target interface for establishing data communication with each of the public transportation systems; Data collection operations are performed on each of the public transportation systems through the target interface to obtain the regional record data for the target area recorded by each of the public transportation systems; Filter the data from all the recorded data of the area that corresponds to all driving roads in the target area, and use the driving data of the target area as the driving data of the target area; The data recorded in each area includes at least one of the following: parking meter data, parking lot data, road engineering data, traffic situation and forecast data, map data, POI data, and road network data.

7. A vehicle data management and control device integrated with a public transportation system, characterized in that, The apparatus is used to perform the vehicle data management method for an integrated public transportation system as described in any one of claims 1-6, and the apparatus comprises: The acquisition module is used to acquire driving data corresponding to the target area to be managed. The driving data is extracted from the traffic data stored in the public transportation system corresponding to the target area. The driving data includes traffic information of all roads in the target area. The segmentation module is used to perform road segmentation on the target area based on the driving data, resulting in multiple segmented roads, each of which includes at least one driving road; The traffic prediction module is used to perform a preset segmented traffic prediction process on each of the divided roads based on the driving data corresponding to the divided road, and obtain the segmented traffic prediction result corresponding to the divided road. The feedback module is used to send at least one of the segmented traffic condition prediction results back to the user who is consulting the corresponding segmented traffic condition prediction result, so as to notify the user of the current traffic condition information of the segmented road corresponding to the segmented traffic condition prediction result.

8. A vehicle data management and control device integrated with a public transportation system, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the vehicle data management method for the integrated public transportation system as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the vehicle data management method for the integrated public transportation system as described in any one of claims 1-6.

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