Expressway vehicle fee evasion inspection method and system based on multi-source data fusion

Abstract

The invention belongs to the technical field of intelligent traffic, and provides an expressway vehicle fee evasion inspection method and system based on multi-source data fusion, and the method comprises the steps: obtaining the timestamps and vehicle feature information of a vehicle when the vehicle is driven into and out of an expressway; the actual dynamic driving time of the vehicle is obtained based on the timestamp, when the actual dynamic driving time exceeds a dynamic driving time threshold value, the potential fee evasion phenomenon exists, and otherwise, whether the potential fee evasion phenomenon exists or not is judged by obtaining path portal frame information in the vehicle driving process; when the path portal frame information is lost, the potential fee evasion phenomenon exists, and otherwise, whether the potential fee evasion phenomenon exists in the vehicle or not is judged according to the vehicle feature information; when the vehicle feature information of the vehicle driving into and out of the highway is not matched, it is judged that a potential fee evasion phenomenon exists, and highway vehicle fee evasion inspection based on multi-source data fusion is completed; therefore, the potential toll evasion behavior of the expressway vehicle is accurately and efficiently identified, and the expressway toll collection rights and interests are

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation technology, specifically relating to a method and system for investigating toll evasion by vehicles on highways based on multi-source data fusion. Background Art

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Highways are multi-lane roads specifically designed for vehicles to travel in separate directions and with fully controlled access. Due to their high construction and management costs, toll collection is generally based on factors such as travel distance and bridge / tunnel conditions. Currently, with the increasing prevalence of non-stop toll collection, toll evasion on highways is becoming more frequent.

[0004] Currently, the main methods of highway toll evasion include changing the vehicle type for billing, inconsistent entry and exit information, card swapping, inconsistent license plates at entry and exit, inconsistent billing and license plate recognition paths, inconsistent vehicle type at entry and exit, abnormal license plates for large vehicles with small labels, abnormal number of axles for large vehicles with small labels, abnormal vehicle type for large vehicles with small labels, malicious U / J-shaped driving, no cameras throughout the journey, exceeding the time limit, and charging under-the-table charges.

[0005] Current methods for detecting toll evasion on highways involve manual analysis by inspectors to identify suspected evaders, followed by manual verification. However, this manual approach requires significant manpower and is inefficient. Furthermore, existing toll evasion detection methods rely on a single data source and are unable to adapt to dynamic changes in weather, vehicle type, and road conditions within fixed time thresholds. They lack multi-dimensional spatiotemporal correlation analysis, resulting in low accuracy in identifying toll evasion. Therefore, how to effectively detect toll evasion through technological means to maintain the normal and orderly operation of highway toll collection is a crucial issue that deserves attention. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a method and system for investigating toll evasion by vehicles on highways based on multi-source data fusion. By acquiring the time of a vehicle's entry and exit from the highway, vehicle type characteristics, and license plate information, and combining this with vehicle gantry information obtained on the highway, the system makes a comprehensive judgment based on the acquired information to determine whether the vehicle exhibits toll evasion behaviors such as abnormal routes. This method accurately and efficiently identifies potential toll evasion by vehicles on highways, thereby protecting the rights and interests of highway toll collectors.

[0007] According to some embodiments, the first solution of the present invention provides a method for investigating highway toll evasion based on multi-source data fusion, which adopts the following technical solution: A method for detecting toll evasion on highways based on multi-source data fusion includes: Obtain the timestamps and vehicle characteristic information of vehicles entering and exiting highways; Based on the obtained timestamp, the actual dynamic driving time of the vehicle on the highway is obtained. When the actual dynamic driving time exceeds the dynamic driving time threshold, the vehicle has a potential toll evasion phenomenon. Otherwise, the potential toll evasion phenomenon of the vehicle is determined by obtaining the path gantry information of the vehicle during its journey on the highway. If the obtained route gantry information is missing, the vehicle may be suspected of evading tolls; otherwise, the vehicle may be suspected of evading tolls based on the obtained vehicle characteristic information. When the vehicle characteristic information obtained does not match when entering and exiting the highway, it is determined that the vehicle has a potential toll evasion problem, and the highway toll evasion investigation based on multi-source data fusion is completed.

[0008] As a further technical limitation, the potential toll evasion phenomena of the vehicle include abnormal travel time, vehicle information mismatch, and missing route gantry information; the abnormal travel time includes J-type toll evasion, traveling long distances to buy short-distance tickets, and no card at the exit; the vehicle information mismatch includes trailer swapping and large vehicles with small labels; the missing route gantry information includes obscured On-Board Unit (OBU) information and reversed CPC (Civil Service Combination Toll Card) cards.

[0009] As a further technical limitation, the process for obtaining the dynamic driving time threshold is as follows: Obtain historical and current traffic data for the target road segment; Key features affecting travel time are extracted based on the acquired historical traffic data; Based on the extracted key features, construct a target road segment traffic sub-model for each key feature; Select the target road segment traffic sub-model based on the acquired current traffic data; The dynamic driving time threshold is calculated by combining the selected target road segment traffic sub-model.

[0010] As a further technical limitation, in the process of determining whether a vehicle has potential toll evasion by acquiring the path gantry information during its highway travel, the number of missing path gantry information is detected in real time. When the number of consecutively missing path gantry information is less than a preset value, a trajectory spatiotemporal reconstruction mechanism is automatically triggered. A time window constraint is constructed by combining the passage timestamps of adjacent path gantry information with the highway design speed. A hidden Markov model is adopted, defining the hidden state set as the actual gantry nodes and their virtual topological connection states. A spatiotemporal transition probability matrix is ​​introduced, and the state transition weights are determined by the gantry spacing and the vehicle's speed. The system dynamically calculates and generates average vehicle speeds; it generates a set of candidate virtual gantries based on road network topology connectivity, and performs spatiotemporal verification by combining the passage time intervals between adjacent gantries; it constructs a Bayesian inference network, and performs multi-dimensional probability calculations by integrating vehicle historical route preferences, real-time traffic conditions, and time series data; it outputs the top N high-confidence trajectory hypotheses, with each path accompanied by a spatiotemporal matching degree evaluation value, and generates a path reconstruction credibility analysis report including time dimension verification; when the number of missing gantries on a path is detected to be no less than a preset value and the spatiotemporal deviation rate between the derived path and the shortest toll path exceeds a set value, the system automatically activates toll evasion investigation.

[0011] As a further technical limitation, in the process of determining whether a vehicle has the potential toll evasion based on the acquired vehicle feature information, vehicle feature information is collected when the vehicle enters and exits the highway, and the number of vehicle axles is detected in real time. The number of vehicle axles obtained by the gantries along the route is retrieved simultaneously, and a multi-stage verification method is used to match the vehicle feature information in order to determine whether the vehicle has the potential toll evasion.

[0012] As a further technical limitation, the actual dynamic driving time of the vehicle on the highway is the time difference between the timestamp of the vehicle leaving the highway and the timestamp of the vehicle entering the highway.

[0013] According to some embodiments, the second aspect of the present invention provides a highway vehicle toll evasion detection system based on multi-source data fusion, employing the following technical solution: A highway toll evasion detection system based on multi-source data fusion includes: The acquisition module is configured to acquire timestamps and vehicle characteristic information of vehicles entering and exiting highways; The judgment module is configured to obtain the actual dynamic driving time of a vehicle on the highway based on the acquired timestamp. If the actual dynamic driving time exceeds the dynamic driving time threshold, the vehicle is suspected of potential toll evasion. Otherwise, the module determines whether the vehicle is suspected of potential toll evasion by acquiring the path gantry information of the vehicle during its highway journey. If the acquired path gantry information is missing, the vehicle is suspected of potential toll evasion. Otherwise, the module determines whether the vehicle is suspected of potential toll evasion based on the acquired vehicle feature information. If the acquired vehicle feature information for entering and exiting the highway does not match, the module determines that the vehicle is suspected of potential toll evasion, thus completing the highway vehicle toll evasion inspection based on multi-source data fusion.

[0014] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium, employing the following technical solution: A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the highway vehicle toll evasion investigation method based on multi-source data fusion as described in the first aspect of the present invention.

[0015] According to some embodiments, the fourth aspect of the present invention provides an electronic device, which adopts the following technical solution: An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the highway vehicle toll evasion investigation method based on multi-source data fusion as described in the first aspect of the present invention.

[0016] According to some embodiments, the fifth aspect of the present invention provides a computer program product, which adopts the following technical solution: A computer program product includes software code, wherein the program in the software code performs the steps of the highway vehicle toll evasion investigation method based on multi-source data fusion as described in the first aspect of the present invention.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention obtains information such as the time a vehicle enters or exits the highway, vehicle type characteristics, and license plate information. Combined with vehicle gantry information obtained on the highway, it makes a comprehensive judgment based on the obtained information to determine whether the vehicle is engaging in toll evasion behaviors such as abnormal routes. By using the vehicle's diverse information, it can accurately and efficiently identify potential toll evasion behaviors and protect the toll collection rights of highways. Attached Figure Description

[0018] The accompanying drawings, which form part of this embodiment, are used to provide a further understanding of this embodiment. The illustrative embodiments and their descriptions are used to explain this embodiment and do not constitute an improper limitation of this embodiment.

[0019] Figure 1 This is a flowchart of the highway toll evasion investigation method based on multi-source data fusion in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram illustrating the steps of the highway toll evasion investigation method based on multi-source data fusion in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the types of toll evasion by vehicles on highways in Embodiment 1 of the present invention; Figure 4 This is a flowchart illustrating the vehicle travel time calculation in Embodiment 1 of the present invention; Figure 5 This is a flowchart of the dynamic threshold calculation for the longest vehicle driving time in Embodiment 1 of the present invention; Figure 6 This is a flowchart of vehicle route restoration in Embodiment 1 of the present invention; Figure 7 This is a flowchart of vehicle information matching in Embodiment 1 of the present invention; Figure 8 This is a structural block diagram of the highway vehicle toll evasion investigation system based on multi-source data fusion in Embodiment 2 of the present invention. Detailed Implementation

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0022] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0023] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are only used to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.

[0024] In this invention, terms such as "fixed connection," "connected," and "linked" should be interpreted broadly, indicating a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can determine the specific meaning of these terms in this invention based on the specific circumstances, and they should not be construed as limitations on the invention.

[0025] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0026] Example 1 Embodiment 1 of this invention introduces a method for investigating toll evasion by vehicles on highways based on multi-source data fusion.

[0027] like Figure 1 and Figure 2 The method for detecting toll evasion on highways based on multi-source data fusion, as shown, includes: Obtain the timestamps and vehicle characteristic information of vehicles entering and exiting highways; Based on the obtained timestamp, the actual dynamic driving time of the vehicle on the highway is obtained. When the actual dynamic driving time exceeds the dynamic driving time threshold, the vehicle has a potential toll evasion phenomenon. Otherwise, the potential toll evasion phenomenon of the vehicle is determined by obtaining the path gantry information of the vehicle during its journey on the highway. If the obtained route gantry information is missing, the vehicle may be suspected of evading tolls; otherwise, the vehicle may be suspected of evading tolls based on the obtained vehicle characteristic information. When the vehicle characteristic information obtained does not match when entering and exiting the highway, it is determined that the vehicle has a potential toll evasion problem, and the highway toll evasion investigation based on multi-source data fusion is completed.

[0028] like Figure 3 The types of toll evasion on highways shown include potential toll evasion phenomena such as abnormal travel time, vehicle information mismatch, and missing route gantry information; abnormal travel time includes J-type toll evasion, traveling long distances to buy short distances, and no card at the exit; vehicle information mismatch includes trailer swapping and large vehicles with small labels; missing route gantry information includes OBU information obscured and CPC card swapping.

[0029] like Figure 4 The flowchart shown is for calculating vehicle travel time. It is applicable to calculating the time taken for a vehicle to travel from entering the highway to exiting the highway, and specifically includes the following steps: S310: When a vehicle passes through a highway entrance toll station, if it is a manual toll lane, the staff will record the vehicle's entry timestamp through the system while the vehicle receives the toll card; if it is an ETC lane, the electronic tag on the vehicle communicates with the lane equipment, and the equipment automatically records the time the vehicle passes through the entrance. S320: When a vehicle arrives at the highway exit toll station, there will also be a corresponding recording operation. In the manual toll lane, the staff will record the time the vehicle arrives at the exit in the system based on the vehicle's toll card information and calculate the travel time; for the ETC lane, the system will automatically identify the vehicle's electronic tag, record the timestamp of the vehicle passing through the exit, and compare it with the entry time for calculation. S330: The system calculates the vehicle's travel time on the highway by subtracting the entry time from the exit time. It compares the actual travel time with the estimated time. If the difference is within a set threshold, the data is considered normal, and billing or processing is performed as usual. If the difference exceeds the threshold, an exception handling procedure is initiated.

[0030] like Figure 5 The illustrated calculation process for the dynamic threshold of the longest vehicle travel time is applicable to calculating dynamic time thresholds for different road segments and scenarios. It specifically includes the following steps: S410: Obtain historical traffic data for the target road segment, including vehicle travel time, traffic flow, weather conditions, and time period information; clean the data, remove outliers and missing values, and perform standardization processing. S420: Based on historical data, key features affecting travel time are extracted, distinguishing between morning peak, evening peak, and off-peak periods. The average vehicle speed for each time period is calculated, severe weather levels are defined, and their attenuation coefficients on vehicle speed are quantified. The travel time distribution patterns of different vehicle types are analyzed by classifying them as passenger cars / freight cars and ETC / CPC users. The importance of features is evaluated through the XGBoost algorithm, and a general prediction model based on XGBoost covers 80% of common scenarios. S430: Adjust model parameters based on current traffic conditions and combine historical data. If the current rainfall is detected to reach the level of a rainstorm, automatically switch to the severe weather sub-model and increase the weather weight. When a sudden accident occurs on the road segment, real-time queue length data is introduced, the "accident impact coefficient" is temporarily added, the maximum travel time threshold of the road segment is appropriately increased, and the normal, Poisson, or Weiber distribution of travel time under different scenarios is fitted. The 95th percentile time value of the probability distribution is taken as the reference value for the longest travel time of the road segment.

[0031] like Figure 6 The flowchart shown is for vehicle trajectory reconstruction and is applicable when vehicle gantry information is incomplete. It includes the following steps: S510: Dynamic gantry missing detection, based on the target vehicle's ETC gantry passage records, detects the number of missing gantries in real time. When the number of consecutive missing gantries is less than... At that time, the system automatically triggers the trajectory spatiotemporal reconstruction mechanism, and constructs a time window constraint by combining the passage timestamps of adjacent gantry and the road design speed; Missing gantry The calculation method is as follows: calculate the gantry density (number of gantry / km) based on the road segment length and the total number of gantry. Divide the density into three intervals: low density (<0.2 gantry / km), medium density (0.2-0.5 gantry / km), and high density (>0.5 gantry / km), and assign them weight coefficients of 0.7, 0.5, and 0.3 respectively. The failure frequency of the gantry equipment on this section of the road over the past month was statistically analyzed. If the failure rate was less than 2%, a correction factor of 0.9 was applied; if it was between 2% and 5%, a correction factor of 1.0 was applied; and if it was greater than 5%, a correction factor of 1.1 was applied. Considering the different requirements for gantry information in simple and complex road sections, in ordinary road sections, the gantry layout is relatively sparse. Even if a certain proportion of gantries are missing, such as 20%-30%, the system can still roughly deduce the trajectory based on the remaining gantry information and general vehicle movement patterns. In complex road sections, the dependence on gantry information is extremely high. Even a 5%-10% or less missing gantry can make trajectory determination difficult. Taking all factors into consideration, 10% is chosen as the baseline proportion for missing gantry information. This is calculated using the formula: "Proportion Threshold = Road Section Gantry Density Weight × Historical Equipment Failure Rate Correction Coefficient × 10%". The percentage threshold is multiplied by the total number of gantries in the road section, and the result is rounded up. S520: Multimodal path reasoning modeling: Hidden Markov Model (HMM) is adopted, and the hidden state set is defined as the actual gantry nodes (G1, G2, ..., Gn) and their virtual topological connection states. A spatiotemporal transition probability matrix is ​​introduced, in which the state transition weights are dynamically calculated and generated by the gantry spacing and the average vehicle speed. S530: Intelligent Route Reconstruction Engine: Innovatively embeds a dynamic interpolation mechanism for virtual nodes into the traditional Viterbi algorithm framework. It generates a set of candidate virtual gantries based on road network topology connectivity, and performs spatiotemporal verification by combining the passage time intervals between adjacent gantries; it constructs a Bayesian inference network, comprehensively considering vehicle historical route preferences, real-time traffic conditions, and time-series data for multi-dimensional probability calculation; it outputs the top N (N≥3) high-confidence trajectory hypotheses, each path accompanied by a spatiotemporal matching degree evaluation value (0-1 interval), generating a route reconstruction credibility analysis report including time-dimensional verification. S540: When the system detects that the number of missing gantry frames is ≥ Furthermore, when the spatiotemporal deviation rate between the derived path and the shortest toll-based path exceeds 15%, the toll evasion detection module is automatically activated. Through multi-feature fusion decision-making, including the path spatiotemporal anomaly index (reconstructed based on gantry passage time series), license plate feature cross-gantry similarity (SSIM≥0.85), and service area dwell time anomaly detection (compared with historical data of similar vehicle types), accurate identification of toll evasion behavior caused by OBU signal obstruction is achieved, forming a complete chain of evidence for investigation.

[0032] like Figure 7 The flowchart shown is applicable to matching export vehicle information and includes the following steps: S610: When a vehicle enters the highway, it collects vehicle feature information through a multispectral high-definition camera (resolution ≥ 8 million pixels, frame rate 60fps) deployed at the entrance, uses an improved YOLOv8 model to detect the number of vehicle axles in real time, and performs cross-verification by combining wheel ground pressure distribution data. S620: Encrypts data such as the number of axles, vehicle 3D contour, license plate information, and vehicle type classification and writes it into the OBU device and the provincial data center to form an unalterable feature vector group. When a vehicle exits the highway, it obtains the entry binding data in real time through V2X communication and simultaneously retrieves the axle number change information recorded by the gantries along the route. S630: Employs a multi-stage verification algorithm to automatically associate data such as service area surveillance video clips, ETC transaction records, and mobile signaling trajectories to construct a complete chain of evidence that includes spatiotemporal features, physical features, and behavioral features; S640: If the weighing and axle count at the exit do not match the information at the entrance, the system will automatically initiate a second weighing and retrieve data from the three adjacent gantry frames for local path analysis.

[0033] This embodiment obtains the time of vehicle entry and exit from the highway, vehicle type characteristics, and license plate information, and combines this with vehicle gantry information obtained on the highway. Based on the obtained information, it makes a comprehensive judgment on whether the vehicle has any toll evasion behavior such as abnormal route. It accurately and efficiently identifies potential toll evasion behavior based on the vehicle's diverse information, thus protecting the toll collection rights of the highway.

[0034] Example 2 Embodiment 2 of the present invention introduces a highway vehicle toll evasion investigation system based on multi-source data fusion.

[0035] like Figure 8 The system shown is a highway toll evasion detection system based on multi-source data fusion, comprising: The acquisition module is configured to acquire timestamps and vehicle characteristic information of vehicles entering and exiting highways; The judgment module is configured to obtain the actual dynamic driving time of a vehicle on the highway based on the acquired timestamp. If the actual dynamic driving time exceeds the dynamic driving time threshold, the vehicle is suspected of potential toll evasion. Otherwise, the module determines whether the vehicle is suspected of potential toll evasion by acquiring the path gantry information of the vehicle during its highway journey. If the acquired path gantry information is missing, the vehicle is suspected of potential toll evasion. Otherwise, the module determines whether the vehicle is suspected of potential toll evasion based on the acquired vehicle feature information. If the acquired vehicle feature information for entering and exiting the highway does not match, the module determines that the vehicle is suspected of potential toll evasion, thus completing the highway vehicle toll evasion inspection based on multi-source data fusion.

[0036] The detailed steps are the same as those provided in Example 1 for the highway vehicle toll evasion investigation method based on multi-source data fusion, and will not be repeated here.

[0037] Example 3 Embodiment 3 of the present invention provides a computer-readable storage medium.

[0038] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the highway vehicle toll evasion investigation method based on multi-source data fusion as described in Embodiment 1 of the present invention.

[0039] The detailed steps are the same as those provided in Example 1 for the highway vehicle toll evasion investigation method based on multi-source data fusion, and will not be repeated here.

[0040] Example 4 Embodiment 4 of the present invention provides an electronic device.

[0041] An electronic device includes a memory, a processor, and a program stored in the memory and running on the processor. When the processor executes the program, it implements the steps in the highway vehicle toll evasion investigation method based on multi-source data fusion as described in Embodiment 1 of the present invention.

[0042] The detailed steps are the same as those provided in Example 1 for the highway vehicle toll evasion investigation method based on multi-source data fusion, and will not be repeated here.

[0043] Example 5 Embodiment 5 of the present invention provides a computer program product.

[0044] A computer program product includes software code, wherein the program in the software code performs the steps of the highway vehicle toll evasion investigation method based on multi-source data fusion as described in Embodiment 1 of the present invention.

[0045] The detailed steps are the same as those provided in Example 1 for the highway vehicle toll evasion investigation method based on multi-source data fusion, and will not be repeated here.

[0046] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0047] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0048] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0049] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0050] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0051] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

[0052] The above description is merely a preferred embodiment of this practice and is not intended to limit the scope of this practice. Various modifications and variations can be made to this practice by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this practice should be included within the protection scope of this practice.

Claims

1. A method for investigating toll evasion on highways based on multi-source data fusion, characterized in that, include: Obtain the timestamps and vehicle characteristic information of vehicles entering and exiting highways; Based on the obtained timestamp, the actual dynamic driving time of the vehicle on the highway is obtained. When the actual dynamic driving time exceeds the dynamic driving time threshold, the vehicle has a potential toll evasion phenomenon. Otherwise, the potential toll evasion phenomenon of the vehicle is determined by obtaining the path gantry information of the vehicle during its journey on the highway. If the obtained route gantry information is missing, the vehicle may be suspected of evading tolls; otherwise, the vehicle may be suspected of evading tolls based on the obtained vehicle characteristic information. When the vehicle characteristic information obtained does not match when entering and exiting the highway, it is determined that the vehicle has a potential toll evasion problem, and the highway toll evasion investigation based on multi-source data fusion is completed.

2. The method for investigating highway toll evasion based on multi-source data fusion as described in claim 1, characterized in that, The potential toll evasion issues of the vehicles include abnormal travel time, mismatched vehicle information, and missing route gantry information; abnormal travel time includes J-type toll evasion, traveling long distances to buy short-distance tickets, and no card at the exit; mismatched vehicle information includes trailer swapping and large vehicles with small labels; missing route gantry information includes obscured onboard electronic tag information and reversed highway composite toll cards.

3. The method for investigating highway toll evasion based on multi-source data fusion as described in claim 1, characterized in that, The process of obtaining the dynamic driving time threshold is as follows: Obtain historical and current traffic data for the target road segment; Key features affecting travel time are extracted based on the acquired historical traffic data; Based on the extracted key features, construct a target road segment traffic sub-model for each key feature; Select the target road segment traffic sub-model based on the acquired current traffic data; The dynamic driving time threshold is calculated by combining the selected target road segment traffic sub-model.

4. The method for investigating highway toll evasion based on multi-source data fusion as described in claim 1, characterized in that, In the process of determining whether a vehicle has potential toll evasion by acquiring the route gantry information of the vehicle during its journey on the highway, the number of missing route gantry information is detected in real time. When the number of consecutively missing route gantry information is less than a preset value, a trajectory spatiotemporal reconstruction mechanism is automatically triggered. A time window constraint is constructed by combining the passage timestamps of adjacent route gantries with the highway design speed. A hidden Markov model is adopted, defining the hidden state set as the actual gantry nodes and their virtual topological connection states. A spatiotemporal transition probability matrix is ​​introduced, and the state transition weights are dynamically calculated and generated by the gantry spacing and the average vehicle speed. A candidate virtual gantry set is generated based on the road network topology connectivity, and spatiotemporal verification is performed by combining the passage time intervals of adjacent gantries. A Bayesian inference network is constructed to perform multi-dimensional probability calculations by integrating vehicle historical route preferences, real-time traffic conditions, and time series data. The top N high-confidence trajectory hypotheses are output, with each path accompanied by a spatiotemporal matching degree evaluation value, generating a path reconstruction credibility analysis report that includes time dimension verification. When the number of missing path gantry is detected to be no less than a preset value and the spatiotemporal deviation rate between the derived path and the shortest toll path exceeds a set value, toll evasion investigation is automatically activated.

5. The method for investigating highway toll evasion based on multi-source data fusion as described in claim 1, characterized in that, In the process of determining whether a vehicle has the potential toll evasion based on the acquired vehicle feature information, vehicle feature information is collected when the vehicle enters and exits the highway, and the number of vehicle axles is detected in real time. The number of vehicle axles obtained from the gantries along the route is retrieved simultaneously. A multi-stage verification method is used to match the vehicle feature information in order to determine whether the vehicle has the potential toll evasion.

6. The method for investigating highway toll evasion based on multi-source data fusion as described in claim 1, characterized in that, The actual dynamic driving time of the vehicle on the highway is the time difference between the timestamp of the vehicle leaving the highway and the timestamp of the vehicle entering the highway.

7. A highway toll evasion detection system based on multi-source data fusion, characterized in that, include: The acquisition module is configured to acquire timestamps and vehicle characteristic information of vehicles entering and exiting highways; The judgment module is configured to obtain the actual dynamic driving time of a vehicle on the highway based on the acquired timestamp. If the actual dynamic driving time exceeds the dynamic driving time threshold, the vehicle is suspected of potential toll evasion. Otherwise, the module determines whether the vehicle is suspected of potential toll evasion by acquiring the path gantry information of the vehicle during its highway journey. If the acquired path gantry information is missing, the vehicle is suspected of potential toll evasion. Otherwise, the module determines whether the vehicle is suspected of potential toll evasion based on the acquired vehicle feature information. If the acquired vehicle feature information for entering and exiting the highway does not match, the module determines that the vehicle is suspected of potential toll evasion, thus completing the highway vehicle toll evasion inspection based on multi-source data fusion.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the highway vehicle toll evasion investigation method based on multi-source data fusion as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the program, it implements the steps of the highway vehicle toll evasion investigation method based on multi-source data fusion as described in any one of claims 1-6.

10. A computer program product, comprising software code, characterized in that, The program in the software code performs the steps of the highway vehicle toll evasion investigation method based on multi-source data fusion as described in any one of claims 1-6.

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