Truck footprint map visualization method and system based on ETC passing data

Abstract

The invention discloses a truck footprint map visualization method and system based on ETC passing data, and belongs to the technical field of visualization. The method comprises the following steps of: constructing a space coordinate passing record set by acquiring an original passing record, a toll station number table, a cost statistical table, a passing frequency table and a payment conservation record table of a truck ETC (Electronic Toll Collection), and calculating a continuity weight; generating a complete track data set; constructing a lightening node and a coverage area; constructing a lightening result set of the site layer and the region layer; performing layered color assignment on the state value of each lightening node in the lightening result set according to a continuous weight; constructing a time comparison panel; constructing a multi-dimensional information presentation result set; calculating a comprehensive achievement score, and mapping the comprehensive achievement score to the lighting result set; constructing an achievement progress set; the method achieves the precise restoration and dynamic display of the track of the truck, and has the advantages of being high in real-time performance, rich in visualization effect and comprehensive in evaluation dimension.

Description

Technical Field

[0001] This invention relates to the field of visualization technology, specifically to a method and system for visualizing truck footprint maps based on ETC traffic data. Background Technology

[0002] In recent years, with the rapid development of intelligent transportation systems and big data technologies, the level of information management in highway traffic has significantly improved. Electronic Toll Collection (ETC), as a core infrastructure within this system, has been widely applied to the identification and tolling of freight vehicles on highways. Currently, some research utilizes ETC traffic data to construct vehicle trajectory models to achieve traffic pattern mining, route congestion prediction, and freight efficiency optimization. However, most existing technologies remain at the stage of single-dimensional traffic data analysis and have not yet fully integrated the multi-table structured data in the ETC system to achieve spatiotemporal reconstruction and visualization of truck traffic trajectories. Furthermore, due to the multi-source heterogeneity of ETC data formats and the discontinuous nature of timestamps, traditional methods are prone to problems such as data fragmentation, trajectory discontinuity, and spatial distortion during trajectory reconstruction, node matching, and visualization mapping, making it difficult to reflect the true operating footprint and dynamic characteristics of trucks.

[0003] The existing ETC toll data application technologies generally suffer from the following shortcomings: First, the data processing methods are simplistic, lacking a comprehensive integration mechanism for multi-dimensional information such as toll station numbers, toll fees, toll frequency, and compliance status, thus failing to form a unified time-series index structure and resulting in insufficient trajectory continuity. Second, there is a lack of a trajectory node construction mechanism based on spatial topological relationships; existing technologies often rely directly on the original toll sequence for connection, without considering the continuity constraints between geographical distance and time differences. Third, most visualization methods use static layer presentation, lacking the ability for time-dimensional linkage mapping and dynamic updates, making it difficult to meet the real-time display requirements of truck footprint maps. Fourth, existing systems have not established a quantitative evaluation system for truck behavior and lack a comprehensive achievement analysis model based on traffic activity and credit information. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for visualizing truck footprint maps based on ETC traffic data, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A method for visualizing truck footprint maps based on ETC (Electronic Toll Collection) data includes the following steps: Step S1: Obtain the original ETC passage record set, toll station number table, fee statistics table, passage frequency table, and payment and compliance record table for trucks; construct a spatial coordinate passage record set and calculate the continuity weight; Step S2: Construct a gantry pair sequence group arranged according to the passage order; calculate the geographic connection vector and construct a trajectory node linked list; establish a trajectory index table; Step S3: Construct illuminated nodes and coverage areas; construct illuminated result sets at the station layer and region layer; Step S4: Assign color to the status values ​​of each illuminated node in the illuminated result set according to the continuity weight; construct a time comparison panel; construct a multi-dimensional information presentation result set; Step S5: Calculate a comprehensive achievement score and map the comprehensive achievement score to the illuminated result set; construct an achievement progress set; Step S6: Receive newly added gantry transaction number data and timestamp data, update the fused dataset; incrementally update the trajectory dataset and illuminated result set, and recalculate the achievement progress set.

[0007] As a preferred embodiment of the truck footprint map visualization method based on ETC passage data described in this invention, the original passage record set, toll station number table, fee statistics table, passage frequency table, and payment compliance record table of truck ETC are obtained from the backend of the truck ETC management system. The original passage record set includes gantry transaction number data, toll station number data, and timestamp data; the toll station number table records the unique identifier number of all toll stations and their corresponding spatial coordinate information; the fee statistics table records the passage fee data of trucks in each ETC transaction; the passage frequency table records the distribution of truck passage frequency on a monthly basis; and the payment compliance record table records the credit status data of truck ETC accounts.

[0008] Based on the spatial coordinate information of the toll station recorded in the toll station number table, the toll station number data in the original passage record set is matched, and spatial coordinate information tags are added to the toll station number data in the original passage record set, thereby updating the original passage record set into a spatial coordinate passage record set.

[0009] As a preferred embodiment of the truck footprint map visualization method based on ETC passage data described in this invention, based on the spatial coordinate passage record set, the gantry transaction number data is used as the main index. The passage fee data, passage frequency data, and credit status data corresponding to the same gantry transaction number are retrieved from the fee statistics table, passage frequency table, and payment and compliance record table, respectively. The data are then fused according to the field correspondence to construct a fused dataset.

[0010] Based on the timestamp data, all records in the fused dataset are rearranged in time series to establish a unified time-series index structure, and continuity weights are calculated based on the time difference and spatial distance between adjacent records.

[0011] As a preferred embodiment of the truck footprint map visualization method based on ETC passage data described in this invention, based on the time-series index structure in the fused dataset, the consecutive gantry transaction numbers under the same truck identifier are sequentially paired using timestamp data as the sorting basis to construct gantry pair sequence groups arranged according to the passage order, and the time difference and spatial distance parameters between each group are recorded.

[0012] Based on the spatial coordinates of adjacent gantries in the gantry pair sequence group, calculate the geographic connection vector, and construct a trajectory segment unit set based on the geographic connection vector; in each trajectory segment unit, attach the corresponding toll fee data and toll frequency data, and construct a trajectory node linked list.

[0013] As a preferred embodiment of the truck footprint map visualization method based on ETC traffic data described in this invention, the trajectory node linked list is merged according to temporal continuity and spatial proximity to construct complete trajectory entries, and a trajectory dataset is generated using truck identifier and timestamp as composite primary keys; a continuity weight is calculated for each trajectory entry in the trajectory dataset, and a trajectory index table is established.

[0014] As a preferred embodiment of the truck footprint map visualization method based on ETC traffic data described in this invention, a track node linked list is extracted from the track dataset, the passage frequency of each toll station is counted, and the passage activity index is calculated. A lighting threshold is preset based on the arithmetic mean of the passage frequency of all toll stations in the track dataset. When the passage activity index of the toll station is greater than or equal to the lighting threshold, the toll station is marked as a lit node in the track dataset.

[0015] The lit nodes are aggregated according to their respective administrative divisions, and the coverage parameter of each administrative division (i.e., the ratio of the number of lit nodes in the division to the total number of toll stations in the division) is calculated. A city coverage threshold is preset. When the coverage parameter is greater than or equal to the city coverage threshold, the administrative division is marked as a coverage area in the trajectory dataset.

[0016] An index for lighting results is established in the trajectory dataset. The status of the lit nodes and the continuity weights calculated in the trajectory dataset are correlated accordingly. The lighting result sets of the station layer and the region layer are constructed using the toll station number and timestamp as composite indexes.

[0017] As a preferred embodiment of the truck footprint map visualization method based on ETC traffic data described in this invention, trajectory segment units are extracted from the trajectory dataset, and a map base layer is constructed using the spatial coordinate sequence of trajectory nodes; the status values ​​of each lit node in the lit result set are assigned colors in layers according to continuous weights, and a dynamic footprint layer is generated using two levels of color encoding: highlight and grayscale.

[0018] In the visualization module, the toll fee data and toll frequency data in the trajectory dataset are mapped to the time dimension to build a time comparison panel; through the synchronized index with the timestamps of the trajectory nodes, the footprint layer and the toll frequency curve are rendered in time linkage.

[0019] A multi-layer information annotation structure is established in the map rendering results. The spatial vector of the trajectory segment unit is associated with the coverage status of the administrative region to which it belongs, and a multi-dimensional information presentation result set is constructed. The multi-dimensional information presentation result set includes a station layer, a region layer and a time layer.

[0020] As a preferred embodiment of the truck footprint map visualization method based on ETC toll data described in this invention, the toll payment and compliance record table is used to extract the compliance time data and the number of abnormalities of the trucks, and combined with the toll data in the toll statistics table and the number of cities covered in the trajectory dataset, a comprehensive achievement score is calculated.

[0021] The comprehensive achievement score is mapped to the lighting result set, and the achievement level is marked on the corresponding trajectory node to construct the achievement progress set.

[0022] An interactive interface is established in the visualization module. When the status of a trajectory node is updated, the corresponding achievement progress set is synchronously updated.

[0023] As a preferred embodiment of the truck footprint map visualization method based on ETC passage data described in this invention, the method receives newly added gantry transaction number data and timestamp data, and compares them with the gantry transaction numbers recorded in the fused dataset; when a new record is detected, the fused dataset is updated based on the spatial coordinate information matched with the toll station number table.

[0024] After updating the fused dataset, the trajectory segment units are regenerated based on the temporal index structure and new continuity weights are calculated. The trajectory dataset and the lighting result set are then incrementally updated.

[0025] When the trajectory dataset and the illuminated result set are updated, the footprint layer and time comparison panel are automatically updated, and the achievement progress set is recalculated.

[0026] A truck footprint map visualization system based on ETC traffic data. The system includes: a data acquisition and weight calculation module, a vector calculation and index table construction module, a result set construction module, a color assignment and multidimensional result set construction module, a progress set construction module, and an update module.

[0027] The data acquisition and weight calculation module acquires the original passage record set, toll station number table, fee statistics table, passage frequency table, and payment and compliance record table for truck ETC; constructs a spatial coordinate passage record set and calculates continuous weights;

[0028] The vector calculation and index table construction module: constructs a gantry pair sequence group arranged according to the order of passage; calculates the geographic connection vector, constructs the trajectory node linked list; and establishes the trajectory index table.

[0029] The result set construction module: constructs the lit nodes and coverage areas; constructs the lit result sets at the site layer and the region layer;

[0030] The coloring and multidimensional result set construction module: assigns color to the state values ​​of each lit node in the lit result set in layers according to continuous weights; constructs a time comparison panel; and constructs a multidimensional information presentation result set.

[0031] The progress set construction module: calculates the comprehensive achievement score, maps the comprehensive achievement score to the lighting result set, and constructs the achievement progress set;

[0032] The update module: receives newly added gantry transaction number data and timestamp data, updates the fused dataset; incrementally updates the trajectory dataset and the lighting result set, and recalculates the achievement progress set.

[0033] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention provides a method and system for visualizing truck footprint maps based on ETC traffic data. By extracting original truck passage records, toll station number tables, fee statistics tables, passage frequency tables, and payment and compliance record tables from the ETC management system, the toll station numbers are matched with spatial coordinate information to construct a spatial coordinate passage record set. Multi-source data fusion is performed using the gantry transaction number as the primary index. Continuity weights are obtained through timestamp rearrangement and calculation of time difference and spatial distance, achieving spatiotemporal integrated structured processing of passage data. Subsequently, gantry pair sequence groups and trajectory node linked lists are constructed based on time series. Trajectory segment units are generated through geographical connection vectors and merged into a complete trajectory dataset based on temporal continuity and spatial proximity, laying the foundation for accurate reconstruction of truck travel paths. Furthermore, by calculating the traffic activity index of each toll station and setting a lighting threshold and city coverage threshold, high-frequency passage nodes are marked as lighting nodes and aggregated to generate coverage areas, thereby establishing a station layer and regional layer. The system first illuminates the result set of each layer, enabling a spatial visualization of freight activity. Then, by assigning layered color to the status values ​​of illuminated nodes according to continuous weights, and combining this with a time comparison panel and a linked rendering mechanism, a multi-dimensional information presentation result set is constructed, encompassing station, regional, and time layers. This provides dynamic visualization of trajectory data in both spatiotemporal dimensions. Subsequently, a comprehensive achievement score is calculated using truck compliance time, number of anomalies, toll fees, and the number of cities covered as input parameters. This score is then mapped to the illuminated result set to generate an achievement progress set, achieving a quantitative assessment and visual display of vehicle operational performance. Finally, by receiving new gantry transaction data and matching it with spatial coordinate information, an incremental update mechanism based on a time-series index is established. This enables dynamic and synchronous updates of trajectory data, the illuminated result set, and the achievement progress set, thus constructing a real-time, continuous, and interactive truck footprint map visualization system. Ultimately, this system achieves accurate reconstruction of truck travel routes, intuitive presentation of regional freight activity, and dynamic analysis of operational performance, providing efficient and reliable technical support for freight monitoring and management decisions. Attached Figure Description

[0034] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0035] Figure 1 This is a schematic diagram illustrating the steps of a truck footprint map visualization method based on ETC traffic data according to the present invention;

[0036] Figure 2 This is a schematic diagram of the structure of a truck footprint map visualization system based on ETC traffic data according to the present invention. Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0038] Please see Figure 1 In this first embodiment: a method for visualizing truck footprint maps based on ETC traffic data is provided, which includes the following steps:

[0039] Step S1: Obtain the original passage record set, toll station number table, fee statistics table, passage frequency table, and payment and compliance record table for truck ETC; construct a spatial coordinate passage record set and calculate the continuity weight.

[0040] Specifically, the system retrieves the original toll record set, toll station number table, fee statistics table, toll frequency table, and payment compliance record table for truck ETC from the truck ETC management system backend. The original toll record set includes gantry transaction number data, toll station number data, and timestamp data. The toll station number table records the unique identifier number and corresponding spatial coordinate information of all toll stations. The fee statistics table records the toll fees for each ETC transaction for trucks. The toll frequency table records the distribution of truck toll frequency on a monthly basis. The payment compliance record table records the credit status data of the truck ETC account.

[0041] Based on the spatial coordinate information of the toll station recorded in the toll station number table, the toll station number data in the original passage record set is matched, and spatial coordinate information tags are added to the toll station number data in the original passage record set, thereby updating the original passage record set into a spatial coordinate passage record set.

[0042] Furthermore, based on the spatial coordinate passage record set, the gantry transaction number data is used as the main index. Passage fee data, passage frequency data, and credit status data corresponding to the same gantry transaction number are retrieved from the fee statistics table, passage frequency table, and payment and compliance record table, respectively. The data are then fused according to the field correspondence to construct a fused dataset.

[0043] Based on the timestamp data, all records in the fused dataset are rearranged into time series to establish a unified time-series index structure. Continuity weights are then calculated based on the time difference and spatial distance between adjacent records, using the following formula:

[0044]

[0045] in, Indicates continuous weights, This represents the time difference between adjacent records. Indicates the spatial distance between adjacent records. This represents the preset time decay coefficient. This represents the preset spatial attenuation coefficient.

[0046] In this invention, original passage records and multiple auxiliary tables (toll station number table, fee statistics table, passage frequency table, and payment compliance record table) are obtained from the ETC management system. The toll station numbers are matched with spatial coordinates to construct a spatial coordinate passage record set. Then, data fusion, temporal rearrangement, and continuous weight calculation are performed using the gantry transaction number as an index, realizing the transformation from multi-source heterogeneous ETC data to a unified spatial-temporal integrated structure. This process not only endows the original data with geographic location attributes but also constructs a continuous quantitative index of passage behavior through the joint analysis of time difference and spatial distance, providing a measurable basis for subsequent trajectory construction and activity calculation. This enables accurate reconstruction of truck trajectories and multi-dimensional fusion of passage characteristics, improving the accuracy of map visualization and the credibility of the analysis.

[0047] Step S2: Construct a gantry pair sequence group arranged in order of passage; calculate the geographic connection vector and construct a trajectory node linked list; establish a trajectory index table.

[0048] Specifically, based on the time-series index structure in the fused dataset, and using timestamp data as the sorting benchmark, consecutive gantry transaction numbers under the same truck identifier are sequentially paired to construct gantry pair sequence groups arranged according to the order of passage, and the time difference and spatial distance parameters between each group are recorded.

[0049] Based on the spatial coordinates of adjacent gantries in the gantry pair sequence group, calculate the geographic connection vector, and construct a trajectory segment unit set based on the geographic connection vector; in each trajectory segment unit, attach the corresponding toll fee data and toll frequency data, and construct a trajectory node linked list.

[0050] Furthermore, the trajectory node linked lists are merged according to temporal continuity and spatial proximity to construct complete trajectory entries, and a trajectory dataset is generated using truck identifiers and timestamps as composite primary keys; a continuity weight is calculated for each trajectory entry in the trajectory dataset, and a trajectory index table is established.

[0051] In this invention, by constructing gantry pair sequence groups based on time-series indexing and calculating the geographical connection vectors between adjacent gantries, a trajectory node linked list and a trajectory index table are established, realizing structured trajectory reorganization of traffic data. This step not only transforms discrete transaction records into spatially continuous trajectory segments but also endows the trajectories with behavioral characteristic expression capabilities by attaching cost and frequency information to the trajectory nodes. Furthermore, by aggregating the time and space of the trajectory node linked list to form complete trajectory entries, the trajectory data possesses logical coherence and indexability. In this way, the system can quickly retrieve and visualize truck driving behavior across different time periods and routes, achieving accurate reconstruction and efficient querying of freight routes.

[0052] Step S3: Construct the lighting nodes and coverage areas; construct the lighting result sets for the site layer and the region layer.

[0053] Specifically, a linked list of trajectory nodes is extracted from the trajectory dataset, the passage frequency of each toll station is counted, and the passage activity index is calculated. A lighting threshold is preset based on the arithmetic mean of the passage frequency of all toll stations in the trajectory dataset. When the passage activity index of the toll station is greater than or equal to the lighting threshold, the toll station is marked as a lit node in the trajectory dataset.

[0054] The calculation formula for the traffic activity index is as follows:

[0055]

[0056] in, This represents the traffic activity index of the i-th toll station. This represents the frequency of passage at the i-th toll station, and N represents the total number of toll stations;

[0057] The formula for calculating the lighting threshold is as follows:

[0058]

[0059] in, Indicates the threshold for lighting up;

[0060] The lit nodes are aggregated according to their respective administrative divisions, and the coverage parameter of each administrative division (i.e., the ratio of the number of lit nodes in the division to the total number of toll stations in the division) is calculated. A city coverage threshold is preset. When the coverage parameter is greater than or equal to the city coverage threshold, the administrative division is marked as a coverage area in the trajectory dataset.

[0061] The formula for calculating the coverage parameter of administrative regions is as follows:

[0062]

[0063] in, This represents the coverage parameter of the c-th administrative region. This represents the number of toll booths that are lit up in the c-th administrative region. This represents the total number of toll stations within the c-th administrative region;

[0064] An index for lighting results is established in the trajectory dataset. The status of the lit nodes and the continuity weights calculated in the trajectory dataset are correlated accordingly. The lighting result sets of the station layer and the region layer are constructed using the toll station number and timestamp as composite indexes.

[0065] In this invention, the frequency of passage at each toll station in the statistical trajectory dataset is collected, and the traffic activity index is calculated. The "lighting status" of nodes and regions is determined using lighting thresholds and city coverage thresholds, thus achieving a mapping from individual trajectories to the spatial distribution of group activity. This step, through a two-layer structure of lit nodes and covered areas, links the micro-characteristics of truck traffic behavior with the macro-characteristics of regional logistics activity, thereby forming lighting result sets at the station and regional levels. This design can intuitively present the distribution of freight intensity and node traffic heat in different regions, providing data support for subsequent regional transportation planning, road congestion analysis, and freight flow assessment, significantly enhancing the insight and decision-making value of the visualized map.

[0066] Step S4: Assign color to the status values ​​of each lit node in the lighting result set according to the continuity weight; construct a time comparison panel; construct a multi-dimensional information presentation result set.

[0067] Specifically, trajectory segment units are extracted from the trajectory dataset, and a map base layer is constructed using the spatial coordinate sequence of trajectory nodes; the state values ​​of each lit node in the lit result set are assigned colors in layers according to continuous weights, and a dynamic footprint layer is generated using two levels of color encoding: highlight and grayscale.

[0068] In the visualization module, the toll fee data and toll frequency data in the trajectory dataset are mapped to the time dimension to build a time comparison panel; through the synchronized index with the timestamps of the trajectory nodes, the footprint layer and the toll frequency curve are rendered in time linkage.

[0069] A multi-layer information annotation structure is established in the map rendering results. The spatial vector of the trajectory segment unit is associated with the coverage status of the administrative region to which it belongs, and a multi-dimensional information presentation result set is constructed. The multi-dimensional information presentation result set includes a station layer, a region layer and a time layer.

[0070] In this invention, by assigning layered color to illuminated nodes according to continuity weights, the spatiotemporal continuity of trajectory nodes is intuitively mapped into a visual color gradient. Simultaneously, a time comparison panel is constructed and time-linked rendering is performed, enabling dynamic and comparable display of trajectory paths, costs, and frequencies over time. Furthermore, a multi-dimensional information presentation structure is established, comprising station, regional, and time layers, comprehensively displaying the geographical, economic, and behavioral characteristics of trajectory nodes in a layered overlay manner. This step, through a multi-dimensional fusion layer structure, achieves spatiotemporal traceability and behavioral quantification of truck operation patterns, improving the intuitiveness of data presentation and providing a visual basis for subsequent behavioral analysis and anomaly identification.

[0071] Step S5: Calculate the comprehensive achievement score and map the comprehensive achievement score to the lighting result set; construct the achievement progress set.

[0072] Specifically, the compliance time and number of abnormalities of trucks are extracted from the payment compliance record table, combined with the toll data in the fee statistics table and the number of cities covered in the trajectory dataset, to calculate the comprehensive achievement score. The calculation formula is as follows:

[0073]

[0074] in, Indicates the overall achievement score. This indicates the duration of compliance (the cumulative duration of time the truck has paid its fees on time). Indicates the maximum compliance time (the maximum possible value or benchmark value in the system). Indicates the number of abnormal events (the number of defaults or abnormal transactions). Indicates toll fees. Indicates the maximum toll fee. Indicates the number of cities covered. Indicates the maximum number of cities covered. , , and These represent the preset weighting factors for compliance duration, number of exceptions, toll fees, and number of cities covered;

[0075] The comprehensive achievement score is mapped to the lighting result set, and the achievement level is marked on the corresponding trajectory node to construct the achievement progress set.

[0076] An interactive interface is established in the visualization module. When the status of a trajectory node is updated, the corresponding achievement progress set is synchronously updated.

[0077] In the present invention, by extracting the compliance duration, number of anomalies, toll charges, and number of cities covered by the freight vehicle, calculating the comprehensive achievement score, and mapping this score to the lighting result set to generate the achievement progress set, a quantitative evaluation and dynamic display of the freight vehicle's operation performance are achieved. This score not only reflects the vehicle's credit performance and travel coverage, but also integrates economic indicators (charges) with behavioral indicators (frequency, compliance), constructing an evaluation dimension that can be dynamically updated. Through the linked update of the achievement progress set and the trajectory node status, the change trend of the freight vehicle's operation performance can be immediately reflected in the visualization interface, providing a visual decision-making basis for enterprises in credit management, vehicle evaluation, and operation optimization.

[0078] Step S6: Receive the newly added gantry transaction number data and timestamp data, and update the fusion data set; perform incremental updates on the trajectory data set and the lighting result set, and recalculate the achievement progress set.

[0079] Specifically, receive the newly added gantry transaction number data and timestamp data, and compare them with the gantry transaction numbers recorded in the fusion data set; when a new record is detected, match the spatial coordinate information based on the toll station number table, and update the fusion data set;

[0080] After updating the fusion data set, regenerate the trajectory segment unit based on the time series index structure and calculate the new continuity weight, and perform incremental updates on the trajectory data set and the lighting result set;

[0081] When the trajectory data set and the lighting result set are updated, automatically update the footprint layer and the time comparison panel, and recalculate the achievement progress set.

[0082] In the present invention, by receiving the newly added gantry transaction number and timestamp data, performing spatial coordinate matching and data set update, an incremental update mechanism based on time series index is established. The system can reconstruct the trajectory segment in real time and recalculate the continuity weight when new data arrives, and automatically update the footprint layer and the achievement progress set. This mechanism ensures the dynamic sustainability of the freight vehicle footprint map, enabling the map data to reflect the latest travel status in real time during operation, and achieving the dynamic fusion and automatic visualization refresh of ETC trajectory data. Therefore, the system has good scalability and timeliness, and can support the real-time monitoring of a large number of freight vehicles and the analysis of multi-period operation behaviors.

[0083] Please refer to Figure 2 , in the second embodiment: Provide a freight vehicle footprint map visualization system based on ETC travel data, which includes: a data acquisition and weight calculation module, a vector calculation and index table construction module, a result set construction module, a coloring and multi-dimensional result set construction module, a progress set construction module, and an update module;

[0084] The data acquisition and weight calculation module acquires the original passage record set, toll station number table, fee statistics table, passage frequency table, and payment and compliance record table for truck ETC; constructs a spatial coordinate passage record set and calculates continuous weights;

[0085] The vector calculation and index table construction module: constructs a gantry pair sequence group arranged according to the order of passage; calculates the geographic connection vector, constructs the trajectory node linked list; and establishes the trajectory index table.

[0086] The result set construction module: constructs the lit nodes and coverage areas; constructs the lit result sets at the site layer and the region layer;

[0087] The coloring and multidimensional result set construction module: assigns color to the state values ​​of each lit node in the lit result set in layers according to continuous weights; constructs a time comparison panel; and constructs a multidimensional information presentation result set.

[0088] The progress set construction module: calculates the comprehensive achievement score, maps the comprehensive achievement score to the lighting result set, and constructs the achievement progress set;

[0089] The update module: receives newly added gantry transaction number data and timestamp data, updates the fused dataset; incrementally updates the trajectory dataset and the lighting result set, and recalculates the achievement progress set.

[0090] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0091] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for visualizing truck footprint maps based on ETC (Electronic Toll Collection) data, characterized in that, The method includes the following steps: Step S1: Obtain the original passage record set, toll station number table, fee statistics table, passage frequency table, and payment and compliance record table for truck ETC; construct a spatial coordinate passage record set and calculate the continuity weight; Step S2: Construct a gantry pair sequence group arranged according to the order of passage; calculate the geographic connection vector and construct the trajectory node linked list; establish a trajectory index table; Step S3: Construct the lighting nodes and coverage areas; construct the lighting result sets for the site layer and the region layer; Step S4: Assign color to the status values ​​of each lit node in the lighting result set according to the continuity weight; construct a time comparison panel; construct a multi-dimensional information presentation result set; Step S5: Calculate the comprehensive achievement score, map the comprehensive achievement score to the lighting result set; construct the achievement progress set; Step S6: Receive newly added gantry transaction number data and timestamp data, update the fused dataset; incrementally update the trajectory dataset and the lighting result set, and recalculate the achievement progress set.

2. The method for visualizing truck footprint maps based on ETC traffic data according to claim 1, characterized in that, The specific implementation process of step S1 includes: The system retrieves the original toll record set, toll station number table, fee statistics table, toll frequency table, and payment compliance record table for truck ETC from the truck ETC management system backend. The original toll record set includes gantry transaction number data, toll station number data, and timestamp data. The toll station number table records the unique identifier number and corresponding spatial coordinate information of all toll stations. The fee statistics table records the toll fees for each ETC transaction for trucks. The toll frequency table records the distribution of truck toll frequency on a monthly basis. The payment compliance record table records the credit status data of the truck ETC account. Based on the spatial coordinate information of the toll station recorded in the toll station number table, the toll station number data in the original passage record set is matched, and spatial coordinate information tags are added to the toll station number data in the original passage record set, thereby updating the original passage record set into a spatial coordinate passage record set.

3. The method for visualizing truck footprint maps based on ETC traffic data according to claim 2, characterized in that, The specific implementation process of step S1 also includes: Based on the spatial coordinate passage record set, the gantry transaction number data is used as the main index. The passage fee data, passage frequency data and credit status data corresponding to the same gantry transaction number are retrieved from the fee statistics table, passage frequency table and payment and compliance record table, respectively. The data are then fused according to the field correspondence to construct a fused dataset. Based on the timestamp data, all records in the fused dataset are rearranged in time series to establish a unified time-series index structure, and continuity weights are calculated based on the time difference and spatial distance between adjacent records.

4. The method for visualizing truck footprint maps based on ETC traffic data according to claim 3, characterized in that, The specific implementation process of step S2 includes: Based on the time-series index structure in the fused dataset, and using timestamp data as the sorting benchmark, consecutive gantry transaction numbers under the same truck identifier are sequentially paired to construct gantry pair sequence groups arranged according to the order of passage, and the time difference and spatial distance parameters between each group are recorded. Based on the spatial coordinates of adjacent gantries in the gantry pair sequence group, calculate the geographic connection vector, and construct a trajectory segment unit set based on the geographic connection vector; in each trajectory segment unit, attach the corresponding toll fee data and toll frequency data, and construct a trajectory node linked list.

5. The method for visualizing truck footprint maps based on ETC traffic data according to claim 4, characterized in that, The specific implementation process of step S2 also includes: The trajectory node linked lists are merged according to temporal continuity and spatial proximity to construct complete trajectory entries. A trajectory dataset is generated using truck identifiers and timestamps as composite primary keys. A continuity weight is calculated for each trajectory entry in the trajectory dataset, and a trajectory index table is established.

6. The method for visualizing truck footprint maps based on ETC traffic data according to claim 5, characterized in that, The specific implementation process of step S3 includes: The trajectory node linked list is extracted from the trajectory dataset, the passage frequency of each toll station is counted, and the passage activity index is calculated. A lighting threshold is preset based on the arithmetic mean of the passage frequency of all toll stations in the trajectory dataset. When the passage activity index of the toll station is greater than or equal to the lighting threshold, the toll station is marked as a lighting node in the trajectory dataset. The lit nodes are aggregated according to their respective administrative divisions, and the coverage parameters of each administrative division are calculated. A city coverage threshold is preset. When the coverage parameter is greater than or equal to the city coverage threshold, the administrative division is marked as a coverage area in the trajectory dataset. An index for lighting results is established in the trajectory dataset. The status of the lit nodes and the continuity weights calculated in the trajectory dataset are correlated accordingly. The lighting result sets of the station layer and the region layer are constructed using the toll station number and timestamp as composite indexes.

7. A method for visualizing truck footprint maps based on ETC traffic data according to claim 6, characterized in that, The specific implementation process of step S4 includes: Trajectory segment units are extracted from the trajectory dataset, and a map base layer is constructed using the spatial coordinate sequence of trajectory nodes; the state values ​​of each lit node in the lit result set are assigned colors in layers according to continuous weights; In the visualization module, the toll fee data and toll frequency data in the trajectory dataset are mapped to the time dimension to build a time comparison panel; through the synchronized index with the timestamps of the trajectory nodes, the footprint layer and the toll frequency curve are rendered in time linkage. A multi-layer information annotation structure is established in the map rendering results. The spatial vector of the trajectory segment unit is associated with the coverage status of the administrative region to which it belongs, and a multi-dimensional information presentation result set is constructed. The multi-dimensional information presentation result set includes a station layer, a region layer and a time layer.

8. The method for visualizing truck footprint maps based on ETC traffic data according to claim 7, characterized in that, The specific implementation process of step S5 includes: Extract the compliance time data and number of abnormalities of the truck from the payment compliance record table, and combine them with the toll data in the fee statistics table and the number of cities covered in the trajectory dataset to calculate the comprehensive achievement score; Map the comprehensive achievement score to the set of highlighted results to construct an achievement progress set; An interactive interface is established in the visualization module. When the status of a trajectory node is updated, the corresponding achievement progress set is synchronously updated.

9. A method for visualizing truck footprint maps based on ETC traffic data according to claim 8, characterized in that, The specific implementation process of step S6 includes: The system receives newly added gantry transaction number data and timestamp data, and compares them with the gantry transaction numbers recorded in the fused dataset; when a new record is detected, the system updates the fused dataset based on the spatial coordinate information matched in the toll station number table. After updating the fused dataset, the trajectory segment units are regenerated based on the temporal index structure and new continuity weights are calculated. The trajectory dataset and the lighting result set are then incrementally updated. When the trajectory dataset and the illuminated result set are updated, the footprint layer and time comparison panel are automatically updated, and the achievement progress set is recalculated.

10. A truck footprint map visualization system based on ETC toll data, executing the truck footprint map visualization method based on ETC toll data as described in any one of claims 1-9, characterized in that, The system includes: a data acquisition and weight calculation module, a vector calculation and index table construction module, a result set construction module, a color assignment and multidimensional result set construction module, a progress set construction module, and an update module; The data acquisition and weight calculation module acquires the original passage record set, toll station number table, fee statistics table, passage frequency table, and payment and compliance record table for truck ETC; constructs a spatial coordinate passage record set and calculates continuous weights; The vector calculation and index table construction module: constructs a gantry pair sequence group arranged according to the order of passage; calculates the geographic connection vector, constructs the trajectory node linked list; and establishes the trajectory index table. The result set construction module: constructs the lit nodes and coverage areas; constructs the lit result sets at the site layer and the region layer; The coloring and multidimensional result set construction module: assigns color to the state values ​​of each lit node in the lit result set in layers according to continuous weights; constructs a time comparison panel; and constructs a multidimensional information presentation result set. The progress set construction module: calculates the comprehensive achievement score, maps the comprehensive achievement score to the lighting result set, and constructs the achievement progress set; The update module: receives newly added gantry transaction number data and timestamp data, updates the fused dataset; incrementally updates the trajectory dataset and the lighting result set, and recalculates the achievement progress set.

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