Expressway vehicle passing characteristic evaluation method and device

Abstract

The invention discloses an expressway vehicle passing characteristic evaluation method and device, and the method comprises the steps: determining a passing identifier of a target passing travel, and carrying out the correlation of a plurality of pieces of flow data of the target passing travel based on the passing identifier of the target passing travel, and obtaining the target passing information corresponding to the target passing travel, the method comprises the steps of determining corresponding feature related information based on target passing information corresponding to a target passing route, generating corresponding passing features based on the feature related information corresponding to the target passing route, and determining passing feature evaluation parameters of the license plate according to all passing features of all target passing routes of each license plate in a target time period. According to the invention, the evaluation efficiency of highway vehicle passing characteristics is improved, and the cost of fee evasion inspection is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of expressways, and in particular to a method and device for evaluating the traffic characteristics of expressways. Background Art

[0002] Auditing is an important part of the highway toll collection business and an important means to safeguard the legitimate interests of the operating entity and ensure the fairness of the toll collection business. After the provincial boundary stations were abolished nationwide, the gantry technology can accurately fit the driving path of passing vehicles on the highway, so as to collect tolls according to the actual driving path. However, it was found in the actual toll collection process that there is still a phenomenon of toll loss due to reasons such as shielding to evade fees, falsely reporting entrances, and running long and buying short.

[0003] In the prior art, auditors often summarize some common traffic characteristics of toll-evading vehicles based on practice, and then check the traffic data with relevant traffic characteristics based on this. This method can find some data of toll-evading vehicles, but since there are a lot of interference data in the traffic data caused by changes in traffic rules, equipment failures, etc., auditors need to invest a lot of energy to deal with it. It can be seen that how to provide a highway vehicle traffic characteristics assessment method to improve the assessment efficiency of highway vehicle traffic characteristics and reduce the cost of toll evasion audits is particularly important. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for evaluating the traffic characteristics of vehicles on highways, which can improve the evaluation efficiency of the traffic characteristics of vehicles on highways and reduce the cost of toll evasion inspection.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses a method for evaluating the traffic characteristics of vehicles on a highway, the method comprising:

[0006] For a target travel itinerary on the expressway, determining a travel identifier for the target travel itinerary;

[0007] Based on the traffic identification of the target traffic itinerary, the gantry flow data, the entrance flow data, the multi-province split flow data, the exit flow data and the gantry brand recognition flow data of the target traffic itinerary are associated to obtain the target traffic information corresponding to the target traffic itinerary;

[0008] Based on the target passage information corresponding to the target passage, determining the feature-related information corresponding to the target passage; the feature-related information at least includes whether the target passage inter-province, the corresponding gantry flow times, the corresponding number of license plate identifications, the corresponding multi-province split flow data, the corresponding exit flow data and the corresponding license plate identification fitting fee;

[0009] Based on the feature-related information corresponding to the target travel itinerary, the travel features of the target travel itinerary are generated; the travel features include at least one or more of the features of no exit for 3 days, no splitting for 7 days, the feature of the number of license plate recognition being greater than the number of door frames, and the feature of the license plate recognition fitting fee being greater than the intra-provincial splitting fee;

[0010] According to all the traffic characteristics of all the target traffic trips of each license plate within the target time period, the traffic characteristic evaluation parameters of the license plate are determined.

[0011] As an optional implementation, in the first aspect of the present invention, the gantry flow data at least includes the access identification, transaction time, gantry ID, entry time, license plate, vehicle model, vehicle type, and user type acquired by each gantry;

[0012] The entry flow data at least includes the access identification, entry station ID, entry time, license plate, vehicle model, vehicle type, and user type obtained at the entry;

[0013] The multi-province split flow data at least includes a pass identifier, an entry station ID, an entry time, an exit station ID, an exit time, a license plate, a vehicle model, a vehicle type, a user type, and a provincial split amount;

[0014] The export flow data at least includes the access identification obtained at the export, the export station ID, the export time, the license plate, the vehicle model, the vehicle type, the user type, the entry time, the actual amount collected, and the actual amount collected in the province;

[0015] The portal frame license plate recognition flow data at least includes the license plate, license plate recognition time, and portal frame ID.

[0016] As an optional implementation, in the first aspect of the present invention, the passage identification based on the target passage trip is used to associate the gantry flow data, entrance flow data, multi-province split flow data, exit flow data and gantry brand recognition flow data of the target passage trip to obtain the target passage information corresponding to the target passage trip, including:

[0017] Based on the passage identification of the target passage stroke, determining the gantry flow data of each high-speed gantry corresponding to the target passage stroke;

[0018] Based on the passage identifier of the target passage stroke, the gantry flow data of each high-speed gantry corresponding to the target passage stroke is associated to obtain the first passage information corresponding to the target passage stroke;

[0019] Based on the passage identifier of the target passage trip, associating the inlet flow data corresponding to the target passage trip with the first passage information corresponding to the target passage trip to obtain the second passage information corresponding to the target passage trip;

[0020] Based on the pass identifier of the target pass itinerary, the multi-province split flow data corresponding to the target pass itinerary is associated with the second pass information corresponding to the target pass itinerary to obtain the third pass information corresponding to the target pass itinerary;

[0021] Based on the passage identifier of the target passage trip, the outlet flow data corresponding to the target passage trip is associated with the third passage information corresponding to the target passage trip to obtain the fourth passage information corresponding to the target passage trip;

[0022] Based on the passage identification of the target passage stroke, the gantry sign identification flow data corresponding to the target passage stroke is associated with the fourth passage information corresponding to the target passage stroke to obtain the target passage information corresponding to the target passage stroke.

[0023] As an optional implementation, in the first aspect of the present invention, determining the traffic characteristic evaluation parameter of the license plate according to all the traffic characteristics of all the target traffic trips of each license plate within the target time period includes:

[0024] Determine the vehicle history feature data corresponding to the license plate according to all the travel features of all the target travel trips of each license plate within the target time period and the number of times each travel feature is generated;

[0025] Based on the vehicle historical feature data corresponding to each license plate, the special traffic parameters corresponding to the license plate are determined; the special traffic parameters include the half-distance shielding traffic parameters, the provincial shielding traffic parameters and the long-distance and short-distance traffic parameters corresponding to the license plate;

[0026] Based on all the special traffic parameters corresponding to each license plate, the traffic characteristic evaluation parameters of the license plate are determined.

[0027] As an optional implementation, in the first aspect of the present invention, determining the special traffic parameter corresponding to each license plate based on the vehicle historical feature data corresponding to the license plate includes:

[0028] Determine, based on the vehicle historical feature data corresponding to each license plate, a feature evaluation parameter of each traffic feature corresponding to the license plate;

[0029] Determine the half-distance shielding weight parameter, intra-province shielding weight parameter and long-run and short-run weight parameter for each traffic feature;

[0030] For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the half-distance shielding weight parameter of each traffic feature, determine the half-distance shielding traffic parameter corresponding to the license plate;

[0031] For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the provincial shielding weight parameter of each traffic feature, determine the provincial shielding traffic parameter corresponding to the license plate;

[0032] For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the running long and buying short weight parameter of each traffic feature, determine the running long and buying short traffic parameter corresponding to the license plate;

[0033] For each license plate, the special traffic parameters corresponding to the license plate are determined based on the half-distance shielding traffic parameters, provincial shielding traffic parameters and long-distance and short-distance traffic parameters corresponding to the license plate.

[0034] As an optional implementation, in the first aspect of the present invention, the determination of the half-distance shielding weight parameter, the intra-province shielding weight parameter and the running long and buying short weight parameter of each pass feature includes:

[0035] Perform association rule mining operations on the traffic characteristics corresponding to several historical travel trips on the expressway and the half-trip blocking behaviors, intra-province blocking behaviors, and long-distance buying behaviors corresponding to the historical travel trips to obtain the traffic characteristics association relationship information corresponding to the historical travel trips; wherein, the half-trip blocking behaviors, intra-province blocking behaviors, and long-distance buying behaviors corresponding to the historical travel trips are determined by manual verification;

[0036] Based on the traffic feature correlation information corresponding to the historical travel itinerary, the half-trip shielding weight parameter, intra-provincial shielding weight parameter and long-run and short-buy weight parameter of each traffic feature are determined.

[0037] As an optional embodiment, in the first aspect of the present invention, the method further comprises:

[0038] For each license plate, determine whether the traffic characteristic evaluation parameters of the license plate meet the preset abnormal traffic characteristic conditions. If so, determine the minimum estimated evasion amount of the license plate based on the target traffic information of all target traffic itineraries of the license plate; when the minimum estimated evasion amount of the license plate exceeds the preset evasion amount threshold, add the license plate to the database to be verified; the database to be verified is used for subsequent manual verification of the evasion behavior of the license plate.

[0039] A second aspect of the present invention discloses a device for evaluating vehicle traffic characteristics on a highway, the device comprising:

[0040] A first determination module is used to determine a passage identifier of a target passage on the expressway;

[0041] An association module is used to associate the gantry flow data, entrance flow data, multi-province split flow data, exit flow data and gantry brand recognition flow data of the target travel stroke based on the travel identification of the target travel stroke, so as to obtain the target travel information corresponding to the target travel stroke;

[0042] The second determination module is used to determine the feature-related information corresponding to the target travel itinerary based on the target travel information corresponding to the target travel itinerary; the feature-related information at least includes whether the target travel itinerary crosses provinces, the corresponding gantry flow times, the corresponding number of license plate identifications, the corresponding multi-province split flow data, the corresponding exit flow data and the corresponding license plate identification fitting fee;

[0043] A generating module, configured to generate the traffic characteristics of the target traffic itinerary based on the characteristic-related information corresponding to the target traffic itinerary; the traffic characteristics include at least one or more of the following characteristics: no exit for 3 days, no splitting for 7 days, the number of license plate recognitions being greater than the number of door frames, and the license plate recognition fitting fee being greater than the intra-provincial splitting fee;

[0044] The third determination module is used to determine the traffic characteristic evaluation parameters of the license plate according to all the traffic characteristics of all the target traffic trips of each license plate within the target time period.

[0045] As an optional implementation, in the second aspect of the present invention, the gantry flow data at least includes the access identification, transaction time, gantry ID, entry time, license plate, vehicle model, vehicle type, and user type acquired by each gantry;

[0046] The entry flow data at least includes the access identification, entry station ID, entry time, license plate, vehicle model, vehicle type, and user type obtained at the entry;

[0047] The multi-province split flow data at least includes a pass identifier, an entry station ID, an entry time, an exit station ID, an exit time, a license plate, a vehicle model, a vehicle type, a user type, and a provincial split amount;

[0048] The export flow data at least includes the access identification obtained at the export, the export station ID, the export time, the license plate, the vehicle model, the vehicle type, the user type, the entry time, the actual amount collected, and the actual amount collected in the province;

[0049] The portal frame license plate recognition flow data at least includes the license plate, license plate recognition time, and portal frame ID.

[0050] As an optional implementation, in the second aspect of the present invention, the association module associates the gantry flow data, entrance flow data, multi-province split flow data, exit flow data and gantry brand recognition flow data of the target travel stroke based on the travel identification of the target travel stroke to obtain the target travel information corresponding to the target travel stroke, and the specific methods include:

[0051] Based on the passage identification of the target passage stroke, determining the gantry flow data of each high-speed gantry corresponding to the target passage stroke;

[0052] Based on the passage identifier of the target passage stroke, the gantry flow data of each high-speed gantry corresponding to the target passage stroke is associated to obtain the first passage information corresponding to the target passage stroke;

[0053] Based on the passage identifier of the target passage trip, associating the inlet flow data corresponding to the target passage trip with the first passage information corresponding to the target passage trip to obtain the second passage information corresponding to the target passage trip;

[0054] Based on the pass identifier of the target pass itinerary, the multi-province split flow data corresponding to the target pass itinerary is associated with the second pass information corresponding to the target pass itinerary to obtain the third pass information corresponding to the target pass itinerary;

[0055] Based on the passage identifier of the target passage trip, the outlet flow data corresponding to the target passage trip is associated with the third passage information corresponding to the target passage trip to obtain the fourth passage information corresponding to the target passage trip;

[0056] Based on the passage identification of the target passage stroke, the gantry sign identification flow data corresponding to the target passage stroke is associated with the fourth passage information corresponding to the target passage stroke to obtain the target passage information corresponding to the target passage stroke.

[0057] As an optional implementation, in the second aspect of the present invention, the third determination module determines the traffic characteristic evaluation parameter of the license plate according to all traffic characteristics of all target traffic trips of each license plate within the target time period, and the specific method includes:

[0058] Determine the vehicle history feature data corresponding to the license plate according to all the travel features of all the target travel trips of each license plate within the target time period and the number of times each travel feature is generated;

[0059] Based on the vehicle historical feature data corresponding to each license plate, the special traffic parameters corresponding to the license plate are determined; the special traffic parameters include the half-distance shielding traffic parameters, the provincial shielding traffic parameters and the long-distance and short-distance traffic parameters corresponding to the license plate;

[0060] Based on all the special traffic parameters corresponding to each license plate, the traffic characteristic evaluation parameters of the license plate are determined.

[0061] As an optional implementation, in the second aspect of the present invention, the third determination module determines the special traffic parameter corresponding to each license plate based on the vehicle historical feature data corresponding to the license plate, and the specific method includes:

[0062] Determine, based on the vehicle historical feature data corresponding to each license plate, a feature evaluation parameter of each traffic feature corresponding to the license plate;

[0063] Determine the half-distance shielding weight parameter, intra-province shielding weight parameter and long-run and short-run weight parameter for each traffic feature;

[0064] For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the half-distance shielding weight parameter of each traffic feature, determine the half-distance shielding traffic parameter corresponding to the license plate;

[0065] For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the provincial shielding weight parameter of each traffic feature, determine the provincial shielding traffic parameter corresponding to the license plate;

[0066] For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the running long and buying short weight parameter of each traffic feature, determine the running long and buying short traffic parameter corresponding to the license plate;

[0067] For each license plate, the special traffic parameters corresponding to the license plate are determined based on the half-distance shielding traffic parameters, provincial shielding traffic parameters and long-distance and short-distance traffic parameters corresponding to the license plate.

[0068] As an optional implementation, in the second aspect of the present invention, the third determination module determines the half-distance shielding weight parameter, the intra-province shielding weight parameter and the running long and buying short weight parameter of each pass feature, and the specific method includes:

[0069] Perform association rule mining operations on the traffic characteristics corresponding to several historical travel trips on the expressway and the half-trip blocking behaviors, intra-province blocking behaviors, and long-distance buying behaviors corresponding to the historical travel trips to obtain the traffic characteristics association relationship information corresponding to the historical travel trips; wherein, the half-trip blocking behaviors, intra-province blocking behaviors, and long-distance buying behaviors corresponding to the historical travel trips are determined by manual verification;

[0070] Based on the traffic feature correlation information corresponding to the historical travel itinerary, the half-trip shielding weight parameter, intra-provincial shielding weight parameter and long-run and short-buy weight parameter of each traffic feature are determined.

[0071] As an optional implementation, in the second aspect of the present invention, the device further includes:

[0072] The verification module is used to determine, for each license plate, whether the traffic characteristic evaluation parameters of the license plate meet the preset abnormal traffic characteristic conditions. If so, the minimum estimated evasion amount of the license plate is determined based on the target traffic information of all target traffic itineraries of the license plate; when the minimum estimated evasion amount of the license plate exceeds the preset evasion amount threshold, the license plate is added to the database to be verified; the database to be verified is used for subsequent manual verification of the evasion behavior of the license plate.

[0073] The third aspect of the present invention discloses another highway vehicle traffic characteristics assessment device, the device comprising:

[0074] A memory storing executable program code;

[0075] a processor coupled to the memory;

[0076] The processor calls the executable program code stored in the memory to execute the steps in the highway vehicle traffic characteristics assessment method disclosed in the first aspect of the present invention.

[0077] The fourth aspect of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the highway vehicle traffic characteristics assessment method disclosed in the first aspect of the present invention.

[0078] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0079] The embodiment of the present invention determines the pass identification of the target pass itinerary, and based on the pass identification of the target pass itinerary, associates multiple flow data of the target pass itinerary to obtain target pass information corresponding to the target pass itinerary, determines corresponding feature-related information based on the target pass information corresponding to the target pass itinerary, generates corresponding pass characteristics based on the feature-related information corresponding to the target pass itinerary, and determines the pass characteristic evaluation parameters of the license plate according to all pass characteristics of all target pass itineraries of each license plate within the target time period. It can be seen that the present invention can obtain the target pass information through the association of the flow data of the target pass itinerary, determine the feature-related information and generate the pass characteristics through the target pass information of the target pass itinerary, and determine the pass characteristic evaluation parameters according to the pass characteristics of the target pass itinerary of the license plate, which is beneficial to improving the evaluation efficiency of the pass characteristics of highway vehicles and reducing the cost of toll evasion inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0081] Figure 1 It is a flow chart of a method for evaluating the traffic characteristics of vehicles on a highway disclosed in an embodiment of the present invention;

[0082] Figure 2 It is a structural schematic diagram of a highway vehicle traffic characteristics evaluation device disclosed in an embodiment of the present invention;

[0083] Figure 3 It is a structural schematic diagram of another highway vehicle traffic characteristics assessment device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0084] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or terminal including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or terminals.

[0086] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0087] The present invention discloses a method and device for evaluating the traffic characteristics of vehicles on highways. The method described in the embodiments of the present invention can obtain target traffic information by associating target traffic flow data, determine feature-related information and generate traffic characteristics through target traffic information of the target traffic travel, and determine traffic characteristic evaluation parameters according to the traffic characteristics of the target traffic travel of the license plate, which is conducive to improving the evaluation efficiency of the traffic characteristics of vehicles on highways and reducing the cost of toll evasion inspection. The following are detailed descriptions.

[0088] Embodiment 1

[0089] See also Figure 1 , Figure 1 1 is a flow chart of a method for evaluating the characteristics of highway vehicle traffic disclosed in an embodiment of the present invention. Figure 1 The described method can be applied to any highway vehicle traffic characteristics assessment scenario, and the embodiments of the present invention are not limited thereto. Figure 1 As shown, the highway vehicle traffic characteristics evaluation method includes the following operations:

[0090] 101. For a target travel itinerary on the expressway, determine a travel mark for the target travel itinerary;

[0091] In the embodiment of the present invention, the pass identifier is a unique identifier automatically generated by the system for the target pass trip when entering the highway, including information such as the entrance type, card number, and pass time;

[0092] 102. Based on the traffic identification of the target traffic itinerary, the gantry flow data, the entrance flow data, the multi-province split flow data, the exit flow data and the gantry plate recognition flow data of the target traffic itinerary are associated to obtain the target traffic information corresponding to the target traffic itinerary;

[0093] 103. Determine feature-related information corresponding to the target travel itinerary based on the target travel information corresponding to the target travel itinerary;

[0094] In the embodiment of the present invention, the feature-related information at least includes whether the target travel itinerary crosses provinces, the corresponding gantry flow times, the corresponding number of license plate identifications, the corresponding multi-province split flow data, the corresponding export flow data, and the corresponding license plate identification fitting fee;

[0095] 104. Generate a travel feature of the target travel itinerary based on feature-related information corresponding to the target travel itinerary;

[0096] In the embodiment of the present invention, the traffic characteristics include at least one or more of the following characteristics: no exit for 3 days, no splitting for 7 days, the number of license plate recognitions being greater than the number of door frames, and the license plate recognition fitting fee being greater than the intra-provincial splitting fee.

[0097] 105. Determine the traffic characteristic evaluation parameters of the license plate according to all the traffic characteristics of all the target traffic trips of each license plate within the target time period.

[0098] In the embodiment of the present invention, the target travel itinerary is used as a basis before the travel characteristics of the target travel itinerary are generated, and the license plate is used as a basis when determining the travel characteristics evaluation parameters of the license plate. In the final evaluation stage, the license plate is used as a basis to make it easier to determine and verify, while in the previous steps, the target travel itinerary is used as a basis to form a complete travel cycle for evaluation. Further, the target time period can be a fixed time period such as a day or a month.

[0099] It can be seen that the implementation of the method described in the present invention can obtain the target traffic information by associating the target traffic flow data, determine the feature-related information and generate the traffic characteristics through the target traffic information of the target traffic itinerary, and determine the traffic characteristic evaluation parameters according to the traffic characteristics of the target traffic itinerary of the license plate, which is beneficial to improving the evaluation efficiency of the traffic characteristics of highway vehicles and reducing the cost of toll evasion inspections.

[0100] In an optional embodiment, the gantry flow data includes at least the access identification, transaction time, gantry ID, entry time, license plate, vehicle model, vehicle type, and user type acquired by each gantry;

[0101] The entry flow data at least includes the access identification, entry station ID, entry time, license plate, vehicle model, vehicle type, and user type obtained at the entry;

[0102] The multi-province split flow data at least includes the pass identification, entry station ID, entry time, exit station ID, exit time, license plate, vehicle model, vehicle type, user type, and provincial split amount;

[0103] The export flow data shall at least include the access identification obtained at the export, the export station ID, the export time, the license plate, the vehicle model, the vehicle type, the user type, the entry time, the actual amount collected, and the actual amount collected in the province;

[0104] The gantry license plate recognition flow data at least includes the license plate, license plate recognition time, and gantry ID.

[0105] In this optional embodiment, before associating different pipeline data, it is necessary to perform data cleaning and data conversion on the pipeline data.

[0106] It can be seen that this optional embodiment can determine the specific data types of gantry flow data, entrance flow data, multi-province split flow data, exit flow data and gantry plate recognition flow data, which is conducive to improving the accuracy of determining target traffic information, and then conducive to the accuracy of determining traffic characteristic evaluation parameters, thereby improving the evaluation efficiency of highway vehicle traffic characteristics and reducing the cost of toll evasion inspections.

[0107] In another optional embodiment, the above step 102 is based on the traffic identification of the target traffic itinerary, and the gantry flow data, entrance flow data, multi-province split flow data, exit flow data and gantry brand recognition flow data of the target traffic itinerary are associated to obtain the target traffic information corresponding to the target traffic itinerary, and the specific method includes:

[0108] Based on the passage identification of the target passage stroke, determine the gantry flow data of each high-speed gantry corresponding to the target passage stroke;

[0109] Based on the traffic identification of the target traffic stroke, the gantry flow data of each high-speed gantry corresponding to the target traffic stroke is associated to obtain the first traffic information corresponding to the target traffic stroke;

[0110] Based on the pass identifier of the target pass itinerary, the inlet flow data corresponding to the target pass itinerary is associated with the first pass information corresponding to the target pass itinerary to obtain the second pass information corresponding to the target pass itinerary;

[0111] Based on the pass identifier of the target pass itinerary, the multi-province split flow data corresponding to the target pass itinerary is associated with the second pass information corresponding to the target pass itinerary to obtain the third pass information corresponding to the target pass itinerary;

[0112] Based on the pass identifier of the target pass itinerary, the export flow data corresponding to the target pass itinerary is associated with the third pass information corresponding to the target pass itinerary to obtain the fourth pass information corresponding to the target pass itinerary;

[0113] Based on the traffic identification of the target traffic stroke, the gantry sign recognition flow data corresponding to the target traffic stroke is associated with the fourth traffic information corresponding to the target traffic stroke to obtain the target traffic information corresponding to the target traffic stroke.

[0114] In this optional embodiment, it can be understood that the passage identification of each flow data is used as the primary key when associating. For example, first, for the association of gantry flow data corresponding to multiple gantries, a vehicle will pass through multiple gantries in one trip. Due to human or equipment failure, the vehicle information read by each gantry will be different, and the gantry time has a cross-day feature. Therefore, when the gantry flow data is extracted from the gantry for association, the mode method can be used to improve the first pass information to ensure the integrity of the gantry data in the first pass information as much as possible; further, in the association process, the related data in different flow data are associated, and if there is a difference in the associated data, the target pass itinerary is supplemented and updated, and the unassociated data is used as the new pass data supplement for the target pass itinerary.

[0115] It can be seen that this optional embodiment can further determine the order and method of associating the gantry flow data, entrance flow data, multi-province split flow data, exit flow data and gantry plate recognition flow data, which is conducive to improving the accuracy of determining the target traffic information, and then conducive to the accuracy of determining the traffic characteristic evaluation parameters, thereby improving the evaluation efficiency of highway vehicle traffic characteristics and reducing the cost of toll evasion inspections.

[0116] In another optional embodiment, the above step 105 determines the traffic characteristic evaluation parameter of the license plate according to all the traffic characteristics of all the target traffic trips of each license plate within the target time period, and the specific method includes:

[0117] Determine the vehicle historical feature data corresponding to the license plate according to all the travel features of all the target travel trips of each license plate within the target time period and the number of times each travel feature is generated;

[0118] Based on the vehicle historical feature data corresponding to each license plate, the special traffic parameters corresponding to the license plate are determined; the special traffic parameters include the half-distance shielding traffic parameters, the provincial shielding traffic parameters and the long-distance and short-distance traffic parameters corresponding to the license plate;

[0119] Based on all the special traffic parameters corresponding to each license plate, the traffic characteristic evaluation parameters of the license plate are determined.

[0120] In this optional embodiment, optionally, based on all the special traffic parameters corresponding to each license plate, the traffic characteristic evaluation parameters of the license plate are determined, which can be directly summed or weighted summed for each special traffic parameter.

[0121] It can be seen that this optional embodiment can determine the vehicle historical feature data corresponding to the license plate and further determine the special traffic parameters corresponding to the license plate based on all the traffic features of all target traffic itineraries of each license plate and the number of times each traffic feature is generated, and determine the traffic feature evaluation parameters based on all the special traffic parameters of the license plate; it is beneficial to improve the accuracy of determining the traffic feature evaluation parameters, thereby improving the evaluation efficiency of highway vehicle traffic features and reducing the cost of toll evasion inspections.

[0122] In another optional embodiment, the above step of determining the special traffic parameter corresponding to each license plate based on the vehicle historical feature data corresponding to the license plate includes:

[0123] Determine, based on the vehicle historical feature data corresponding to each license plate, a feature evaluation parameter of each traffic feature corresponding to the license plate;

[0124] Determine the half-distance shielding weight parameter, intra-province shielding weight parameter and long-run and short-run weight parameter for each traffic feature;

[0125] For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the half-distance shielding weight parameter of each traffic feature, determine the half-distance shielding traffic parameter corresponding to the license plate;

[0126] For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the provincial shielding weight parameter of each traffic feature, determine the provincial shielding traffic parameter corresponding to the license plate;

[0127] For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the running long and buying short weight parameter of each traffic feature, determine the running long and buying short traffic parameter corresponding to the license plate;

[0128] For each license plate, the special traffic parameters corresponding to the license plate are determined based on the half-distance shielding traffic parameters, provincial shielding traffic parameters and long-distance and short-distance traffic parameters corresponding to the license plate.

[0129] In this optional embodiment, the method for determining the half-distance shielding weight parameters, provincial shielding weight parameters and long-run and short-run weight parameters of each traffic feature can be customized by technical personnel or by constructing a correlation function, etc., and the present invention is not limited thereto.

[0130] It can be seen that this optional embodiment can determine the feature evaluation parameters of each traffic feature corresponding to the license plate based on the vehicle historical feature data corresponding to each license plate, and determine the half-distance shielding weight parameter, provincial shielding weight parameter and long-term and short-term weight parameter of each traffic feature. Different special traffic parameters are determined according to the different weight parameters and feature evaluation parameters of the traffic features, which is conducive to improving the accuracy of determining the special traffic parameters corresponding to the license plate, and then to improving the accuracy of determining the traffic feature evaluation parameters, thereby improving the evaluation efficiency of the traffic features of highway vehicles and reducing the cost of toll evasion inspections.

[0131] In another optional embodiment, the above steps determine the half-distance shielding weight parameter, the intra-province shielding weight parameter and the long-distance buying and short-distance buying weight parameter of each traffic feature, specifically in the following manner:

[0132] Perform association rule mining operations on the traffic characteristics corresponding to several historical travel trips on the expressway and the half-trip blocking behaviors, intra-province blocking behaviors, and long-distance buying behaviors corresponding to the historical travel trips to obtain the traffic characteristics association relationship information corresponding to the historical travel trips; wherein, the half-trip blocking behaviors, intra-province blocking behaviors, and long-distance buying behaviors corresponding to the historical travel trips are determined by manual verification;

[0133] Based on the traffic feature correlation information corresponding to the historical travel itinerary, the half-trip shielding weight parameter, intra-provincial shielding weight parameter and long-run and short-buy weight parameter of each traffic feature are determined.

[0134] In this optional embodiment, further optionally, the association rule mining operation can also be replaced by a machine learning method, which continuously iterates and adjusts the half-trip shielding weight parameters, intra-province shielding weight parameters and long-run and short-run weight parameters of each traffic feature through historical traffic itinerary training.

[0135] It can be seen that this optional embodiment can perform association rule mining operations on the traffic characteristics corresponding to several historical travel itineraries on the highway and the half-trip shielding behaviors, provincial shielding behaviors and long-running and short-selling behaviors corresponding to the historical travel itineraries, obtain the traffic characteristic association relationship information corresponding to the historical travel itineraries, and further determine the semi-different weight parameters of each traffic characteristic, which is conducive to improving the accuracy of determining the half-trip shielding weight parameters, provincial shielding weight parameters and long-running and short-selling weight parameters, and then improving the accuracy of determining the special traffic parameters corresponding to the license plate, which is conducive to the accuracy of determining the traffic characteristic evaluation parameters.

[0136] In yet another optional embodiment, the method may further include:

[0137] For each license plate, determine whether the traffic characteristic evaluation parameters of the license plate meet the preset abnormal traffic characteristic conditions. If so, determine the minimum estimated evasion amount of the license plate based on the target traffic information of all target traffic itineraries of the license plate; when the minimum estimated evasion amount of the license plate exceeds the preset evasion amount threshold, add the license plate to the database to be verified; the database to be verified is used for subsequent manual verification of the evasion behavior of the license plate.

[0138] In this optional embodiment, the traffic characteristic evaluation parameter satisfies the preset abnormal traffic characteristic condition in such a way that the traffic characteristic evaluation parameter value reaches a threshold value corresponding to the abnormal traffic characteristic condition.

[0139] It can be seen that this optional embodiment is capable of determining whether the traffic characteristic evaluation parameters of the license plate meet the preset abnormal traffic characteristic conditions. If so, it further determines the minimum estimated evasion amount of the license plate, and adds the license plate to the database to be verified when the minimum estimated evasion amount exceeds the evasion amount threshold. This is conducive to determining the subsequent processing method based on the traffic characteristic evaluation parameters to reduce the cost of evasion inspections.

[0140] Embodiment 2

[0141] See also Figure 2 , Figure 2 Schematic diagram of a highway vehicle traffic characteristics evaluation device disclosed in an embodiment of the present invention. Figure 2 The described device can be applied to any highway vehicle traffic characteristics assessment scenario, and the embodiments of the present invention are not limited thereto. Figure 2 As shown, the highway vehicle traffic characteristics assessment device may include:

[0142] The first determination module 201 is used to determine the pass identifier of the target pass trip on the highway;

[0143] The association module 202 is used to associate the gantry flow data, the entrance flow data, the multi-province split flow data, the exit flow data and the gantry sign recognition flow data of the target travel stroke based on the travel identification of the target travel stroke, so as to obtain the target travel information corresponding to the target travel stroke;

[0144] The second determination module 203 is used to determine the feature-related information corresponding to the target travel itinerary based on the target travel information corresponding to the target travel itinerary; the feature-related information at least includes whether the target travel itinerary crosses provinces, the corresponding gantry flow times, the corresponding number of license plate identifications, the corresponding multi-province split flow data, the corresponding exit flow data and the corresponding license plate identification fitting fee;

[0145] The generating module 204 is used to generate the traffic characteristics of the target traffic itinerary based on the characteristic related information corresponding to the target traffic itinerary; the traffic characteristics include at least one or more of the characteristics of no exit in 3 days, no split in 7 days, the number of plate recognitions is greater than the number of door frames, and the plate recognition fitting fee is greater than the intra-provincial split fee;

[0146] The third determination module 205 is used to determine the traffic characteristic evaluation parameters of the license plate according to all the traffic characteristics of all the target traffic trips of each license plate within the target time period.

[0147] It can be seen that the device described in the present invention can obtain target traffic information by associating target traffic flow data, determine feature-related information and generate traffic characteristics through the target traffic information of the target traffic itinerary, and determine traffic characteristic evaluation parameters according to the traffic characteristics of the target traffic itinerary of the license plate, which is beneficial to improving the evaluation efficiency of highway vehicle traffic characteristics and reducing the cost of toll evasion inspections.

[0148] In an optional embodiment, the gantry flow data includes at least the access identification, transaction time, gantry ID, entry time, license plate, vehicle model, vehicle type, and user type acquired by each gantry;

[0149] The entry flow data at least includes the access identification, entry station ID, entry time, license plate, vehicle model, vehicle type, and user type obtained at the entry;

[0150] The multi-province split flow data at least includes the pass identification, entry station ID, entry time, exit station ID, exit time, license plate, vehicle model, vehicle type, user type, and provincial split amount;

[0151] The export flow data shall at least include the access identification obtained at the export, the export station ID, the export time, the license plate, the vehicle model, the vehicle type, the user type, the entry time, the actual amount collected, and the actual amount collected in the province;

[0152] The gantry license plate recognition flow data at least includes the license plate, license plate recognition time, and gantry ID.

[0153] It can be seen that the implementation of this optional embodiment can determine the specific data types of gantry flow data, entrance flow data, multi-province split flow data, exit flow data and gantry plate recognition flow data, which is beneficial to improving the accuracy of determining target traffic information, and further to the accuracy of determining traffic characteristic evaluation parameters, thereby improving the evaluation efficiency of highway vehicle traffic characteristics and reducing the cost of toll evasion inspections.

[0154] In another optional embodiment, the association module 202 associates the gantry flow data, the entrance flow data, the multi-province split flow data, the exit flow data and the gantry brand recognition flow data of the target travel stroke based on the travel identification of the target travel stroke to obtain the target travel information corresponding to the target travel stroke, and the specific method includes:

[0155] Based on the passage identification of the target passage stroke, determine the gantry flow data of each high-speed gantry corresponding to the target passage stroke;

[0156] Based on the traffic identification of the target traffic stroke, the gantry flow data of each high-speed gantry corresponding to the target traffic stroke is associated to obtain the first traffic information corresponding to the target traffic stroke;

[0157] Based on the pass identifier of the target pass itinerary, the inlet flow data corresponding to the target pass itinerary is associated with the first pass information corresponding to the target pass itinerary to obtain the second pass information corresponding to the target pass itinerary;

[0158] Based on the pass identifier of the target pass itinerary, the multi-province split flow data corresponding to the target pass itinerary is associated with the second pass information corresponding to the target pass itinerary to obtain the third pass information corresponding to the target pass itinerary;

[0159] Based on the pass identifier of the target pass itinerary, the export flow data corresponding to the target pass itinerary is associated with the third pass information corresponding to the target pass itinerary to obtain the fourth pass information corresponding to the target pass itinerary;

[0160] Based on the traffic identification of the target traffic stroke, the gantry sign recognition flow data corresponding to the target traffic stroke is associated with the fourth traffic information corresponding to the target traffic stroke to obtain the target traffic information corresponding to the target traffic stroke.

[0161] It can be seen that the implementation of this optional embodiment can further determine the order and manner of association of gantry flow data, entrance flow data, multi-province split flow data, exit flow data and gantry plate recognition flow data, which is conducive to improving the accuracy of determining target traffic information, and then to the accuracy of determining traffic characteristic evaluation parameters, thereby improving the evaluation efficiency of highway vehicle traffic characteristics and reducing the cost of toll evasion inspections.

[0162] In another optional embodiment, the third determination module 205 determines the traffic characteristic evaluation parameter of the license plate according to all traffic characteristics of all target traffic trips of each license plate within the target time period, and the specific method includes:

[0163] Determine the vehicle historical feature data corresponding to the license plate according to all the travel features of all the target travel trips of each license plate within the target time period and the number of times each travel feature is generated;

[0164] Based on the vehicle historical feature data corresponding to each license plate, the special traffic parameters corresponding to the license plate are determined; the special traffic parameters include the half-distance shielding traffic parameters, the provincial shielding traffic parameters and the long-distance and short-distance traffic parameters corresponding to the license plate;

[0165] Based on all the special traffic parameters corresponding to each license plate, the traffic characteristic evaluation parameters of the license plate are determined.

[0166] It can be seen that the implementation of this optional embodiment can determine the vehicle historical feature data corresponding to the license plate and further determine the special traffic parameters corresponding to the license plate based on all the traffic features of all target traffic itineraries of each license plate and the number of times each traffic feature is generated, and determine the traffic feature evaluation parameters based on all the special traffic parameters of the license plate; it is beneficial to improve the accuracy of determining the traffic feature evaluation parameters, thereby improving the evaluation efficiency of highway vehicle traffic features and reducing the cost of toll evasion inspections.

[0167] In another optional embodiment, the third determination module 205 determines the special traffic parameter corresponding to each license plate based on the vehicle historical feature data corresponding to the license plate, and the specific method includes:

[0168] Determine, based on the vehicle historical feature data corresponding to each license plate, a feature evaluation parameter of each traffic feature corresponding to the license plate;

[0169] Determine the half-distance shielding weight parameter, intra-province shielding weight parameter and long-run and short-run weight parameter for each traffic feature;

[0170] For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the half-distance shielding weight parameter of each traffic feature, determine the half-distance shielding traffic parameter corresponding to the license plate;

[0171] For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the provincial shielding weight parameter of each traffic feature, determine the provincial shielding traffic parameter corresponding to the license plate;

[0172] For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the running long and buying short weight parameter of each traffic feature, determine the running long and buying short traffic parameter corresponding to the license plate;

[0173] For each license plate, the special traffic parameters corresponding to the license plate are determined based on the half-distance shielding traffic parameters, provincial shielding traffic parameters and long-distance and short-distance traffic parameters corresponding to the license plate.

[0174] It can be seen that the implementation of this optional embodiment can determine the feature evaluation parameters of each traffic feature corresponding to the license plate based on the vehicle historical feature data corresponding to each license plate, and determine the half-distance shielding weight parameter, provincial shielding weight parameter and long-term and short-term weight parameter of each traffic feature. Different special traffic parameters are determined according to the different weight parameters and feature evaluation parameters of the traffic features, which is conducive to improving the accuracy of determining the special traffic parameters corresponding to the license plate, and then to improving the accuracy of determining the traffic feature evaluation parameters, thereby improving the evaluation efficiency of the traffic features of highway vehicles and reducing the cost of toll evasion inspections.

[0175] In another optional embodiment, the third determination module 205 determines the half-distance shielding weight parameter, the intra-province shielding weight parameter and the running long and buying short weight parameter of each pass feature, and the specific method includes:

[0176] Perform association rule mining operations on the traffic characteristics corresponding to several historical travel trips on the expressway and the half-trip blocking behaviors, intra-province blocking behaviors, and long-distance buying behaviors corresponding to the historical travel trips to obtain the traffic characteristics association relationship information corresponding to the historical travel trips; wherein, the half-trip blocking behaviors, intra-province blocking behaviors, and long-distance buying behaviors corresponding to the historical travel trips are determined by manual verification;

[0177] Based on the traffic feature correlation information corresponding to the historical travel itinerary, the half-trip shielding weight parameter, intra-provincial shielding weight parameter and long-run and short-buy weight parameter of each traffic feature are determined.

[0178] It can be seen that the implementation of this optional embodiment can perform association rule mining operations on the traffic characteristics corresponding to several historical travel itineraries on the highway and the half-trip shielding behaviors, provincial shielding behaviors and long-distance buying behaviors corresponding to the historical travel itineraries, obtain the traffic characteristic association relationship information corresponding to the historical travel itineraries, and further determine the semi-different weight parameters of each traffic characteristic, which is conducive to improving the accuracy of determining the half-trip shielding weight parameters, provincial shielding weight parameters and long-distance buying weight parameters, and then improving the accuracy of determining the special traffic parameters corresponding to the license plate, which is conducive to the accuracy of determining the traffic characteristic evaluation parameters.

[0179] In yet another optional embodiment, the device may further include:

[0180] The verification module 206 is used to determine, for each license plate, whether the traffic characteristic evaluation parameters of the license plate meet the preset abnormal traffic characteristic conditions. If so, the minimum estimated evasion amount of the license plate is determined based on the target traffic information of all target traffic itineraries of the license plate; when the minimum estimated evasion amount of the license plate exceeds the preset evasion amount threshold, the license plate is added to the database to be verified; the database to be verified is used for subsequent manual verification of the evasion behavior of the license plate.

[0181] It can be seen that the implementation of this optional embodiment can determine whether the traffic characteristic evaluation parameters of the license plate meet the preset abnormal traffic characteristic conditions. If so, the minimum estimated evasion amount of the license plate is further determined, and the license plate is added to the database to be verified when the minimum estimated evasion amount exceeds the evasion amount threshold. This is conducive to determining the subsequent processing method based on the traffic characteristic evaluation parameters to reduce the cost of evasion inspection.

[0182] Embodiment 3

[0183] See also Figure 3 , Figure 3 FIG. 1 is a schematic diagram of the structure of another highway vehicle traffic characteristics evaluation device disclosed in an embodiment of the present invention. Figure 3 The highway vehicle traffic characteristics evaluation device shown may include:

[0184] A memory 301 storing executable program codes;

[0185] a processor 302 coupled to the memory 301;

[0186] The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the highway vehicle traffic characteristics assessment method described in the first embodiment of the present invention.

[0187] Embodiment 4

[0188] The embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the highway vehicle traffic characteristics evaluation method described in the first embodiment of the present invention.

[0189] Embodiment 5

[0190] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the highway vehicle traffic characteristics assessment method described in Example 1.

[0191] The system embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative labor.

[0192] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, a magnetic disk storage, a magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0193] Finally, it should be noted that the method and device for evaluating the traffic characteristics of vehicles on highways disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical schemes described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. A method for evaluating the traffic characteristics of highway vehicles, characterized in that: The method comprises: For a target travel itinerary on the expressway, determining a travel identifier for the target travel itinerary; Based on the traffic identification of the target traffic itinerary, the gantry flow data, the entrance flow data, the multi-province split flow data, the exit flow data and the gantry brand recognition flow data of the target traffic itinerary are associated to obtain the target traffic information corresponding to the target traffic itinerary; Based on the target passage information corresponding to the target passage, determining the feature-related information corresponding to the target passage; the feature-related information at least includes whether the target passage inter-province, the corresponding gantry flow times, the corresponding number of license plate identifications, the corresponding multi-province split flow data, the corresponding exit flow data and the corresponding license plate identification fitting fee; Based on the feature-related information corresponding to the target travel itinerary, the travel features of the target travel itinerary are generated; the travel features include at least one or more of the features of no exit for 3 days, no splitting for 7 days, the feature of the number of license plate recognition being greater than the number of door frames, and the feature of the license plate recognition fitting fee being greater than the intra-provincial splitting fee; According to all the traffic characteristics of all the target traffic trips of each license plate within the target time period, the traffic characteristic evaluation parameters of the license plate are determined.

2. The highway vehicle traffic characteristics evaluation method according to claim 1, characterized in that: The gantry flow data at least includes the access identification, transaction time, gantry ID, entry time, license plate, vehicle model, vehicle type, and user type obtained by each gantry; The entry flow data at least includes the access identification, entry station ID, entry time, license plate, vehicle model, vehicle type, and user type obtained at the entry; The multi-province split flow data at least includes a pass identifier, an entry station ID, an entry time, an exit station ID, an exit time, a license plate, a vehicle model, a vehicle type, a user type, and a provincial split amount; The export flow data at least includes the access identification obtained at the export, the export station ID, the export time, the license plate, the vehicle model, the vehicle type, the user type, the entry time, the actual amount collected, and the actual amount collected in the province; The portal frame license plate recognition flow data at least includes the license plate, license plate recognition time, and portal frame ID.

3. The highway vehicle traffic characteristics evaluation method according to claim 1 or 2, characterized in that: The passage identification based on the target passage itinerary is used to associate the gantry flow data, the entrance flow data, the multi-province split flow data, the exit flow data and the gantry brand recognition flow data of the target passage itinerary to obtain the target passage information corresponding to the target passage itinerary, including: Based on the passage identification of the target passage stroke, determining the gantry flow data of each high-speed gantry corresponding to the target passage stroke; Based on the passage identifier of the target passage stroke, the gantry flow data of each high-speed gantry corresponding to the target passage stroke is associated to obtain the first passage information corresponding to the target passage stroke; Based on the passage identifier of the target passage trip, associating the inlet flow data corresponding to the target passage trip with the first passage information corresponding to the target passage trip to obtain the second passage information corresponding to the target passage trip; Based on the pass identifier of the target pass itinerary, the multi-province split flow data corresponding to the target pass itinerary is associated with the second pass information corresponding to the target pass itinerary to obtain the third pass information corresponding to the target pass itinerary; Based on the passage identifier of the target passage trip, the outlet flow data corresponding to the target passage trip is associated with the third passage information corresponding to the target passage trip to obtain the fourth passage information corresponding to the target passage trip; Based on the passage identification of the target passage stroke, the gantry sign identification flow data corresponding to the target passage stroke is associated with the fourth passage information corresponding to the target passage stroke to obtain the target passage information corresponding to the target passage stroke.

4. The highway vehicle traffic characteristics evaluation method according to claim 1, characterized in that: Determining the traffic characteristic evaluation parameters of the license plate according to all the traffic characteristics of all the target traffic trips of each license plate within the target time period includes: Determine the vehicle historical feature data corresponding to the license plate according to all the travel features of all the target travel trips of each license plate within the target time period and the number of times each travel feature is generated; Based on the vehicle historical feature data corresponding to each license plate, the special traffic parameters corresponding to the license plate are determined; the special traffic parameters include the half-distance shielding traffic parameters, the provincial shielding traffic parameters and the long-distance and short-distance traffic parameters corresponding to the license plate; Based on all the special traffic parameters corresponding to each license plate, the traffic characteristic evaluation parameters of the license plate are determined.

5. The highway vehicle traffic characteristics evaluation method according to claim 4, characterized in that: The method of determining the special traffic parameters corresponding to each license plate based on the vehicle historical feature data corresponding to the license plate includes: Determine, based on the vehicle historical feature data corresponding to each license plate, a feature evaluation parameter of each traffic feature corresponding to the license plate; Determine the half-distance shielding weight parameter, intra-province shielding weight parameter and long-run and short-run weight parameter for each traffic feature; For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the half-distance shielding weight parameter of each traffic feature, determine the half-distance shielding traffic parameter corresponding to the license plate; For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the provincial shielding weight parameter of each traffic feature, determine the provincial shielding traffic parameter corresponding to the license plate; For each license plate, based on the feature evaluation parameter of each traffic feature corresponding to the license plate and the running long and buying short weight parameter of each traffic feature, determine the running long and buying short traffic parameter corresponding to the license plate; For each license plate, the special traffic parameters corresponding to the license plate are determined based on the half-distance shielding traffic parameters, provincial shielding traffic parameters and long-distance and short-distance traffic parameters corresponding to the license plate.

6. The highway vehicle traffic characteristics assessment method according to claim 5, characterized in that: The determination of the half-distance shielding weight parameter, the intra-province shielding weight parameter and the long-run and short-run weight parameter for each traffic feature includes: Perform association rule mining operations on the traffic characteristics corresponding to several historical travel trips on the expressway and the half-trip blocking behaviors, intra-province blocking behaviors, and long-distance buying behaviors corresponding to the historical travel trips to obtain the traffic characteristics association relationship information corresponding to the historical travel trips; wherein, the half-trip blocking behaviors, intra-province blocking behaviors, and long-distance buying behaviors corresponding to the historical travel trips are determined by manual verification; Based on the traffic feature correlation information corresponding to the historical travel itinerary, the half-trip shielding weight parameter, intra-provincial shielding weight parameter and long-run and short-buy weight parameter of each traffic feature are determined.

7. The highway vehicle traffic characteristics evaluation method according to claim 1, characterized in that: The method further comprises: For each license plate, determine whether the traffic characteristic evaluation parameters of the license plate meet the preset abnormal traffic characteristic conditions. If so, determine the minimum estimated evasion amount of the license plate based on the target traffic information of all target traffic itineraries of the license plate; when the minimum estimated evasion amount of the license plate exceeds the preset evasion amount threshold, add the license plate to the database to be verified; the database to be verified is used for subsequent manual verification of the evasion behavior of the license plate.

8. A highway vehicle traffic characteristics assessment device, characterized in that: The device comprises: A first determination module is used to determine a passage identifier of a target passage on the expressway; An association module is used to associate the gantry flow data, entrance flow data, multi-province split flow data, exit flow data and gantry brand recognition flow data of the target travel stroke based on the travel identification of the target travel stroke, so as to obtain the target travel information corresponding to the target travel stroke; The second determination module is used to determine the feature-related information corresponding to the target travel itinerary based on the target travel information corresponding to the target travel itinerary; the feature-related information at least includes whether the target travel itinerary crosses provinces, the corresponding gantry flow times, the corresponding number of license plate identifications, the corresponding multi-province split flow data, the corresponding exit flow data and the corresponding license plate identification fitting fee; A generating module, configured to generate the traffic characteristics of the target traffic itinerary based on the characteristic-related information corresponding to the target traffic itinerary; the traffic characteristics include at least one or more of the following characteristics: no exit for 3 days, no splitting for 7 days, the number of license plate recognitions being greater than the number of door frames, and the license plate recognition fitting fee being greater than the intra-provincial splitting fee; The third determination module is used to determine the traffic characteristic evaluation parameters of the license plate according to all the traffic characteristics of all the target traffic trips of each license plate within the target time period.

9. A highway vehicle traffic characteristics assessment device, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the highway vehicle traffic characteristics assessment method as described in any one of claims 1-7.

10. A computer storable medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the highway vehicle traffic characteristics assessment method as described in any one of claims 1-7.

Images