ETC-based in-transit vehicle digital monitoring method and system

Abstract

The invention relates to the technical field of traffic control systems, and particularly discloses an ETC-based in-transit vehicle digital monitoring method and system, and the method comprises the steps: data acquisition, abnormal parking evaluation, road section congestion degree judgment, and road section dredging plan optimization. The method comprises the following steps: determining a vehicle abnormal parking evaluation coefficient, counting the number of abnormal vehicles, timely finding potential safety hazards, combining abnormal vehicle driving data and portal road section data, determining a road section congestion degree index, matching and implementing a dredging plan, obtaining a dredged road section and vehicle driving information, calculating a second dredging degree value, and matching a threshold value. And comparing with the first index, and comprehensively judging whether to optimize the plan or not, thereby realizing the digital monitoring of the total in-transit vehicles in the highway network, improving the overall monitoring efficiency of the in-transit vehicles, and ensuring the smoothness of the highway network.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic control systems, and in particular to a method and system for digitally monitoring en route vehicles based on ETC. Background Art

[0002] At present, most provinces and cities have cancelled main-line toll stations on expressways and implemented networked toll collection. ETC gantry systems have been set up between two roads, i.e., expressways and other roads, including the intersections of other expressways or local ordinary roads, and between provincial boundaries. A highway toll network with the largest number of users, the largest road network, and the most advanced technology has been built in the world. The refined management and services of expressways have put forward urgent demands for digital monitoring of all vehicles on the road.

[0003] For example, the invention patent with announcement number CN111325978B announces a full-process monitoring and warning system and method for abnormal behavior of vehicles on highways, including a road-side monitoring system, a background cloud integration system and a vehicle warning system; the road-side monitoring system and the vehicle warning system are both connected to the background cloud integration system; the road-side monitoring system is used to obtain the real-time driving speed, lane and license plate information of the vehicle, and the road-side monitoring system sends the monitored information to the background cloud integration system in one direction; the background cloud integration system is used to calculate the data sent by the road-side monitoring system, extract the abnormal behavior information of the vehicle and send it to the relevant vehicles; the vehicle warning system realizes the warning function for the driver, including: a data receiving module and a voice broadcast module; the data receiving module receives the abnormal behavior information of the vehicle from the background cloud integration system, and then uses the voice broadcast module to transmit the warning information to the driver of the vehicle.

[0004] For example, the invention patent with announcement number CN108597217B announces a highway accident monitoring and prompting method, which belongs to the field of highway safety technology. It adopts the segment lane division method and the video vehicle detector speed measurement method, and combines the average vehicle speed and the maximum vehicle speed threshold and the minimum vehicle speed threshold to intelligently monitor and prompt the occurrence of highway traffic accidents. If the existence of a dangerous section or a dangerous section is detected, an alarm message will be displayed in the preceding section prompt screen of the section to remind the rear vehicles. In addition, the section information will be sent to the traffic management department to deal with dangerous accidents.

[0005] Combining the above technical solutions, it is found that there are vehicle monitoring technical solutions for highways, which are mainly aimed at monitoring the overall operation of the road network. This situation can only achieve partial monitoring of some vehicles, which will lead to relatively one-sided intelligent monitoring of vehicles and ultimately face inaccurate monitoring of all vehicles. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method and system for digital monitoring of vehicles in transit based on ETC, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a method for digital monitoring of vehicles in transit based on ETC, including: data acquisition: acquiring the first real-time road section data of the gantry pair in the expressway network and the first driving data of each vehicle in transit in the area to which the gantry pair belongs; abnormal vehicle assessment: determining the first abnormal parking assessment coefficient of each vehicle in transit according to the first driving data of each vehicle in transit in the area to which the gantry pair belongs, verifying it with the predefined abnormal parking assessment reference coefficient, and counting the number of abnormal vehicles according to the verification result; road section congestion level determination: obtaining the first abnormal parking assessment coefficient of each abnormal vehicle. Driving data, and the first real-time road section data of the gantry pair in the expressway network are integrated to determine the congestion level index of the first road section of the current gantry pair, so as to match the road section unblocking plan, and according to the road section unblocking plan, unblock the vehicles on the road section of the current gantry pair; optimization of road section unblocking plan: obtain the road section information after unblocking and the vehicle driving information after unblocking, obtain the congestion level index of the second road section of the current gantry pair, and compare it with the congestion level index of the first road section of the current gantry pair, and comprehensively judge whether to optimize the road section unblocking plan, so as to complete the digital monitoring of all vehicles on the road in the expressway network.

[0008] As a further method, the first abnormal parking assessment coefficient of each vehicle in transit is determined by the following specific determination process: performing difference processing on the entry time point of each vehicle in transit entering the first gantry and the entry time point of each vehicle in transit entering the second gantry to obtain the gantry pair travel time of each vehicle in transit; extracting the parking adaptation time and the engine adaptation temperature from the information database in the highway network; and obtaining the first abnormal parking assessment coefficient of each vehicle in transit based on the gantry pair travel time of each vehicle in transit, the number of stops of each vehicle in transit within the first monitoring period, the corresponding time of each stop of each vehicle in transit within the first monitoring period, the parking adaptation time and the free passage time of the gantry pair.

[0009] As a further method, the first abnormal parking assessment coefficient of each on-road vehicle is verified with a predefined abnormal parking assessment reference coefficient, and the number of abnormal vehicles is counted according to the verification result. The specific statistical process is: if the first abnormal parking assessment coefficient of a certain on-road vehicle is greater than or equal to the abnormal parking assessment reference coefficient, the on-road vehicle corresponding to the first abnormal parking assessment coefficient is recorded as an abnormal vehicle, and a number of on-road vehicles whose first abnormal parking assessment coefficient is greater than or equal to the abnormal parking assessment reference coefficient are counted and recorded as the number of abnormal vehicles.

[0010] As a further method, the congestion index of the first road section of the current gantry pair is determined, and the specific determination process is: obtaining the first environmental data of the current gantry pair section, specifically the mean particle concentration of the current gantry pair section in the first monitoring period; matching the mean particle concentration of the current gantry pair section in the first monitoring period with the road visibility corresponding to each predefined mean particle concentration interval, thereby matching to obtain the road visibility of the current gantry pair section in the first monitoring period; performing standard deviation processing on the instantaneous speed of each abnormal vehicle at each first monitoring time point to obtain the speed variation coefficient of each abnormal vehicle in the first monitoring period; based on the road visibility of the current gantry pair section in the first monitoring period, the speed variation coefficient of each abnormal vehicle in the first monitoring period, the number of vehicles passing through the gantry pair in the first monitoring period, the length of vehicle congestion in the gantry pair in the first monitoring period, and the number of abnormal vehicles, a comprehensive analysis is performed to obtain the congestion index of the first road section of the current gantry pair, and the specific method is as follows:

[0011]

[0012] Where YD is the congestion index of the first section of the current gantry pair, DL is the road visibility of the current gantry pair section in the first monitoring cycle, and V h is the speed variation coefficient of the hth abnormal vehicle in the first monitoring period, h is the number of abnormal vehicles, h=1,2,3,...,GS, GS is the number of abnormal vehicles, G is the number of vehicles on the way passing through the gantry pair in the first monitoring period, CD is the congestion length of vehicles on the way of the gantry pair in the first monitoring period, y2 is the road section congestion weight factor corresponding to the road visibility predefined in the information database of the expressway network, y3 is the road section congestion weight factor corresponding to the mean of the speed variation coefficient predefined in the information database of the expressway network, y4 is the road section congestion weight factor corresponding to the abnormal vehicle proportion predefined in the information database of the expressway network, and y5 is the road section congestion weight factor corresponding to the congestion length of vehicles on the way predefined in the information database of the expressway network.

[0013] As a further method, the matching obtains the road section clearing plan, and the specific matching process is: matching the first road section congestion level index of the current gantry pair with the road section clearing plan corresponding to the predefined first road section congestion level index intervals, thereby obtaining the road section clearing plan of the current gantry pair.

[0014] As a further method, the judgment whether to optimize the road section clearing plan is as follows: data processing is performed on the first section congestion level index of the current gantry pair and the second section congestion level threshold of the current gantry pair to obtain the road section congestion level difference of the current gantry pair. If the road section congestion level difference of the current gantry pair is greater than or equal to zero, there is no need to optimize the road section clearing plan. If the road section congestion level difference of the current gantry pair is less than zero, the road section clearing plan needs to be optimized.

[0015] The second aspect of the present invention provides a system for digital monitoring of vehicles in transit based on ETC, including: a data acquisition module, used to acquire first real-time road section data belonging to a gantry pair in a highway network and first driving data of each vehicle in transit in the area belonging to the gantry pair; an abnormal vehicle assessment module, used to determine the first abnormal parking assessment coefficient of each vehicle in transit according to the first driving data of each vehicle in transit in the area belonging to the gantry pair, verify it with a predefined abnormal parking assessment reference coefficient, and count the number of abnormal vehicles according to the verification result; a road section congestion degree determination module, used to acquire the driving data of each abnormal vehicle, and comprehensively The real-time road section data of the first gantry pair in the expressway network is combined to determine the congestion level index of the first road section of the current gantry pair, thereby matching the road section unblocking plan, and unblocking the vehicles on the road section of the current gantry pair according to the road section unblocking plan; the road section unblocking plan optimization module is used to obtain the road section information after unblocking and the vehicle driving information after unblocking, obtain the unblocking level value of the second road section of the current gantry pair, and compare it with the congestion level index of the first road section of the current gantry pair, and comprehensively judge whether to optimize the road section unblocking plan, so as to complete the digital monitoring of all vehicles on the road in the expressway network.

[0016] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0017] (1) The present invention provides a method and system for digital monitoring of vehicles in transit based on ETC. By obtaining the driving data of vehicles in the road section and area to which the gantry in the expressway network belongs, determining the evaluation coefficient of abnormal vehicle parking, and counting the number of abnormal vehicles, potential safety hazards can be discovered in a timely manner. The abnormal vehicle driving data and the gantry road section data are combined to determine the road section congestion index, match the unblocking plan and implement it, obtain the unblocked road section and vehicle driving information, calculate the second unblocking degree value, match the threshold, compare it with the first index, and comprehensively judge whether to optimize the plan. The above steps realize the digital monitoring of all vehicles in transit in the expressway network, including abnormal parking detection, road section congestion assessment and unblocking plan optimization, improve the overall monitoring efficiency of vehicles in transit, and ensure the smooth flow of the road network.

[0018] (2) The present invention can quickly discover abnormal parking behaviors of vehicles on the road by performing real-time judgment and verification on the abnormal parking evaluation coefficients of vehicles on the road. Through the verification results, the number of vehicles on the road with abnormal parking can be accurately counted, providing accurate data support for the judgment of the congestion level of subsequent road sections, thereby improving resource utilization efficiency and the accuracy of digital monitoring of vehicles on the road.

[0019] (3) The present invention collects road section data in real time through the gantries in the expressway network, which can quickly determine the congestion level of the current road section and provide timely and accurate information support to relevant management departments. According to the congestion level index, the corresponding road section clearing plan can be quickly matched to ensure the adaptability and flexibility of the plan, shorten the duration of traffic congestion, and ensure smooth traffic order.

[0020] (4) The present invention can completely monitor road condition information, as well as the operating conditions of all individual vehicles on the road, and conduct monitoring and early warning of all individual vehicles. It can achieve efficient and accurate monitoring of all vehicles on the road in the expressway network, and has high availability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0022] Figure 1 The figure is a schematic flow chart of the method steps of the present invention.

[0023] Figure 2 It is a schematic diagram of system module connection of the present invention. DETAILED DESCRIPTION

[0024] The technical solutions 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, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] Reference Figure 1 As shown, the first aspect of the present invention provides a method for digital monitoring of in-transit vehicles based on ETC, including: data acquisition: acquiring the first real-time road section data of the gantry pair in the highway network and the first driving data of each in-transit vehicle in the area to which the gantry pair belongs.

[0026] It should be explained that the above-mentioned gantry pair is a gantry pair composed of two adjacent gantries, which is used for real-time monitoring and data collection of vehicles on the highway. The gantry pair involved in the embodiment of the present invention is the ETC gantry pair.

[0027] Specifically, the first real-time road section data of the gantry pair in the expressway network includes the free passage time of the gantry pair, the number of vehicles passing through the gantry pair in the first monitoring period, and the congestion length of vehicles passing through the gantry pair in the first monitoring period.

[0028] The free passage time of the above-mentioned gantry pair is specifically obtained by averaging multiple historical passage times of the gantry pair.

[0029] For some gantry pairs with remote geographical locations and less traffic, their free travel time can also be calculated using historical data. When the gantry pair lacks traffic data, it is deemed that there is no congestion and the free travel time is used as a reference.

[0030] The above-mentioned number of vehicles on the way and the length of vehicle congestion on the way can be extracted from the monitoring report within the expressway network.

[0031] The above-mentioned first monitoring period is a period of time used for initial vehicle information monitoring of vehicles in transit. The first monitoring period is determined by traffic supervisors based on a comprehensive analysis of factors such as the real-time flow of vehicles in transit, the monitoring environment, and the status of road sections.

[0032] The first driving data of each vehicle in transit in the area to which the gantry belongs specifically includes the entry time point of each vehicle in transit entering the first gantry, the entry time point of each vehicle in transit entering the second gantry, the number of stops of each vehicle in transit during the first monitoring period, and the corresponding duration of each stop.

[0033] The first driving data of each on-the-way vehicle in the area to which the above-mentioned gantry belongs can be extracted from the on-the-way vehicle real-time information table by establishing an on-the-way vehicle real-time information table for each on-the-way vehicle.

[0034] The above-mentioned real-time information table of vehicles in transit is established, with vehicles as the basic unit. Files are created as soon as vehicles appear, covering all status information of all vehicles in transit in the expressway network. Vehicle flow data is updated in real time according to the gantry, accurately recording the latest and most complete status information of all vehicles in transit. Files are closed at a regular time to avoid occupying too much memory and improve the performance of the information table.

[0035] The specific establishment process is as follows: "License plate-entry time point" is the primary key. The same record may have multiple pass_ids (license plate identification), or there may be two records for the same license plate; vehicle_id: primary key, updated on the primary key, and should be deleted after exit; en_time: primary key, which can be used to calculate the en-route time without having to list a separate en-route time table; pass_ids: list, recording all pass_ids under the primary key (vehicle_id, en_time); pass_id: set to 0 when initialized, identifying the main pass_id in the pass_id list; this variable can be temporarily recorded as 0 when the flow is missing or before the recognition is completed, without affecting the implementation of other functions; en_station_id: in-route vehicle entrance information; last_gantry_hex: information of the previous logical gantry; last_gantry_time: the entrance time point of entering the first gantry; diff_times: interval time series table, using streaming calculation, when the new g When antry_hex and trans_time appear, immediately compare the last gantry time to calculate diff_time and store it in the sequence table; relative_velocitys: relative speed sequence, using streaming calculation logic, when the new diff_time is calculated, it is compared with the average time of the current two gantries, and the relative ratio is calculated and included in the sequence table. When used, the average value of the sequence or the average value of the recent items can be required as the representative of the relative speed of the trip. If the ratio is greater than the threshold of 2.5, it is very likely that there is a temporary stop; rest_time: the number of temporary stops, using streaming calculation logic, if it is determined to be a temporary stop based on the specific data of relative_velocitys, such as the streaming updated relative_velocitys is greater than 2.5, then the variable +1; pic_id: license plate recognition photo code, if the vehicle in transit is also associated with the license plate recognition data, the license plate recognition picture id is saved to facilitate the relevant departments to retrieve relevant pictures in time and obtain more comprehensive information.

[0036] The above information is combined to form a real-time information table of vehicles in transit, thereby obtaining the first driving data of each vehicle in transit in the area to which the gantry belongs.

[0037] It should be explained that the timed cancellation of the above-mentioned real-time information table of vehicles in transit is as follows:

[0038] Initialize two variables when creating the file, out=0, ex_t ime is set to empty.

[0039] If the same vehicle_id is scanned and there is an exit flow, and the pass_id is in the pass_ids list of the vehicle, then the pass_id of the in-transit vehicle record is marked as the main pass_id of the in-transit vehicle record (that is, it is the main pass_id because it is the final billing exit), and the ex_time variable is marked, and the out variable is marked as 1, indicating that the trip has been completely completed; if the exit flow is not obtained yet, and the same vehicle_id has entry information at a later time, then the out variable is marked as 2, and the ex_time is marked as the new entry time, indicating that the trip has actually ended but the exit information is missing. After the exit flow is obtained, the ex_time is updated and the out is re-marked as 1; for the data that has been exported, the streaming process only marks it and does not clear it temporarily. At the same time, a scheduled task scan table is started, and it is cleared when the current time is at least T (perhaps 1 hour) from the exit time, T represents the data deletion interval threshold duration, and there is no new in-transit data, so that multiple trips that are logically continuous can be spliced.

[0040] Abnormal vehicle assessment: Based on the first driving data of each vehicle in transit in the area to which the gantry belongs, the first abnormal parking assessment coefficient of each vehicle in transit is determined, and the coefficient is verified with the predefined abnormal parking assessment reference coefficient. The number of abnormal vehicles is counted based on the verification result.

[0041] Furthermore, the specific determination process of determining the first abnormal parking assessment coefficient of each on-the-way vehicle is as follows:

[0042] The entry time point of each in-transit vehicle entering the first gantry is subtracted from the entry time point of each in-transit vehicle entering the second gantry to obtain the gantry travel time of each in-transit vehicle.

[0043] The parking adaptation time is extracted from the information database in the highway network.

[0044] According to the comprehensive analysis of the gantry pair driving time of each on-road vehicle, the number of stops of each on-road vehicle in the first monitoring cycle, the corresponding duration of each stop of each on-road vehicle in the first monitoring cycle, the parking adaptation duration and the free passage time of the gantry pair, the first abnormal parking assessment coefficient of each on-road vehicle is obtained. The specific method is as follows:

[0045]

[0046] Where TC gis the first abnormal parking assessment coefficient of the g-th vehicle on the way. In this embodiment, it is a quantitative indicator for evaluating the abnormal parking of vehicles on the way. When the first abnormal parking assessment coefficient of the vehicle on the way is higher, it may mean that there is a risk of abnormal parking for the vehicle on the way, and it is necessary to pay special attention to it. The abnormal parking mainly refers to the abnormal parking phenomenon caused by factors such as too many parking times and too long parking time of the vehicle on the way.

[0047] g is the serial number of each vehicle on the road, g = 1, 2, 3, ..., G.

[0048] G is the number of vehicles on the way passing through the gantry pair in the first monitoring period, which refers to the number of all vehicles on the way passing through the gantry pair in the first monitoring period.

[0049] ΔT g It is the travel time of the g-th vehicle on the way, which refers to the period from when the vehicle on the way enters the gantry to when it passes the next gantry, that is, the travel time of the vehicle on the way on the gantry pair section.

[0050] ΔT is the free passage time of the gantry pair, which refers to the reference value of the predefined passage time of the gantry pair.

[0051] CS g is the number of stops of the g-th vehicle in transit during the first monitoring period, which refers to the number of stops of the vehicle in transit due to various reasons, such as traffic accidents, road waterlogging, illegal parking, etc. during the first monitoring period.

[0052] SC gt It is the duration corresponding to the tth stop of the g-th vehicle in transit during the first monitoring period, and refers to the duration of each stop of the vehicle in transit during the first monitoring period.

[0053] t is the number of each stop, t = 1, 2, 3, ..., CS g , C.S. g The number of stops.

[0054] TS is the parking adaptation time, which refers to the standard value corresponding to the predefined parking time.

[0055] It should be explained that the above-mentioned parking adaptation time refers to the standard value corresponding to the parking time in the emergency lane or emergency parking strip when a vehicle on the road may break down or encounter an emergency situation.

[0056] y1 is the abnormal parking assessment factor corresponding to the unit value of the parking time deviation value predefined in the information database within the expressway network.

[0057] In a specific embodiment, the abnormal parking assessment factor corresponding to the unit value of the parking time deviation value can be directly obtained from the information library in the highway network during use. The factor represents the numerical value of the degree of influence on the first abnormal parking assessment coefficient of the vehicle in transit during the first abnormal parking assessment of the vehicle in transit. The corresponding relationship can be a pre-set mapping relationship. For example, the parking time deviation value and the abnormal parking assessment factor corresponding to the unit value of the parking time deviation value preset in the information library in the highway network form a mapping set, and the real-time parking time deviation value is brought into the mapping set to obtain the abnormal parking assessment factor corresponding to the unit value of the parking time deviation value. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this embodiment, its value range is (0, 1).

[0058] In this embodiment, the numerical relationship between the driving time of the gantry pair of the vehicle in transit and the free passage time of the gantry pair can be partly attributed to the number of times the vehicle in transit stops. The more times the vehicle stops, the longer the time it takes for the vehicle to stop, which leads to an increase in the difference between the actual driving time of the vehicle in transit and the free passage time. Similarly, the sum of the corresponding time of each stop is also an important factor affecting the difference between the driving time and the free passage time. The longer the duration of each stop, the longer the accumulated parking time, which leads to an increase in the difference between the driving time and the free passage time, so that the possibility of the vehicle in transit being judged as abnormal parking is increased.

[0059] Specifically, the first abnormal parking assessment coefficient of each on-the-way vehicle is verified with the abnormal parking assessment reference coefficient predefined in the information database within the expressway network, and the number of abnormal vehicles is counted according to the verification result. The specific statistical process is:

[0060] If the first abnormal parking assessment coefficient of a vehicle on the way is greater than or equal to the abnormal parking assessment reference coefficient, the vehicle on the way corresponding to the first abnormal parking assessment coefficient is recorded as an abnormal vehicle, and a number of vehicles on the way whose first abnormal parking assessment coefficient is greater than or equal to the abnormal parking assessment reference coefficient are counted and recorded as the number of abnormal vehicles.

[0061] If the first abnormal parking assessment coefficient of a vehicle on the way is less than the abnormal parking assessment reference coefficient, there is no need to count the vehicle on the way.

[0062] It should be explained that the increase in the number of abnormal vehicles may mean that there are more abnormal parking behaviors on the road section, which may cause traffic congestion and aggravate the congestion level of the road section. In addition, by observing the changing trend of the number of abnormal vehicles, the congestion trend of the road section can be predicted. If the number of abnormal vehicles continues to increase, it may mean that the congestion situation is worsening; conversely, if the number of abnormal vehicles decreases, it may mean that the congestion situation is easing. By timely identifying and handling abnormal vehicles, traffic delays and congestion caused by abnormal parking behaviors can be reduced, which helps to improve the traffic efficiency of the entire road section.

[0063] Determination of road section congestion level: Acquire the driving data of each abnormal vehicle, and integrate the first real-time road section data of the gantry pair in the expressway network to determine the congestion level index of the first road section of the current gantry pair, thereby matching the road section unblocking plan, and perform unblocking operations on the vehicles on the way on the current gantry pair section according to the road section unblocking plan.

[0064] Furthermore, the driving data of each abnormal vehicle is specifically the instantaneous speed of each abnormal vehicle at each first monitoring time point.

[0065] The above instantaneous speed can be obtained by extracting it from the speedometer of the vehicle on the road.

[0066] The above-mentioned first monitoring time points are specifically a plurality of first monitoring time points obtained by dividing the first monitoring period into time points.

[0067] The above division method specifically divides the first monitoring period into first monitoring time points according to preset 30 seconds.

[0068] Specifically, the congestion index of the first section of the current gantry pair is determined by the following specific determination process:

[0069] The first environmental data of the current gantry to the road section is obtained, specifically, the average value of the particle concentration of the current gantry to the road section in the first monitoring cycle.

[0070] The above-mentioned particle concentration can be calculated by the laser scattering method, which utilizes the laser scattering effect. When the laser beam encounters particles in the air, scattering occurs, and the concentration of the particles is calculated by detecting the intensity change of the scattered light.

[0071] It should be explained that the current gantry may be at a relatively short distance to the road section, which satisfies the detection distance of laser scattering.

[0072] According to the mean particle matter concentration of the current gantry-to-road section in the first monitoring period, the road visibility corresponding to the predefined mean particle matter concentration intervals is matched. The specific matching process is: obtaining a mapping set of the particle matter concentration and road visibility of the current gantry-to-road section in the first monitoring period from the information database in the expressway network, first determining the interval to which the particle matter concentration of the current gantry-to-road section in the first monitoring period belongs, and assigning the road visibility corresponding to the interval to the current gantry-to-road section corresponding to the particle matter concentration, so as to obtain the road visibility of the current gantry-to-road section in the first monitoring period by matching.

[0073] The instantaneous speed of each abnormal vehicle at each first monitoring time point is processed by standard deviation to obtain the speed variation coefficient of each abnormal vehicle in the first monitoring period.

[0074] According to the road visibility of the current gantry pair section in the first monitoring cycle, the speed change coefficient of each abnormal vehicle in the first monitoring cycle, the number of vehicles passing through the gantry pair in the first monitoring cycle, the congestion length of vehicles on the way in the gantry pair in the first monitoring cycle and the number of abnormal vehicles, a comprehensive analysis is performed to obtain the congestion index of the first section of the current gantry pair. The specific method is as follows:

[0075]

[0076] Wherein, YD is the congestion index of the first section of the current gantry pair, which is used to measure the vehicle congestion degree of the current gantry pair section in this embodiment. The larger the congestion index of the first section is, the lower the traffic quality of the current gantry pair section is, and it is necessary to take unblocking measures as soon as possible.

[0077] DL is the road visibility of the current gantry to the road section in the first monitoring cycle, which refers to the maximum distance at which the objects ahead can be clearly seen on the current gantry to the road section in the first monitoring cycle.

[0078] V h is the speed variation coefficient of the hth abnormal vehicle in the first monitoring period, which reflects the speed variation of the abnormal vehicle through the current gantry on the road section in the first monitoring period. When the speed variation coefficient is large, it means that the speed of the abnormal vehicle fluctuates greatly and there may be frequent stops; when the speed variation coefficient is small, it indicates that the speed of the vehicle is relatively stable and its driving process may be smoother.

[0079] h is the number of each abnormal vehicle, h = 1, 2, 3, ..., GS, GS is the number of abnormal vehicles.

[0080] G is the number of vehicles passing through the gantry during the first monitoring cycle.

[0081] CD is the congestion length of vehicles on the way during the first monitoring period of the gantry. It refers to the length of the road section where vehicles on the way are blocked due to excessive traffic flow, traffic accidents, road construction or other reasons, forming a continuous queue. This length reflects the severity and scope of traffic congestion.

[0082] y2 is the road section congestion weight factor corresponding to the road visibility predefined in the highway network information database, y3 is the road section congestion weight factor corresponding to the mean of the speed variation coefficient predefined in the highway network information database, y4 is the road section congestion weight factor corresponding to the abnormal vehicle proportion predefined in the highway network information database, y5 is the road section congestion weight factor corresponding to the on-the-way vehicle congestion length predefined in the highway network information database.

[0083] Among them, the road section congestion weight factor corresponding to road visibility, the road section congestion weight factor corresponding to the mean speed variation coefficient, the road section congestion weight factor corresponding to the abnormal vehicle proportion and the road section congestion weight factor corresponding to the congestion length of vehicles in transit are all directly obtained from the information database in the expressway network; in this embodiment, the road section congestion weight factor corresponding to road visibility ranges from 0.3 to 0.4, the road section congestion weight factor corresponding to the mean speed variation coefficient ranges from 0.5 to 0.68, the road section congestion weight factor corresponding to the abnormal vehicle proportion ranges from 0.4 to 0.6, and the road section congestion weight factor corresponding to the congestion length of vehicles in transit ranges from 0.3 to 0.5.

[0084] In this embodiment, the reduced visibility of the current gantry to the road section will affect the line of sight of the driver of the vehicle on the road, making it difficult for the driver to accurately judge the road conditions and vehicle dynamics ahead, thereby possibly reducing the speed of the vehicle. The cautious driving behavior of the driver will lead to a decrease in road capacity, thereby causing traffic congestion and increasing the length of the congestion of vehicles on the road. Similarly, the abnormal vehicle speed change coefficient reflects the instability or volatility of the vehicle speed. When the speed of an abnormal vehicle, such as a faulty vehicle, changes greatly, it will affect the driving speed and stability of the surrounding vehicles. This speed instability may lead to discontinuity of traffic flow, thereby causing traffic congestion and increasing the length of the congestion. The abnormal vehicle proportion reflects the proportion of abnormal vehicles on the road to the number of vehicles on the road. When this ratio is high, it means that there are more abnormal vehicles on the road. These vehicles may occupy road resources due to failures, accidents or other reasons, reducing road capacity. A high abnormal vehicle proportion may lead to frequent traffic congestion and an increase in the length of congestion, thereby aggravating the congestion of the road section.

[0085] Furthermore, the matching obtains a road section dredging plan, and the specific matching process is as follows:

[0086] The first road section congestion index of the current gantry pair is matched with the road section clearing plan corresponding to each first road section congestion index interval predefined in the information database within the expressway network. The specific matching process is: obtain a mapping set of the first road section congestion index of the current gantry pair and the road section clearing plan from the information database within the expressway network, first determine the interval to which the first road section congestion index of the current gantry pair belongs, and assign the road section clearing plan corresponding to the interval to the current gantry pair corresponding to the first road section congestion index, so as to obtain the road section clearing plan of the current gantry pair by matching.

[0087] In a specific embodiment of the above-mentioned road section clearing plan, for example, when the congestion index of the first section of the current gantry pair is 1.5, it can be considered that the road congestion level of the current gantry pair is relatively low, and the plan measures may be to strengthen traffic monitoring and guidance, and timely release road condition information through traffic broadcasts, electronic display screens, etc., to guide drivers to plan routes reasonably; when the congestion index of the first section of the current gantry pair is 5.5, it can be considered that the road congestion level of the current gantry pair is relatively high, and the plan measures may be to implement temporary traffic control measures, such as closing some lanes, restricting vehicle traffic, etc.

[0088] Optimization of road section clearing plan: obtain the road section information after clearing and the vehicle driving information after clearing, obtain the clearing degree value of the second section of the current gantry pair, match the congestion degree threshold of the second section of the current gantry pair, and compare it with the congestion degree index of the first section of the current gantry pair, and comprehensively judge whether to optimize the road section clearing plan, so as to complete the digital monitoring of all on-the-road vehicles in the expressway network.

[0089] Specifically, the obtained second section dredging degree value of the current gantry pair is matched to obtain the second section congestion degree threshold of the current gantry pair. The specific analysis process is as follows:

[0090] The road section information after dredging is specifically the number of vehicles passing through the gantry pair in the second monitoring period, wherein the number of vehicles passing through can be extracted through the flow information of the gantry pair.

[0091] The above-mentioned second monitoring period is a period of time used for continuous vehicle information monitoring of the completed road clearing plan. The second monitoring period is determined by traffic supervision personnel based on a comprehensive analysis of factors such as the real-time flow of vehicles on the road, the monitoring environment, and the road status.

[0092] The vehicle driving information after dredging is specifically the average speed of each passing vehicle in the second monitoring period, wherein the speed can be calculated by the corresponding distance of the gantry pair and the corresponding duration of the vehicle passing the gantry pair.

[0093] The average speed of each passing vehicle in the second monitoring period is multiplied by the unblocking degree influencing parameter corresponding to the predefined speed average in the highway intranet information database, and the multiplication result is recorded as the speed variation coefficient of each passing vehicle in the second monitoring period.

[0094] The influencing parameter of the degree of dredging corresponding to the speed mean is directly obtained from the information database in the expressway network; in this embodiment, the value range of the influencing parameter of the degree of dredging corresponding to the speed mean is (0, 1).

[0095] Get the associated information of the road section clearing plan of the current gantry pair, including the clearing time of the road section clearing plan and the number of cleared vehicles.

[0096] The above-mentioned dredging time and number of vehicles dredged can be extracted from the execution report of the dredging plan.

[0097] According to the comprehensive analysis of the speed change coefficient of each passing vehicle in the second monitoring period, the dredging time of the road section dredging plan, the number of passing vehicles of the gantry pair in the second monitoring period, and the number of dredged vehicles in the road section dredging plan, the dredging degree value of the second road section of the current gantry pair is obtained. The specific method is as follows:

[0098]

[0099] Wherein, SS is the second road section clearing degree value of the current gantry pair, which is used to measure the vehicle clearing degree of the current gantry pair road section after the road section clearing plan is executed in this embodiment. The smaller the second road section clearing degree value is, the lower the quality of the road section clearing plan of the current gantry pair road section is, and the road section clearing plan of the current gantry pair road section needs to be optimized.

[0100] V f is the speed variation coefficient of the f-th passing vehicle in the second monitoring period, which reflects the speed variation of the passing vehicle in the second monitoring period. When the speed variation coefficient is large, it means that the speed of the passing vehicle fluctuates greatly, and there may still be frequent stops; when the speed variation coefficient is small, it indicates that the speed of the vehicle is relatively stable, and its driving process may be smoother.

[0101] f is the number of each passing vehicle, f = 1, 2, 3, ..., G'.

[0102] G′ is the number of vehicles passing through the gantry pair in the second monitoring period, which refers to the number of all vehicles passing through the gantry pair in the second monitoring period.

[0103] ST is the number of vehicles that have been cleared according to the road section clearing plan, which refers to the number of vehicles that have successfully resumed traffic through the implementation of the road clearing plan.

[0104] SC is the duration of the road clearing plan, which refers to the period from the implementation of the road clearing plan to the resumption of traffic for all vehicles.

[0105] s1 is the unblocking influence coefficient corresponding to the speed variation coefficient predefined in the information database of the expressway network, s2 is the unblocking influence coefficient corresponding to the proportion of unblocked vehicles predefined in the information database of the expressway network, and s3 is the unblocking influence coefficient corresponding to the unblocking time predefined in the information database of the expressway network.

[0106] Among them, the dredging influence coefficient corresponding to the speed variation coefficient, the dredging influence coefficient corresponding to the proportion of cleared vehicles, and the dredging influence coefficient corresponding to the dredging time are all directly obtained from the information database in the expressway network; in this embodiment, the dredging influence coefficient corresponding to the speed variation coefficient, the dredging influence coefficient corresponding to the proportion of cleared vehicles, and the dredging influence coefficient corresponding to the dredging time are all in the range of (0, 1).

[0107] In this embodiment, if the speed variation coefficient of the current gantry pair of road sections is small, it means that the driver can maintain a relatively stable speed in the current road section, that is, the dredging quality of the current road section is good; the proportion of dredged vehicles refers to the ratio between the number of vehicles that have successfully passed the congested road section and resumed normal driving and the total number of passing vehicles. This ratio reflects the efficiency and effectiveness of the road section dredging plan. At the beginning of the dredging work, the number of dredged vehicles may be zero. As the dredging work progresses, the number of dredged vehicles will gradually increase, thereby improving the dredging degree of the second road section of the current gantry pair; when the speed variation coefficient is large, the dredging time may be extended. This is because the driver needs to drive more carefully to avoid traffic accidents.

[0108] The second road section clearness value of the current gantry pair is matched with the second road section congestion level thresholds corresponding to each second road section clearness value interval predefined in the information database within the expressway network. The specific matching process is: obtaining a mapping set between the second road section clearness value of the current gantry pair and the second road section congestion level thresholds from the information database within the expressway network, determining the specific interval of the second road section clearness value of the current gantry pair, and assigning the second road section congestion level threshold corresponding to the interval to the current gantry pair corresponding to the second road section clearness value, thereby matching and obtaining the second road section congestion level threshold of the current gantry pair.

[0109] It should be explained that, if the clearness value of the second road section of the current gantry pair is larger, the congestion level threshold of the second road section of the current gantry pair obtained by matching is smaller.

[0110] Furthermore, the specific judgment process of whether to optimize the road section dredging plan is as follows:

[0111] The congestion level index of the first section of the current gantry pair and the congestion level threshold of the second section of the current gantry pair are processed to obtain the congestion level difference of the section of the current gantry pair. The specific method is as follows:

[0112]

[0113] Wherein, Δθ is the congestion level difference of the current gantry pair, Δθ1 is the congestion level difference of the current gantry pair that does not need to be optimized, Δθ2 is the congestion level difference of the current gantry pair that needs to be optimized, YD is the congestion level index of the first section of the current gantry pair, and YD′ is the congestion level threshold of the second section of the current gantry pair.

[0114] If the difference in congestion levels of the road section of the current gantry pair is Δθ2, the road section clearing plan needs to be optimized. If the difference in congestion levels of the road section of the current gantry pair is Δθ1, there is no need to optimize the road section clearing plan.

[0115] The above-mentioned optimization of the road section clearing plan can be aimed at handling abnormal vehicles and clarifying the parking categories of abnormal vehicles, such as illegal parking, faulty parking, accident parking, etc. If the abnormal vehicle has a faulty parking situation, the traffic supervision personnel can provide assistance to the abnormal vehicle and move the abnormal vehicle away from the normal driving section as soon as possible, so as to reduce the congestion level of the road section.

[0116] It should be explained that, based on the various data parameters involved in the present invention, all vehicles on the road can be subjected to failure to arrive monitoring, fatigue driving monitoring, section speeding monitoring and other abnormal behavior monitoring, among which failure to arrive monitoring is taken as an example.

[0117] In a specific embodiment, all vehicles on the way are monitored for not arriving due to the due date, specifically:

[0118] Use "Gantry-Maximum Reappearance Time" to avoid errors caused by temporary traffic jams and evacuations. The maximum reappearance time refers to the maximum time for vehicles in transit to enter the next gantry. It can be obtained through historical data and ultimately determine the "warning time" DT corresponding to each gantry.

[0119] Scan the real-time table of vehicles in transit every 5 minutes, calculate the di f_time between the current time and the last time the vehicle appeared in transit, and then adjust it to di f_time' according to the inherent instability measure of the relative speed of the vehicle in transit, compare it with the DT corresponding to its last_gantry_hex, screen the vehicles whose di f_time' is greater than DT, and establish a temporary parking risk table (features include: license plate primary key, last_gantry_hex, last_gantry_time, DT).

[0120] In addition, a gantry-adjacent service area static table is maintained to indicate whether there is a service area next to each last_gantry_hex and the flow of the service area will not be uploaded. 0 means no service area, 1 means there is a service area but the service area flow is not guaranteed to be uploaded, and 2 means there is a service area and the flow of the service area is guaranteed to be uploaded completely.

[0121] For the temporary parking risk table generated by the above-mentioned timed task, exclude the vehicles with last_gantry_hex belonging to 1 and the vehicles with last_gantry_hex belonging to 2 and clearly found in the service area flow, and rank the remaining vehicles according to the disappearance time (di f_time'-DT). This means that the longer the vehicle disappears, the greater the temporary parking risk, and the more it is a high-risk vehicle that truly deserves the attention of traffic regulators.

[0122] Reference Figure 2 As shown, the second aspect of the present invention provides a system for digital monitoring of vehicles in transit based on ETC, including: a data acquisition module, an abnormal vehicle assessment module, a road section congestion level determination module and a road section clearing plan optimization module.

[0123] The second aspect of the present invention provides a system for digital monitoring of vehicles in transit based on ETC, which also includes: an information library within the highway network for storing parking adaptation time, engine adaptation temperature and preset values ​​of various factors.

[0124] The data acquisition module is connected to the abnormal parking assessment module, the data acquisition module and the abnormal vehicle assessment module are both connected to the road section congestion degree determination module, the road section congestion degree determination module is connected to the road section clearing plan optimization module, and the abnormal vehicle assessment module and the road section congestion degree determination module are both connected to the information database in the highway network.

[0125] The data acquisition module is used to acquire the first real-time road section data to which the gantry pair belongs in the expressway network and the first driving data of each on-the-way vehicle in the area to which the gantry pair belongs.

[0126] The abnormal vehicle assessment module is used to determine the first abnormal parking assessment coefficient of each on-the-way vehicle according to the first driving data of each on-the-way vehicle in the area to which the gantry belongs, verify it with the predefined abnormal parking assessment reference coefficient, and count the number of abnormal vehicles according to the verification result.

[0127] The road section congestion level determination module is used to obtain the driving data of each abnormal vehicle, and integrate the first real-time road section data of the gantry pair in the expressway network to determine the first road section congestion level index of the current gantry pair, thereby matching the road section unblocking plan, and performing unblocking operations on the vehicles in transit on the current gantry pair road section according to the road section unblocking plan.

[0128] The road section dredging plan optimization module is used to obtain the road section information after dredging and the vehicle driving information after dredging, obtain the second section dredging degree value of the current gantry pair, match the second section congestion degree threshold of the current gantry pair, and compare it with the first section congestion degree index of the current gantry pair, and comprehensively judge whether to optimize the road section dredging plan, so as to complete the digital monitoring of all on-the-way vehicles in the highway network.

[0129] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for digital monitoring of vehicles in transit based on ETC, characterized in that: include: Data acquisition: acquiring the first real-time road section data of the gantry pair in the expressway network and the first driving data of each on-the-way vehicle in the area to which the gantry pair belongs; Abnormal vehicle assessment: Based on the first driving data of each on-the-way vehicle in the area, the first abnormal parking assessment coefficient of each on-the-way vehicle is determined, and the coefficient is verified with the predefined abnormal parking assessment reference coefficient. The number of abnormal vehicles is counted according to the verification result; Determination of road congestion level: Acquire the driving data of each abnormal vehicle, and integrate the first real-time road section data of the gantry pair in the expressway network to determine the first road section congestion level index of the current gantry pair, so as to match the road section unblocking plan, and perform unblocking operations on the vehicles on the road section of the current gantry pair according to the road section unblocking plan; Optimization of road section clearing plan: obtain the road section information after clearing and the vehicle driving information after clearing, obtain the clearing degree value of the second section of the current gantry pair, match the congestion degree threshold of the second section of the current gantry pair, and compare it with the congestion degree index of the first section of the current gantry pair, and comprehensively judge whether to optimize the road section clearing plan, so as to complete the digital monitoring of all on-the-road vehicles in the expressway network.

2. The method for digital monitoring of vehicles in transit based on ETC according to claim 1 is characterized by: The first real-time road section data of the gantry pair in the expressway network specifically includes the free passage time of the gantry pair, the number of vehicles passing through the gantry pair in the first monitoring period, and the congestion length of vehicles passing through the gantry pair in the first monitoring period; The first driving data of each vehicle in transit in the area to which the gantry belongs specifically includes the entry time point of each vehicle in transit entering the first gantry, the entry time point of each vehicle in transit entering the second gantry, the number of stops of each vehicle in transit during the first monitoring period, and the corresponding duration of each stop.

3. The method for digital monitoring of vehicles in transit based on ETC according to claim 2 is characterized by: The specific determination process of determining the first abnormal parking evaluation coefficient of each on-the-way vehicle is as follows: Performing difference processing on the entry time point of each in-transit vehicle entering the first gantry and the entry time point of each in-transit vehicle entering the second gantry to obtain the gantry pair travel time of each in-transit vehicle; Extract the parking adaptation time from the information database in the expressway network; Based on a comprehensive analysis of the gantry pair travel time of each vehicle in transit, the number of stops of each vehicle in transit during the first monitoring cycle, the corresponding duration of each stop of each vehicle in transit during the first monitoring cycle, the parking adaptation time and the free passage time of the gantry pair, the first abnormal parking assessment coefficient of each vehicle in transit is obtained.

4. The method for digital monitoring of vehicles in transit based on ETC according to claim 3 is characterized by: The first abnormal parking assessment coefficient of each on-the-way vehicle is verified with a predefined abnormal parking assessment reference coefficient, and the number of abnormal vehicles is counted according to the verification result. The specific statistical process is: If the first abnormal parking assessment coefficient of a vehicle on the way is greater than or equal to the abnormal parking assessment reference coefficient, the vehicle on the way corresponding to the first abnormal parking assessment coefficient is recorded as an abnormal vehicle, and a number of vehicles on the way whose first abnormal parking assessment coefficient is greater than or equal to the abnormal parking assessment reference coefficient are counted and recorded as the number of abnormal vehicles.

5. The method for digital monitoring of vehicles in transit based on ETC according to claim 1 is characterized in that: The driving data of each abnormal vehicle is specifically the instantaneous speed of each abnormal vehicle at each first monitoring time point.

6. The method for digital monitoring of vehicles in transit based on ETC according to claim 1 is characterized by: The specific determination process of determining the congestion index of the first section of the current gantry pair is as follows: Obtaining first environmental data of the current gantry to the road section, specifically, the average particle concentration of the current gantry to the road section in the first monitoring cycle; According to the average particle concentration of the current gantry over the road section in the first monitoring cycle, the road visibility corresponding to each predefined average particle concentration interval is matched, so as to obtain the road visibility of the current gantry over the road section in the first monitoring cycle; Perform standard deviation processing on the instantaneous speed of each abnormal vehicle at each first monitoring time point to obtain a speed variation coefficient of each abnormal vehicle in the first monitoring period; According to the road visibility of the current gantry pair section in the first monitoring cycle, the speed change coefficient of each abnormal vehicle in the first monitoring cycle, the number of vehicles passing through the gantry pair in the first monitoring cycle, the congestion length of vehicles on the way in the gantry pair in the first monitoring cycle and the number of abnormal vehicles, a comprehensive analysis is performed to obtain the congestion index of the first section of the current gantry pair. The specific method is as follows: Where YD is the congestion index of the first section of the current gantry pair, DL is the road visibility of the current gantry pair section in the first monitoring cycle, and V h is the speed variation coefficient of the hth abnormal vehicle in the first monitoring period, h is the number of abnormal vehicles, h=1,2,3,...,GS, GS is the number of abnormal vehicles, G is the number of vehicles on the way passing through the gantry pair in the first monitoring period, CD is the congestion length of vehicles on the way of the gantry pair in the first monitoring period, y2 is the road section congestion weight factor corresponding to the road visibility predefined in the information database of the expressway network, y3 is the road section congestion weight factor corresponding to the mean of the speed variation coefficient predefined in the information database of the expressway network, y4 is the road section congestion weight factor corresponding to the abnormal vehicle proportion predefined in the information database of the expressway network, and y5 is the road section congestion weight factor corresponding to the congestion length of vehicles on the way predefined in the information database of the expressway network.

7. The method for digital monitoring of vehicles in transit based on ETC according to claim 6 is characterized by: The matching obtains the road section dredging plan, and the specific matching process is as follows: The first road section congestion index of the current gantry pair is matched with the road section clearing plan corresponding to each predefined first road section congestion index interval, so as to obtain the road section clearing plan of the current gantry pair.

8. The method for digital monitoring of vehicles in transit based on ETC according to claim 1 is characterized by: The clearing degree value of the second section of the current gantry pair is obtained, and the congestion degree threshold of the second section of the current gantry pair is matched. The specific analysis process is as follows: The road section information after dredging is specifically the number of vehicles on the way passing through the gantry in the second monitoring period; The vehicle driving information after the dredging is specifically the average speed of each passing vehicle in the second monitoring period; The average speed of each passing vehicle in the second monitoring period is multiplied by the dredging degree influencing parameter corresponding to the predefined average speed in the information database of the expressway network, and the multiplication result is recorded as the speed variation coefficient of each passing vehicle in the second monitoring period; Obtain the associated information of the road section dredging plan of the current gantry pair, including the dredging time of the road section dredging plan and the number of vehicles that have been dredged; The second section dredging degree value of the current gantry pair is obtained based on a comprehensive analysis of the speed change coefficient of each passing vehicle in the second monitoring period, the dredging time of the section dredging plan, the number of vehicles on the way of the gantry pair in the second monitoring period, and the number of vehicles dredged in the section dredging plan; The second road section clearness value of the current gantry pair is matched with the second road section congestion level threshold corresponding to each predefined second road section clearness value interval, so as to obtain the second road section congestion level threshold of the current gantry pair.

9. The method for digital monitoring of vehicles in transit based on ETC according to claim 1, characterized in that: The specific judgment process of whether to optimize the road section dredging plan is as follows: The first section congestion index of the current gantry pair and the second section congestion threshold of the current gantry pair are processed to obtain the section congestion difference of the current gantry pair. If the section congestion difference of the current gantry pair is greater than or equal to zero, there is no need to optimize the section clearing plan. If the section congestion difference of the current gantry pair is less than zero, it is necessary to optimize the section clearing plan.

10. A system using the ETC-based digital monitoring method for vehicles in transit as claimed in any one of claims 1 to 9, characterized in that: include: A data acquisition module, used to acquire first real-time road section data belonging to the gantry pair in the expressway network and first driving data of each on-the-way vehicle in the area belonging to the gantry pair; An abnormal vehicle assessment module is used to determine a first abnormal parking assessment coefficient of each on-the-way vehicle according to the first driving data of each on-the-way vehicle in the area to which the gantry belongs, verify it with a predefined abnormal parking assessment reference coefficient, and count the number of abnormal vehicles according to the verification result; The road section congestion level determination module is used to obtain the driving data of each abnormal vehicle, and to determine the congestion level index of the first road section of the current gantry pair based on the first real-time road section data of the gantry pair in the expressway network, so as to match the road section unblocking plan and perform unblocking operations on the vehicles on the road section of the current gantry pair according to the road section unblocking plan; The road section clearing plan optimization module is used to obtain the road section information after clearing and the vehicle driving information after clearing, obtain the clearing degree value of the second section of the current gantry pair, match the congestion degree threshold of the first section of the current gantry pair, and compare it with the congestion degree index of the first section of the current gantry pair, and comprehensively judge whether to optimize the road section clearing plan, so as to complete the digital monitoring of all on-the-way vehicles in the expressway network.

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