Method and system for digital monitoring of vehicles in transit based on ETC

Abstract

The present invention relates to the technical field of traffic control systems, and specifically discloses a method and system for digital monitoring of vehicles in transit based on ETC. The method comprises: data acquisition, abnormal parking assessment, road section congestion degree determination, and road section unblocking plan optimization. By acquiring vehicle driving data of the gantries in the highway network for the road sections and regions to which they belong, determining the vehicle abnormal parking assessment coefficient, and counting the number of abnormal vehicles, potential safety hazards can be discovered in a timely manner. The abnormal vehicle driving data and the gantries road section data are combined to determine the road section congestion degree index, match the unblocking plan and implement it, obtain the unblocked road section and vehicle driving information, calculate a second unblocking degree value, match it with a threshold, compare it with the first index, and comprehensively judge whether to optimize the plan. The above steps realize digital monitoring of all vehicles in transit in the highway network, improve the overall monitoring efficiency of vehicles in transit, and ensure the smooth flow of the road 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 vehicles in transit based on ETC. Background Art

[0002] At present, most provinces and cities have cancelled toll stations on the main lines of expressways and implemented networked toll collection. ETC gantry systems have been installed between two roads, that is, between expressways and other roads, including the intersections of other expressways or local ordinary roads, and between provincial borders. This has built the world's largest expressway toll network with the largest number of users, the largest road network, and the most advanced technology. 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 unidirectionally to the background cloud integration system; the background cloud integration system is used to calculate the data sent by the road-side monitoring system, extract the vehicle abnormal behavior information 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 vehicle abnormal behavior information 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 section 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 a dangerous section or a dangerous section is detected, an alarm message will be displayed on 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 the dangerous accident.

[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 shortcomings of the existing technology, 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 based on 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 based on the verification result; road section congestion level determination: obtaining the first abnormal parking assessment coefficient of each abnormal vehicle Driving data, and integrated with the first real-time road section data of the gantry pair in the expressway network, determine the congestion level index of the first road section of the current gantry pair, thereby matching the road section unblocking plan, and performing unblocking operations on the vehicles on the road section of the current gantry pair according to the road section unblocking plan; optimization of the 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, and the specific determination process is: 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 driving 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 driving time of each vehicle in transit, the number of stops of each vehicle in transit in the first monitoring period, the corresponding time of each stop of each vehicle in transit in 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 vehicle in transit is verified with a predefined abnormal parking assessment reference coefficient, and the number of abnormal vehicles is counted based on the verification result. The specific statistical process is: if the first abnormal parking assessment coefficient of a vehicle in transit is greater than or equal to the abnormal parking assessment reference coefficient, the vehicle in transit corresponding to the first abnormal parking assessment coefficient is recorded as an abnormal vehicle, and a number of vehicles in transit 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 value of the particle concentration of the current gantry pair section in the first monitoring period; matching the mean value of the particle concentration of the current gantry pair section in the first monitoring period with the road visibility corresponding to each predefined mean value interval of the particle concentration, thereby obtaining 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, and obtaining 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 in transit of the gantry pair in the first monitoring period, the length of vehicle congestion in transit of 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 within 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 within the expressway network, y4 is the road section congestion weight factor corresponding to the abnormal vehicle proportion predefined in the information database within 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 within 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 index of the current gantry pair with the road section clearing plan corresponding to the predefined first road section congestion index intervals, thereby obtaining the road section clearing plan of the current gantry pair.

[0014] As a further method, the judgment on whether to optimize the road section clearing plan is as follows: data processing is performed on the first section congestion index of the current gantry pair and the second section congestion threshold of the current gantry pair to obtain the difference in the road section congestion levels of the current gantry pair. If the difference in the road section congestion levels 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 difference in the road section congestion levels 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 for acquiring 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 to which the gantry pair belongs; an abnormal vehicle assessment module for determining a first abnormal parking assessment coefficient of each vehicle in transit based on the first driving data of each vehicle in transit in the area to which the gantry pair belongs, performing verification with a predefined abnormal parking assessment reference coefficient, and counting the number of abnormal vehicles based on the verification result; a road section congestion level determination module for acquiring driving data of each abnormal vehicle and comprehensively analyzing the abnormal parking assessment coefficient. The real-time road section data of the first gantry pair in the expressway network is combined to determine the congestion index of the first road section of the current gantry pair, thereby matching the road section unblocking plan and performing unblocking operations on 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 degree value of the second road section of the current gantry pair, and compare it with the congestion 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 sections and areas to which the gantries in the expressway network belong, 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 section data are combined to determine the congestion index of the section, match the unblocking plan and implement it, obtain the unblocked 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, 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 detect 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 that have parked abnormally 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 gantries within the highway 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 high efficiency and accuracy in monitoring all vehicles on the road in the highway network, and has high availability. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The present invention is further described with reference to the accompanying drawings. However, 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 effort.

[0022] Figure 1 Schematic diagram of the method steps of the present invention.

[0023] Figure 2 This is a schematic diagram of system module connections 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 vehicles in transit 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 vehicle in transit in the area to which the gantry pair belongs.

[0026] It should be explained that the above-mentioned gantry pair is a gantry pair consisting 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 passage 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 passage 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 of 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 determination of the first monitoring period is obtained by traffic supervision personnel based on a comprehensive analysis of factors such as the real-time flow of vehicles in transit, the monitoring environment, and the road section status.

[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 times each vehicle in transit stops during the first monitoring period, and the corresponding duration of each stop.

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

[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 a vehicle appears, and the status information of all vehicles in transit in the expressway network is fully covered. Vehicle flow data is updated in real time based on the gantry, and the latest and most complete status information of all vehicles in transit is accurately recorded. Files are closed at regular intervals to avoid occupying too much memory and improve the performance of the information table.

[0035] The specific establishment process is: with "license plate-entry time point" as the primary key, the same record may have multiple pass_ids (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, you can use this 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; when the flow is missing or before the recognition is completed, this variable can be temporarily recorded as 0, without affecting the implementation of other functions; en_station_id: in-transit 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 them with the previous gantry time to calculate diff_time and store them 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 will be saved to facilitate the relevant departments to retrieve the 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 noted 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 an exit transaction is detected for the same vehicle_id and its pass_id is in the vehicle's pass_ids list, the pass_id of the in-transit vehicle record is marked as the primary pass_id for that in-transit vehicle record (i.e., it is the primary pass_id for the final exit). The ex_time variable is also marked, and the out variable is set to 1, indicating that the trip has completely completed. If no exit transaction is available yet, but the same vehicle_id has entry information at a later time, the out variable is marked to 2, and ex_time is marked with the new entry time, indicating that the trip has actually completed but lacks exit information. Once the exit transaction is available, ex_time is updated and out is marked back to 1. For data that has already been exited, the streaming process only marks it and does not purge it for now. Simultaneously, a scheduled task is initiated to scan the table. Purge data only when the current time is at least T (perhaps 1 hour) from the exit time, where T represents the data deletion interval threshold, and there is no new in-transit data. This allows for the concatenation of logically continuous trips.

[0040] Abnormal vehicle assessment: Based on the first driving data of each vehicle in transit in the area, the gantry determines the first abnormal parking assessment coefficient of each vehicle in transit, verifies it with the predefined abnormal parking assessment reference coefficient, and counts the number of abnormal vehicles based on the verification results.

[0041] Furthermore, the first abnormal parking evaluation coefficient of each vehicle in transit is determined by the following specific determination process:

[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] Based on the comprehensive analysis of the gantry pair driving time of each vehicle in transit, the number of stops of each vehicle in transit during the first monitoring period, the corresponding duration of each stop of each vehicle in transit during the first monitoring period, 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. The specific method is as follows:

[0045]

[0046] Where, TC gis the first abnormal parking assessment coefficient of the g-th vehicle in transit. In this embodiment, it is a quantitative indicator used to evaluate abnormal parking situations of vehicles in transit. When the first abnormal parking assessment coefficient of a vehicle in transit is higher, it may mean that the vehicle in transit is at risk of abnormal parking and requires special attention. Abnormal parking mainly refers to the phenomenon of abnormal parking caused by factors such as excessive number of stops or excessively long parking durations of vehicles in transit.

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

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

[0049] ΔT g The g-th vehicle-in-transit travel time on the gantry pair is the time from when the vehicle enters the gantry to when it passes the next gantry, that is, the travel time of the vehicle on-transit 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 The number of stops of the g-th vehicle in transit during the first monitoring period refers to the number of stops of the vehicle in transit during the first monitoring period due to various reasons, such as traffic accidents, road flooding, illegal parking, etc.

[0052] SC gt The duration corresponding to the tth stop of the g-th vehicle in transit during the first monitoring period 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 , CS 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 duration deviation value can be directly obtained from the information database in the highway network. 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 duration deviation value and the abnormal parking assessment factor corresponding to the unit value of the parking duration deviation value preset in the information database in the highway network form a mapping set, and the real-time parking duration deviation value is brought into the mapping set to obtain the abnormal parking assessment factor corresponding to the unit value of the parking duration 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 the vehicle spends on parking, 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 time of each stop, the longer the cumulative 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 an abnormal stop is increased.

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

[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 in transit is less than the abnormal parking assessment reference coefficient, there is no need to count the vehicle in transit.

[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 are likely to 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. On the contrary, 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 performing 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 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 number 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 a preset 30 seconds.

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

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

[0070] The above-mentioned particle concentration can be determined by the laser scattering method. By utilizing the laser scattering effect, when a 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 can be at a relatively short distance from the road section, which meets the detection distance of laser scattering.

[0072] According to the average particle matter concentration of the current gantry-to-road section in the first monitoring cycle, it is matched with the road visibility corresponding to each predefined particle matter concentration average interval. The specific matching process is: obtain the mapping set of the particle matter concentration and road visibility of the current gantry-to-road section in the first monitoring cycle from the information database in the expressway network, first determine the interval to which the particle matter concentration of the current gantry-to-road section in the first monitoring cycle belongs, and assign 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 cycle through matching.

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

[0074] Based on 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 on the way through the gantry pair in the first monitoring cycle, the congestion length of the vehicles on the way through 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 degree of vehicle congestion in the current gantry pair section in this embodiment. The larger the congestion index of the first section, the lower the traffic quality of the current gantry pair section, and unblocking measures need to be taken 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 when it passes through the current gantry-pair 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 vehicle's speed 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 period.

[0081] CD is the congestion length of vehicles on the way during the first monitoring period of the gantry pair. 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 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, and y5 is the road section congestion weight factor corresponding to the congestion length of on-road vehicles 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 proportion of abnormal vehicles, and the road section congestion weight factor corresponding to the congestion length of vehicles in transit are all directly obtained from the information database within the expressway network; in this embodiment, the road section congestion weight factor corresponding to road visibility has a value range of 0.3 to 0.4, the road section congestion weight factor corresponding to the mean speed variation coefficient has a value range of 0.5 to 0.68, the road section congestion weight factor corresponding to the proportion of abnormal vehicles has a value range of 0.4 to 0.6, and the road section congestion weight factor corresponding to the congestion length of vehicles in transit has a value range of 0.3 to 0.5.

[0084] In this embodiment, the reduced visibility of the current gantry over the road section affects the line of sight of drivers of vehicles on the road, making it difficult for them to accurately judge the road conditions and vehicle dynamics ahead, which may result in a reduction in speed. The drivers' cautious driving behavior will lead to a decrease in road capacity, which in turn causes traffic congestion and increases the length of on-road vehicle jams. Similarly, the abnormal vehicle speed variation coefficient reflects the instability or volatility of vehicle speed. When the speed of an abnormal vehicle, such as a disabled vehicle, varies significantly, it will affect the driving speed and stability of surrounding vehicles. This speed instability may lead to discontinuity in traffic flow, causing traffic congestion and increasing the length of the congestion. The abnormal vehicle proportion reflects the proportion of abnormal vehicles to the total 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 increased congestion length, thereby aggravating the congestion level of the road section.

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

[0086] The first-section congestion index of the current gantry pair is matched with the section clearing plans corresponding to the first-section congestion index intervals predefined in the information database within the expressway network. The specific matching process is: obtaining a mapping set of the first-section congestion index of the current gantry pair and the section clearing plan from the information database within the expressway network, first determining the interval to which the first-section congestion index of the current gantry pair belongs, and assigning the section clearing plan corresponding to the interval to the current gantry pair corresponding to the first-section congestion index, thereby matching and obtaining the section clearing plan of the current gantry pair.

[0087] In a specific embodiment of the above-mentioned road section clearing plan, for example, when the congestion index of the first road section of the current gantry pair is 1.5, it can be considered that the current road congestion level of the 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 road section of the current gantry pair is 5.5, it can be considered that the current road congestion level of the gantry pair is relatively high, and the plan measures may be to implement temporary traffic control measures, such as closing some lanes, restricting vehicle passage, 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 vehicles on the road in the expressway network.

[0089] Specifically, the obtained second section unblocking degree value of the current gantry pair is matched with 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 to continuously monitor vehicle information for the completion of the 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 time length 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 mean speed 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 mean speed is (0, 1).

[0095] Obtain 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 the number of vehicles dredged can be obtained from the implementation report of the dredging plan.

[0097] Based on a comprehensive analysis of the speed variation 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 in this embodiment to measure the vehicle clearing degree of the current gantry pair section after the execution of the road section clearing plan. 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 section is, and the road section clearing plan of the current gantry pair 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 clearing plan, which refers to the number of vehicles that have successfully restored 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] The dredging impact coefficient corresponding to the speed variation coefficient, the dredging impact coefficient corresponding to the proportion of vehicles that have been dredged, and the dredging impact coefficient corresponding to the dredging time are all directly obtained from the information database within the expressway network; in this embodiment, the dredging impact coefficient corresponding to the speed variation coefficient, the dredging impact coefficient corresponding to the proportion of vehicles that have been dredged, and the dredging impact 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 is small, it means that the driver can maintain a relatively stable speed in the current section, that is, the dredging quality of the current section is good; the proportion of dredged vehicles refers to the ratio between the number of vehicles that have successfully passed the congested section and resumed normal driving and the total number of passing vehicles. This ratio reflects the efficiency and effectiveness of the 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 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 threshold 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 threshold 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 section congestion level threshold of the current gantry pair.

[0109] It should be explained that, if the clearing degree value of the second road section of the current gantry pair is greater, the congestion degree 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 current gantry pair. The specific method is as follows:

[0112]

[0113] Where 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 require optimization, Δθ2 is the congestion level difference of the current gantry pair that requires optimization, 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 used to deal with abnormal vehicles and clarify the parking categories of abnormal vehicles, such as illegal parking, fault parking, accident parking, etc. If the abnormal vehicle has a fault parking situation, 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, thereby reducing 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 monitored for other abnormal behaviors such as failure to arrive, fatigue driving monitoring, and speeding in intervals, among which failure to arrive is taken as an example.

[0117] In a specific embodiment, all vehicles on the way are monitored for their failure to arrive, 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 it takes 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] The real-time table of vehicles in transit is scanned every 5 minutes. The dif_time between the current time and the last time a vehicle was seen is calculated. This time is then adjusted to dif_time' based on the inherent instability measure of the vehicle's relative speed. This time is then compared with the DT corresponding to its last_gantry_hex value. Vehicles with dif_time' greater than DT are screened out to establish a temporary parking risk table (features include: license plate primary key, last_gantry_hex, last_gantry_time, and 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 service area flow is guaranteed to be uploaded completely.

[0121] For the temporary parking risk table generated by the above-mentioned scheduled task, exclude vehicles with last_gantry_hex equal to 1, and vehicles with last_gantry_hex equal to 2 that have been clearly found in the service area flow, and rank the remaining vehicles according to their disappearance time (dif_time'-DT). This means that the longer the vehicle disappears, the greater the temporary parking risk, and the more high-risk vehicles are that truly deserve the attention of traffic supervisors.

[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 level determination module, the road section congestion level determination module is connected to the road section clearing plan optimization module, and the abnormal vehicle assessment module and the road section congestion level determination module are both connected to the information database within the highway network.

[0125] The data acquisition module is used to obtain 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.

[0126] The abnormal vehicle assessment module is used to determine the first abnormal parking assessment coefficient of each vehicle in transit based on the first driving data of each vehicle in transit 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 based on 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 on the road section of the current gantry pair 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 vehicles on the road in the expressway network.

[0129] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments 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 scope of protection 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 first real-time road section data of the gantry pair in the expressway network and first driving data of each vehicle in transit in the area to which the gantry pair belongs; Abnormal vehicle assessment: Based on the first driving data of each vehicle in the area under the gantry, the first abnormal parking assessment coefficient of each vehicle in the area 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. Determining the degree of road congestion: Acquire the driving data of each abnormal vehicle and integrate the real-time road section data of the first gantry pair in the expressway network to determine the congestion level index of the first road section of the current gantry pair. Based on this, a road section unblocking plan is obtained and unblocking operations are performed on the vehicles on the road section of the current gantry pair according to the road section unblocking plan. Optimization of road section unblocking plans: Obtaining road section information and vehicle travel information after unblocking, obtaining the unblocking degree value of the second section of the current gantry pair, matching the congestion degree threshold of the second section of the current gantry pair, and comparing it with the congestion degree index of the first section of the current gantry pair, comprehensively determining whether to optimize the road section unblocking plan, thereby completing digital monitoring of all vehicles on the road within the expressway network; The obtained second section unblocking degree value of the current gantry pair is matched with the second section congestion degree threshold of the current gantry pair. The specific analysis process is as follows: The road section information after dredging is specifically the number of vehicles on the road 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; Multiply the average speed of each passing vehicle in the second monitoring period by the traffic flow influencing parameter corresponding to the predefined average speed in the information database of the expressway network, and record the multiplication result 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 dredged; The second section dredging degree value of the current gantry pair is obtained based on a comprehensive analysis of the speed variation coefficient of each passing vehicle in the second monitoring period, the dredging time of the road section dredging plan, the number of vehicles on the way during the second monitoring period, and the number of vehicles dredged according to the road section dredging plan; The second road section clearing degree value of the current gantry pair is matched with the second road section congestion degree threshold corresponding to each predefined second road section clearing degree value interval, so as to obtain the second road section congestion degree threshold of the current gantry pair.

2. The method for digital monitoring of vehicles in transit based on ETC according to claim 1, characterized in that: 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 times each vehicle in transit stops 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, characterized in that: The specific determination process of determining the first abnormal parking evaluation coefficient of each vehicle on the way is as follows: Performing a 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; The parking adaptation time is extracted from the information database of the highway network; Based on a comprehensive analysis of the gantry pair driving time of each vehicle in transit, the number of stops of each vehicle in transit during the first monitoring period, the corresponding duration of each stop of each vehicle in transit during the first monitoring period, 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, characterized in that: The first abnormal parking assessment coefficient of each vehicle in transit is verified with a predefined abnormal parking assessment reference coefficient, and the number of abnormal vehicles is counted based on the verification result. The specific statistical process is as follows: 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, 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, characterized in that: 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-road section, specifically, the average particle concentration of the current gantry-to-road section in the first monitoring period; Matching the average particle concentration of the current gantry over the road section during the first monitoring period with the road visibility corresponding to each predefined average particle concentration interval to obtain the road visibility of the current gantry over the road section during the first monitoring period; Performing 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; Based on 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 on the way through the gantry pair in the first monitoring cycle, the congestion length of the vehicles on the way through 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 within 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 within the expressway network, y4 is the road section congestion weight factor corresponding to the abnormal vehicle proportion predefined in the information database within 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 within the expressway network.

7. The method for digital monitoring of vehicles in transit based on ETC according to claim 6, characterized in that: The 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, thereby obtaining 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, 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 difference in congestion levels of the current gantry pair. If the difference in congestion levels 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 difference in congestion levels of the current gantry pair is less than zero, the road section clearing plan needs to be optimized.

9. A system using the ETC-based digital monitoring method for vehicles in transit as described in any one of claims 1 to 8, characterized in that: include: A data acquisition module is used to acquire first real-time road section data of the gantry pair in the expressway network and first driving data of each vehicle in transit in the area to which the gantry pair belongs; An abnormal vehicle assessment module is used to determine a first abnormal parking assessment coefficient for each vehicle in transit based on the first driving data of each vehicle in transit in the area under the gantry, verify the coefficient with a predefined abnormal parking assessment reference coefficient, and calculate the number of abnormal vehicles based on the verification result; The road section congestion level determination module is used to obtain the driving data of each abnormal vehicle and integrate the real-time road section data of the first gantry pair in the expressway network to determine the congestion level index of the first road section of the current gantry pair. Based on this, a road section unblocking plan is obtained and unblocking operations are performed on the vehicles on the road section of the current gantry pair according to the road section unblocking plan. 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 dredging 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 dredging plan, so as to complete the digital monitoring of all vehicles on the road in the expressway network.

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