A method, device and medium for diversion induction control of high-speed toll station

Abstract

The embodiments of this specification disclose a method, device and medium for diversion induction control of a highway toll station, which relates to the field of intelligent transportation technology and is used to alleviate the congestion problem of highway toll stations. The method includes: obtaining the traffic data and scene data of the highway toll station area to be induced by pre-installing multiple types of acquisition equipment; establishing a digital twin model of the highway toll station area to be induced based on the traffic data and scene data; classifying the vehicles in the area based on the flow data of the gantry pre-transaction system to obtain the first vehicle and the second vehicle; determining the current traffic data and current scene data corresponding to the vehicle based on the digital twin model, and extracting the traffic flow feature data; clustering the neighborhood topology of the traffic flow feature data to obtain the cluster center; determining the traffic state estimation of the vehicle based on the cluster center, so as to perform lane induction diversion for the first vehicle and the second vehicle based on the traffic state estimation.

Description

Technical Field

[0001] This specification relates to the field of smart highway technology, and in particular to a method, device and medium for diversion induction control of a highway toll station. Background Art

[0002] With the development of economy and technology, more and more vehicles are traveling on intercity highways and ring highways. In areas near airports, urban areas and other areas and during specific time periods, the traffic volume at the exits of highway toll stations increases, and there are many special situations and congestion is prone to occur, resulting in more serious traffic congestion on the road sections where highway toll stations are located. Therefore, how to alleviate the congestion problem at highway toll stations under high traffic volume has become an important part of the traffic maintenance process.

[0003] At present, in order to alleviate the congestion problem of highway entrances and exits caused by manual toll collection at highway toll stations under high traffic conditions, the highway electronic toll collection system (ETC) can use mobile payment to complete the toll collection mode, interact with vehicles through DSRC dedicated shortwave, and determine vehicle information and vehicle passing conditions. However, due to the limited number of ETC users at present, in order to ensure the normal passage of vehicles paying manually, manual toll collection channels and ETC channels are set up at highway toll stations. However, due to the problem of driver's line of sight obstruction or vehicle-mounted ETC terminal failure, based on the current manual selection of toll lanes for passage, it is easy to cause non-ETC vehicles or faulty ETC vehicles to mistakenly enter the ETC lanes, causing congestion to worsen. And affected by the recognition response speed of the ETC toll collection system, the waiting time for ETC vehicles to lift the bar is also long. Summary of the invention

[0004] In order to solve the above technical problems, one or more embodiments of this specification provide a diversion induction control method for a high-speed toll station.

[0005] One or more embodiments of this specification adopt the following technical solutions:

[0006] One or more embodiments of this specification provide a method for controlling a diversion induction of a high-speed toll station, which is applied to a diversion induction control system including a gantry pre-transaction system, an identification and diversion system, and a verification and release system. The method includes:

[0007] Acquire the traffic data and scene data of the area of ​​the high-speed toll station to be induced by using multiple types of acquisition equipment pre-installed in the gantry pre-transaction system, the identification and diversion system, and the verification and release system;

[0008] Determine the detection area corresponding to each of the traffic data and the scene data, so as to fuse the detection area to establish a digital twin model of the high-speed toll station area to be induced;

[0009] Detecting the on-board unit of the vehicle entering the high-speed toll station area to be induced according to the gantry pre-transaction system, so as to record the flow data of the high-speed toll station area to be induced according to the on-board unit;

[0010] Based on the flow data, the vehicles traveling in the area of ​​the to-be-induced high-speed toll station are classified to obtain a first traveling vehicle and a second traveling vehicle; wherein the first traveling vehicle is a vehicle that can pass directly, and the second traveling vehicle is a vehicle that cannot pass directly;

[0011] Determine the current traffic data and current scene data corresponding to the first moving vehicle and the second moving vehicle based on the digital twin model, so as to respectively extract the traffic flow characteristic data corresponding to the first moving vehicle and the second moving vehicle;

[0012] Clustering the traffic flow characteristic data through the neighborhood topology of the traffic flow characteristic data to obtain the cluster center corresponding to the traffic flow characteristic data;

[0013] The traffic state estimation of the first and second traveling vehicles in the high-speed toll station area to be induced is determined according to the cluster center, so as to perform lane induction diversion on the first and second traveling vehicles based on the traffic state estimation.

[0014] Optionally, in one or more embodiments of the present specification, before acquiring the traffic data and scene data of the area of ​​the high-speed toll station to be induced by using multiple types of acquisition devices pre-installed in the gantry pre-transaction system, the identification and diversion system, and the verification and release system, the method further includes:

[0015] Based on the locations of the gantry pre-transaction system, the identification and diversion system, and the verification and release system in the area of ​​the high-speed toll station to be induced, the area of ​​the high-speed toll station to be induced is divided into detection areas, and a first detection area corresponding to the gantry pre-transaction system, a second detection area corresponding to the identification and diversion system, and a third detection area corresponding to the verification and release system are determined;

[0016] Determine the types of acquisition equipment corresponding to the first detection area, the second detection area, and the third detection area according to the preset highway design strategy of the to-be-induced highway toll station area;

[0017] Determine the candidate collection devices for each area according to the corresponding collection device type, and determine the initial installation position of each candidate collection device based on the effective collection range of the candidate collection device and the area of ​​each area;

[0018] Obtaining the effective range of the same type of acquisition devices at the initial installation position of each area, determining the coverage range of the same type of acquisition devices according to the effective range of each of the same type of acquisition devices, and determining the remaining coverage area according to the division range of each area and the coverage range;

[0019] Determining whether to add the same type of acquisition equipment based on the ratio of the remaining coverage area to the corresponding area and the area value of the remaining coverage area;

[0020] If so, determine the center position of each of the remaining coverage areas, determine the additional installation position based on the center position and the effective coverage range of the same type of collection equipment, and install each of the collection equipment according to the initial installation position and the additional installation position, so as to obtain the traffic data and scene data of the highway toll station area to be induced based on multiple types of collection equipment.

[0021] Optionally, in one or more embodiments of the present specification, determining the detection area corresponding to each of the traffic data and the scene data, so as to fuse the detection area to establish a digital twin model of the high-speed toll station area to be induced, specifically includes:

[0022] Performing data preprocessing operations on the traffic data and the scene data to obtain processed traffic data and processed scene data; wherein the data preprocessing operations include: data cleaning, data denoising, and data standardization;

[0023] Acquire the first detection area, the second detection area, and the connection area of ​​the second detection area, and screen the processed traffic data and the processed scene data based on the position points corresponding to the connection areas to determine the data to be fused;

[0024] Based on the timestamps and spatial coordinates corresponding to the data to be fused, time-aligning and spatial-aligning the data to be fused to obtain candidate fused data;

[0025] Extract features from the candidate fusion data according to their data types, determine feature vectors of the candidate fusion data, match the candidate fusion data based on the feature vectors, obtain associated candidate fusion data, and fuse the associated fusion data based on Kalman filtering to obtain fused scene data and fused traffic data;

[0026] Input the fused scene data into a preset modeling tool to establish a scene digital twin model of the high-speed toll station area to be induced, and input the fused traffic data into a preset modeling tool to establish a traffic digital twin model of the high-speed toll station area to be induced;

[0027] Spatial matching is performed based on the spatial information of the scene digital twin model and the traffic digital twin model to obtain a digital twin model of the high-speed toll station area to be induced.

[0028] Optionally, in one or more embodiments of the present specification, classifying the vehicles traveling in the to-be-induced high-speed toll station area based on the flow data to obtain the first traveling vehicle and the second traveling vehicle specifically includes:

[0029] If the license plate identification of each vehicle traveling in the area of ​​the to-be-induced high-speed toll station is obtained based on the flow data, and the payment result corresponding to each license plate identification; wherein the payment result includes: success and failure;

[0030] Obtain the license plate identification corresponding to the failed payment result, determine the ETC device corresponding to the license plate identification according to the digital twin model, and call the operation data of the ETC device to determine the operation status of the ETC device;

[0031] Based on the payment result and the ETC device, each of the traveling vehicles is classified to obtain a first traveling vehicle and a second traveling vehicle.

[0032] Optionally, in one or more embodiments of the present specification, determining the current traffic data and current scene data corresponding to the first moving vehicle and the second moving vehicle based on the digital twin model to respectively extract the traffic flow characteristic data corresponding to the first moving vehicle and the second moving vehicle specifically includes:

[0033] Simulating current traffic data and current scene data corresponding to the first traveling vehicle and the second traveling vehicle based on the digital twin model to obtain simulated traffic data corresponding to the first traveling vehicle and the second traveling vehicle;

[0034] Acquire historical traffic data of the area of ​​the to-be-induced expressway toll station, and determine direct traffic impact data and indirect traffic impact data of the simulated traffic data according to the historical traffic data matching the simulated traffic data; wherein the direct traffic impact data includes: vehicle speed related data and road capacity related data, and the indirect traffic impact data includes: weather related data and noise related data;

[0035] Based on the association between the direct traffic impact data and the indirect traffic impact data, a plurality of connected graphs including the direct traffic impact data and the indirect traffic impact data are generated, and based on the number of data components of each of the connected graphs, a first weight value corresponding to the direct impact data and the indirect impact data in each of the connected graphs is determined;

[0036] Determining second weight values ​​corresponding to each of the directly affected data and the indirectly affected data based on the number of lower-layer data corresponding to the directly affected data and the indirectly affected data in each of the connected graphs;

[0037] Based on the traffic restriction conditions of the high-speed toll station area to be induced, determining abnormal values ​​of the direct traffic impact data and the indirect traffic impact data, and determining a third weight value of the direct traffic impact data and the indirect traffic impact data based on the abnormal values;

[0038] Based on the weighted value of the first weight value, the second weight value and the third weight value, the importance of each of the direct traffic impact data and the indirect traffic impact data is determined to extract the traffic flow characteristic data corresponding to the first traveling vehicle and the second traveling vehicle based on the importance.

[0039] Optionally, in one or more embodiments of the present specification, clustering the traffic flow characteristic data by using the neighborhood topology of the traffic flow characteristic data to obtain the cluster center corresponding to the traffic flow characteristic data specifically includes:

[0040] Determine the similarity between the traffic flow characteristic data based on cosine similarity, take the data with similarity greater than a preset threshold as the neighborhood of the traffic flow characteristic data, and construct a neighborhood topological structure corresponding to the traffic flow characteristic data;

[0041] Randomly extract a preset number of traffic flow characteristic data as initial cluster centers, calculate the distance between each traffic flow characteristic data and the initial cluster center according to the neighborhood topological structure, and classify the traffic flow characteristic data to the initial cluster center with the closest distance to obtain a clustering result;

[0042] The initial cluster center is iteratively updated according to the clustering result. If it is determined that the cluster center corresponding to the clustering result remains unchanged, the cluster center corresponding to the traffic flow characteristic data is obtained.

[0043] Optionally, in one or more embodiments of the present specification, determining the traffic state estimation of the first and second traveling vehicles in the to-be-induced high-speed toll station area according to the cluster center, so as to perform lane induction diversion on the first and second traveling vehicles based on the traffic state estimation, specifically includes:

[0044] Using the traffic state corresponding to the cluster center as an estimate of the traffic state of the first traveling vehicle and the second traveling vehicle in the area of ​​the high-speed toll station to be induced;

[0045] determining an induction ratio between the first traveling vehicle and the second traveling vehicle based on the traffic state estimation;

[0046] Obtaining the traffic attributes corresponding to the verification and release systems of the lanes where the first and second vehicles are located, and determining whether the first and second vehicles match the traffic attributes;

[0047] If the induction rate is greater than the preset induction rate threshold or the traffic attributes do not match, a diversion simulation is performed on the first and second driving vehicles based on the digital twin model of the highway toll station area to be induced to determine the lane induced diversion result of the first and second driving vehicles.

[0048] Optionally, in one or more embodiments of the present specification, after determining the traffic state estimation of the first and second traveling vehicles in the high-speed toll station area to be induced according to the cluster center, and performing lane induction diversion on the first and second traveling vehicles based on the traffic state estimation, the method further includes:

[0049] Acquiring the exit information of the first detection area and the entrance information of the second monitoring area recorded by the onboard units of the first traveling vehicle and the second traveling vehicle based on the identification and diversion system;

[0050] Comparing the first detection area exit information with the second monitoring area entrance information to obtain a comparison result;

[0051] If the comparison result is inconsistent, reclassifying the moving vehicle based on the entrance information of the second monitoring area, and iteratively performing lane induction diversion based on the classification result;

[0052] If the comparison result is consistent, the entrance information of the third monitoring area is obtained based on the verification and release system, and the first detection area exit information and the third monitoring area entrance information are matched with instructions according to the built-in release conditions of the verification and release system, so as to control the lifting bar in the third monitoring area based on the matched instructions to realize the diversion passage of the moving vehicles.

[0053] One or more embodiments of this specification provide a diversion induction control device for a high-speed toll station, which is applied to a diversion induction control system including a gantry pre-transaction system, an identification diversion system, and a verification and release system. The device includes:

[0054] at least one processor; and,

[0055] a memory communicatively connected to the at least one processor; wherein,

[0056] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: execute any of the above-mentioned methods.

[0057] One or more embodiments of the present specification provide a non-volatile computer storage medium, which is applied to a diversion induction control system including a gantry pre-trading system, an identification and diversion system, and a verification and release system, and stores computer executable instructions, and the computer executable instructions are configured to be able to execute any of the methods described above.

[0058] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects:

[0059] Through the diversion induction method, pre-trading based on the gantry trading system is realized, and ramp pre-trading is realized to support the operation of the exit ramp pre-trading system. The establishment of a digital twin model based on the fused scene data and traffic data can better understand and simulate the situation in the area of ​​the high-speed toll station to be induced, and provide an accurate reference for subsequent decision-making and optimization. By classifying the moving vehicles, it is convenient to carry out targeted guidance for the first moving vehicle and the second moving vehicle based on different lane signs, and alleviate the congestion problem of the toll station entrance and exit caused by non-ETC vehicles entering the ETC lane. By combining the digital twin model to obtain the corresponding traffic flow characteristic data for cluster analysis, the traffic state estimation is obtained, and the diversion simulation is carried out based on the digital twin model. By simulating the diversion results, the effects of different diversion strategies are evaluated, and the best diversion scheme is selected, which can reduce traffic congestion and queue length in the area of ​​the high-speed toll station to be induced, and improve traffic efficiency and road capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. In the drawings:

[0061] Figure 1 A schematic flow chart of a method for controlling the diversion induction of a high-speed toll station provided in an embodiment of this specification;

[0062] Figure 2 A schematic diagram of the layout of a high-speed toll station area to be induced provided in an embodiment of this specification;

[0063] Figure 3 A schematic diagram of the internal structure of a traffic diversion induction control device for a high-speed toll station provided in an embodiment of this specification;

[0064] Figure 4 A schematic diagram of the internal structure of a non-volatile storage medium provided in an embodiment of this specification. DETAILED DESCRIPTION

[0065] The embodiments of this specification provide a method, device and medium for diversion induction control of a high-speed toll station.

[0066] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0067] like Figure 1 As shown, the embodiment of this specification provides a flow chart of a method for controlling the diversion induction of a high-speed toll station. Figure 1 It can be seen that in one or more embodiments of this specification, a method for controlling the diversion induction of a high-speed toll station is applied to a diversion induction control system including a gantry pre-transaction system, an identification diversion system, and a verification and release system, and the method includes:

[0068] S101: Acquire the traffic data and scene data of the area of ​​the high-speed toll station to be induced by using multiple types of acquisition devices pre-installed in the gantry pre-transaction system, the identification and diversion system, and the verification and release system.

[0069] In order to avoid the problem of driver's line of sight being blocked or the problem of vehicle-mounted ETC terminal failure, the vehicle congestion caused by non-ETC vehicles or faulty ETC vehicles mistakenly entering the ETC lane. Figure 2 The gantry pre-trading system, identification and diversion system, and verification and release system diversion induction control system shown in the figure realize ramp pre-trading to support the operation of the exit ramp pre-trading system, that is, to alleviate the congestion of the entrance and exit of the high-speed toll station through the multi-mode fusion toll station fast passage. Specifically, in a certain embodiment of this specification, the passage data and scene data of the high-speed toll station area to be induced are obtained by pre-setting multiple types of acquisition equipment with the gantry transaction system, the identification and diversion system, and the verification and release system.

[0070] Further, in one or more embodiments of the present specification, before obtaining the traffic data and scene data of the high-speed toll station area to be induced by pre-installing multiple types of acquisition devices in the gantry pre-transaction system, the identification and diversion system, and the verification and release system, in order to ensure the accurate acquisition of the data of the high-speed toll station area to be induced, thereby improving the reliability of subsequent diversion induction, the method also includes the following process:

[0071] First, based on the location of the gantry pre-transaction system, the identification and diversion system, and the verification and release system in the area of ​​the high-speed toll station to be induced, the detection area of ​​the high-speed toll station to be induced is divided, and the first detection area corresponding to the gantry pre-transaction system, the second detection area corresponding to the identification and diversion system, and the third detection area corresponding to the verification and release system are determined. Then, according to the preset highway design strategy of the high-speed toll station area to be induced, the types of collection equipment corresponding to the first detection area, the second detection area, and the third detection area are determined. Thus, the to-be-selected collection equipment of each area is determined according to the corresponding collection equipment type, and the initial installation position of each to-be-selected collection equipment is determined according to the effective collection range of the to-be-selected collection equipment and the area area of ​​each area. At the same time, in order to further adjust the initial installation position to improve the coverage of data collection, the effective range of the same type of collection equipment at the initial installation position of each area is obtained in the embodiment of this specification, so as to determine the coverage of the same type of collection equipment according to the effective range of each same type of collection equipment, so as to determine the remaining coverage area according to the division range of each area and the coverage range. Then, according to the proportion of the remaining coverage area to the corresponding area and the area value of the remaining coverage area, it is determined whether to supplement the same type of collection equipment. If so, then determine the center position of each remaining coverage area, determine the additional installation position based on the center position and the effective coverage range of the same type of collection equipment, and install each collection device according to the initial installation position and the additional installation position, so as to obtain the traffic data and scene data of the area of ​​the high-speed toll station to be induced based on multiple types of collection devices. Through the initial installation position and further adjustments, every corner of the area of ​​the high-speed toll station to be induced can be covered, so as to obtain comprehensive traffic data and scene data. By comparing the proportion and area value of the remaining coverage area with the corresponding area, it can be determined whether additional collection equipment is needed, thereby improving the economy and feasibility in the actual installation process. Through the combined use of multiple types of collection equipment, more comprehensive and diverse traffic data and scene data can be obtained, providing more accurate analysis and decision-making basis.

[0072] S102: Determine the detection areas corresponding to each of the traffic data and the scene data, so as to fuse the detection areas and establish a digital twin model of the high-speed toll station area to be induced.

[0073] After obtaining the traffic data and scene data of the high-speed toll station area to be induced based on the above steps, in order to facilitate the analysis and prediction of the traffic conditions in the high-speed toll station area to be induced, the detection areas corresponding to the various traffic data and scene data are determined in the embodiments of this specification, so as to fuse the detection areas and establish a digital twin model of the high-speed toll station area to be induced. Specifically, in one or more embodiments of this specification, the detection areas corresponding to the various traffic data and scene data are determined, so as to fuse the detection areas and establish a digital twin model of the high-speed toll station area to be induced, which specifically includes the following processes:

[0074] First, data preprocessing operations are performed on the traffic data and the scene data to obtain processed traffic data and processed scene data; wherein, the data preprocessing operations that need to be explained include: data cleaning, data denoising, and data standardization. Then, the first detection area, the second detection area, and the connection area of ​​the second detection area are obtained, and the processed traffic data and the processed scene data are screened based on the position points corresponding to the connection areas to determine the data to be fused. According to the timestamps and spatial coordinates corresponding to each data to be fused, the data to be fused are time-aligned and space-aligned to obtain candidate fused data. By screening and aligning the data to be fused, it can be ensured that the fused data has consistent time and space information, which improves the comparability and reliability of the data. At the same time, according to the data type of each candidate fused data, feature extraction is performed on the candidate fused data, and the feature vector of each candidate fused data is determined. The candidate fused data is matched based on the feature vector to obtain associated candidate fused data and the associated fused data is fused based on the Kalman filter to obtain fused scene data and fused traffic data. Then, the fused scene data is input into the preset modeling tool to establish the scene digital twin model of the high-speed toll station area to be induced, and the fused traffic data is input into the preset modeling tool to establish the traffic digital twin model of the high-speed toll station area to be induced. The digital twin model of the high-speed toll station area to be induced is obtained by spatially matching the spatial information of the scene digital twin model and the traffic digital twin model. The establishment of a digital twin model based on the fused scene data and traffic data can better understand and simulate the situation of the high-speed toll station area to be induced, and provide an accurate reference for subsequent decision-making and optimization.

[0075] S103: Detecting the on-board unit of the vehicle entering the high-speed toll station area to be induced according to the gantry pre-transaction system, so as to record the flow data of the high-speed toll station area to be induced according to the on-board unit.

[0076] In order to achieve the effect of early payment and super-fast lane release, the gantry pre-transaction system installs ETC equipment in a gantry manner on the toll station ramp, which is equivalent to pre-setting one or more ETC transaction lanes. The ramp is responsible for pre-transactions. By advancing the ETC vehicle toll from in-lane transactions to ramp transactions, it helps to perform seamless toll deductions on ETC vehicles before they enter the toll station lanes. At this time, the gantry pre-transaction system detects the on-board unit of the vehicle entering the toll station area to be induced, and can determine the payment flow data of the toll station area to be induced based on the vehicle information of the on-board unit.

[0077] S104: Classify the vehicles traveling in the area of ​​the high-speed toll station to be induced based on the flow data to obtain a first vehicle and a second vehicle; wherein the first vehicle is a vehicle that can pass directly, and the second vehicle is a vehicle that cannot pass directly.

[0078] After obtaining the flow data, it can be determined whether the pre-transaction has been performed after the vehicle passes through the gantry ETC device of the gantry pre-transaction system, and then the vehicles in the area of ​​the highway toll station to be induced are classified according to the result of the pre-transaction, and a first type of vehicle that can pass directly and a second type of vehicle that cannot pass directly are obtained. This facilitates the subsequent notification of the pre-transaction status of ETC vehicles and CPC card vehicles to the owners according to the pre-transaction results of the ETC vehicles, for example, through variable information signs, and guides vehicles with failed pre-transactions to the manual lanes for processing, effectively diverting vehicles in advance, avoiding congestion caused by vehicles with failed transactions entering the ETC lanes, and inducing diversion, so as to achieve smooth passage of vehicles in the toll station square.

[0079] Specifically, in one or more embodiments of the present specification, the vehicles traveling in the area of ​​the to-be-induced expressway toll station are classified based on the flow data to obtain the first traveling vehicle and the second traveling vehicle, which specifically includes the following process:

[0080] If the license plate identification of each vehicle in the area of ​​the to-be-induced high-speed toll station and the payment results corresponding to each license plate identification are obtained based on the flow data. The payment results include: success and failure. Then obtain the license plate identification corresponding to the failed payment result, determine the ETC device corresponding to the license plate identification according to the digital twin model, and call the operating data of the ETC device to determine the operating status of the ETC device. In order to avoid the inability of ETC vehicles to conduct pre-transactions due to failure of the ETC toll collection equipment, this manual classifies each vehicle based on the payment results and the ETC equipment to obtain the first vehicle and the second vehicle. For example, in a certain application scenario, the following flow data is available: Vehicle 1: license plate number ABC123, payment result: successful Vehicle 2: license plate number DEF456, payment result: failed Vehicle 3: license plate number GHI789, payment result: successful Vehicle 4: license plate number JKL012, payment result: failed. At this time, first, based on the flow data, we can obtain the license plate identification and payment results of each vehicle in the area of ​​the to-be-induced expressway toll station, and determine that ABC123 paid successfully, DEF456 paid unsuccessfully, GHI789 paid successfully, and JKL012 paid unsuccessfully. At this time, in order to determine whether the vehicle that failed to pay can continue to conduct ETC transactions to avoid the problem of reduced traffic efficiency caused by the failure of the payment equipment and the subsequent induction of the vehicle to the manual payment lane, the ETC device corresponding to the license plate identification will be determined based on the digital twin model, and the operation data of the ETC device will be called to determine the operation status of the ETC device, so as to further analyze the classification type corresponding to the vehicle.

[0081] S105: Determine the current traffic data and current scene data corresponding to the first moving vehicle and the second moving vehicle based on the digital twin model, so as to respectively extract the traffic flow characteristic data corresponding to the first moving vehicle and the second moving vehicle.

[0082] In order to achieve adaptive induction for the moving vehicles based on the traffic flow characteristic data corresponding to each vehicle, and avoid lane congestion caused by untimely lane changes due to artificial lane selection, in the embodiments of this specification, the current traffic data and current scene data corresponding to the first moving vehicle and the second moving vehicle are determined based on the digital twin model, so as to extract the traffic flow characteristic data corresponding to the first moving vehicle and the second moving vehicle respectively. Specifically, in one or more embodiments of this specification, the current traffic data and current scene data corresponding to the first moving vehicle and the second moving vehicle are determined based on the digital twin model, so as to extract the traffic flow characteristic data corresponding to the first moving vehicle and the second moving vehicle respectively. In order to comprehensively consider the simulated traffic data and the historical traffic data, and accurately extract the traffic flow characteristic data corresponding to the first moving vehicle and the second moving vehicle according to the correlation and feature importance between different data, the specific process is as follows:

[0083] First, the current traffic data and current scene data corresponding to the first and second traveling vehicles are simulated based on the digital twin model to obtain the simulated traffic data corresponding to the first and second traveling vehicles. Then, the historical traffic data of the high-speed toll station area to be induced is obtained to determine the direct traffic impact data and indirect traffic impact data of the simulated traffic data according to the historical traffic data matching the simulated traffic data; wherein the direct traffic impact data includes: vehicle speed related data, road capacity related data, etc., and the indirect traffic impact data includes: meteorological related data, noise related data, etc. According to the correlation between the direct traffic impact data and the indirect traffic impact data, multiple connected graphs containing the direct traffic impact data and the indirect traffic impact data are generated, and based on the number of data components of each connected graph, the first weight value corresponding to the direct impact data and the indirect impact data in each connected graph is determined. Then, based on the number of lower-level data corresponding to the direct impact data and the indirect impact data in each connected graph, the second weight value corresponding to each direct impact data and the indirect impact data is determined. Based on the traffic restriction conditions of the highway toll station area to be induced, the abnormal values ​​of the direct traffic impact data and the indirect traffic impact data are determined, so as to determine the third weight value of the direct traffic impact data and the indirect traffic impact data based on the abnormal values; according to the weighted value of the first weight value, the second weight value and the third weight value obtained above, the importance of each direct traffic impact data and the indirect traffic impact data is determined, so as to extract the traffic flow characteristic data corresponding to the first traveling vehicle and the second traveling vehicle based on the importance.

[0084] S106: Clustering the traffic flow characteristic data according to the neighborhood topology of the traffic flow characteristic data to obtain the cluster center corresponding to the traffic flow characteristic data.

[0085] In order to facilitate the determination of the traffic state corresponding to the moving vehicle, this specification clusters the traffic flow characteristic data by the neighborhood topology of the traffic flow characteristic data to obtain the cluster center corresponding to the traffic flow characteristic data. Specifically, in one or more embodiments of this specification, clustering the traffic flow characteristic data by the neighborhood topology of the traffic flow characteristic data to obtain the cluster center corresponding to the traffic flow characteristic data specifically includes the following process:

[0086] First, the similarity between traffic flow feature data is determined based on cosine similarity, so that data with similarity greater than a preset threshold is used as the neighborhood of traffic flow feature data, and the neighborhood topology structure corresponding to the traffic flow feature data is constructed. By using cosine similarity, the similarity between traffic flow feature data can be effectively measured, thereby constructing a neighborhood topology structure. Then, a preset number of traffic flow feature data are randomly selected as initial clustering centers, and the distance between each traffic flow feature data and the initial clustering center is calculated according to the neighborhood topology structure, and the traffic flow feature data is classified into the nearest initial clustering center to obtain a clustering result. By randomly selecting a preset number of traffic flow feature data as the initial clustering center, the clustering result can be prevented from being affected by the initial center. The random selection method can ensure the randomness of the clustering process and increase the robustness of the algorithm. According to the neighborhood topology structure, the distance between each traffic flow feature data and the initial clustering center is calculated, and the data is classified into the nearest initial clustering center. In this way, similar data can be effectively clustered together to obtain preliminary clustering results. Then, the initial cluster center is iteratively updated according to the clustering result. If it is determined that the cluster center corresponding to the clustering result remains unchanged or the number of iterations is reached, the cluster center corresponding to the traffic flow characteristic data can be obtained.

[0087] S107: Determine a traffic state estimate of the first and second traveling vehicles in the high-speed toll station area to be induced according to the cluster center, so as to perform lane-induced diversion of the first and second traveling vehicles based on the traffic state estimate.

[0088] According to the cluster center obtained in the above step S106, the traffic state estimation of the first and second traveling vehicles in the area of ​​the high-speed toll station to be induced is determined, so as to perform lane induction diversion on the first and second traveling vehicles according to the traffic state estimation. Specifically, in one or more embodiments of the present specification, the traffic state estimation of the first and second traveling vehicles in the area of ​​the high-speed toll station to be induced is determined according to the cluster center, so as to perform lane induction diversion on the first and second traveling vehicles based on the traffic state estimation, specifically including the following process:

[0089] First, the traffic state corresponding to the cluster center is used as the traffic state estimation of the first and second vehicles in the high-speed toll station area to be induced. Then, the induction rate of the first and second vehicles is determined based on the traffic state estimation. The traffic attributes corresponding to the verification and release system of the lanes where the first and second vehicles are located are obtained, and it is determined whether the first and second vehicles match the traffic attributes, so as to avoid the diversion decision errors caused by too high induction rate or mismatch of traffic attributes, and improve the accuracy and effectiveness of diversion decisions. If the induction rate is greater than the preset induction rate threshold or the traffic attributes do not match, then it can be determined that the first and second vehicles are simulated according to the digital twin model of the high-speed toll station area to be induced, and the lane induced diversion results of the first and second vehicles are determined. By simulating the diversion results, the effects of different diversion strategies are evaluated, and the best diversion scheme is selected. At the same time, the digital twin model can also predict the impact and changes of the diversion results, providing reference and decision-making basis for traffic management. Through reasonable diversion control, traffic congestion and queue length in the high-speed toll station area to be induced can be reduced, and traffic efficiency and road capacity can be improved. At the same time, through the simulation and prediction of the digital twin model, the diversion strategy can be optimized to further improve the management and control of traffic conditions.

[0090] Further, in one or more embodiments of the present specification, after determining the traffic state estimation of the first and second traveling vehicles in the area of ​​the high-speed toll station to be induced according to the cluster center, and performing lane induction diversion on the first and second traveling vehicles based on the traffic state estimation, the method further includes the following process:

[0091] First, based on the identification and diversion system, the exit information of the first detection area and the entrance information of the second monitoring area recorded by the on-board units of the first and second traveling vehicles are obtained. Then, in order to avoid the problem that some paying vehicles are not detected in the first detection area due to information delay and the transaction is completed, the comparison result is obtained by comparing the exit information of the first detection area with the entrance information of the second monitoring area. If the comparison result is inconsistent, in order to improve the accuracy of diversion, the traveling vehicles are reclassified according to the entrance information of the second monitoring area, and the above diversion processing process is iterated based on the classification result to perform lane induction diversion. If the comparison result is consistent, the entrance information of the third monitoring area is obtained through the verification and release system, and the exit information of the first detection area and the entrance information of the third monitoring area are matched according to the built-in release conditions of the verification and release system, so as to control the lifting bar in the third monitoring area based on the matched instructions, so as to realize the automatic diversion of the traveling vehicles. That is to say, the ETC vehicle has three transaction authentications: ramp pre-transaction, front antenna of the toll lane, and rear antenna of the toll lane, which ensures that the vehicle completes the charging in the three transaction authentications, greatly improving the charging efficiency of the ETC vehicle. The ramp pre-transaction and dual-antenna model effectively improves transaction accuracy and efficiency, further improves the toll station's traffic efficiency, reduces users' travel time, and promotes the improvement of traffic smoothness. Vehicles that successfully complete ramp transactions only need to be verified in the ETC lane, which reduces the transaction time in the lane and greatly improves the vehicle's passing speed.

[0092] In an application scenario of this specification, the diversion induction control of the high-speed toll station can also be performed based on the following process. First, the gantry pre-transaction system detects the ETC vehicle through the ETC gantry installed at the ramp, and performs ETC pre-transaction after detecting the on-board unit OBU. If the transaction is successful, an ETC transaction flow is generated. The transaction flow information and format are the same as the transaction flow generated by the existing ETC lane, and the flow information is shared with the identification and diversion system and the verification and release system through the message middleware. The gantry pre-transaction system simultaneously collects its own software and hardware operating status information, and transmits it to the corresponding monitoring system through multicast mode. The identification and diversion system receives the pre-transaction information of the gantry pre-transaction system, and uses the information display screen between the ramp and the toll station to prompt the owner of the vehicle to conduct ETC transactions in the gantry pre-transaction system, and guides the vehicle with failed ETC transactions to go directly to the manual lane for processing. At the same time, the monitoring system corresponding to the gantry pre-transaction system will monitor the software and hardware operating status of the gantry pre-transaction system. Among them, it should be noted that the process of ETC pre-transaction after the on-board unit is detected is as follows: if the on-board unit number and license plate number are in the traded information received from the ramp pre-transaction system and the transaction time is within a certain time before the current time, for example, within 4 hours in a certain scenario, then the lane software lifts the barrier to release. If the on-board unit number is not in the traded information received by the ramp pre-transaction system, then the lane software reads the entry and exit information files in the on-board unit and the ETC card. If the entry and exit status bits in the file are in the traded state, for example, the entry status bit is the entry state, and the exit status bit is the exit state, and within a certain time, for example, within 4 hours in a certain scenario, then it can be considered that the on-board unit has successfully completed the transaction in the ramp pre-transaction system of this station, the barrier is lifted to release, and the operation of retrieving the traffic permit is performed at the same time. If the on-board unit number is not in the traded information received by the ramp pre-transaction system, and the entry and exit information files in the on-board unit and the ETC card indicate that the on-board unit is still in an untraded state, then the lane software performs the transaction according to the existing ETC transaction process.

[0093] like Figure 3 As shown, the embodiment of this specification provides a schematic diagram of the internal structure of a diversion induction control device of a high-speed toll station, which is composed of Figure 3 It can be seen that a diversion induction control device of a high-speed toll station in the embodiment of this specification is applied to a diversion induction control system including a gantry pre-transaction system, an identification diversion system, and a verification and release system. The device includes:

[0094] at least one processor; and,

[0095] a memory communicatively connected to the at least one processor; wherein,

[0096] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to: execute any of the above-mentioned methods.

[0097] like Figure 4 As shown, the embodiment of this specification provides a schematic diagram of the internal structure of a non-volatile storage medium, which is composed of Figure 4 It can be seen that a non-volatile storage medium in an embodiment of the present specification is applied to a diversion induction control system including a gantry pre-trading system, an identification and diversion system, and a verification and release system, and stores computer executable instructions 401, and the computer executable instructions 401 can: execute any of the methods described above.

[0098] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0099] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0100] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. A method for controlling traffic diversion induction at a high-speed toll station, characterized in that: Applied to a diversion induction control system including a gantry pre-trading system, an identification diversion system, and a verification release system, the method includes: Acquire the traffic data and scene data of the area of ​​the high-speed toll station to be induced by using multiple types of acquisition equipment pre-installed in the gantry pre-transaction system, the identification and diversion system, and the verification and release system; Determine the detection area corresponding to each of the traffic data and the scene data, so as to fuse the detection area to establish a digital twin model of the high-speed toll station area to be induced; Detecting the on-board unit of the vehicle entering the high-speed toll station area to be induced according to the gantry pre-transaction system, so as to record the flow data of the high-speed toll station area to be induced according to the on-board unit; Based on the flow data, the vehicles traveling in the area of ​​the to-be-induced high-speed toll station are classified to obtain a first traveling vehicle and a second traveling vehicle; wherein the first traveling vehicle is a vehicle that can pass directly, and the second traveling vehicle is a vehicle that cannot pass directly; Determine the current traffic data and current scene data corresponding to the first moving vehicle and the second moving vehicle based on the digital twin model, so as to respectively extract the traffic flow characteristic data corresponding to the first moving vehicle and the second moving vehicle; Clustering the traffic flow characteristic data through the neighborhood topology of the traffic flow characteristic data to obtain the cluster center corresponding to the traffic flow characteristic data; Determine, according to the cluster center, an estimated traffic state of the first and second traveling vehicles in the area of ​​the to-be-induced high-speed toll station, so as to perform lane-induced diversion on the first and second traveling vehicles based on the estimated traffic state; Determining the current traffic data and current scene data corresponding to the first moving vehicle and the second moving vehicle based on the digital twin model to respectively extract the traffic flow characteristic data corresponding to the first moving vehicle and the second moving vehicle, specifically includes: Simulating current traffic data and current scene data corresponding to the first traveling vehicle and the second traveling vehicle based on the digital twin model to obtain simulated traffic data corresponding to the first traveling vehicle and the second traveling vehicle; Acquire historical traffic data of the area of ​​the to-be-induced expressway toll station, and determine direct traffic impact data and indirect traffic impact data of the simulated traffic data according to the historical traffic data matching the simulated traffic data; wherein the direct traffic impact data includes: vehicle speed related data and road capacity related data, and the indirect traffic impact data includes: weather related data and noise related data; Based on the association between the direct traffic impact data and the indirect traffic impact data, a plurality of connected graphs including the direct traffic impact data and the indirect traffic impact data are generated, and based on the number of data components of each of the connected graphs, a first weight value corresponding to the direct impact data and the indirect impact data in each of the connected graphs is determined; Determining second weight values ​​corresponding to each of the directly affected data and the indirectly affected data based on the number of lower-layer data corresponding to the directly affected data and the indirectly affected data in each of the connected graphs; Based on the traffic restriction conditions of the high-speed toll station area to be induced, determining abnormal values ​​of the direct traffic impact data and the indirect traffic impact data, and determining a third weight value of the direct traffic impact data and the indirect traffic impact data based on the abnormal values; Based on the weighted value of the first weight value, the second weight value and the third weight value, the importance of each of the direct traffic impact data and the indirect traffic impact data is determined to extract the traffic flow characteristic data corresponding to the first traveling vehicle and the second traveling vehicle based on the importance.

2. A method for controlling traffic diversion induction at a high-speed toll station according to claim 1, characterized in that: Before acquiring the traffic data and scene data of the area of ​​the high-speed toll station to be induced by using multiple types of acquisition devices pre-installed in the gantry pre-transaction system, the identification and diversion system, and the verification and release system, the method further includes: Based on the locations of the gantry pre-transaction system, the identification and diversion system, and the verification and release system in the area of ​​the high-speed toll station to be induced, the area of ​​the high-speed toll station to be induced is divided into detection areas, and a first detection area corresponding to the gantry pre-transaction system, a second detection area corresponding to the identification and diversion system, and a third detection area corresponding to the verification and release system are determined; Determine the types of acquisition equipment corresponding to the first detection area, the second detection area, and the third detection area according to the preset highway design strategy of the to-be-induced highway toll station area; Determine the candidate collection devices for each area according to the corresponding collection device type, and determine the initial installation position of each candidate collection device based on the effective collection range of the candidate collection device and the area of ​​each area; Obtaining the effective range of the same type of acquisition devices at the initial installation position of each area, determining the coverage range of the same type of acquisition devices according to the effective range of each of the same type of acquisition devices, and determining the remaining coverage area according to the division range of each area and the coverage range; Determining whether to add the same type of acquisition equipment based on the ratio of the remaining coverage area to the corresponding area and the area value of the remaining coverage area; If so, determine the center position of each of the remaining coverage areas, determine the additional installation position based on the center position and the effective coverage range of the same type of collection equipment, and install each of the collection equipment according to the initial installation position and the additional installation position, so as to obtain the traffic data and scene data of the highway toll station area to be induced based on multiple types of collection equipment.

3. A method for controlling the flow diversion induction of a high-speed toll station according to claim 2, characterized in that: Determining the detection area corresponding to each of the traffic data and the scene data, so as to fuse the detection area to establish a digital twin model of the high-speed toll station area to be induced, specifically including: Performing data preprocessing operations on the traffic data and the scene data to obtain processed traffic data and processed scene data; wherein the data preprocessing operations include: data cleaning, data denoising, and data standardization; Acquire the first detection area, the second detection area, and the connection area of ​​the second detection area, and screen the processed traffic data and the processed scene data based on the position points corresponding to the connection areas to determine the data to be fused; Based on the timestamps and spatial coordinates corresponding to the data to be fused, time-aligning and spatial-aligning the data to be fused to obtain candidate fused data; Extract features from the candidate fusion data according to their data types, determine feature vectors of the candidate fusion data, match the candidate fusion data based on the feature vectors, obtain associated candidate fusion data, and fuse the associated fusion data based on Kalman filtering to obtain fused scene data and fused traffic data; Input the fused scene data into a preset modeling tool to establish a scene digital twin model of the high-speed toll station area to be induced, and input the fused traffic data into a preset modeling tool to establish a traffic digital twin model of the high-speed toll station area to be induced; Spatial matching is performed based on the spatial information of the scene digital twin model and the traffic digital twin model to obtain the digital twin model of the high-speed toll station area to be induced.

4. The method for controlling the flow diversion induction of a high-speed toll station according to claim 1, characterized in that: Classifying the vehicles traveling in the area of ​​the to-be-induced high-speed toll station based on the flow data to obtain a first traveling vehicle and a second traveling vehicle specifically includes: If the license plate identification of each vehicle traveling in the area of ​​the to-be-induced high-speed toll station is obtained based on the flow data, and the payment result corresponding to each license plate identification; wherein the payment result includes: success and failure; Obtain the license plate identification corresponding to the failed payment result, determine the ETC device corresponding to the license plate identification according to the digital twin model, and call the operation data of the ETC device to determine the operation status of the ETC device; Based on the payment result and the ETC device, each of the traveling vehicles is classified to obtain a first traveling vehicle and a second traveling vehicle.

5. The method for controlling the flow diversion induction of a high-speed toll station according to claim 1, characterized in that: Clustering the traffic flow characteristic data by using the neighborhood topology of the traffic flow characteristic data to obtain the cluster center corresponding to the traffic flow characteristic data specifically includes: Determine the similarity between the traffic flow characteristic data based on cosine similarity, take the data with similarity greater than a preset threshold as the neighborhood of the traffic flow characteristic data, and construct a neighborhood topological structure corresponding to the traffic flow characteristic data; Randomly extract a preset number of traffic flow characteristic data as initial cluster centers, calculate the distance between each traffic flow characteristic data and the initial cluster center according to the neighborhood topological structure, and classify the traffic flow characteristic data to the initial cluster center with the closest distance to obtain a clustering result; The initial cluster center is iteratively updated according to the clustering result. If it is determined that the cluster center corresponding to the clustering result remains unchanged, the cluster center corresponding to the traffic flow characteristic data is obtained.

6. A method for controlling traffic diversion induction at a high-speed toll station according to claim 1, characterized in that: Determining a traffic state estimate of the first and second traveling vehicles in the to-be-induced high-speed toll station area according to the cluster center, so as to perform lane induction diversion on the first and second traveling vehicles based on the traffic state estimate, specifically includes: Using the traffic state corresponding to the cluster center as an estimate of the traffic state of the first traveling vehicle and the second traveling vehicle in the area of ​​the high-speed toll station to be induced; determining an induction ratio between the first traveling vehicle and the second traveling vehicle based on the traffic state estimation; Obtaining the traffic attributes corresponding to the verification and release systems of the lanes where the first and second vehicles are located, and determining whether the first and second vehicles match the traffic attributes; If the induction rate is greater than the preset induction rate threshold or the traffic attributes do not match, a diversion simulation is performed on the first and second driving vehicles based on the digital twin model of the highway toll station area to be induced to determine the lane induced diversion result of the first and second driving vehicles.

7. The method for controlling the flow diversion induction of a high-speed toll station according to claim 1, characterized in that: After determining the traffic state estimation of the first and second traveling vehicles in the high-speed toll station area to be induced according to the cluster center, and performing lane induction diversion on the first and second traveling vehicles based on the traffic state estimation, the method further includes: Acquiring the exit information of the first detection area and the entrance information of the second monitoring area recorded by the onboard units of the first traveling vehicle and the second traveling vehicle based on the identification and diversion system; Comparing the first detection area exit information with the second monitoring area entrance information to obtain a comparison result; If the comparison result is inconsistent, reclassifying the moving vehicle based on the entrance information of the second monitoring area, and iteratively performing lane induction diversion based on the classification result; If the comparison result is consistent, the entrance information of the third monitoring area is obtained based on the verification and release system, and the first detection area exit information and the third monitoring area entrance information are matched with instructions according to the built-in release conditions of the verification and release system, so as to control the lifting bar in the third monitoring area based on the matched instructions to realize the diversion passage of the moving vehicles.

8. A diversion induction control device for a high-speed toll station, applied to a diversion induction control system including a gantry pre-transaction system, an identification diversion system, and a verification and release system, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can: execute any of the methods described in claims 1-7.

9. A non-volatile storage medium, applied to a diversion induction control system including a gantry pre-transaction system, an identification and diversion system, and a verification and release system, storing computer executable instructions, wherein the computer executable instructions can: execute any of the methods described in claims 1-7 above.

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