A payment method, system and electronic device based on a high-speed composite pass card

Abstract

This invention relates to the field of highway technology, and in particular to a payment method, system, and electronic device based on a highway composite toll card. The method includes: acquiring traffic flow data within a preset distance range of the toll station exit; performing congestion prediction based on the traffic flow data and preset congestion factors to obtain congested exit information; acquiring historical passage data and entrance information of users en route; performing exit prediction based on the historical passage data and entrance information to obtain the first toll station exit information and the first toll bill for the users en route; matching the first toll station exit information and the congested exit information; when a match is successful, pushing the first toll bill to the user terminal of the users en route; and generating payment information based on the payment information when the first toll bill is received. This method solves the problem of toll station congestion caused by existing CPC card passage methods and improves the passage efficiency of vehicles holding CPC cards.

Description

Technical Field

[0001] This invention relates to the field of highway technology, and in particular to a toll payment method, system and electronic device based on a highway composite toll card. Background Technology

[0002] With the rapid development of technology, the toll collection model for highways is also evolving. Currently, the toll collection structure for highways in my country mainly includes manual toll collection (MTC) and electronic toll collection (ETC).

[0003] With the elimination of provincial border toll stations, traffic volume on highways in various provinces has increased rapidly. However, some high-traffic toll plazas, due to various limitations, are unable to expand their capacity, leading to decreased vehicle speeds and congestion. While ETC (Electronic Toll Collection) can improve traffic efficiency through non-stop toll deduction, it is limited to vehicles equipped with dedicated OBU (On-Board Unit) devices and requires entry into dedicated ETC lanes. Furthermore, the ETC application process is cumbersome, and after-sales service quality varies, resulting in a large number of drivers still choosing to use CPC (Cost Per Card) for toll collection. The CPC card, a composite toll card for highways, is used in manual / semi-automatic toll collection (MTC) systems. The CPC card can identify vehicle entry and exit information at toll stations and accurately record the vehicle's actual travel route, providing crucial data for inter-provincial toll calculation and settlement.

[0004] When using the CPC card system, a CPC card needs to be obtained at the toll station entrance, and the highway toll must be paid in sequence at the MTC lane toll station exit. Therefore, the existing CPC card system requires queuing to pay, which can easily cause congestion at toll stations and make it difficult to meet the needs of smooth traffic flow. In particular, for toll stations with high traffic volume, the existing CPC card system will exacerbate the congestion in MTC lanes and mixed lanes.

[0005] Therefore, how to maximize the passage efficiency of vehicles holding CPC cards and alleviate congestion at toll stations under the existing lane size and without affecting the current toll structure has become an urgent problem to be solved. Summary of the Invention

[0006] This invention provides a payment method, system, and electronic device based on a high-speed composite toll card, which can improve the passage efficiency of vehicles holding CPC cards.

[0007] This invention provides a payment method based on a high-speed composite toll card, the method comprising:

[0008] Obtain traffic flow data within a preset distance range of the toll station exit;

[0009] Congestion prediction is performed based on the traffic flow data and preset congestion factors to obtain congestion exit information;

[0010] Obtain historical passage data and entry information of users currently en route;

[0011] Based on the historical traffic data and the entrance information, exit prediction is performed to obtain the first toll station exit information and the first toll bill for the user in transit.

[0012] The first toll station exit information and the congested exit information are matched. When the match is successful, the first toll bill is pushed to the user terminal of the user in transit.

[0013] When the payment information of the first toll bill is received, payment information is generated based on the payment information.

[0014] Optionally, the step of predicting congestion based on the traffic flow data and preset congestion factors to obtain congestion exit information includes:

[0015] Based on the traffic flow data, the predicted number of vehicles at the toll station exit during the predicted time period is calculated.

[0016] Based on the predicted number of vehicles and the preset congestion factor, the congestion parameters at the toll station exit are calculated.

[0017] When the congestion parameter is not less than a preset threshold, the toll station exit is marked as a congested exit, and the predicted time period is marked as a congested time period; and the congested exit and the congested time period are used as the congested exit information.

[0018] Optionally, the traffic flow data includes: gantry identifier, number of vehicles at the gantry, and collection period; calculating the predicted number of vehicles for the predicted period based on the traffic flow data includes:

[0019] Based on the gantry identifier and a preset first association relationship, the historical diversion ratio associated with the gantry identifier is determined; the first association relationship is the association between the gantry identifier and the historical diversion ratio.

[0020] Based on the collection period and the preset second association relationship, the historical period diversion ratio corresponding to the collection period is determined from the historical classification ratio;

[0021] The predicted number of vehicles for the predicted period is calculated based on the historical diversion ratio and the number of gantry vehicles.

[0022] Optionally, the predicted number of vehicles includes: the number of trucks and the number of buses; the preset congestion factor includes the external dimensions of trucks and buses; the preset threshold includes the capacity threshold of the toll plaza.

[0023] The calculation of congestion parameters at the toll station exit based on the predicted number of vehicles and a preset congestion factor includes:

[0024] The truck capacity is obtained by multiplying the number of trucks by their external dimensions, and the bus capacity is obtained by multiplying the number of buses by their external dimensions. The truck capacity and the bus capacity are then summed to obtain the congestion parameters at the toll station exit.

[0025] Optionally, the method further includes:

[0026] If no payment confirmation result for the first toll bill is received within a first preset time, the real-time on-the-go data of the user in transit is obtained.

[0027] Based on the real-time on-the-way data and the entrance information, update the first toll station exit information and the first toll bill to obtain the second toll station exit information and the second toll bill.

[0028] The step of matching the first toll station exit information and the congested exit information, and pushing the first toll bill to the user terminal of the user in transit when the match is successful, includes:

[0029] The second toll station exit information and the congested exit information are matched. When the match is successful, the second toll bill is pushed to the user terminal of the user on the way.

[0030] Optionally, the method further includes:

[0031] If no payment confirmation result for the second toll bill is received within the second preset time, and the estimated fee information sent by the preset target gantry is received, a third toll bill is generated based on the estimated fee information.

[0032] When the user in transit is determined to be a user who can make payment without a password, a payment request without a password is initiated to the third-party payment platform based on the estimated fee information. Upon receiving the payment callback information from the third-party payment platform, payment information without a password is generated based on the payment callback information without a password.

[0033] Optionally, the method further includes:

[0034] When it is determined that the user in transit is not a user who pays without a password, the third toll bill is pushed to the user's terminal. Upon receiving the user's confirmation payment instruction, a payment request is initiated to the third-party payment platform. Upon receiving the payment callback information from the third-party payment platform, payment information is generated based on the payment callback information.

[0035] Optionally, the method further includes:

[0036] When a temporary correction instruction is received, the instruction is parsed to obtain the correction period and correction object information. Based on the correction period and correction object information, the historical period diversion ratio is corrected.

[0037] Another aspect of the present invention provides a payment system based on a high-speed composite toll card, the system comprising:

[0038] The traffic flow data acquisition module is used to acquire traffic flow data within a preset distance range from the toll station exit;

[0039] The congestion prediction module is used to predict congestion based on the traffic flow data and preset congestion factors, and obtain congestion exit information.

[0040] The on-the-go user data acquisition module is used to acquire historical passage data and entry information of on-the-go users.

[0041] The exit prediction module is used to predict the exit based on the historical passage data and the entrance information, and to obtain the first toll station exit information and the first toll bill of the user in transit.

[0042] The matching module is used to match the exit information of the first toll station with the congested exit information. When the match is successful, the first toll bill is pushed to the user terminal of the user in transit.

[0043] The information generation module is used to generate payment information based on the payment information when the payment information of the first toll bill is received.

[0044] Another aspect of the present invention provides an electronic device, the device comprising a processor and a memory:

[0045] The memory is used to store program code and transmit the program code to the processor;

[0046] The processor is used to execute the method described above according to the instructions in the program code.

[0047] As can be seen from the above technical solutions, the present invention has the following advantages:

[0048] This invention obtains traffic flow data within a preset distance range of a toll station exit and performs congestion prediction based on the traffic flow data and preset congestion factors to obtain congested exit information, thereby updating and predicting the congestion status of toll station exits. Furthermore, it obtains historical passage data and entrance information of users on the road and performs exit prediction based on the historical passage data and entrance information to obtain the first toll station exit information and the first toll bill for the users on the road, thus predicting the destination toll station exit for the users on the road. Finally, it matches the first toll station exit information with the congested exit information; when a match is found... Upon successful completion, if there is congestion at the destination toll station exit for the user en route, the system will push the first toll bill to the user's terminal, reminding the user to pay the toll before reaching the toll booth. When the system receives the payment information from the first toll bill, it generates payment information based on the payment information. This allows the user to simply return the CPC card upon reaching the toll booth and proceed, solving the problem of congestion at toll booths caused by queuing at the exit booths for payment in the existing CPC card system. This improves the passage efficiency of vehicles using CPC cards. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A flowchart illustrating a payment method based on a high-speed composite toll card, provided in Embodiment 1 of the present invention;

[0051] Figure 2 This is a schematic diagram of the toll station exit gantry erection provided in Embodiment 1 of the present invention;

[0052] Figure 3 A flowchart illustrating a payment method based on a high-speed composite toll card, provided in Embodiment 2 of the present invention;

[0053] Figure 4 A flowchart illustrating a payment method based on a high-speed composite toll card, provided in Embodiment 3 of the present invention;

[0054] Figure 5 This is a schematic diagram of a payment system based on a high-speed composite toll card provided in Embodiment 7 of the present invention;

[0055] Figure 6 System architecture provided for this invention Figure 1 ;

[0056] Figure 7 System architecture provided for this invention Figure 2 . Detailed Implementation

[0057] This invention provides a payment method, system, and electronic device based on a high-speed composite toll card, which can improve the passage efficiency of vehicles holding CPC cards.

[0058] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0059] Example 1:

[0060] Please see Figure 1 Embodiment 1 of the present invention provides a payment method based on a high-speed composite toll card, the steps of which include:

[0061] 101. Obtain traffic flow data within a preset distance range of the toll station exit.

[0062] It should be noted that this embodiment uses the toll station exit as a reference. Several gantries are set up along the highway within a preset distance before and after the toll station. Each gantry has a unique identifier, which distinguishes the traffic flow data collected by different gantries. Each gantry is equipped with hardware such as an RSU antenna and a license plate capture device. The RSU antenna interacts with the CPC card and collects estimated toll information from the CPC card, including highway entrance information, toll amount, vehicle type, and license plate type, and synchronizes this estimated toll information to the system database. Therefore, by utilizing the interaction information between the gantry and the CPC card, the traffic flow data passing through the gantry can be determined. Since the instantaneous traffic volume at the gantry is very small, to improve the accuracy of the prediction, the gantry counts the number of vehicles within a preset time period and updates the vehicle count according to a preset data update cycle.

[0063] The preset distance range can be determined based on the actual situation of the toll station, and the data update cycle is determined based on the distance between the gantry and the toll station. In this embodiment, in order to provide effective early warning of traffic flow at the toll station exit and enable toll station administrators to perform efficient scheduling, priority is given to acquiring traffic flow data within a 30km radius before and after the toll station exit. It is understood that there can be multiple toll station exits. For example, all toll stations within the highway network can be preferred. This embodiment takes the acquisition of data from one toll station exit as an example, specifically focusing on the 30km radius before and after the toll station exit as an example, as explained below:

[0064] Under normal circumstances, vehicles arrive at the toll station from two directions: the main lane and the feeder lane. For example... Figure 2 As shown, the mainline lanes towards the toll station are equipped with gantries 1, 2, and 3, located approximately 30km, 20km, and 10km from the toll station, respectively. The branch lanes have gantries 4, 5, and 6, located approximately 30km, 20km, and 10km from the ramps, respectively. This embodiment obtains the number of vehicles counted at gantries 1 through 6.

[0065] 102. Based on traffic flow data and preset congestion factors, congestion prediction is performed to obtain congestion exit information.

[0066] It should be noted that the distance between the gantry and the toll station exit can be determined based on the gantry markings. Based on this distance and the vehicle's speed, the travel time required for the vehicle to travel from the gantry to the toll station can be determined. Therefore, by combining the travel time and the gantry traffic flow data, the traffic flow data of the toll station at different times can be predicted in advance.

[0067] Congestion prediction refers to determining whether the predicted traffic flow data at toll stations exceeds the capacity of the toll station. If so, the toll station is considered congested, and its exit is marked as a congested exit. The predicted time period is also marked as a congested time period. The capacity of a toll station is primarily considered based on the number of vehicles it can allow to pass and / or the capacity of the toll plaza. Congestion factors are used to assist in calculating the number and / or volume of vehicles corresponding to the traffic flow data. Congested exit information includes the congested time period and the congested exit.

[0068] It should be noted that there can be one or more toll station exits. In the case of multiple exits, congestion prediction for each toll station exit is made using traffic flow data from each toll station and preset congestion factors.

[0069] 103. Obtain historical passage data and entry information of users currently on the road.

[0070] It should be noted that "users on the way" refers to registered users who have entered the tollbooth entrance and received and activated their CPC cards. "Registered users" refers to users who have completed authorization registration in the system, submitted the corresponding user information (such as driver's license information, vehicle information, etc.), and completed information binding and verification.

[0071] Understandably, before using the system for the first time, vehicle owners submit their vehicle registration information and user information to bind their frequently used vehicle. After receiving the information submitted by the vehicle owner, the system reviews the information and completes the registration upon approval. The registration channel chosen by the vehicle owner can be one or more of the following: a mini-program, official account, web link, APP, or registration platform provided by the system front-end. The specific registration channel can be selected according to the actual situation, and this embodiment does not impose specific limitations.

[0072] After a vehicle passes through the tollbooth entrance, tollbooth personnel write the vehicle information, tollbooth entrance information, and entry time into the CPC card. This information is then synchronized to the system. The system generates an order based on the received information and filters the order (excluding orders for vehicles on holidays or special occasions). The system then compares the vehicle information in the order with pre-stored information on registered users to determine if the user is registered. If so, it retrieves the user's historical passage data and entrance information from the system database. It's understood that the system database stores historical passage data for all vehicles within the highway network and real-time CPC card data read by the gantries, among other road network data.

[0073] 104. Based on historical traffic data and entrance information, predict the exit of the toll station to obtain the exit information of the first toll station and the first toll bill.

[0074] It should be noted that historical passage data includes vehicle license plate information, gantry information, historical exits, historical entrances, historical routes, historical travel times, and historical toll bills. Among these, historical entrances and exits, historical routes, historical travel times, and historical toll bills are correlated. Gantry information includes gantry identification and historical read times.

[0075] The toll station exit prediction specifically includes: matching entrance information with historical entrances in historical traffic data to determine a first target historical entrance that matches the entrance information; determining a first target historical exit, a first target historical travel path, a first target historical travel time, and a first target historical toll bill corresponding to the first target historical entrance based on the first target historical entrance and its association; when there are multiple first target historical exits, calculating the passage frequency of each first target historical exit, and using the first target historical exit with a passage frequency greater than a preset frequency threshold as the first predicted exit for this prediction, and using the first target historical travel path corresponding to the first predicted exit as the first target path for this prediction; determining the predicted arrival time of the user on the way to the predicted exit based on the first target path and the travel speed of the user on the way; using the first target historical toll bill corresponding to the predicted exit as the first toll bill for this prediction, or calculating the first toll bill by combining entrance information, predicted exit, and the historical travel path between entrance information and predicted exit. The first toll station exit information includes the first predicted exit and the first predicted arrival time.

[0076] In this embodiment, when a user on the road just exits the toll station entrance, the system obtains the user's historical passage data and entrance information to predict the user's destination toll station exit in advance, and generates a toll bill in advance for the user to confirm and pay. This facilitates the user to prepay the toll in advance, thereby helping to alleviate congestion in mixed lanes and MTC lanes.

[0077] It should be noted that there is no sequential relationship between steps 101-102 and steps 103-104. For example, steps 103-104 can precede steps 101-102.

[0078] 105. Match the exit information of the first toll station with the congested exit information. When the match is successful, push the first toll bill to the user terminal of the user on the way.

[0079] It should be noted that the system matches the first predicted exit and first predicted arrival time in the first toll station exit information with the congested exit and congestion time in the congestion exit information. A successful match occurs when the congested exit and predicted exit match, and the predicted arrival time matches the congestion time. This indicates that the toll station will be congested when the user arrives at that exit. Once a match is successful, the first toll bill corresponding to the predicted exit is sent to the user, reminding them to pay the toll in advance.

[0080] Understandably, if multiple predicted exits are successfully matched, the first toll bill corresponding to each predicted exit will be sent to users en route. Users can then select and confirm the bill according to their needs and pay the toll in advance. The specific push notification method can be determined based on the actual situation, such as through a mini-program push or a combination of SMS and H5 links.

[0081] 106. When the payment information for the first toll bill is received, the payment information is generated based on the payment information.

[0082] It should be noted that after receiving the first toll bill, users en route can confirm their predicted exit and pay the corresponding first toll bill at the predicted exit, either en route, at the toll plaza, or at the tollbooth exit. Therefore, upon receiving payment information for the first toll bill from an en route user, the system generates payment information based on this information and synchronizes it to the existing mixed-lane toll collection system. This payment information includes entry and exit information, travel route, toll amount, license plate, vehicle type, and vehicle class. Thus, when the en route user returns their CPC card at the tollbooth exit, staff only need to verify the license plate and vehicle class in the payment information to quickly allow passage. This solves the problem of queuing at the tollbooth exit for payment, which easily causes congestion, and improves the passage efficiency for vehicles using CPC cards.

[0083] This embodiment acquires traffic flow data within a preset distance range of the toll station exit and performs congestion prediction based on the traffic flow data and preset congestion factors to obtain congested exit information, thereby updating and predicting the congestion status of the toll station exit. It also acquires historical passage data and entry information of users on the way and performs exit prediction based on this data to obtain the first toll station exit information and the first toll bill for the users on the way, thus predicting the destination toll station exit for the users on the way. By matching the first toll station exit information with the congested exit information, if a match is successful, it indicates that the destination toll station exit for the users on the way is congested, and the first toll bill is pushed to... The system automatically alerts users on the road to pay tolls before reaching the toll booth. Upon receiving payment information for the first toll bill, it generates payment information based on this information. This allows users to simply return their CPC card upon arrival at the toll booth and proceed without further delay. This solves the problem of congestion at toll booths caused by queuing at the exit booths when using CPC cards. It improves the passage efficiency of vehicles with CPC cards, alleviates congestion in MTC lanes and mixed lanes, and enhances the overall capacity of the toll plaza, while also improving the convenience of toll payment.

[0084] Example 2:

[0085] Please see Figure 3 The present invention provides a payment method based on a high-speed composite toll card in Embodiment 2. Building upon Embodiment 1, this embodiment further defines step 102, which includes the following steps:

[0086] 201. Based on traffic flow data, determine the predicted number of vehicles at the toll station exit during the predicted time period.

[0087] It should be noted that the traffic flow data includes: gantry identification, number of vehicles at the gantry, data collection period, and traffic flow statistics duration. The gantry identification can be the gantry's number, identifier, or other identifying information used to distinguish different gantries. The data collection period is calculated by adding the traffic flow statistics duration to the start time of traffic flow data collection at the gantry. The prediction period is derived from the data collection period and the advance prediction time interval. The advance prediction interval is determined by the distance between the gantry and the toll station exit and the vehicle speed. The gantry identification is used to indicate the gantry's position, and the distance between the gantry and the toll station exit can be determined based on the gantry identification.

[0088] The specific steps determined in step 201 are as follows:

[0089] S01: Determine the historical diversion ratio associated with the gantry identifier based on the gantry identifier and the preset first association relationship; the first association relationship is the association relationship between the gantry identifier and the historical diversion ratio.

[0090] It should be noted that since the distances between each gantry and the toll station exit vary, using traffic flow data from different gantry units to predict toll station traffic flow can yield the traffic volume for different prediction time intervals. During the data collection period, vehicle diversion occurs between gantries and between gantries and toll station exits. In this embodiment, vehicle diversion can be represented by historical diversion ratios. These historical diversion ratios include the historical diversion ratio of the gantry units and the diversion ratio of vehicles entering the toll station.

[0091] The first association is pre-built in the database, and its construction steps include: determining the location of the gantry based on the gantry identifier, determining all diversion nodes between the gantry and the toll station exit based on the gantry location, and establishing an association between the gantry identifier and the historical diversion ratio of all diversion nodes.

[0092] Based on branch lanes and mainline lanes, gantries can be divided into branch line gantries and mainline gantries. Historical gantry diversion rates include both branch line gantry and mainline gantry historical diversion rates. Diversion rates entering the toll station include the diversion rates from branch line lanes and the diversion rates from mainline lanes.

[0093] The following is an explanation using the examples listed in Implementation Example 1:

[0094] like Figure 2 As shown in Tables 1 and 2, assuming a vehicle speed of 70 km / h, the average travel time for vehicles on the main lanes from gantries 1, 2, and 3 to the toll station is approximately 26 min, 17 min, and 8 min, respectively. Similarly, the average travel time for vehicles on the branch lanes from gantries 4, 5, and 6 to the ramps is also approximately 26 min, 17 min, and 8 min, respectively.

[0095] Table 1 Basic Information of Mainline Gantry

[0096]

[0097]

[0098] Table 2 Basic Information of Branch Line Gantry

[0099] gantry Distance from gantry to ramp Average time for a vehicle to travel from the gantry to the ramp (at a speed of 70 km / h) Gantry 4 Approximately 30km Approximately 26 minutes gantry 5 Approximately 20km Approximately 17 minutes Gantry 6 Approximately 10km Approximately 8 minutes

[0100] Since the distance from the ramp to the toll station is negligible, and the situation for branch lanes is the same as that for main lanes, branch lanes can be superimposed with the downstream lane data. This embodiment mainly uses traffic flow data statistics of the main lanes as an example for explanation.

[0101] Specifically, assuming a vehicle speed of 70 km / h, for the main lane, the data update cycles for gantry 1, gantry 2, and gantry 3 are 5 minutes, 5 minutes, and 3 minutes, respectively. When the first vehicle passes through gantry 1, gantry 1 starts timing, counting the number of vehicles passing through gantry 1 in 5 minutes. Some of these vehicles will arrive at the toll station in approximately 21 to 26 minutes. Therefore, based on the vehicle data from gantry 1, using the timing start point of gantry 1 as a reference point, the number of vehicles at the toll station approximately 21 to 26 minutes in advance can be predicted. The prediction time range is 21 to 26 minutes. When the first vehicle passes through gantry 2, gantry 2 starts timing, counting the number of vehicles passing through gantry 2 in 5 minutes. Some of these vehicles will arrive at the toll station in approximately 12 to 17 minutes. Similarly, based on the number of vehicles at gantry 2 and the data collection time point, the number of vehicles at the toll station 12 to 17 minutes after the data collection time point can be determined. When the first vehicle passes through gantry 3, gantry 3 starts timing, counting the number of vehicles that pass through gantry 3 in 3 minutes. Some of these vehicles will arrive at the toll station in about 5 to 8 minutes. The above information is shown in Table 3.

[0102] Table 3. Duration of gantry traffic flow statistics, data update cycle, and advance prediction time interval.

[0103] gantry Traffic flow statistics duration Data update cycle Predict time intervals in advance Gantry 1 0-5min 5min 21-26min Gantry 2 0-5min 5min 12-17min Gantry 3 0-3min 3min 5-8min

[0104] like Figure 2 As shown, the diversion nodes between gantry 1 and the toll station exit are: the diversion node between gantry 1 and gantry 2, the diversion node between gantry 2 and gantry 3, and the diversion node between gantry 3 and the toll station. Therefore, the historical diversion ratios associated with gantry 1 include: the historical diversion ratio P1 passing through gantry 2, the historical diversion ratio P2 passing through gantry 3, and the ratio P3 of the mainline lane entering the toll station. The historical diversion ratios associated with gantry 2 include: the historical diversion ratio P2 passing through gantry 3, and the ratio P3 of the mainline lane entering the toll station. The historical diversion ratios associated with gantry 3 include: the ratio P3 of the mainline lane entering the toll station.

[0105] Specifically, regarding the historical diversion ratio, since highway traffic volume usually changes significantly with holidays and seasons, with holidays showing the greatest correlation and seasons showing the least, in this embodiment, the historical diversion ratio is preferably the historical diversion ratio of each time period of the average daily traffic volume during holidays in the previous year or the historical diversion ratio of each time period of the average daily traffic volume during a certain quarter of the previous year (including the first quarter from January to March, the second quarter from April to June, the third quarter from July to September, and the fourth quarter from October to December).

[0106] It is understandable that the historical diversion ratio is calculated in advance based on the traffic flow data of the previous year and stored in the database. The calculation method is as follows:

[0107] The gantry vehicle number diversion ratio is obtained by calculating the ratio of the number of vehicles in the two gantry lines.

[0108] The historical diversion ratio of branch lanes entering the toll station is calculated by subtracting the number of vehicles from the next gantry in the order of the toll station from the number of vehicles in the main lane. The difference in the number of vehicles is then divided by the number of vehicles in the gantry closest to the toll station.

[0109] Historical diversion ratio of branch lanes entering the toll station: calculated from the traffic flow at the toll station, the traffic flow and diversion ratio of the last gantry of the branch lane, the traffic flow of the first gantry of the main line lane, and the diversion ratio of all gantries of the main line lane other than the first gantry.

[0110] The calculation method for the historical diversion ratio is further illustrated using the above case as an example:

[0111] The 24 hours of a day are divided into 48 time periods, each lasting 30 minutes. The traffic flow Sp at the toll station, the traffic flow S1, S2, and S3 at the main line gantries 1, 2, and 3, and the traffic flow S6 and S7 at the branch line gantries 6 and 7 are measured and statistically analyzed for each time period.

[0112] The ratio of vehicles passing through gantry 2 is:

[0113]

[0114] The ratio of vehicles passing through gantry 3 is:

[0115]

[0116] The percentage of vehicles entering the toll station from the branch lane is:

[0117]

[0118] The percentage of vehicles entering the toll station from the main lane is:

[0119]

[0120] Finally, the historical diversion ratio for each time period corresponding to each gantry is recorded in the historical diversion ratio table, as shown in Table 4. When the historical diversion ratio needs to be obtained, the historical diversion ratio corresponding to the gantry can be retrieved based on the gantry identifier and the first association relationship.

[0121] It is understood that Table 4 is only an example of data format recording. In actual application, specific gantry vehicle data can be filled into the table.

[0122] Table 4 Historical Diversion Ratio

[0123]

[0124] S02: Based on the collection period and the preset second correlation, determine the historical time period diversion ratio corresponding to the collection period from the historical classification ratio.

[0125] As shown in Table 4 above, each time period corresponds to a historical diversion ratio, and the historical diversion ratio for each time period is the historical time period diversion ratio. In this embodiment, a second association relationship between the data collection time period and the historical time period diversion ratio is pre-constructed and stored in the database.

[0126] Based on the data collection period and the second correlation, the historical diversion ratio corresponding to the data collection period is determined from the historical diversion ratios obtained in step S01. The data collection period can be determined based on the gantry's timing start point, traffic flow statistics duration, and data update cycle. For example, the traffic flow statistics duration for gantry 1 is 0-5 minutes, and the data update cycle is 5 minutes. During the 0:30 time period, the diversion ratio for gantry 2 is A, the diversion ratio for gantry 3 is B, and the diversion ratio for the mainline lane entering the toll station is C. Assuming the timing start point is 0:00, based on the timing start point and data update cycle, the first data collection period can be determined to be 0:00-0:05. Since this data collection period falls within the 0:30 time period, the historical diversion ratios associated with gantry 1 are A, B, and C.

[0127] S03: Calculate the predicted number of vehicles for the predicted period based on the historical diversion ratio, the number of gantry vehicles, and the duration of traffic flow statistics.

[0128] It should be noted that the number of vehicles at the gantry includes both the number of vehicles at the mainline gantry and the number of vehicles at the branch line gantry. The traffic flow statistics duration includes both the mainline gantry and branch line gantry traffic flow statistics durations. By comparing the traffic flow statistics durations of the mainline and branch line gantry, and based on the comparison results, the corresponding calculation formula is used to calculate the advance prediction time interval and the predicted number of vehicles within that interval. The prediction time period is then calculated based on the data collection period and the advance prediction time interval. The number of vehicles corresponding to the advance prediction time interval is the predicted number of vehicles for the prediction time period.

[0129] Specifically, it is as follows:

[0130] (1) When the traffic flow statistics duration of the mainline gantry is the same as that of the branchline gantry, that is, when k = l, the formula for calculating the predicted number of vehicles is:

[0131]

[0132] The time interval for advance prediction is as follows:

[0133]

[0134] (2) When the traffic flow statistics duration of the mainline gantry is less than that of the branchline gantry, i.e., k < l, the formula for calculating the predicted number of vehicles is:

[0135]

[0136] The time interval for advance prediction is as follows:

[0137]

[0138] (3) When the traffic flow statistics duration of the mainline gantry is greater than that of the branchline gantry, i.e., k > l, the formula for calculating the predicted number of vehicles is:

[0139]

[0140] The time interval for advance prediction is as follows:

[0141]

[0142] Where k is the mainline gantry traffic flow statistics duration in minutes; l is the branch line gantry traffic flow statistics duration in minutes; i is the i-th minute of the mainline gantry traffic flow statistics duration; j is the j-th minute of the branch line gantry traffic flow statistics duration; s mThe cumulative traffic flow per minute at the main line gantry; s b The cumulative traffic flow per minute for the branch line gantry; d m d represents the distance from the mainline gantry to the toll station, in km (kilometers); b v represents the distance from the branch line gantry to the toll station, in km (kilometers); v represents the average speed of vehicles on this road segment, in km / h (kilometers per hour). The advance prediction time interval for the mainline lanes is expressed in minutes. P represents the advance prediction time interval for branch lanes, in minutes; m is the m-th diversion node between the mainline gantry and the toll station; b is the b-th diversion node between the branch lane gantry and the toll station. m The historical diversion ratio of the m-th diversion node on the mainline lane, P b Let be the historical diversion ratio of the b-th diversion node on the branch lane, x be the total number of diversion nodes on the main line lane, and y be the total number of diversion nodes on the branch lane.

[0143] The following further illustrates this point, using the aforementioned case as an example:

[0144] According to Table 3 above, the advance prediction time range for gantry 1 on the mainline lane is 21-26 minutes. The distance between gantry 4 and the ramp is the same as the distance between gantry 1 and the toll station. Since the distance between the ramp and the toll station is negligible, the advance prediction time range for both gantry 4 and gantry 1 is also 21-26 minutes. Based on this, the predicted number of vehicles at the toll station within 21-26 minutes can be determined as follows:

[0145]

[0146] Similarly, the formulas for calculating the number of predicted vehicles for other advance prediction time intervals in the above cases are shown in Table 5.

[0147] Table 5. Advance prediction time intervals and corresponding predicted number of vehicles

[0148]

[0149] 202. Based on the predicted number of vehicles and the preset congestion factor, the congestion parameters at the toll station exit are calculated.

[0150] It should be noted that when judging the congestion situation of toll stations based on the capacity of the toll plaza, the congestion factors include: the external dimensions of trucks and the external dimensions of buses.

[0151] It is understandable that when a vehicle holding a CPC card passes through the gantry, the gantry can read the vehicle information (such as license plate, vehicle type, and vehicle class) stored in the CPC card, and count the number of different types of vehicles passing through based on the vehicle information. Therefore, the number of vehicles counted by the gantry includes both passenger vehicles and freight vehicles. In this embodiment, the predicted number of vehicles includes both predicted passenger vehicles and predicted freight vehicles.

[0152] Multiply the number of trucks by their external dimensions to obtain the truck capacity, and multiply the number of buses by their external dimensions to obtain the bus capacity. Sum the truck capacity and bus capacity to obtain the congestion parameters at the toll station exit.

[0153] When judging the congestion situation at a toll station based on its vehicle throughput capacity, the congestion factor is a constant, preferably 1 in this embodiment. In this case, the congestion parameter equals the predicted number of vehicles.

[0154] 203. When the congestion parameter is not less than the preset threshold, the toll station exit will be marked as a congested exit; and the congested exit and the congested time period will be used as congested exit information.

[0155] When judging the congestion of a toll station based on the capacity of the toll plaza, the preset threshold is the capacity threshold of the toll plaza. When the congestion parameter is not less than the capacity threshold of the toll plaza, it means that the toll plaza of the toll station has reached saturation or limit within the predicted period. Therefore, the toll station exit corresponding to the predicted period is marked as a congested exit, the predicted period is marked as a congested period, and congested exit information is generated.

[0156] When assessing toll station congestion based on vehicle throughput capacity, a preset threshold is set for the number of passing vehicles. If the congestion parameter is not less than this threshold, it indicates that the predicted number of vehicles exceeds the toll station's throughput capacity during the predicted time period, and the toll station is congested during that period. The toll station exit corresponding to this predicted time period is marked as a congested exit, and the predicted time period is marked as a congested time period, generating congested exit information. This congested exit information includes both the congested exit and the congested time period.

[0157] This embodiment calculates the predicted number of vehicles for a given time period based on traffic flow data, and calculates the congestion parameters at the toll station exit based on the predicted number of vehicles and a preset congestion factor. When the congestion parameters are not less than a preset threshold, the toll station exit is marked as a congested exit and the predicted time period is marked as a congested time period. The congested exit and the congested time period are used as congested exit information, thereby realizing the prediction of congested exits at toll stations.

[0158] In another preferred embodiment, the congestion level of the vehicle toll station can also be classified according to the above judgment results, as follows:

[0159] When the congestion parameter is not less than the threshold for the number of vehicles passing through, the congestion level of the toll station is set to Level 1; when the congestion parameter is approximately equal to the capacity threshold of the toll plaza, the congestion level is set to Level 2; and when the congestion parameter is not less than the capacity threshold of the toll plaza, the congestion level is set to Level 3. The congestion level increases progressively from Level 1 to Level 3. This embodiment, by setting congestion levels, facilitates personnel scheduling at the toll station based on the congestion level, thereby further improving the traffic efficiency of the toll station.

[0160] In another preferred embodiment, it further includes:

[0161] When a temporary correction instruction is received, the instruction is parsed to obtain the correction period and correction target information. Based on the correction period and correction target information, the historical diversion ratio is corrected.

[0162] It should be noted that when a temporary correction instruction is received, it indicates that a sudden temporary situation may have occurred within the preset distance range of the toll station, such as road closures due to toll station or ramp construction, or sudden highway accidents (such as car crashes). If the previous historical diversion ratio is used to calculate the predicted number of vehicles at this time, the accuracy of the prediction will be reduced. Therefore, it is necessary to correct the historical diversion ratio according to the correction period and correction object in the temporary correction instruction, so as to use a new diversion ratio that is more in line with the actual environment of the temporary event to calculate the predicted number of vehicles. For example, if the ramp between gantry 1 and gantry 2 is temporarily closed from 16:00 to 17:00, then S1 = S2, P1 = 1, and a corrected diversion ratio table is generated, as shown in Table 6.

[0163] In this embodiment, when a temporary correction instruction is received, the instruction is parsed to obtain the correction period and correction object information. Based on the correction period and correction object information, the historical diversion ratio is corrected to make the historical diversion ratio fit the current special situation, thereby improving the accuracy of toll station congestion prediction.

[0164] In another preferred embodiment, when a restoration instruction is received, the restoration instruction is parsed to obtain restoration time period information and restoration object, and the historical diversion ratio is restored based on the restoration time period information and restoration object.

[0165] It should be noted that when a restoration command is received, it indicates that the temporary event has ended, and the corrected historical diversion ratio needs to be restored to the initial data. For example, if the ramp between gantry 1 and gantry 2 reopens between 16:00 and 17:00, and the traffic flow data of gantry 2 is restored from S1 to S2, then the ratio passing through gantry 2 will be restored from 1 to S2 / S1, and P2 will be restored from the state of S3 / S1 to S3 / S2.

[0166] Table 6 Historical Diversion Ratio Correction

[0167]

[0168]

[0169] Example 3:

[0170] Please see Figure 4 This third embodiment provides a payment method based on a high-speed composite toll card. Building upon either embodiment one or two, after step 105, if no payment confirmation result for the first toll bill is received within a first preset time, the method further includes:

[0171] 301. Obtain real-time on-the-go data of users currently en route.

[0172] It should be noted that if no payment confirmation result for the first toll bill is received within the first preset time, it indicates that the toll station exit predicted above may not be the actual destination exit for the user in transit.

[0173] Real-time in-transit data refers to the gantry data of the gantry nodes along the route of users in transit up to the current time. It includes: gantry identifier, CPC card data, and read time. CPC card data refers to the data read from the card's storage when the gantry interacts with the CPC card.

[0174] Understandably, every time a vehicle with a CPC card passes through a gantry, the CPC card will interact with the gantry. The gantry will read the data stored in the CPC card (such as license plate information, station information, toll amount, entrance information, etc.) and upload it to the system via multicast.

[0175] 302. Update the exit information and toll bill of the first toll station based on real-time on-the-go data, historical traffic data and entrance information to obtain the exit information and toll bill of the second toll station. Match the exit information of the second toll station with the congested exit information. When the match is successful, push the toll bill of the second toll bill to the user terminal of the user on the road.

[0176] It should be noted that the exit information and toll bill of the first toll station are updated based on real-time on-the-go data and entrance information to obtain the exit information and toll bill of the second toll station. The update steps include:

[0177] S11: Determine the actual driving route of users on the road based on entry information and real-time on-the-road data.

[0178] It should be noted that, based on the gantry identification and reading time, the gantry nodes that the users on the way pass through are determined, and the path is reconstructed and drawn in combination with the entrance information, so as to obtain the actual driving path of the users on the way.

[0179] S12: Match the actual driving route with historical traffic data to obtain the total number of historical traffic flows corresponding to the actual traffic route, the historical traffic route of the second target, the number of passages of the historical traffic route of the second target, and the historical exit of the second target corresponding to the historical traffic route of the second target.

[0180] It should be noted that the actual driving route is matched with the historical travel routes in the historical travel data of users on the road to determine whether there is a matching historical travel route. If so, the historical travel route corresponding to the actual driving route, the historical exit corresponding to the historical route, the number of transactions at the historical exit, and the total number of transactions are counted and output.

[0181] S13: Calculate the passage ratio of the historical passage path based on the passage flow count and the total passage flow count of the historical passage path; update the second target historical passage path with the highest passage ratio to the second target passage path, and update the second target historical exit corresponding to the second target historical exit with the highest passage ratio to the second predicted exit; and calculate the second predicted arrival time of the on-the-way users to the predicted exit based on the driving speed of the on-the-way users and the target passage path.

[0182] It should be noted that the pass rate of historical paths is the ratio of the number of times a historical path has been used to the total number of historical paths. If only one historical path is obtained, the second target historical path with the highest pass rate is itself.

[0183] For example: Assuming the entry point information for a user in transit is Gate A, and the currently passed gantry billing nodes are gantry 1 and gantry 2, the actual travel path of the user in transit can be reconstructed as: Gate A - Gantry 1 - Gantry 2. Matching the path from Gate A to Gantry 1 to Gantry 2 with the user's historical travel data yields the following matching results:

[0184] (Ⅰ) A entrance - gantry 1 - gantry 2 - gantry 3 - gantry 5 - exit b, the number of passages is 5;

[0185] (II) A entrance - gantry 1 - gantry 2 - gantry 3 - gantry 6 - exit c, the number of passages is 3;

[0186] (Ⅲ) A entrance - gantry 1 - gantry 2 - gantry 4 - gantry 7 - exit d, the number of passages is 2;

[0187] As shown above, the total number of passage flows satisfying the conditions of entrance A and gantry 1-gantry 2 is 10. The corresponding historical passage paths are paths (Ⅰ), (Ⅱ), and (Ⅲ), and the historical exits corresponding to these historical paths are b, c, and d, with corresponding counts of 5, 3, and 2 times, respectively. The calculated percentages of these historical passage paths are 50%, 30%, and 20%, respectively. Therefore, historical path (Ⅰ) is the second target passage path, and exit b is the second predicted exit.

[0188] It is understood that the above case is only one example and is not a limitation of the implementation of this embodiment. If there are more occurrences or other matching results, such as multiple different passage paths between entrance A and exit b, the above calculation method can be referred to to obtain the passage ratio of different paths at the same exit, and then the target passage path and the second predicted arrival time can be determined based on the passage ratio.

[0189] S14: Update the first toll bill based on the entrance information, the second predicted exit, and the second target passage path to obtain the second toll bill;

[0190] It should be noted that the toll is recalculated based on the second target travel route, the second predicted exit, and the entrance information to obtain the second toll bill.

[0191] S15: Match the exit information of the second toll station with the congested exit information. When the match is successful, push the second toll bill to the user terminal of the user on the way.

[0192] As mentioned above, the exit information for the second toll station includes the second predicted arrival time and the second predicted exit. The matching principle can be found in step 105, and will not be repeated here.

[0193] In this embodiment, if no payment confirmation result is received within a first preset time, real-time on-the-go data is obtained, and the exit information of the first toll station and the first toll bill are updated based on the real-time on-the-go data and the entrance information to obtain the exit information of the second toll station and the second toll bill. This corrects the destination toll station exit for the on-the-go user. By matching the exit information of the second toll station and the second toll bill, when the match is successful, the second toll bill is pushed to the user terminal of the on-the-go user, providing the on-the-go user with an exit prediction that better meets the user's needs, thereby facilitating the on-the-go user to prepay the toll in advance.

[0194] In another preferred embodiment, step S5 is followed by...

[0195] S16: When real-time on-the-go data of the next node of the on-the-go user is received, execute steps S11-S15.

[0196] It should be noted that every time a vehicle passes through a gantry, the gantry will upload the real-time on-the-go data of the user. When a new gantry node receives real-time on-the-go data, the actual driving route is updated according to the latest real-time on-the-go data, and then the information of the second exit toll station is updated. This provides users with a more suitable exit and toll bill, so that users can prepay the toll in advance.

[0197] 303. If the payment confirmation result of the second toll bill is not received within the second preset time, and the estimated fee information sent by the preset target gantry is received, a third toll bill is generated based on the estimated fee information. When it is determined that the user in transit is a user who pays without a password, a request for payment without a password is initiated to the third-party payment platform based on the estimated fee information. Upon receiving the payment without a password callback information from the third-party payment platform, payment without a password is generated based on the payment without a password callback information.

[0198] It should be noted that the preset target gantry refers to the gantry set up on the exit ramp of the toll station. Receiving the estimated toll information from the preset target gantry indicates that the vehicle is already on the exit ramp and is about to arrive at the toll plaza. The location of the preset target gantry can be determined based on the actual ramp conditions. The system database pre-stores the association between preset target gantry information and the toll station exit information represented by the preset target gantry. If no payment confirmation result is received within the second preset time, it indicates that the user has not prepaid the toll, which may be because the predicted exit is not the user's destination, or because the user is unable to confirm payment. If the estimated toll information is received from the preset target gantry at this time, it indicates that the user is currently on the exit ramp of the destination toll station and is about to arrive at the destination toll station.

[0199] Specifically, when a vehicle holding a CPC card passes through a preset target gantry, the gantry reads the estimated toll information stored in the CPC card, including highway entrance information, license plate number, toll amount, vehicle type, and vehicle class. Based on the estimated toll information sent by the preset target gantry and the toll station exit name represented by the preset target gantry stored in the database, a third toll bill can be calculated. It is understood that the calculation of toll fees can refer to relevant existing highway toll regulations, which will not be elaborated upon in this embodiment. The third toll bill includes entrance information, exit information, license plate number, vehicle type, vehicle class, toll station information, total toll amount, etc.

[0200] During the user registration phase, once user information is approved, users in transit can choose whether to sign up for password-free payment with a third-party payment platform. Users who have signed up for password-free payment and whose password-free mode is actively activated are considered password-free payment users. Therefore, after generating a third-party toll bill, the system checks whether the user in transit is a password-free payment user. If so, it indicates that the user supports password-free payment, and a password-free payment request is initiated to the third-party payment platform authorized by the user. After receiving the request, the third-party payment platform pays the corresponding fee and returns the corresponding password-free payment callback information. Payment information is then generated based on the payment callback information.

[0201] In this embodiment, when no payment confirmation result for the second toll bill is received within a second preset time, and the estimated toll information sent by the preset target gantry is received, a third toll bill is generated based on the estimated toll information. When it is determined that the user in transit is a user who pays without a password, a request for payment without a password is initiated to the third-party payment platform based on the estimated toll information. Upon receiving the payment without a password callback information from the third-party payment platform, payment without a password is generated based on the payment without a password callback information. This further improves the convenience of toll payment for users in transit and further enhances the vehicle traffic efficiency of the MTC lane and mixed traffic lane at the toll station exit.

[0202] 304. When it is determined that the user in transit is not a user who pays without a password, the third-party toll bill is pushed to the user's terminal. Upon receiving the user's confirmation payment instruction, a payment request is initiated to the third-party payment platform. Upon receiving the payment callback information from the third-party payment platform, payment information is generated based on the payment callback information.

[0203] It should be noted that when a user in transit is determined to be a non-password-free user, it means that the user in transit does not support password-free payment or the password-free mode activation status is abnormal, and the fee cannot be deducted directly according to the password-free payment mode. Therefore, the generated third-party toll bill is sent to the user terminal of the user in transit. When the payment confirmation instruction is received from the user in transit, a payment request is initiated to the third-party payment platform. When the payment callback information is received from the third-party payment platform, the payment information is generated according to the payment callback information.

[0204] Example 4:

[0205] Based on Embodiment 1, Embodiment 2, or Embodiment 3, the payment method provided in Embodiment 4 further includes: when receiving a first bill query instruction from a user in transit or a user not in transit, parsing the first bill query instruction to obtain first passage information, querying the corresponding fourth passage fee bill based on the first passage information, and pushing the fourth passage fee bill to the user in transit or a user not in transit; when receiving a confirmation payment instruction from the user in transit or a user not in transit, initiating a payment request to a third-party payment platform, and generating payment information based on the payment callback information received from the third-party payment platform.

[0206] It should be noted that non-traveling users who have not enabled message notifications or password-free access, or who have not bound vehicle information, can proactively initiate a bill inquiry by scanning a QR code or following a public WeChat account and inputting their corresponding toll information (such as tollbooth exit, license plate, license plate color, CPC card number, etc.). For users who have passed through tollbooths before, they can click on "Historical License Plate Inquiry" to automatically generate a first bill inquiry instruction containing license plate information. Upon receiving the toll bill, the user sends a payment confirmation instruction to the system through their user terminal to complete the toll payment. It is understood that users on the road can inquire about and prepay their bills at toll plazas, service areas, or other locations. The specific location and inquiry channel can be chosen according to the actual situation, and this embodiment does not impose specific limitations.

[0207] In this implementation, when a query instruction is received, the query instruction is parsed to obtain the passage information. Based on the passage information, the corresponding toll bill is retrieved from the system database and sent to the corresponding user terminal. Upon receiving a payment confirmation instruction from a user in transit or a user not in transit, a payment request is initiated to a third-party payment platform. Upon receiving payment callback information from the third-party payment platform, payment information is generated based on the payment callback information. Thus, both users not in transit and users in transit who have not bound vehicle information can pay tolls in advance. This embodiment further improves the vehicle passage efficiency at toll station exits.

[0208] In another preferred embodiment, it further includes:

[0209] When a second bill query instruction is received, the system parses the instruction to obtain the second passage information. Based on this second passage information, it queries and outputs the corresponding fifth passage fee bill. Upon receiving a confirmation instruction for the fifth passage fee bill, it determines whether the user corresponding to the bill is a password-free payment user. If so, it initiates a payment request to the third-party payment platform and generates payment information based on the payment callback information received from the platform. If not, it initiates a payment confirmation request to the user's terminal and, upon receiving a payment confirmation instruction from the terminal, initiates a payment request to the third-party payment platform and generates payment information based on the payment callback information received from the platform.

[0210] It should be noted that the second bill inquiry command can be a query command generated by the CPC card number entered by the toll collector, or it can be triggered by the toll collector swiping the CPC card using an RFID device. In this embodiment, if a user in transit does not pay the fee in advance and has already returned the CPC card at the toll booth, the toll collector can swipe the card using an RFID device, or swipe the card to retrieve the corresponding toll bill. After verifying the toll bill, the toll collector can input a confirmation command. Upon receiving the confirmation command, the system determines whether the user of the toll bill is a password-free payment user. If so, the password-free payment process is initiated; otherwise, a payment confirmation request is sent to the user terminal corresponding to the toll bill. After the user confirms the payment, a payment request is sent to the third-party payment platform to complete the payment, reducing the time for users to scan the code for payment and further improving passage efficiency.

[0211] Example 5:

[0212] Based on Embodiment 1, Embodiment 2, Embodiment 3, or Embodiment 4, the payment method provided in Embodiment 5 further includes:

[0213] When a refund instruction is received, the system parses the instruction to obtain the refund user information, sends a refund request to the third-party payment platform based on the refund user information, and generates a refund result based on the refund callback information received from the third-party payment platform.

[0214] It should be noted that the refund information includes the user's account, the refund path, and the refund amount. When a refund instruction is received, a refund request is initiated to the third-party payment platform based on the user's account, the refund path, and the refund amount. The third-party payment platform responds to the refund request and returns the refund amount to the user's account.

[0215] In this embodiment, it can be understood that when a user has prepaid the toll and returns their CPC card at the tollbooth exit, the tollbooth exit staff swipes the card or enters the CPC card number into the lane toll collection system. The lane toll collection system at the tollbooth exit retrieves the corresponding payment information based on the received CPC card information and verifies it. If any discrepancies are found, such as inconsistent license plate or vehicle type, or incorrect toll, a lane refund is automatically initiated (the paid toll will be returned to the user's account). The user then re-pays the toll according to the existing standard procedure. If the user has signed up for password-free payment, they can directly use password-free payment when re-paying the toll. Additionally, if a user makes a payment error due to special circumstances, the roadside customer service can process a refund in the system backend. The system will then initiate a refund request to a third-party payment platform, allowing the platform to return the refund amount to the user.

[0216] Example 6:

[0217] Based on Embodiment 1, Embodiment 2, Embodiment 3, Embodiment 4, or Embodiment 5, the payment method provided in Embodiment 6 further includes: when receiving real-time entry information of a user in transit, determining whether the user in transit has signed up for password-free payment based on the user information of the user in transit; if so, activating the password-free payment mode, and determining in real time whether the status of the password-free payment mode is normal; if so, determining that the user in transit is a password-free payment user.

[0218] It should be noted that the status of the password-free payment mode can be determined by calling the in-transit user status query API. Based on the query results, it can be determined whether the in-transit user has activated, authorized, has outstanding fees, or is on the blacklist.

[0219] In one specific embodiment, the method further includes: turning off the password-free payment mode after generating payment information for users who make password-free payments.

[0220] This embodiment improves the smoothness and security of payment by monitoring the password-free payment mode of users on the road in real time and turning it off when not on the road, thereby further improving the vehicle passage efficiency at the toll station exit.

[0221] Example 7:

[0222] See Figure 5 Embodiment 7 of the present invention provides a payment system based on a high-speed composite toll card, which specifically includes:

[0223] The traffic flow data acquisition module 401 is used to acquire traffic flow data within a preset distance range of the toll station exit;

[0224] The congestion prediction module 402 is used to predict congestion based on traffic flow data and preset congestion factors, and obtain congestion exit information.

[0225] The on-the-go user data acquisition module 403 is used to acquire the historical passage data and entry information of on-the-go users;

[0226] The exit prediction module 404 is used to predict the exit based on historical traffic data and entrance information, and to obtain the first toll station exit information and the first toll bill for users on the way.

[0227] The matching module 405 is used to match the exit information of the first toll station with the congested exit information. When the match is successful, the first toll bill is pushed to the user terminal of the user on the way.

[0228] The information generation module 406 is used to generate payment information based on the payment information when the payment information of the first toll bill is received.

[0229] In one specific embodiment, the congestion prediction module 402 specifically includes:

[0230] The first calculation unit is used to calculate the predicted number of vehicles at the toll station exit during the predicted time period based on traffic flow data.

[0231] The second calculation unit is used to calculate the congestion parameters at the toll station exit based on the predicted number of vehicles and the preset congestion factor.

[0232] The labeling unit is used to label the toll station exit as a congested exit and the predicted time period as a congested time period when the congestion parameter is not less than a preset threshold; and to use the congested exit and the congested time period as congested exit information.

[0233] In one specific embodiment, the first computing unit includes:

[0234] The first determining subunit is used to determine the historical diversion ratio associated with the gantry based on the gantry identifier and a preset first association relationship; the first association relationship is the association relationship between the gantry identifier and the historical diversion ratio.

[0235] The second determining subunit is used to determine the historical time period diversion ratio corresponding to the collection time period from the historical classification ratio based on the collection time period and the preset second association relationship.

[0236] The third determining sub-unit is used to calculate the predicted number of vehicles for the predicted period based on the historical diversion ratio and the number of gantry vehicles.

[0237] In one specific embodiment, the second calculation unit is specifically used to multiply the number of trucks by the truck's outer dimensions to obtain the truck capacity, and multiply the number of buses by the bus's outer dimensions to obtain the bus capacity; and sum the truck capacity and bus capacity to obtain the congestion parameters at the toll station exit.

[0238] In a preferred embodiment, the system further includes:

[0239] The real-time on-the-go data acquisition module is used to acquire the real-time on-the-go data of users when no payment confirmation result for the first toll bill is received within a first preset time.

[0240] The update module is used to update the exit information of the first toll station and the first toll bill based on real-time on-the-way data and entrance information, and to obtain the exit information of the second toll station and the second toll bill.

[0241] Update the matching module to match the exit information of the second toll station with the congested exit information. When the match is successful, the second toll bill will be pushed to the user terminal of the user on the way.

[0242] In one specific embodiment, the system further includes:

[0243] The toll prepayment bill generation module is used to generate a third toll bill based on the estimated fee information when no payment confirmation result for the second toll bill is received within a second preset time, and the estimated fee information sent by the preset target gantry is received.

[0244] The password-free payment information generation module is used to initiate a password-free payment request to the third-party payment platform based on the estimated fee information when it is determined that the user in transit is a password-free payment user, and to generate password-free payment information based on the password-free payment callback information received from the third-party payment platform.

[0245] In a preferred embodiment, the system further includes:

[0246] The correction module is used to parse the temporary correction instruction when it receives it, obtain the correction period and correction object information, and correct the historical period diversion ratio based on the correction period and correction object information.

[0247] In a specific application example, when building this system in practice, you can refer to, for example... Figure 6The system architecture shown can be divided into three zones based on the flow of data interaction: the Internet zone, the security front-end zone, and the toll collection zone. The Internet zone includes third-party payment platforms and mobile terminals. A firewall separates the Internet zone from the security front-end zone. The security front-end zone includes pre-payment interface services, an unknown threat detection system, a network access control system, a front-end interactive unit, and a security management module. The security management module includes log auditing, database auditing, vulnerability scanning, and a bastion host. A network gateway separates the security front-end zone from the toll collection authority. The toll collection zone includes a core switch, a pre-payment mini-program backend, and the toll collection system. The toll collection system includes a toll support system and a transmission system. The pre-payment mini-program backend includes the pre-payment mini-program and the pre-payment backend. The pre-payment backend interacts with the front-end pre-payment mini-program backend and the toll support system, providing users with functions such as pre-payment bill inquiry, payment, and refund. The toll support system receives billing data from the gantry and generates pre-payment bills, which are then sent to the pre-payment backend via the transmission system.

[0248] In another specific application example, such as Figure 7 As shown, the system architecture provided in this embodiment, divided according to the application layer, can be divided into user terminals, front-end systems, back-end applications, and technical support. The user terminals include a pre-payment mini-program, a pre-payment push module, and a congestion exit notification module. The front-end system includes a CPC estimated toll system, a hybrid lane toll system, a pre-transaction multicast system, a congestion prediction module, and a route prediction module. The back-end applications include a toll support platform, a mobile payment back-end, an invoice platform, and a pre-payment system management platform. The lowest-level technical support includes microservice architecture, queue partitioning technology, ramp estimated toll technology, and data visualization technology. It is understood that the above application-layer architecture can be constructed according to actual conditions.

[0249] In another preferred embodiment, the present invention provides an electronic device, the device including a processor and a memory:

[0250] The memory is used to store program code and transmit the program code to the processor;

[0251] The processor is used to execute the above method embodiment according to the instructions in the program code.

[0252] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0253] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0254] Furthermore, in the various embodiments of the present invention, the functional units can be integrated into one processing unit, or each functional unit can be a separate physical entity, or two or more functional units can be integrated into one processing unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0255] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0256] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0257] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A payment method based on a high-speed composite toll card, characterized in that, The method includes: Obtain traffic flow data within a preset distance range of the toll station exit; Congestion prediction is performed based on the traffic flow data and preset congestion factors to obtain congestion exit information; Obtain historical passage data and entry information of users currently en route; Based on the historical traffic data and the entrance information, exit prediction is performed to obtain the first toll station exit information and the first toll bill for the user in transit. The first toll station exit information and the congested exit information are matched. When the match is successful, the first toll bill is pushed to the user terminal of the user in transit. When the payment information of the first toll bill is received, payment information is generated based on the payment information; The step of predicting congestion based on the traffic flow data and preset congestion factors to obtain congestion exit information includes: Based on the traffic flow data, the predicted number of vehicles at the toll station exit during the predicted time period is calculated; Based on the predicted number of vehicles and the preset congestion factor, the congestion parameters at the toll station exit are calculated. When the congestion parameter is not less than a preset threshold, the toll station exit is marked as a congested exit, and the predicted time period is marked as a congested time period; and the congested exit and the congested time period are used as the congested exit information; The traffic flow data includes: gantry identification, number of vehicles at the gantry, collection period, and traffic flow statistics duration; the calculation of the predicted number of vehicles at the toll station exit during the predicted period based on the traffic flow data includes: Based on the gantry identifier and a preset first association relationship, the historical diversion ratio associated with the gantry identifier is determined; the first association relationship is the association between the gantry identifier and the historical diversion ratio. Based on the collection period and the preset second association relationship, the historical time period diversion ratio corresponding to the collection period is determined from the historical diversion ratio; The predicted number of vehicles for the predicted period is calculated based on the historical time period diversion ratio, the number of gantry vehicles, and the traffic flow statistics duration.

2. The method according to claim 1, characterized in that, The predicted number of vehicles includes: the number of trucks and the number of buses; the preset congestion factor includes the external dimensions of trucks and buses; the preset threshold includes the capacity threshold of the toll plaza. The calculation of congestion parameters at the toll station exit based on the predicted number of vehicles and a preset congestion factor includes: The truck capacity is obtained by multiplying the number of trucks by their external dimensions, and the bus capacity is obtained by multiplying the number of buses by their external dimensions. The truck capacity and the bus capacity are then summed to obtain the congestion parameters at the toll station exit.

3. The method according to claim 1, characterized in that, If no payment confirmation result for the first toll bill is received within a first preset time, the method further includes: Obtain the real-time on-the-go data of the users in transit; Based on the real-time on-the-way data, the historical passage data, and the entrance information, update the first toll station exit information and the first toll bill to obtain the second toll station exit information and the second toll bill. The step of matching the first toll station exit information and the congested exit information, and pushing the first toll bill to the user terminal of the user in transit when the match is successful, includes: The second toll station exit information and the congested exit information are matched. When the match is successful, the second toll bill is pushed to the user terminal of the user on the way.

4. The method according to claim 3, characterized in that, The method further includes: If no payment confirmation result for the second toll bill is received within the second preset time, and the estimated fee information sent by the preset target gantry is received, a third toll bill is generated based on the estimated fee information. When the user in transit is determined to be a user who can make payment without a password, a payment request without a password is initiated to the third-party payment platform based on the estimated fee information. Upon receiving the payment callback information from the third-party payment platform, payment information without a password is generated based on the payment callback information without a password.

5. The method according to claim 4, characterized in that, The method further includes: When it is determined that the user in transit is not a user who pays without a password, the third toll bill is pushed to the user's terminal. Upon receiving the user's confirmation payment instruction, a payment request is initiated to the third-party payment platform. Upon receiving the payment callback information from the third-party payment platform, payment information is generated based on the payment callback information.

6. The method according to claim 1, characterized in that, The method further includes: When a temporary correction instruction is received, the instruction is parsed to obtain the correction period and correction object information. Based on the correction period and correction object information, the historical period diversion ratio is corrected.

7. A payment system based on a high-speed composite toll card, characterized in that, The system includes: The traffic flow data acquisition module is used to acquire traffic flow data within a preset distance range from the toll station exit; The congestion prediction module is used to predict congestion based on the traffic flow data and preset congestion factors, and obtain congestion exit information. The on-the-go user data acquisition module is used to acquire historical passage data and entry information of on-the-go users. The exit prediction module is used to predict the exit based on the historical passage data and the entrance information, and to obtain the first toll station exit information and the first toll bill of the user in transit. The matching module is used to match the exit information of the first toll station with the congested exit information. When the match is successful, the first toll bill is pushed to the user terminal of the user in transit. The information generation module is used to generate payment information based on the payment information when the payment information of the first toll bill is received; The congestion prediction module includes: The first calculation unit is used to calculate the predicted number of vehicles at the toll station exit during the predicted time period based on the traffic flow data. The second calculation unit is used to calculate the congestion parameters of the toll station exit based on the predicted number of vehicles and the preset congestion factor. The labeling unit is used to label the toll station exit as a congested exit and the predicted time period as a congested time period when the congestion parameter is not less than a preset threshold; and to use the congested exit and the congested time period as the congested exit information. The traffic flow data includes: gantry identification, number of vehicles on the gantry, collection period, and traffic flow statistics duration; The first computing unit includes: The first determining subunit is used to determine the historical diversion ratio associated with the gantry identifier based on the gantry identifier and a preset first association relationship; the first association relationship is the association relationship between the gantry identifier and the historical diversion ratio. The second determining subunit is used to determine the historical diversion ratio corresponding to the collection period from the historical diversion ratio based on the collection period and the preset second association relationship; The third determining subunit is used to calculate the predicted number of vehicles for the predicted period based on the historical time period diversion ratio, the number of gantry vehicles, and the traffic flow statistics duration.

8. An electronic device, characterized in that, The device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the method as described in any one of claims 1-6 according to instructions in the program code.

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