A method and system for dynamically regulating service capacity of a highway toll station

Abstract

The application provides a highway toll station service capacity dynamic regulation method and system, relates to the highway management and control technical field, and the method comprises the following steps: collecting traffic operation state sensing data, real-time operation data and AI event detection data of the toll station of the highway, and fusing and generating comprehensive operation state evaluation data set; based on the comprehensive operation state evaluation data set, it is judged whether the closing condition of the toll station is met, if the closing condition is met, the closing operation of the toll station is executed, and the temporary closing prompt information is pushed to the vehicle end; if the closing condition is not met, the number of entrance and exit lanes of the toll station is dynamically allocated according to the entrance and exit traffic flow and the average service capacity of the entrance and exit lanes of the toll station, and the slow-down prompt information is pushed to the vehicle end, so that the service capacity of the highway toll station is dynamically regulated to guarantee the smooth road and improve the operation efficiency.

Description

Technical Field

[0001] This invention relates to the field of highway management and control technology, and in particular to a method and system for dynamic regulation of the service capacity of highway toll stations. Background Technology

[0002] With the continuous improvement of the expressway network and the sustained growth of vehicle travel, expressway traffic congestion is becoming increasingly frequent. As a crucial connection point between expressways and ordinary roads, the service capacity of toll stations directly affects the overall traffic efficiency and the public's travel experience. A scientifically sound method for configuring toll station service capacity can dynamically match toll collection capacity with changes in traffic demand, thereby effectively reducing average vehicle delays, alleviating queuing congestion, and improving the overall operational efficiency of the road.

[0003] Currently, highway toll stations generally adopt a static configuration method, with a fixed number of lanes. Service capacity adjustment mainly relies on manual experience and judgment, resulting in a slow response speed and difficulty in adapting to rapid changes in traffic flow. During periods of traffic flow fluctuation or peak holiday periods, problems such as low lane resource utilization, severe congestion in some lanes, and excessively long vehicle queuing times often occur.

[0004] Therefore, it is necessary to provide a method and system for dynamic regulation of the service capacity of highway toll stations to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention provides a method for dynamically adjusting the service capacity of highway toll stations, the method comprising: Collect traffic operation status perception data, real-time operation data, and AI event detection data from highway toll stations, and integrate them to generate a comprehensive operation status assessment dataset; Based on the comprehensive operational status assessment dataset, it is determined whether the closing conditions of the toll station are met. If the closing conditions are met, the closing operation of the toll station is executed, and a temporary closure prompt message is pushed to the vehicle. If the closing conditions are not met, the number of entrance and exit lanes of the toll station will be dynamically allocated based on the traffic flow at the entrance and exit of the toll station and the average service capacity of the entrance and exit lanes, and a slow-down reminder message will be pushed to the vehicle.

[0006] Preferably, the collection of traffic operation status perception data, real-time operation data, and AI event detection data from highway toll stations, and the fusion of these data to generate a comprehensive operation status assessment dataset, specifically includes: The traffic operation monitoring system at the toll station collects traffic operation status perception data and obtains real-time operation data of the toll station; wherein, the traffic operation status perception data includes at least the hourly traffic flow at the entrance. Hourly traffic flow at exits And the downstream traffic flow speed v; the real-time operating data includes at least lane opening status, toll collection method and vehicle queuing status; An AI-based event detection platform is introduced to acquire real-time information on emergencies at the toll station and its upstream and downstream road sections; the emergency information includes at least traffic accidents, vehicle congestion, and equipment malfunctions. The comprehensive operational status assessment is formed by integrating the traffic operation status perception data, the real-time operation data, and the AI ​​event detection data.

[0007] Preferably, the closure conditions include severe weather or traffic congestion on the downstream section of the toll station.

[0008] Preferably, the process for determining whether the downstream section of the toll station is in a state of traffic congestion is as follows: Obtain the downstream road segment service capacity C and the downstream road segment baseline traffic congestion threshold coefficient of the toll station. Downstream road segment baseline traffic flow speed threshold ; Based on real-time weather type, time period characteristics, and traffic flow fluctuation coefficient, a dynamic correction model for congestion determination threshold is used to adjust the baseline traffic congestion threshold coefficient for the downstream road segment. and the downstream road segment reference traffic flow speed threshold Dynamic corrections are performed to obtain the real-time traffic congestion threshold coefficient M and the real-time traffic flow velocity threshold K of the downstream road segment; in, , , This is a weather correction factor; This is the flow fluctuation correction factor, and , Real-time peak hour traffic flow for downstream road sections; Real-time off-peak hourly traffic flow for downstream road sections; For weather safety correction factors; This is a time period correction factor; This refers to real-time traffic weighting coefficients. like and If so, it is determined that the downstream section of the toll station is in a state of traffic congestion.

[0009] Preferably, if the closing condition is not met, the number of entrance and exit lanes of the toll station is dynamically allocated based on the traffic flow at the entrance and exit of the toll station and the average service capacity of the entrance and exit lanes, specifically including: If the closing conditions are not met, the proportion of ETC vehicles in the entrance traffic flow of the toll station will be calculated. MTC vehicle ratio The proportion of vehicles requiring manual toll collection And the proportion of ETC vehicles in the outbound traffic flow MTC vehicle ratio The proportion of vehicles requiring manual toll collection ;in, , ; Based on the traffic flow at the toll station's entrances and exits and the average service capacity of the entrance and exit lanes, combined with the vehicle type ratio, the number of entrance lanes at the toll station is calculated. and number of exit lanes as follows: In the formula, Average service capacity of the entrance lanes at toll stations; Average service capacity of the exit lanes at the toll station; The queuing delay at the toll station is calculated as follows: In the formula, The total service capacity of the entrance lanes at the toll station; The total service capacity of the exit lanes at the toll station; The total number of lanes at the toll station is N. Based on the lane type-traffic direction collaborative optimization allocation model, and taking into account the operating efficiency, service level and resource utilization of the toll station, the number of entrance and exit lanes and lane types are collaboratively allocated with the goal of maximizing the comprehensive utilization rate of lane resources at the toll station. The objective function of the lane type-traffic direction collaborative optimization allocation model is: The constraints of the lane type-traffic direction collaborative optimization allocation model are: In the formula, F represents the comprehensive utilization rate of lane resources at the toll station; This refers to the number of ETC lanes, MTC lanes, and manual lanes at the toll station entrance. This refers to the number of ETC lanes, MTC lanes, and manual lanes at the toll station exit. The final number of entrance lanes for the toll station; The final number of exit lanes for the toll station; This is the floor function operator; The weighting coefficients are optimized for multiple objectives and are used to characterize the relative importance of lane resource cost, vehicle queuing delay, and vehicle traffic efficiency to the overall utilization rate of lane resources. when At that time, , ; when At that time, if ,but , ;like ,but , .

[0010] Preferably, the total service capacity of the entrance lanes of the toll station as follows: In the formula, The service capacity of the f-th entrance lane of the toll station; The average service capacity of the entrance lanes at the toll station as follows: ; Total service capacity of the exit lanes at the toll station as follows: In the formula, The service capacity of the g-th exit lane at the toll station; The average service capacity of the exit lanes at the toll station as follows: .

[0011] Preferably, if the entrance lane of the toll station is a manual lane, then the service capacity of the entrance lane is the same as the service capacity of the manual lane. The calculation formula is as follows: In the formula, The average service time for manual lanes is the average time from when a vehicle starts receiving service to when it finishes service and starts to leave on a manual lane. This is the baseline processing time for vehicle type s; This refers to the proportion of vehicle type s, i.e., the percentage of vehicle type s in the total number of vehicle types. This is a weather correction factor; Traffic condition correction factor; Q is traffic flow; For traffic capacity; Sensitivity coefficient; This is a correction factor for labor efficiency. This is the efficiency reduction factor; Let Q(j) be the traffic flow fluctuation coefficient; Q(j) is the traffic flow at historical time j. The average traffic flow for historical period J; t(j) represents the service time fluctuation coefficient; t(j) represents the service time at historical time j. The average service time for historical period J; To reduce the weighting factor for efficiency, in manual lanes, The value is 0.3. The value is 0.7; If the entrance lane of the toll station is an MTC lane, then the service capacity of the entrance lane is the service capacity of the MTC lane. The calculation formula is as follows: In the formula, The average service time for the MTC lane is the average time from when a vehicle starts receiving service to when it finishes service and starts to leave the MTC lane. This is the MTC efficiency correction factor, in the MTC lane. The value is 0.5. The value is 0.5; If the entrance lane of the toll station is an ETC lane, then the service capacity of the entrance lane is the service capacity of the ETC lane. The calculation formula is as follows: In the formula, The average service time for an ETC lane is the average time from when a vehicle starts receiving service to when it finishes receiving service and starts leaving the ETC lane. This is the ETC efficiency correction factor, used in ETC lanes. The value is 0.7. The value is 0.3; When the exit lanes of the toll station are manual lanes, MTC lanes, and ETC lanes, the corresponding service capacity is calculated in the same way as above.

[0012] Preferably, when dynamically allocating the number of entrance and exit lanes of the toll station, if the toll station has tidal flow lane facilities, the number of entrance and exit lanes is set using the tidal flow lane method; if the toll station does not have the tidal flow lane facilities, the number of entrance and exit lanes is controlled by lane indicators.

[0013] Preferably, the temporary shutdown prompt and the slow-down prompt are pushed to the vehicle via an information board, an audio-visual prompt device, and an in-vehicle navigation terminal.

[0014] A dynamic control system for the service capacity of highway toll stations, the system comprising: The data acquisition module is used to collect traffic operation status perception data, real-time operation data and AI event detection data from highway toll stations, and integrate them to generate a comprehensive operation status assessment dataset. The closing condition determination module is used to determine whether the closing conditions of the toll station are met based on the comprehensive operation status evaluation dataset. If the closing conditions are met, the closing operation of the toll station is executed, and a temporary closing prompt message is pushed to the vehicle. The lane allocation module is used to dynamically allocate the number of entrance and exit lanes of the toll station based on the entrance and exit traffic flow and the average service capacity of the entrance and exit lanes if the closing conditions are not met, and push slow down prompt information to the vehicle.

[0015] Compared with related technologies, the method and system for dynamic control of service capacity of highway toll stations provided by the present invention have the following beneficial effects: This invention collects traffic operation status perception data, real-time operation data, and AI event detection data from highway toll stations and integrates them to generate a comprehensive operation status assessment dataset. Based on this dataset, it determines whether the conditions for closing the toll station are met. If the conditions are met, the toll station is closed, and a temporary closure notification is sent to the vehicle. If the conditions are not met, the number of entrance and exit lanes is dynamically allocated based on the traffic flow and average service capacity of the entrance and exit lanes, and a slow-down notification is sent to the vehicle. This dynamically adjusts the service capacity of highway toll stations to ensure smooth traffic flow and improve operational efficiency.

[0016] This invention dynamically adjusts toll station closure conditions, lane allocation, and lane direction settings by collecting real-time traffic operation data from highway toll stations and combining it with AI event detection results. This achieves scientific and efficient regulation of toll station service capacity. The invention fully considers key parameters such as toll station entrance and exit traffic flow, downstream road capacity, and operating speed. While ensuring no traffic congestion on the main road, it scientifically determines toll station closure conditions and lane opening / closing schemes, ensuring the effectiveness of the dynamic toll station control strategy. This effectively alleviates traffic pressure at toll stations and downstream areas, improving the overall traffic efficiency of the highway mainline and toll stations. Attached Figure Description

[0017] Figure 1 A flowchart of a method for dynamically adjusting the service capacity of a highway toll station, provided as an embodiment of the present invention; Figure 2 A system block diagram of a dynamic control system for the service capacity of a highway toll station provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] like Figure 1 The diagram shown is a flowchart of a method for dynamically adjusting the service capacity of a highway toll station according to an embodiment of the present invention. Figure 1 The execution entity of the method shown can be a software and / or hardware device. The execution entity of this application can include, but is not limited to, at least one of the following: user equipment, network equipment, etc. User equipment can include, but is not limited to, computers, smartphones, personal digital assistants (PDAs), and the aforementioned electronic devices. Network equipment can include, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers. Cloud computing is a type of distributed computing, consisting of a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. Steps S1 to S3 are detailed as follows: S1 collects traffic operation status perception data, real-time operation data, and AI event detection data from highway toll stations, and integrates them to generate a comprehensive operation status assessment dataset; S2, based on the comprehensive operation status evaluation dataset, determine whether the closing conditions of the toll station are met. If the closing conditions are met, execute the closing operation of the toll station and push a temporary closure prompt message to the vehicle. S3. If the closing conditions are not met, the number of entrance and exit lanes of the toll station is dynamically allocated based on the traffic flow at the entrance and exit of the toll station and the average service capacity of the entrance and exit lanes, and a slow-down prompt message is pushed to the vehicle.

[0020] In practical applications, the toll station traffic operation monitoring system accurately collects core traffic operation status perception data, covering hourly traffic flow at entrances, hourly traffic flow at exits, and traffic flow speed on downstream road sections. Simultaneously, it acquires real-time operational data of the toll station, including lane opening status, toll collection methods, and vehicle queuing conditions, providing a comprehensive understanding of the toll station's basic operational status. An AI-based event detection platform is introduced to capture real-time information on emergencies at the toll station and upstream / downstream road sections, such as traffic accidents, vehicle congestion, and equipment malfunctions. Through multi-source data fusion technology, traffic operation status data, real-time operation data, and AI event detection data are integrated to form a comprehensive operational status assessment dataset, effectively avoiding decision-making biases caused by the limitations of a single data dimension.

[0021] Based on a comprehensive operational status assessment dataset, the system automatically determines whether the conditions for closing a toll station are met. These conditions are clearly defined into two core scenarios: first, severe weather such as heavy rain, heavy snow, or dense fog, which would seriously affect driving safety, and continuing to keep the toll station open could lead to traffic accidents; second, traffic congestion downstream of the toll station. Quantitative indicators are used to accurately determine congestion levels, preventing a continuous influx of vehicles that could worsen congestion and ensuring smooth traffic flow on the main road. When either condition is met, the system immediately executes the closure operation. Simultaneously, it pushes a "XX toll station is temporarily closed, please choose an alternative route" message to vehicles through multiple channels, including information boards, audio-visual prompts, and in-vehicle navigation terminals. This synchronizes the closure decision with travel guidance, minimizing the impact on public travel.

[0022] If the closure conditions are not met, the required number of lanes at the toll station entrances and exits is scientifically calculated based on traffic flow data and the average service capacity of the lanes. The average lane service capacity fully considers the differences in service efficiency among manual, MTC, and ETC lanes to ensure a high degree of consistency between the calculated lane demand and actual operation. Then, a differentiated lane allocation strategy is developed based on the total number of lanes at the toll station: when the total number of lanes meets the sum of the required lanes for entrances and exits, the calculated number of lanes is allocated accordingly; when the total number of lanes is insufficient, priority is given to allocating lanes for entrances and exits with greater demand, achieving optimal allocation of lane resources. If the toll station has tidal flow lane facilities, lane direction is flexibly adjusted using tidal flow lanes; if it does not have such facilities, lane control is achieved through lane indicators. Simultaneously, a "Tidal flow toll station, please slow down" message is pushed to vehicles through multiple channels to guide safe and orderly passage, ultimately achieving dynamic matching between toll station service capacity and traffic demand, effectively alleviating queuing congestion and improving overall traffic efficiency.

[0023] The above methods can solve the problems of slow response, low resource utilization, and poor congestion relief in traditional static lane configuration and manual control, and achieve dynamic adaptation of highway toll station service capacity to real-time traffic demand, thereby improving highway traffic efficiency and public travel experience.

[0024] In the specific implementation process, the collection of traffic operation status perception data, real-time operation data, and AI event detection data from highway toll stations is integrated to generate a comprehensive operation status assessment dataset, which specifically includes: The traffic operation monitoring system at the toll station collects traffic operation status perception data and obtains real-time operation data of the toll station; wherein, the traffic operation status perception data includes at least the hourly traffic flow at the entrance. Hourly traffic flow at exits And the downstream traffic flow speed v; the real-time operating data includes at least lane opening status, toll collection method and vehicle queuing status; An AI-based event detection platform is introduced to acquire real-time information on emergencies at the toll station and its upstream and downstream road sections; the emergency information includes at least traffic accidents, vehicle congestion, and equipment malfunctions. The comprehensive operational status assessment is formed by integrating the traffic operation status perception data, the real-time operation data, and the AI ​​event detection data.

[0025] The traffic operation monitoring system deployed at toll stations accurately captures traffic operation status perception data, focusing on collecting hourly traffic flow at entrances, hourly traffic flow at exits, and traffic flow speed in downstream road sections. These three types of data directly reflect the traffic demand intensity at toll stations and the upstream and downstream traffic patterns, and can be used to assess traffic congestion risks and calculate lane demand. Simultaneously, real-time operational data of the toll stations is acquired, covering lane opening status, toll collection methods, and vehicle queuing conditions. Lane opening status and toll collection methods determine the upper limit of the toll station's actual service capacity, while vehicle queuing conditions directly reflect the degree of matching between current service capacity and traffic demand.

[0026] An AI-based event detection platform has been introduced, leveraging the real-time and accurate nature of AI algorithms to dynamically capture information on emergencies at toll stations and upstream / downstream road sections. These emergencies include core disruption types such as traffic accidents, vehicle congestion, and equipment malfunctions. Such emergencies directly disrupt normal traffic flow, causing a sharp drop in traffic efficiency or temporary impairment of service capacity. If not promptly incorporated into control decisions, they can easily lead to secondary congestion or waste of service resources.

[0027] Data fusion technology is employed to integrate and process traffic operation status perception data, real-time operation data, and AI event detection data. Preprocessing operations such as data format standardization, spatiotemporal alignment, and outlier removal address the issues of heterogeneous dimensions, spatiotemporal asynchrony, and inconsistent data quality across multiple data sources. Furthermore, feature association and semantic fusion are used to uncover potential correlations between different types of data. For example, malfunctioning equipment events are correlated with the service capacity of corresponding lanes, and traffic accidents are correlated with traffic flow speeds in downstream road sections. Ultimately, a comprehensive operational status assessment dataset reflecting the traffic situation, facility operation status, and sudden disturbances at toll stations is formed. This provides comprehensive data support for subsequent control decisions, ensuring the scientific validity and effectiveness of control strategies.

[0028] The process for determining whether the downstream section of the toll station is experiencing traffic congestion is as follows: Obtain the downstream road segment service capacity C and the downstream road segment baseline traffic congestion threshold coefficient of the toll station. Downstream road segment baseline traffic flow speed threshold ; Based on real-time weather type, time period characteristics, and traffic flow fluctuation coefficient, a dynamic correction model for congestion determination threshold is used to adjust the baseline traffic congestion threshold coefficient for the downstream road segment. and the downstream road segment reference traffic flow speed threshold Dynamic corrections are performed to obtain the real-time traffic congestion threshold coefficient M and the real-time traffic flow velocity threshold K of the downstream road segment; in, , , This is a weather correction factor; This is the flow fluctuation correction factor, and , Real-time peak hour traffic flow for downstream road sections; Real-time off-peak hourly traffic flow for downstream road sections; For weather safety correction factors; This is a time period correction factor; This refers to real-time traffic weighting coefficients. like and If so, it is determined that the downstream section of the toll station is in a state of traffic congestion.

[0029] In practical applications, the service capacity parameters, baseline traffic congestion threshold coefficient, and baseline traffic flow speed threshold of the downstream road segment are obtained from the target toll station. Based on real-time collected weather type, time period characteristics, and traffic flow fluctuation coefficient, a preset congestion judgment threshold dynamic correction model is used to dynamically correct the baseline traffic congestion threshold coefficient and baseline traffic flow speed threshold of the downstream road segment, respectively, to obtain real-time traffic congestion threshold coefficient and real-time traffic flow speed threshold of the downstream road segment adapted to the current real-time traffic environment. The correction parameters introduced in the correction process include weather correction coefficient, traffic flow fluctuation correction coefficient, weather safety correction coefficient, time period correction coefficient, and real-time traffic weight coefficient. The traffic flow fluctuation correction coefficient is determined based on the real-time peak hour traffic flow and real-time off-peak hour traffic flow of the downstream road segment. Each correction coefficient corresponds to the correction weight of the baseline threshold for different influence dimensions to ensure that the corrected real-time threshold is adapted to the current traffic scenario and avoids the judgment deviation of the static threshold.

[0030] When the corresponding judgment conditions of real-time traffic parameters and real-time traffic congestion threshold coefficient of the downstream road segment are met simultaneously, as well as the corresponding judgment conditions of real-time traffic flow speed and real-time traffic flow speed threshold of the downstream road segment, the downstream road segment of the toll station is determined to be in a state of traffic congestion.

[0031] If the closing conditions are not met, the number of entrance and exit lanes of the toll station will be dynamically allocated based on the traffic flow at the toll station's entrances and exits and the average service capacity of the entrance and exit lanes. Specifically, this includes: If the closing conditions are not met, the proportion of ETC vehicles in the entrance traffic flow of the toll station will be calculated. MTC vehicle ratio The proportion of vehicles requiring manual toll collection And the proportion of ETC vehicles in the outbound traffic flow MTC vehicle ratio The proportion of vehicles requiring manual toll collection ;in, , ; Based on the traffic flow at the toll station's entrances and exits and the average service capacity of the entrance and exit lanes, combined with the vehicle type ratio, the number of entrance lanes at the toll station is calculated. and number of exit lanes as follows: In the formula, Average service capacity of the entrance lanes at toll stations; Average service capacity of the exit lanes at the toll station; The queuing delay at the toll station is calculated as follows: In the formula, The total service capacity of the entrance lanes at the toll station; The total service capacity of the exit lanes at the toll station; The total number of lanes at the toll station is N. Based on the lane type-traffic direction collaborative optimization allocation model, and taking into account the operating efficiency, service level and resource utilization of the toll station, the number of entrance and exit lanes and lane types are collaboratively allocated with the goal of maximizing the comprehensive utilization rate of lane resources at the toll station. The objective function of the lane type-traffic direction collaborative optimization allocation model is: The constraints of the lane type-traffic direction collaborative optimization allocation model are: In the formula, F represents the comprehensive utilization rate of lane resources at the toll station; This refers to the number of ETC lanes, MTC lanes, and manual lanes at the toll station entrance. This refers to the number of ETC lanes, MTC lanes, and manual lanes at the toll station exit. The final number of entrance lanes for the toll station; The final number of exit lanes for the toll station; This is the floor function operator; The weighting coefficients are optimized for multiple objectives and are used to characterize the relative importance of lane resource cost, vehicle queuing delay, and vehicle traffic efficiency to the overall utilization rate of lane resources. when At that time, , ; when At that time, if ,but , ;like ,but , .

[0032] When the lane closure conditions are not met, the proportions of ETC vehicles, MTC vehicles, and manually tolled vehicles in the toll station entrance traffic flow and the corresponding proportions of the three vehicle types in the exit traffic flow are statistically analyzed to obtain the traffic flow type distribution characteristics at the toll station entrance and exit.

[0033] Based on the traffic flow at the entrance and exit of the toll station, the average service capacity of the entrance and exit lanes, and the statistically obtained vehicle type ratio, the required number of entrance lanes and exit lanes for the toll station are calculated respectively. At the same time, based on the total service capacity of the entrance lanes and the total service capacity of the exit lanes, the queuing delay of the toll station is calculated, thereby quantifying the current traffic service pressure and operating status of the toll station.

[0034] The system obtains the total number of lanes at the toll station and, through a pre-defined lane type-traffic direction collaborative optimization allocation model, considers the toll station's operational efficiency, service level, and resource utilization rate to maximize the comprehensive utilization rate of lane resources. It collaboratively allocates the number and type of entrance and exit lanes. This model has corresponding objective functions and constraints. The objective function quantifies the comprehensive utilization rate of lane resources, while the constraints include total lane count constraints, lane quantity constraints for each type, and lane capacity matching constraints. The model also includes rules for rounding up the lane count and final allocation determination. Based on the calculated numerical relationship between the number of entrance and exit lanes, the final number of entrance and exit lanes is determined, achieving dynamic lane allocation.

[0035] Total service capacity of the entrance lanes at the toll station as follows: In the formula, The service capacity of the f-th entrance lane of the toll station; The average service capacity of the entrance lanes at the toll station as follows: ; Total service capacity of the exit lanes at the toll station as follows: In the formula, The service capacity of the g-th exit lane at the toll station; The average service capacity of the exit lanes at the toll station as follows: .

[0036] The total service capacity of the toll station entrance lanes is obtained by summing the single-lane service capacities of all entrance lanes within the toll station, which is used to accurately quantify the overall traffic service capacity limit of the toll station entrance side.

[0037] Based on the calculated total service capacity of the entrance lanes, the average service capacity of the entrance lanes of the toll station is calculated by combining the total number of entrance lanes of the toll station. This average service capacity is used to characterize the average traffic service level of a single lane on the entrance side of the toll station.

[0038] By summing the single-lane service capabilities of all exit lanes within the toll station, the total service capability of the toll station exit lanes is obtained, which is used to accurately quantify the overall traffic service capacity limit of the toll station exit side.

[0039] Based on the calculated total service capacity of the exit lanes, the average service capacity of the exit lanes of the toll station is calculated by combining the total number of exit lanes of the toll station. This average service capacity is used to characterize the average traffic service level of a single lane on the exit side of the toll station.

[0040] If the entrance lane of the toll station is a manual lane, then the service capacity of the entrance lane is the same as the service capacity of the manual lane. The calculation formula is as follows: In the formula, The average service time for manual lanes is the average time from when a vehicle starts receiving service to when it finishes service and starts to leave on a manual lane. This is the baseline processing time for vehicle type s; This refers to the proportion of vehicle type s, i.e., the percentage of vehicle type s in the total number of vehicle types. This is a weather correction factor; Traffic condition correction factor; Q is traffic flow; For traffic capacity; Sensitivity coefficient; This is a correction factor for labor efficiency. This is the efficiency reduction factor; Let Q(j) be the traffic flow fluctuation coefficient; Q(j) is the traffic flow at historical time j. The average traffic flow for historical period J; t(j) represents the service time fluctuation coefficient; t(j) represents the service time at historical time j. The average service time for historical period J; To reduce the weighting factor for efficiency, in manual lanes, The value is 0.3. The value is 0.7; If the entrance lane of the toll station is an MTC lane, then the service capacity of the entrance lane is the service capacity of the MTC lane. The calculation formula is as follows: In the formula, The average service time for the MTC lane is the average time from when a vehicle starts receiving service to when it finishes service and starts to leave the MTC lane. This is the MTC efficiency correction factor, in the MTC lane. The value is 0.5. The value is 0.5; If the entrance lane of the toll station is an ETC lane, then the service capacity of the entrance lane is the service capacity of the ETC lane. The calculation formula is as follows: In the formula, The average service time for an ETC lane is the average time from when a vehicle starts receiving service to when it finishes receiving service and starts leaving the ETC lane. This is the ETC efficiency correction factor, used in ETC lanes. The value is 0.7. The value is 0.3; When the exit lanes of the toll station are manual lanes, MTC lanes, and ETC lanes, the corresponding service capacity is calculated in the same way as above.

[0041] Manual toll lanes, as a traditional toll collection method, have service capacity limited by the efficiency of manual operation, making them the type of lane with the lowest traffic efficiency among the three types. When a toll station entrance lane is a manual lane, its service capacity is taken as the manual lane's service capacity. The average service time for a manual lane is the average time from when a vehicle begins receiving service to when it completes service and departs. The calculation process incorporates multi-dimensional correction parameters, including the baseline processing time and corresponding vehicle type percentage for each type of vehicle, weather correction coefficient, traffic condition correction coefficient, traffic flow, capacity, sensitivity coefficient, manual efficiency correction coefficient, efficiency reduction coefficient, flow fluctuation coefficient, service time fluctuation coefficient, and efficiency reduction weighting coefficient. Among these, the flow fluctuation coefficient is determined based on the traffic flow at a historical moment and the average traffic flow over a historical period, and the service time fluctuation coefficient is determined based on the service time at a historical moment and the average service time over a historical period. Furthermore, in the manual lane scenario, the efficiency reduction weighting coefficient... Take fixed values ​​of 0.3 and 0.7 respectively.

[0042] MTC lanes, as a hybrid mode combining manual and electronic toll collection, have a service capacity between that of manual and ETC lanes. When the tollbooth entrance lane is an MTC lane, its service capacity is set to the MTC lane's capacity. The average service time for an MTC lane is the average time it takes for a vehicle in the MTC lane to start receiving service, complete service, and depart. An MTC efficiency correction coefficient is introduced in the calculation, and in the MTC lane scenario, an efficiency reduction weighting coefficient is applied. All are set to a fixed value of 0.5.

[0043] ETC lanes, based on electronic toll collection technology, offer the highest traffic efficiency. When an ETC lane is used at the toll plaza entrance, its service capacity is set to the ETC lane's service capacity. The average service time for an ETC lane is the average duration from when a vehicle begins receiving service to when it completes service and departs. An ETC efficiency correction coefficient is introduced during the calculation, and in the ETC lane scenario, an efficiency reduction weighting coefficient is applied. Take fixed values ​​of 0.7 and 0.3 respectively.

[0044] Furthermore, when the exit lanes of a toll station are manual lanes, MTC lanes, and ETC lanes, the calculation method for the corresponding lane service capacity is the same as that for the entrance lanes of the same type. When dynamically allocating the number of entrance and exit lanes at the toll station, if the toll station has tidal flow lane facilities, the number of entrance and exit lanes is set using the tidal flow lane method; if the toll station does not have tidal flow lane facilities, the number of entrance and exit lanes is controlled through lane indicators.

[0045] The implementation of dynamic lane allocation needs to be differentiated according to the facility configuration of toll stations to ensure the flexibility and feasibility of lane adjustments, and to ensure that the dynamic allocation strategy can be implemented effectively, so as to achieve a precise match between service capacity and traffic demand.

[0046] If a toll station is equipped with tidal flow lane facilities, meaning it has dedicated lanes with flexible traffic direction adjustment and corresponding isolation and guidance facilities, then the number of entrance and exit lanes will be set using the tidal flow lane method. By adjusting the traffic direction of the tidal flow lanes, the number of lanes in the high-demand direction can be quickly increased, while the proportion of lanes in the low-demand direction can be reduced. This allows for the dynamic flow of lane resources between entrances and exits, adapting to the tidal changes in traffic flow and maximizing the utilization rate of lane resources.

[0047] When toll stations lack tidal flow lane facilities, lane indicators are used to control the number of entrance and exit lanes. As a visual guidance device, the lane indicators can switch between displaying the lane's traffic attribute (entrance or exit) in real time. Staff or the system can adjust the display status of the lane indicators based on the dynamic allocation results to clarify the function of each lane and guide vehicles to travel according to the assigned lane attributes. This also enables dynamic adjustment of the number of entrance and exit lanes, ensuring the effective implementation of control strategies under existing facility conditions.

[0048] The temporary shutdown prompt and the slow-down prompt are pushed to the vehicle via the information board, audio-visual prompt device and vehicle navigation terminal.

[0049] The push notifications for "temporarily closed" and "slow down" messages employ a multi-channel collaborative mechanism to ensure vehicles receive crucial traffic guidance promptly, guaranteeing the effectiveness of traffic control strategies and ensuring driving safety. Specifically, information is delivered through three types of carriers: first, on-site information boards at toll stations, displaying visual text to inform vehicles before they enter the station; second, audio-visual warning devices, enhancing the warning effect through sound and light linkage to meet information reception needs in complex road conditions; and third, in-vehicle navigation terminals, accurately pushing personalized prompts to guide vehicles in route planning or adjusting driving status in advance. These three channels complement each other, ensuring that both "temporarily closed" and "slow down" messages reach all vehicles.

[0050] like Figure 2 The diagram shown is a system block diagram of a dynamic control system for the service capacity of a highway toll station provided in an embodiment of the present invention. The system includes: The data acquisition module is used to collect traffic operation status perception data, real-time operation data and AI event detection data from highway toll stations, and integrate them to generate a comprehensive operation status assessment dataset. The closing condition determination module is used to determine whether the closing conditions of the toll station are met based on the comprehensive operation status evaluation dataset. If the closing conditions are met, the closing operation of the toll station is executed, and a temporary closing prompt message is pushed to the vehicle. If the lane allocation module does not meet the closing conditions, it dynamically allocates the number of entrance and exit lanes of the toll station based on the traffic flow at the entrance and exit of the toll station and the average service capacity of the entrance and exit lanes, and pushes a slow-down prompt message to the vehicle.

[0051] Figure 2 The apparatus of the illustrated embodiment can be used to perform corresponding actions. Figure 1 The steps in the method embodiments shown are implemented in a similar manner and have similar technical effects, and will not be repeated here.

[0052] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the steps of the method for dynamic control of service capacity of a highway toll station as described in any of the above.

[0053] like Figure 3 The diagram shown is a hardware structure schematic of an electronic device according to an embodiment of the present invention. The electronic device 30 includes: a processor 31, a memory 32, and a computer program; wherein... The memory 32 is used to store the computer program, and the memory may also be flash memory. The computer program is, for example, an application program or functional module that implements the above method.

[0054] Processor 31 is configured to execute the computer program stored in the memory to implement the various steps performed by the device in the above method. For details, please refer to the relevant descriptions in the preceding method embodiments.

[0055] Alternatively, the memory 32 can be either standalone or integrated with the processor 31.

[0056] When the memory 32 is a device independent of the processor 31, the device may further include: Bus 33 is used to connect the memory 32 and the processor 31.

[0057] A readable storage medium storing a computer program, which, when executed by a processor, is used to implement the steps of a method for dynamically adjusting the service capacity of a highway toll station as described in any of the above claims.

[0058] The readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application-Specific Integrated Circuit (ASIC). Alternatively, the ASIC can be located in a user equipment. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0059] The present invention also provides a program product including executable instructions stored in a readable storage medium. At least one processor of the device can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the device to implement the methods provided in the various embodiments described above.

[0060] In the embodiments of the above-described device, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0061] Through the above embodiments, the present invention, through a method and system for dynamically adjusting the service capacity of highway toll stations, collects traffic operation status perception data, real-time operation data, and AI event detection data from highway toll stations, and integrates them to generate a comprehensive operation status assessment dataset. Based on the comprehensive operation status assessment dataset, it determines whether the conditions for closing the toll station are met. If the conditions are met, the toll station is closed, and a temporary closure prompt message is pushed to the vehicle. If the conditions are not met, the number of entrance and exit lanes of the toll station is dynamically allocated according to the traffic flow at the toll station entrance and exit and the average service capacity of the entrance and exit lanes, and a slow-down prompt message is pushed to the vehicle. This dynamically adjusts the service capacity of highway toll stations to ensure smooth traffic and improve operational efficiency.

[0062] This invention dynamically adjusts toll station closure conditions, lane allocation, and lane direction settings by collecting real-time traffic operation data from highway toll stations and combining it with AI event detection results. This achieves scientific and efficient regulation of toll station service capacity. The invention fully considers key parameters such as toll station entrance and exit traffic flow, downstream road capacity, and operating speed. While ensuring no traffic congestion on the main road, it scientifically determines toll station closure conditions and lane opening / closing schemes, ensuring the effectiveness of the dynamic toll station control strategy. This effectively alleviates traffic pressure at toll stations and downstream areas, improving the overall traffic efficiency of the highway mainline and toll stations.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; 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 or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamically adjusting the service capacity of highway toll stations, characterized in that, The method includes: Collect traffic operation status perception data, real-time operation data, and AI event detection data from highway toll stations, and integrate them to generate a comprehensive operation status assessment dataset; Based on the comprehensive operational status assessment dataset, it is determined whether the closing conditions of the toll station are met. If the closing conditions are met, the closing operation of the toll station is executed, and a temporary closure prompt message is pushed to the vehicle. If the closing conditions are not met, the number of entrance and exit lanes of the toll station will be dynamically allocated based on the traffic flow at the entrance and exit of the toll station and the average service capacity of the entrance and exit lanes, and a slow-down reminder message will be pushed to the vehicle.

2. The method for dynamic adjustment of service capacity of highway toll stations according to claim 1, characterized in that, The process involves collecting traffic operation status perception data, real-time operation data, and AI event detection data from highway toll stations, and fusing them to generate a comprehensive operation status assessment dataset, specifically including: The traffic operation monitoring system at the toll station collects traffic operation status perception data and obtains real-time operation data of the toll station; wherein, the traffic operation status perception data includes at least the hourly traffic flow at the entrance. Hourly traffic flow at exits And the downstream traffic flow speed v; the real-time operating data includes at least lane opening status, toll collection method and vehicle queuing status; An AI-based event detection platform is introduced to acquire real-time information on emergencies at the toll station and its upstream and downstream road sections; the emergency information includes at least traffic accidents, vehicle congestion, and equipment malfunctions. The comprehensive operational status assessment is formed by integrating the traffic operation status perception data, the real-time operation data, and the AI ​​event detection data.

3. The method for dynamic adjustment of service capacity of highway toll stations according to claim 1, characterized in that, The conditions for closure include severe weather or traffic congestion on the downstream section of the toll station.

4. The method for dynamic adjustment of service capacity of highway toll stations according to claim 3, characterized in that, The process for determining whether the downstream section of the toll station is experiencing traffic congestion is as follows: Obtain the downstream road segment service capacity C and the downstream road segment baseline traffic congestion threshold coefficient of the toll station. Downstream road segment baseline traffic flow speed threshold ; Based on real-time weather type, time period characteristics, and traffic flow fluctuation coefficient, a dynamic correction model for congestion determination threshold is used to adjust the baseline traffic congestion threshold coefficient for the downstream road segment. and the downstream road segment reference traffic flow speed threshold Dynamic corrections are performed to obtain the real-time traffic congestion threshold coefficient M and the real-time traffic flow velocity threshold K of the downstream road segment; in, , , This is a weather correction factor; This is the flow fluctuation correction factor, and , Real-time peak hour traffic flow for downstream road sections; Real-time off-peak hourly traffic flow for downstream road sections; For weather safety correction factors; This is a time period correction factor; This refers to real-time traffic weighting coefficients. like and If so, it is determined that the downstream section of the toll station is in a state of traffic congestion.

5. The method for dynamic adjustment of service capacity of highway toll stations according to claim 1, characterized in that, If the closing conditions are not met, the number of entrance and exit lanes of the toll station will be dynamically allocated based on the traffic flow at the toll station's entrances and exits and the average service capacity of the entrance and exit lanes. Specifically, this includes: If the closing conditions are not met, the proportion of ETC vehicles in the entrance traffic flow of the toll station will be calculated. MTC vehicle ratio The proportion of vehicles requiring manual toll collection And the proportion of ETC vehicles in the outbound traffic flow MTC vehicle ratio The proportion of vehicles requiring manual toll collection ;in, , ; Based on the traffic flow at the toll station's entrances and exits and the average service capacity of the entrance and exit lanes, combined with the vehicle type ratio, the number of entrance lanes at the toll station is calculated. and number of exit lanes as follows: In the formula, Average service capacity of the entrance lanes at toll stations; Average service capacity of the exit lanes at the toll station; The queuing delay at the toll station is calculated as follows: In the formula, The total service capacity of the entrance lanes at the toll station; The total service capacity of the exit lanes at the toll station; The total number of lanes at the toll station is N. Based on the lane type-traffic direction collaborative optimization allocation model, and taking into account the operating efficiency, service level and resource utilization of the toll station, the number of entrance and exit lanes and lane types are collaboratively allocated with the goal of maximizing the comprehensive utilization rate of lane resources at the toll station. The objective function of the lane type-traffic direction collaborative optimization allocation model is: The constraints of the lane type-traffic direction collaborative optimization allocation model are: In the formula, F represents the comprehensive utilization rate of lane resources at the toll station; This refers to the number of ETC lanes, MTC lanes, and manual lanes at the toll station entrance. This refers to the number of ETC lanes, MTC lanes, and manual lanes at the toll station exit. The final number of entrance lanes for the toll station; The final number of exit lanes for the toll station; This is the floor function operator; The weighting coefficients are optimized for multiple objectives and are used to characterize the relative importance of lane resource cost, vehicle queuing delay, and vehicle traffic efficiency to the overall utilization rate of lane resources. when At that time, , ; when At that time, if ,but , ;like ,but , .

6. The method for dynamic adjustment of service capacity of highway toll stations according to claim 5, characterized in that, Total service capacity of the entrance lanes at the toll station as follows: In the formula, The service capacity of the f-th entrance lane of the toll station; The average service capacity of the entrance lanes at the toll station as follows: ； Total service capacity of the exit lanes at the toll station as follows: In the formula, The service capacity of the g-th exit lane at the toll station; The average service capacity of the exit lanes at the toll station as follows: 。 7. The method for dynamic adjustment of service capacity of highway toll stations according to claim 6, characterized in that, If the entrance lane of the toll station is a manual lane, then the service capacity of the entrance lane is the same as the service capacity of the manual lane. The calculation formula is as follows: In the formula, The average service time for manual lanes is the average time from when a vehicle starts receiving service to when it finishes service and starts to leave on a manual lane. This is the baseline processing time for vehicle type s; This refers to the proportion of vehicle type s, i.e., the percentage of vehicle type s in the total number of vehicle types. This is a weather correction factor; Traffic condition correction factor; Q is traffic flow; For traffic capacity; Sensitivity coefficient; This is a correction factor for labor efficiency. This is the efficiency reduction factor; Let Q(j) be the traffic flow fluctuation coefficient; Q(j) is the traffic flow at historical time j. The average traffic flow for historical period J; t(j) represents the service time fluctuation coefficient; t(j) represents the service time at historical time j. The average service time for historical period J; To reduce the weighting factor for efficiency, in manual lanes, The value is 0.

3. The value is 0.7; If the entrance lane of the toll station is an MTC lane, then the service capacity of the entrance lane is the service capacity of the MTC lane. The calculation formula is as follows: In the formula, The average service time for the MTC lane is the average time from when a vehicle starts receiving service to when it finishes service and starts to leave the MTC lane. This is the MTC efficiency correction factor, in the MTC lane. The value is 0.

5. The value is 0.5; If the entrance lane of the toll station is an ETC lane, then the service capacity of the entrance lane is the service capacity of the ETC lane. The calculation formula is as follows: In the formula, The average service time for an ETC lane is the average time from when a vehicle starts receiving service to when it finishes receiving service and starts leaving the ETC lane. This is the ETC efficiency correction factor, used in ETC lanes. The value is 0.

7. The value is 0.3; When the exit lanes of the toll station are manual lanes, MTC lanes, and ETC lanes, the corresponding service capacity is calculated in the same way as above.

8. The method for dynamic adjustment of service capacity of highway toll stations according to claim 1, characterized in that, When dynamically allocating the number of entrance and exit lanes of the toll station, if the toll station has tidal flow lane facilities, the number of entrance and exit lanes is set using the tidal flow lane method; if the toll station does not have the tidal flow lane facilities, the number of entrance and exit lanes is controlled by lane indicators.

9. The method for dynamic adjustment of service capacity of highway toll stations according to claim 1, characterized in that, The temporary shutdown prompt and the slow-down prompt are pushed to the vehicle via the information board, audio-visual prompt device and vehicle navigation terminal.

10. A dynamic control system for the service capacity of a highway toll station, applied to the dynamic control method for the service capacity of a highway toll station as described in any one of claims 1-9, characterized in that, The system includes: The data acquisition module is used to collect traffic operation status perception data, real-time operation data and AI event detection data from highway toll stations, and integrate them to generate a comprehensive operation status assessment dataset. The closing condition determination module is used to determine whether the closing conditions of the toll station are met based on the comprehensive operation status evaluation dataset. If the closing conditions are met, the closing operation of the toll station is executed, and a temporary closing prompt message is pushed to the vehicle. The lane allocation module is used to dynamically allocate the number of entrance and exit lanes of the toll station based on the entrance and exit traffic flow and the average service capacity of the entrance and exit lanes if the closing conditions are not met, and push slow down prompt information to the vehicle.

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