Expressway toll collection special situation paperless processing method

Abstract

The application discloses a highway toll collection special situation paperless processing method, and relates to the technical field of transportation, comprising the following steps: S01, acquiring a highway network topology graph based on the distribution positions of highway management toll collection sites and exit sites and a passing route; S02, verifying toll collection data according to a business rule; the verification of the toll collection data comprises the following steps: S021, extracting a transaction time sequence of the toll collection data, and verifying the toll collection data based on the transaction time sequence; S022, extracting a path time sequence based on a path in the toll collection data, and verifying the toll collection data based on the path time sequence; and S023, outputting a lane opening strategy based on the verification results of the path time sequence and the transaction time sequence; the method can improve the moving effect of vehicles, reduce the congestion of highway toll collection sites, and thus improve the processing efficiency of toll collection data.

Description

Technical Field

[0001] This invention relates to the field of transportation technology, and in particular to a paperless processing method for special situations involving highway toll collection. Background Technology

[0002] In recent years, with the rapid development of transportation and the continuous increase in car ownership, congestion at highway toll stations has become a frequent occurrence. Some toll stations have been found to have too many lanes, resulting in idle lanes and wasted resources. Therefore, it is particularly important to design the number of lanes at toll stations in a reasonable manner.

[0003] There are several methods for calculating the number of lanes, including empirical methods, lane capacity calculation methods, and methods based on queuing theory. Among these, queuing theory is the most suitable method. A lane number calculation table is created using this theory, and the number of lanes is selected from the table based on the comprehensive service time and design hourly traffic volume of the toll station lanes.

[0004] However, by opening up the number of lanes for obtaining vehicle information, the system fails to consider that lanes should be adjusted accordingly to prevent congestion when vehicles travel on different paths. Summary of the Invention

[0005] This application provides a paperless method for handling special situations in highway toll collection, which solves the problem of adjusting lane opening based on vehicle movement in the prior art, improves the efficiency of toll collection, and reduces congestion during vehicle passage.

[0006] This application provides a paperless processing method for special situations in highway toll collection, including:

[0007] S01. Obtain the highway network topology map based on the distribution location of highway management toll stations and exit stations, and the routes taken. S02, In accordance with business rules, verify the charging data; The verification of the charging data includes: S021, Extract the transaction time series of the fee data, and verify the fee data based on the transaction time series; S022, Extract the path-based time series from the charging data, and verify the charging data based on the path time series; S023, based on the verification results of path time series and transaction time series, output the lane opening strategy.

[0008] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: By combining vehicle movement paths with vehicle passage times, the efficiency of lane adjustment during vehicle passage can be improved, which helps identify when traffic is congested or busy. By selecting different vehicle movement conditions, and obtaining information on different vehicle numbers and movement times, the open lanes of all toll stations on the corresponding highway sections can be adjusted to improve vehicle movement efficiency, reduce congestion at highway toll stations, and thus improve the processing efficiency of toll data. Attached Figure Description

[0009] Figure 1 A flowchart illustrating an embodiment of a paperless processing method for special situations in highway toll collection; Figure 2 A flowchart illustrating Example 2 of a paperless processing method for special situations in highway toll collection; Figure 3 A flowchart illustrating Example 3 of a paperless processing method for special situations in highway toll collection; Figure 4 This is a flowchart illustrating Example 4 of a paperless processing method for special situations in highway toll collection. Detailed Implementation

[0010] To facilitate understanding of the present invention, a more complete description of this application will be given below with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to enable a more thorough and complete understanding of the disclosure of the present invention.

[0011] It should be noted that the terms "vertical," "horizontal," "up," "down," "left," "right," and similar expressions used in this article are for illustrative purposes only and do not represent the only possible implementation.

[0012] Unless otherwise defined, 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; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0013] Example 1 To improve toll collection efficiency, under special circumstances on highways, the movement of vehicles in different lanes is first obtained, along with the status of each toll station along the movement path, to obtain the vehicle's movement path. Based on the transaction status of each toll station during the vehicle's movement, the optimal time that can be reached for each movement is determined. Based on the optimal time predicted for each vehicle's movement path, the number of currently open lanes is adjusted to improve processing efficiency and reduce lane pressure.

[0014] like Figure 1 As shown, the paperless processing method for special situations in highway toll collection, as described in this application, includes: S01. Obtain the highway network topology map based on the distribution location of highway management toll stations and exit stations, and the routes taken.

[0015] The highway network topology map is used to determine the location of lanes and toll stations on the current highway, determine the possible paths and times that vehicles may take when passing through, and adjust the number of lanes accordingly by predicting the passing paths and times.

[0016] S02. In accordance with business rules, the toll data is verified. The toll data is the transaction data generated when vehicles pass through the highway. Based on the toll data, such as the entrance toll station, entrance time, passage time, toll amount, passage frequency of each lane, and vehicles entering each station, the toll data is verified to obtain features related to lane adjustment, which are then used to adjust the number of lanes open on each highway segment.

[0017] In this embodiment of the invention, the verification of the charging data includes: S021, Extract the transaction time series of the toll collection data, and verify the toll collection data based on the transaction time series; wherein, the transaction time series is the time information corresponding to the time, frequency and number of transactions that occur at each toll station; S022, Extract the path-based time series from the charging data, and verify the charging data based on the path time series; S023, based on the verification results of path time series and transaction time series, output the lane opening strategy.

[0018] Suppose there are three toll stations, A, B, and C, on a certain section of highway. Analysis of the path time series and transaction time series reveals the following: Toll station A experiences severe traffic congestion during peak hours, resulting in increased average travel time.

[0019] Traffic flow at toll station B is relatively stable, but transaction times are longer.

[0020] Toll station C experiences fewer vehicles and lower lane utilization during off-peak hours.

[0021] Based on these circumstances, the following lane opening strategies can be formulated: During peak hours, the number of lanes open at toll station A will be increased to alleviate congestion.

[0022] Optimize the transaction process at toll plaza B at all times to improve efficiency.

[0023] During off-peak hours, reduce the number of lanes open at toll station C to save costs.

[0024] Preferably, the implementation of the lane opening strategy involves adjusting the real-time strategy based on average travel time, transaction time, and lane utilization. By comparing the average travel time before and after implementing the strategy, the effectiveness of the strategy in alleviating congestion can be evaluated. By comparing the transaction time before and after implementing the strategy, the effectiveness of the strategy in improving the operating efficiency of the toll station can be evaluated. By comparing the lane utilization before and after implementing the strategy, the benefits of the strategy can be evaluated.

[0025] Example 2 like Figure 2 As shown, the specific implementation method of step S021 is as follows: S0211, Based on the transaction time series, obtain the first feature of the highway based on the location of the toll station.

[0026] The first feature is used to obtain the location information of each toll station at the time of the transaction, so as to obtain the lanes in use at that time and the frequency of vehicle passage in each lane.

[0027] S0212, compare the vehicle data passing through the toll station under each transaction time series, and extract the second feature based on the passing speed from the toll data.

[0028] The second feature is used to determine the average speed of each lane at each passage under each time condition, and the speed at which each lane passes under each toll condition.

[0029] S0213, based on the first feature and the second feature, determine the comparison result for each lane according to the lane usage status of the toll data.

[0030] When comparing the first feature with the yield feature, it is determined whether the vehicle movement time is reasonable for each movement, and the first feature is compared with the second feature to obtain the passage status of each lane. Based on the passage status of each lane, the current data is verified to be reasonable.

[0031] Specifically, the comparison method for the first feature and the second feature is as follows: Extract speed features related to traffic speed from the first and second features, and based on the speed features, obtain M1 first features and M2 second features for N toll conditions; Based on N charging situations, the mean, median, and standard deviation of M1 primary features and M2 secondary features corresponding to each charging situation are obtained as three comparison groups; the similarity between the M1 primary features and the M2 secondary features is compared, and the M1 primary features and M2 secondary features are respectively placed into the three comparison groups; By comparing the data changes of the control group under different toll conditions, the trend and significance of the traffic speed change under different toll conditions are obtained as the comparison results of the first feature and the second feature.

[0032] Another way to achieve the comparison results is as follows: Three comparison groups are obtained. The first intermediate result is determined based on the comparison group corresponding to the mean and median. The second intermediate result is determined based on the comparison group corresponding to the standard deviation. The comparison result between the first feature and the second feature is determined based on the first intermediate result, the second intermediate result and the corresponding charging status.

[0033] By comparing the average and median of the same characteristic under different toll conditions, the trend of traffic speed under different toll conditions can be observed. For example, if the average and median of a certain characteristic are both lower when the toll is high than when the toll is low, then it can be considered that the high toll may lead to a decrease in traffic speed.

[0034] By comparing the standard deviation of the same feature under different toll conditions, we can observe whether the fluctuation range of passage speed differs. For example, if the standard deviation of a certain feature is much larger under free conditions than under toll conditions, it indicates that the passage speed is more unstable under free conditions.

[0035] In this step, the data is divided into N subsets based on toll conditions, which include situations common at highway toll stations such as low tolls, high tolls, and slow toll collection. For each subset of toll conditions, M1 speed-related features are selected from the first feature and M2 speed-related features are selected from the second feature. For each toll condition, the values ​​of the M1 first features and M2 second features are compared to quantitatively determine whether there is a significant difference between the two features. Through quantitative analysis, the trend and significance of traffic speed changes under different toll conditions are summarized. The similarity between the first and second features is calculated using the Pearson correlation coefficient.

[0036] Before obtaining the comparison results of the first feature and the second feature, the process also includes: The first extracted text and the second extracted text in the comparison group are obtained respectively. The first extracted text is used to extract the first feature and the second feature that do not conform to the real-time data and remove the charging status that does not conform to the current time period. The second text is used to remove the text information in the second feature that does not conform to the first feature.

[0037] The non-comparison text table is invoked to remove interfering text from the first and second extracted texts. For example, some words have the same meaning but express different ideas. By removing this part of the text information, the trend of the comparison results becomes more obvious.

[0038] S0214, Based on the comparison results of each lane, input the corresponding first feature and second feature into the first feature set.

[0039] Determining the comparison result for each lane using the first and second features effectively classifies or identifies lane usage. This classification / identification helps understand which lanes are busiest and which are relatively idle, thus providing a basis for lane management and optimization. Inputting the corresponding first and second features into the first feature set integrates relevant data, providing a rich, structured dataset for subsequent consideration of vehicle movement paths.

[0040] Example 3 like Figure 3 As shown, the specific implementation method of step S022 is as follows: S0221, Based on the path time series, obtain the third feature corresponding to the vehicle movement segment.

[0041] The third feature is used to determine the movement of vehicles on different highway sections, such as speed, type of vehicle, and path of movement of vehicles when passing through different lane positions.

[0042] S0222, compare the fourth feature based on vehicle path fitting for each path time series.

[0043] The fourth feature is used to indicate the overlap of vehicle movement paths at each time point, thereby allowing the calculation of the probability that a vehicle will go to the toll station and adjusting the number of lanes.

[0044] S0223, based on the third and fourth features, determine whether the path movement of each vehicle is reasonable.

[0045] Based on the obtained third and fourth features, the probability of the possible path for each vehicle is predicted. Based on the distribution of lanes on each path, it is determined whether the current path setting is reasonable.

[0046] When comparing the third and fourth features, the comparison focuses on the rationality of the vehicle movement path. This is to obtain data on missed detections, evasions, and unreasonable movements of vehicles during their movement, thereby assisting staff in adjusting the current toll collection system in real time.

[0047] Preferably, when comparing whether each vehicle route is reasonable, the method further includes: Input a list of paths and select the target path based on the third feature; The similarity of the target path is determined based on the fourth feature, and the rationality of the vehicle path is determined based on the similarity of the target path.

[0048] Selecting the target path based on the third feature includes: At least one candidate path is obtained based on the road conditions corresponding to the third feature, and the cumulative distance of the candidate path is obtained. The cumulative distance refers to the total distance of all routes on a road. When the cumulative distance is less than a first preset value and the candidate path includes multiple waypoints, the candidate path is determined as the target path.

[0049] Judging the rationality of a vehicle route based on the similarity to the target route includes: Obtain the overlap distance of at least one target path among multiple target paths, and determine that each overlap distance includes at least one waypoint. The overlap distance is used to represent the cumulative sum of overlapping distances in the target paths. When the overlap distance is greater than a second preset value and the similarity of the corresponding target paths is greater than a third preset value, it is determined to be a reasonable target path.

[0050] The first preset value is used to determine whether the current candidate path is a normal driving path. If the cumulative distance of the candidate paths is too small, it means that the current vehicle's movement path is abnormal and the data is missing or incorrect. When the overlap distance between target paths is greater than the second threshold, it means that the current vehicle is likely to drive to the toll station corresponding to the next waypoint. The toll station predicts the upcoming vehicle and makes corresponding adjustments. The third preset value is used to determine the similarity of the routes in order to predict the routes.

[0051] After obtaining a suitable target route, based on the geographical location of the target route, determine whether each waypoint is the vehicle's destination. If so, obtain the transit time of the corresponding waypoint, add all transit times together, and add them to the transit time table. If not, record the transit time corresponding to each waypoint, determine the probability of the next waypoint based on the current highway segment, and add it to the time table.

[0052] By identifying similar situations when vehicles pass through different times, a scenario corresponding to each time can be obtained. By adjusting the similarity between the current situation and the preset scenario, the pressure on frequently used lanes can be reduced, thereby adjusting the lane usage and improving vehicle passage efficiency.

[0053] S0224, when the vehicle path movement is reasonable, based on the third and fourth features, compare the similarity between the total passage time and the vehicle movement path under the optimal passage time, and extract the fifth feature related to the path movement; the fifth feature is used to obtain the similarity of the vehicle passing through different lanes and different paths when the passage time is different; the similarity between the total passage time and the optimal passage time can be determined by the distance between the paths.

[0054] S0225, Based on the acquired fifth feature, select the corresponding first feature set and output the final alignment result.

[0055] Determine the path similarity of the target path in each fifth feature, and select the first feature set corresponding to the target path; Based on the path similarity of the first feature set, the fifth feature, and the target path, determine the path selection preference when paths overlap; The path selection status is determined based on path selection preferences, and the output is the final comparison result.

[0056] Based on the fifth feature, corresponding features similar to the current situation are selected from the first and second feature sets, thereby predicting the possible movement of vehicle lanes and facilitating adjustments to lane opening.

[0057] The purpose of selecting the first and second feature sets for the fifth feature is to improve vehicle movement efficiency and reduce congestion at highway toll stations by selecting different vehicle movement conditions under different vehicle numbers and movement times, thereby improving the processing efficiency of toll data.

[0058] Example 4 To ensure that the acquired lane opening strategy is more aligned with peak and off-peak season handling methods, different selection methods are adopted for the lanes opened on highways. The difference between the optimal passage time and the actual passage time under different usage patterns is verified to evaluate the lane opening strategy.

[0059] like Figure 4 As shown, the evaluation method for the lane opening strategy in step S023 is as follows: S0231, obtain the number of lanes open at each toll station in the lane opening strategy, and compare the lane utilization rate corresponding to the number of lanes open.

[0060] S0232, when the lane utilization rate exceeds the preset utilization rate value, determine the average travel time of the lane, and when the average travel time is greater than the first preference value, adjust the lane opening strategy.

[0061] The first preference value is used to determine whether the average lane travel time is too high. Under different road conditions, users may prefer to use a few lanes for driving. If the utilization rate is high but the lane travel time is long, it means that the vehicles passing through the current lane are special, and other lanes can be opened to share the pressure of this lane.

[0062] Specifically, lane utilization rate can be defined as the percentage of actual usage time out of the total available time: Lane Utilization Rate = (Actual Usage Time / Total Available Time) × 100%. Actual usage time can be obtained from transaction records in the toll collection system, while total available time can be calculated based on the toll station's operating hours.

[0063] Average travel time can be calculated as the average time a vehicle takes from entering to leaving the toll station. This can be done by recording the timestamps of vehicles passing through the toll station, subtracting the entry time from the exit time to obtain the travel time for each vehicle, and then averaging the results.

[0064] S0233, when the lane utilization rate is less than the preset utilization rate value, determine the lane transaction time; when the lane transaction time is greater than the second preference value, adjust the lane opening strategy.

[0065] The second preference value is used to determine if a lane has a long transaction time but low utilization, indicating that the current lane is occupied by large vehicles or other slow-moving vehicles that need to be inspected. In this case, the lane opening is adjusted according to the current path situation.

[0066] Specifically, transaction time can be defined as the average time required for a vehicle to complete payment. This can be obtained from transaction records in the toll collection system, specifically the time difference between the vehicle's arrival at the toll station and the completion of payment.

[0067] Regarding the settings for the preset utilization rate value, the first preference value, and the second preference value: The preset utilization rate value is set based on historical data and actual conditions. A certain percentage of the historical average utilization rate (such as 90%) can be taken as the threshold.

[0068] The first preference value represents the maximum average travel time that can be tolerated when lane utilization exceeds the preset value. This value can be set based on historical data and user surveys, and should generally be lower than a certain percentage (such as 120%) of the historical average travel time.

[0069] The second preference value represents the maximum tolerable transaction time when lane utilization is lower than a preset value. Similarly, this value should be set based on historical data and user surveys, and should generally be lower than a certain percentage (e.g., 130%) of the historical average transaction time.

[0070] Methods for adjusting lane opening policies: When lane utilization exceeds the preset utilization value and average travel time is greater than the first preference value, it is advisable to reduce the number of open lanes or optimize the lane allocation algorithm to reduce congestion.

[0071] When lane utilization is less than the preset utilization value and transaction time is greater than the second preference value, it is advisable to increase the number of open lanes or optimize the processing speed of the toll collection system to improve transaction efficiency.

[0072] By precisely defining and calculating lane utilization, average travel time, and transaction time, we can gain a more accurate understanding of the actual usage and efficiency of lanes. By comprehensively considering lane utilization, average travel time, transaction time, and other relevant indicators, we can develop more comprehensive optimization strategies.

[0073] The optimized formula for the number of dynamic lanes can be expressed as: Dynamic lane opening quantity = base lane quantity + (congestion coefficient) Adjustment factor) + (multi-factor comprehensive coefficient) Special factors) in: Basic number of lanes: The number of lanes under normal circumstances.

[0074] Congestion coefficient: A coefficient that represents the degree of traffic congestion, calculated based on real-time traffic information, and ranges from 0 to 1.

[0075] Adjustment factor: A coefficient calculated based on predicted and historical data, used to adjust the number of lanes open. The adjustment factor is set according to the lane utilization rate.

[0076] Multi-factor comprehensive coefficient: The coefficient is determined by taking into account factors such as traffic flow, average vehicle speed, and road capacity, and is based on the average value of the intersection of the fifth feature set and the first feature set.

[0077] Special factors: coefficients that take into account special factors such as weather and holidays.

[0078] For example: Taking a highway as an example, this section of highway normally has four lanes. According to real-time traffic information, the current congestion coefficient is 0.8, indicating relatively congested traffic. Based on predicted and historical data, the adjustment factor for peak hours is 1.5. Simultaneously, considering factors such as traffic flow and average vehicle speed, the multi-factor comprehensive coefficient is calculated to be 0.6. Furthermore, since today is a holiday, a special factor of 0.2 needs to be added.

[0079] Calculations are performed based on the optimized formula: The number of lanes open dynamically = 4 + (0.8) 1.5)+(0.6 0.2) = 5.64 Depending on the actual situation, the number of lanes must be an integer, so it is possible to open 6 lanes to alleviate the current traffic congestion.

[0080] It is important to note that the specific formulas and parameter settings need to be adjusted and optimized based on actual conditions to achieve better results. Furthermore, in practical applications, the costs and benefits of lane opening, as well as the weights and relationships between different factors, must be considered to ensure the accuracy and feasibility of dynamically determining the number of lanes open.

[0081] The above is just an example; in actual applications, further adjustments and optimizations may be needed depending on the specific circumstances.

[0082] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A paperless processing method for special situations in highway toll collection, characterized in that, include: S01. Obtain the highway network topology map based on the distribution location of highway management toll stations and exit stations, and the routes taken. S02, In accordance with business rules, verify the charging data; The verification of the charging data includes: S021, Extract the transaction time series of the fee data, and verify the fee data based on the transaction time series; S0211, Based on the transaction time series, obtain the first feature of the highway based on the location of toll stations; the first feature is used to obtain the location information of each toll station at the time of the transaction, so as to obtain the lane used at that time and the frequency of vehicle passage in each lane; S0212, compare the vehicle data passing through the toll station under each transaction time series, and extract the second feature based on the passing speed from the toll data; the second feature is used to determine the average speed of each lane at each passing time under each time condition, and the passing speed under each toll condition; S0213, Based on the first feature and the second feature, determine the comparison result for each lane according to the lane usage status of the toll data; The comparison method for the first and second features is as follows: Extract speed features related to traffic speed from the first and second features, and based on these speed features, obtain M1 first features and M2 second features for N toll conditions; based on the N toll conditions, obtain the mean, median, and standard deviation of the M1 first features and M2 second features corresponding to each toll condition as three comparison groups; compare the similarity between the M1 first features and M2 second features, and place the M1 first features and M2 second features into the three comparison groups respectively; compare the data changes of the comparison groups under different toll conditions, and obtain the trend and significance of traffic speed changes under different toll conditions as the comparison results of the first and second features. S0214, Based on the comparison results of each lane, input the corresponding first feature and second feature into the first feature set; S022, Extract the path-based time series from the charging data, and verify the charging data based on the path time series; S0221, Based on the path time series, obtain the third feature corresponding to the vehicle movement segment; the third feature is used to determine the vehicle movement under different highway segments, including the movement speed, the type of vehicle passing through, and the movement path generated by the vehicle passing through different lane positions when moving. S0222, compare the fourth feature based on vehicle path fitting for each path time series; the fourth feature is used to represent the overlap of vehicle movement paths at each time, thereby deduce the probability that a vehicle will go to the toll station and adjust the number of lane settings. S0223, based on the third and fourth features, determine whether the movement of each vehicle path is reasonable; when comparing whether each vehicle path is reasonable, it also includes: inputting a path list, selecting a target path according to the third feature; judging the similarity of the target paths according to the fourth feature, and judging the reasonableness of the vehicle path based on the similarity of the target paths; judging the reasonableness of the vehicle path based on the similarity of the target paths includes: obtaining the overlap distance of at least one target path among multiple target paths, determining that each overlap distance includes at least one waypoint, the overlap distance is used to represent the cumulative sum of the overlap distances in the target paths, when the overlap distance is greater than a second preset value, and the similarity of the corresponding target path is greater than a third preset value, it is determined to be a reasonable target path; S0224, When the vehicle path movement is reasonable, based on the third and fourth features, compare the similarity between the total passage time and the vehicle path movement under the optimal passage time, and extract the fifth feature related to the path movement; the fifth feature is used to obtain the similarity of vehicles passing through different lanes and different paths when different passage times are used; when obtaining the similarity between the total passage time and the optimal passage time, the similarity of the paths is determined by the distance between the paths. S0225, Based on the acquired fifth feature, select the corresponding first feature set and output the final comparison result, including: determining the path similarity of the target path in each fifth feature, selecting the first feature set corresponding to the target path; determining the path selection preference when paths overlap based on the first feature set, the fifth feature, and the path similarity of the target path; determining the path selection status based on the path selection preference, and outputting the final comparison result. S023, based on the verification results of path time series and transaction time series, output the lane opening strategy; S0231, Obtain the number of lanes open at each toll station in the lane opening strategy, and compare the lane utilization rate corresponding to the number of lanes open; S0232, when the lane utilization rate exceeds the preset utilization rate value, determine the average travel time of the lane; when the average travel time is greater than the first preference value, adjust the lane opening strategy. S0233, when the lane utilization rate is less than the preset utilization rate value, determine the lane transaction time; when the lane transaction time is greater than the second preference value, adjust the lane opening strategy.

2. The paperless processing method for special situations in highway toll collection as described in claim 1, characterized in that, Three comparison groups are obtained. The first intermediate result is determined based on the comparison group corresponding to the mean and median. The second intermediate result is determined based on the comparison group corresponding to the standard deviation. The comparison result between the first feature and the second feature is determined based on the first intermediate result, the second intermediate result and the corresponding charging status.

3. The paperless processing method for special situations in highway toll collection as described in claim 1, characterized in that, Selecting the target path based on the third feature includes: At least one candidate path is obtained based on the road conditions corresponding to the third feature, and the cumulative distance of the candidate path is obtained. The cumulative distance refers to the total distance of all routes along a road. When the cumulative distance is less than a first preset value and the candidate path includes multiple waypoints, the candidate path is determined as the target path.

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